diff --git "a/raw_rss_feeds/https___arxiv_org_rss_cs.xml" "b/raw_rss_feeds/https___arxiv_org_rss_cs.xml" --- "a/raw_rss_feeds/https___arxiv_org_rss_cs.xml" +++ "b/raw_rss_feeds/https___arxiv_org_rss_cs.xml" @@ -7,12975 +7,12 @@ http://www.rssboard.org/rss-specification en-us - Thu, 01 Jan 2026 05:00:05 +0000 + Fri, 02 Jan 2026 05:00:16 +0000 rss-help@arxiv.org - Thu, 01 Jan 2026 00:00:00 -0500 + Fri, 02 Jan 2026 00:00:00 -0500 Saturday Sunday - - Enriching Historical Records: An OCR and AI-Driven Approach for Database Integration - https://arxiv.org/abs/2512.23710 - arXiv:2512.23710v1 Announce Type: new -Abstract: This research digitizes and analyzes the Leidse hoogleraren en lectoren 1575-1815 books written between 1983 and 1985, which contain biographic data about professors and curators of Leiden University. It addresses the central question: how can we design an automated pipeline that integrates OCR, LLM-based interpretation, and database linking to harmonize data from historical document images with existing high-quality database records? We applied OCR techniques, generative AI decoding constraints that structure data extraction, and database linkage methods to process typewritten historical records into a digital format. OCR achieved a Character Error Rate (CER) of 1.08 percent and a Word Error Rate (WER) of 5.06 percent, while JSON extraction from OCR text achieved an average accuracy of 63 percent and, based on annotated OCR, 65 percent. This indicates that generative AI somewhat corrects low OCR performance. Our record linkage algorithm linked annotated JSON files with 94% accuracy and OCR-derived JSON files with 81%. This study contributes to digital humanities research by offering an automated pipeline for interpreting digitized historical documents, addressing challenges like layout variability and terminology differences, and exploring the applicability and strength of an advanced generative AI model. - oai:arXiv.org:2512.23710v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Computational Linguistics in the Netherlands Journal 14 (2025) 401-420 - Zahra Abedi, Richard M. K. van Dijk, Gijs Wijnholds, Tessa Verhoef - - - CAT: A Metric-Driven Framework for Analyzing the Consistency-Accuracy Relation of LLMs under Controlled Input Variations - https://arxiv.org/abs/2512.23711 - arXiv:2512.23711v1 Announce Type: new -Abstract: We introduce \textsc{CAT}, a framework designed to evaluate and visualize the \emph{interplay} of \emph{accuracy} and \emph{response consistency} of Large Language Models (LLMs) under controllable input variations, using multiple-choice (MC) benchmarks as a case study. Current evaluation practices primarily focus on model capabilities such as accuracy or benchmark scores and, more recently, measuring consistency is being considered an essential property for deploying LLMs in high-stake, real-world applications. We argue in this paper that although both dimensions should still be evaluated independently, their inter-dependency also need to be considered for a more nuanced evaluation of LLMs. At the core of \textsc{CAT} are the \emph{Consistency-Accuracy Relation (CAR)} curves, which visualize how model accuracy varies with increasing consistency requirements, as defined by the \emph{Minimum-Consistency Accuracy (MCA)} metric. We further propose the \emph{Consistency-Oriented Robustness Estimate (CORE)} index, a global metric that combines the area and shape of the CAR curve to quantify the trade-off between accuracy and consistency. We present a practical demonstration of our framework across a diverse set of generalist and domain-specific LLMs, evaluated on multiple MC benchmarks. We also outline how \textsc{CAT} can be extended beyond MC tasks to support long-form, open-ended evaluations through adaptable scoring functions. - oai:arXiv.org:2512.23711v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Paulo Cavalin, Cassia Sanctos, Marcelo Grave, Claudio Pinhanez, Yago Primerano - - - STED and Consistency Scoring: A Framework for Evaluating LLM Structured Output Reliability - https://arxiv.org/abs/2512.23712 - arXiv:2512.23712v1 Announce Type: new -Abstract: Large Language Models (LLMs) are increasingly deployed for structured data generation, yet output consistency remains critical for production applications. We introduce a comprehensive framework for evaluating and improving consistency in LLM-generated structured outputs. Our approach combines: (1) STED (Semantic Tree Edit Distance), a novel similarity metric balancing semantic flexibility with structural strictness when comparing JSON outputs, and (2) a consistency scoring framework aggregating multiple STED measurements across repeated generations to quantify reliability. Through systematic experiments on synthetic datasets with controlled schema, expression, and semantic variations, we demonstrate STED achieves superior performance ($0.86-0.90$ similarity for semantic equivalents, $0.0$ for structural breaks) compared to existing metrics including TED, BERTScore, and DeepDiff. Applying our framework to benchmark six LLMs reveals significant variations: Claude-3.7-Sonnet demonstrates exceptional consistency, maintaining near-perfect structural reliability even at high temperatures ($T=0.9$), while models like Claude-3-Haiku and Nova-Pro exhibit substantial degradation requiring careful tuning. Our framework enables practical applications including targeted model selection for structured tasks, iterative prompt refinement for reproducible results, and diagnostic analysis to identify inconsistency root causes. This work provides theoretical foundations and practical tools for ensuring reliable structured output generation in LLM-based production systems. - oai:arXiv.org:2512.23712v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Guanghui Wang, Jinze Yu, Xing Zhang, Dayuan Jiang, Yin Song, Tomal Deb, Xuefeng Liu, Peiyang He - - - PyBangla at BLP-2025 Task 2: Enhancing Bangla-to-Python Code Generation with Iterative Self-Correction and Multilingual Agents - https://arxiv.org/abs/2512.23713 - arXiv:2512.23713v1 Announce Type: new -Abstract: LLMs excel at code generation from English prompts, but this progress has not extended to low-resource languages. We address Bangla-to-Python code generation by introducing BanglaCodeAct, an agent-based framework that leverages multi-agent prompting and iterative self-correction. Unlike prior approaches relying on task-specific fine-tuning, BanglaCodeAct employs an open-source multilingual LLM within a Thought-Code-Observation loop, enabling dynamic generation, testing, and refinement of code from Bangla instructions. We benchmark several small-parameter open-source LLMs and evaluate their effectiveness on the mHumanEval dataset for Bangla NL2Code. Our results show that Qwen3-8B, when deployed with BanglaCodeAct, achieves the best performance, with pass@1 accuracy of 94.0\% on the development set and 71.6\% on the blind test set. These results establish a new benchmark for Bangla-to-Python translation and highlight the potential of agent-based reasoning for reliable code generation in low-resource languages. Experimental scripts are publicly available at github.com/jahidulzaid/PyBanglaCodeActAgent. - oai:arXiv.org:2512.23713v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Jahidul Islam, Md Ataullha, Saiful Azad - - - PharmaShip: An Entity-Centric, Reading-Order-Supervised Benchmark for Chinese Pharmaceutical Shipping Documents - https://arxiv.org/abs/2512.23714 - arXiv:2512.23714v1 Announce Type: new -Abstract: We present PharmaShip, a real-world Chinese dataset of scanned pharmaceutical shipping documents designed to stress-test pre-trained text-layout models under noisy OCR and heterogeneous templates. PharmaShip covers three complementary tasks-sequence entity recognition (SER), relation extraction (RE), and reading order prediction (ROP)-and adopts an entity-centric evaluation protocol to minimize confounds across architectures. We benchmark five representative baselines spanning pixel-aware and geometry-aware families (LiLT, LayoutLMv3-base, GeoLayoutLM and their available RORE-enhanced variants), and standardize preprocessing, splits, and optimization. Experiments show that pixels and explicit geometry provide complementary inductive biases, yet neither alone is sufficient: injecting reading-order-oriented regularization consistently improves SER and EL and yields the most robust configuration, while longer positional coverage stabilizes late-page predictions and reduces truncation artifacts. ROP is accurate at the word level but challenging at the segment level, reflecting boundary ambiguity and long-range crossings. PharmaShip thus establishes a controlled, reproducible benchmark for safety-critical document understanding in the pharmaceutical domain and highlights sequence-aware constraints as a transferable bias for structure modeling. We release the dataset at https://github.com/KevinYuLei/PharmaShip. - oai:arXiv.org:2512.23714v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tingwei Xie, Tianyi Zhou, Yonghong Song - - - Wind Speed Weibull Model Identification in Oman, and Computed Normalized Annual Energy Production (NAEP) From Wind Turbines Based on Data From Weather Stations - https://arxiv.org/abs/2512.23715 - arXiv:2512.23715v1 Announce Type: new -Abstract: Using observation records of wind speeds from weather stations in the Sultanate of Oman between 2000 and 2023, we compute estimators of the two Weibull distribution parameters (namely, the Weibull distribution's shape parameter and the Weibull distribution's scale parameter) in 10 weather station locations within eight Omani governorates. The 10 weather station locations in Oman and their corresponding governorates are Seeb (in Muscat), Salalah (in Dhofar), Buraimi (in Al Buraimi), Masirah (in Ash Sharqiyah South), Thumrait (in Dhofar), Sur (in Ash Sharqiyah South), Khasab (in Musandam), Sohar (in Sohar), Fahud (in Az Zahirah), and Saiq (in Ad Dakhiliyah). The obtained wind speed distributions at these weather stations are then used to predict the annual energy production (AEP) for a proposed reference amount of 1 MWp of wind turbine capacity, and this specific AEP is designated here by the term "normalized annual energy production (NAEP)." The direction of the wind is also analyzed statistically over the same period to identify the more probable wind directions. Four locations were clearly distinguishable as being windy compared to the others. The simulated probability of exceeding a feasible 6 m/s (21.6 km/h) wind speed in these locations is 41.71% in Thumrait, 37.77% in Masirah, 29.53% in Sur, and 17.03% in Fahud. The NAEP values in these four locations are estimated as 1.727 GWh/MWp/year, 1.419 GWh/MWp/year, 1.038 GWh/MWp/year, and 0.602 GWh/MWp/year, respectively. The wind in the location of Thumrait is not only the fastest (on average) among the selected locations but also the most unidirectional, blowing almost always from the south-south-east (SSE) direction, and both features make this non-coastal location in southern Oman, with an altitude of about 467 m, an attractive site for utility-scale wind farms. - oai:arXiv.org:2512.23715v1 - cs.CE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1002/eng2.70089 - Engineering Reports. 7(3), article e70089 (2025) - Osama A. Marzouk - - - Noise-Driven Persona Formation in Reflexive Neural Language Generation - https://arxiv.org/abs/2512.23716 - arXiv:2512.23716v1 Announce Type: new -Abstract: This paper introduces the Luca-Noise Reflex Protocol (LN-RP), a computational framework for analyzing noise-driven persona emergence in large language models. By injecting stochastic noise seeds into the initial generation state, we observe nonlinear transitions in linguistic behavior across 152 generation cycles. Our results reveal three stable persona modes with distinct entropy signatures, and demonstrate that external noise sources can reliably induce phase transitions in reflexive generation dynamics. Quantitative evaluation confirms consistent persona retention and significant differences across modes (p < 0.01). The protocol provides a reproducible method for studying reflexive generation, emergent behavior, and longrange linguistic coherence in LLMs. - oai:arXiv.org:2512.23716v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Toshiyuki Shigemura - - - HarmTransform: Transforming Explicit Harmful Queries into Stealthy via Multi-Agent Debate - https://arxiv.org/abs/2512.23717 - arXiv:2512.23717v1 Announce Type: new -Abstract: Large language models (LLMs) are equipped with safety mechanisms to detect and block harmful queries, yet current alignment approaches primarily focus on overtly dangerous content and overlook more subtle threats. However, users can often disguise harmful intent through covert rephrasing that preserves malicious objectives while appearing benign, which creates a significant gap in existing safety training data. To address this limitation, we introduce HarmTransform, a multi-agent debate framework for systematically transforming harmful queries into stealthier forms while preserving their underlying harmful intent. Our framework leverages iterative critique and refinement among multiple agents to generate high-quality, covert harmful query transformations that can be used to improve future LLM safety alignment. Experiments demonstrate that HarmTransform significantly outperforms standard baselines in producing effective query transformations. At the same time, our analysis reveals that debate acts as a double-edged sword: while it can sharpen transformations and improve stealth, it may also introduce topic shifts and unnecessary complexity. These insights highlight both the promise and the limitations of multi-agent debate for generating comprehensive safety training data. - oai:arXiv.org:2512.23717v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Shenzhe Zhu - - - Network Traffic Analysis with Process Mining: The UPSIDE Case Study - https://arxiv.org/abs/2512.23718 - arXiv:2512.23718v1 Announce Type: new -Abstract: Online gaming is a popular activity involving the adoption of complex systems and network infrastructures. The relevance of gaming, which generates large amounts of market revenue, drove research in modeling network devices' behavior to evaluate bandwidth consumption, predict and sustain high loads, and detect malicious activity. In this context, process mining appears promising due to its ability to combine data-driven analyses with model-based insights. In this paper, we propose a process mining-based method that analyzes gaming network traffic, allowing: unsupervised characterization of different states from gaming network data; encoding such states through process mining into interpretable Petri nets; and classification of gaming network traffic data to identify different video games being played. We apply the method to the UPSIDE case study, involving gaming network data of several devices interacting with two video games: Clash Royale and Rocket League. Results demonstrate that the gaming network behavior can be effectively and interpretably modeled through states represented as Petri nets with sufficient coherence (94.02% inter-device similarity) and specificity (174.99% inter-state separation) while maintaining a good classification accuracy of the two different video games (73.84% AUC). - oai:arXiv.org:2512.23718v1 - cs.LG - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Francesco Vitale, Paolo Palmiero, Massimiliano Rak, Nicola Mazzocca - - - A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulation - https://arxiv.org/abs/2512.23719 - arXiv:2512.23719v1 Announce Type: new -Abstract: Artificial intelligence is beginning to ease long-standing bottlenecks in the CAD-to-mesh pipeline. This survey reviews recent advances where machine learning aids part classification, mesh quality prediction, and defeaturing. We explore methods that improve unstructured and block-structured meshing, support volumetric parameterizations, and accelerate parallel mesh generation. We also examine emerging tools for scripting automation, including reinforcement learning and large language models. Across these efforts, AI acts as an assistive technology, extending the capabilities of traditional geometry and meshing tools. The survey highlights representative methods, practical deployments, and key research challenges that will shape the next generation of data-driven meshing workflows. - oai:arXiv.org:2512.23719v1 - cs.CE - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Steven Owen, Nathan Brown, Nikos Chrisochoides, Rao Garimella, Xianfeng Gu, Franck Ledoux, Na Lei, Roshan Quadros, Navamita Ray, Nicolas Winovich, Yongjie Jessica Zhang - - - An Electronic Ising Machine - https://arxiv.org/abs/2512.23720 - arXiv:2512.23720v1 Announce Type: new -Abstract: We develop a custom printed circuit board (PCB) as a low-power and high-speed accelerator for NP-Hard graph problems. Based on the annealing principle, it uses an analog computing architecture of coupled nonlinear electronic oscillators. Using an energy-based representation of the input problem, the system is shown to naturally follow the gradient towards stable phase alignments that encode solutions. We introduce the motivational theory, give an overview of our detailed circuit design, simulations, and experiments, and provide insight on the emerging development of novel physics-based computing devices. - oai:arXiv.org:2512.23720v1 - cs.ET - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Matt Bowring, Ben Anderdson, Ben Tiffany - - - Emergent World Beliefs: Exploring Transformers in Stochastic Games - https://arxiv.org/abs/2512.23722 - arXiv:2512.23722v1 Announce Type: new -Abstract: Transformer-based large language models (LLMs) have demonstrated strong reasoning abilities across diverse fields, from solving programming challenges to competing in strategy-intensive games such as chess. Prior work has shown that LLMs can develop emergent world models in games of perfect information, where internal representations correspond to latent states of the environment. In this paper, we extend this line of investigation to domains of incomplete information, focusing on poker as a canonical partially observable Markov decision process (POMDP). We pretrain a GPT-style model on Poker Hand History (PHH) data and probe its internal activations. Our results demonstrate that the model learns both deterministic structure, such as hand ranks, and stochastic features, such as equity, without explicit instruction. Furthermore, by using primarily nonlinear probes, we demonstrated that these representations are decodeable and correlate with theoretical belief states, suggesting that LLMs are learning their own representation of the stochastic environment of Texas Hold'em Poker. - oai:arXiv.org:2512.23722v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Adam Kamel, Tanish Rastogi, Michael Ma, Kailash Ranganathan, Kevin Zhu - - - New Exam Security Questions in the AI Era: Comparing AI-Generated Item Similarity Between Naive and Detail-Guided Prompting Approaches - https://arxiv.org/abs/2512.23729 - arXiv:2512.23729v1 Announce Type: new -Abstract: Large language models (LLMs) have emerged as powerful tools for generating domain-specific multiple-choice questions (MCQs), offering efficiency gains for certification boards but raising new concerns about examination security. This study investigated whether LLM-generated items created with proprietary guidance differ meaningfully from those generated using only publicly available resources. Four representative clinical activities from the American Board of Family Medicine (ABFM) blueprint were mapped to corresponding Entrustable Professional Activities (EPAs), and three LLMs (GPT-4o, Claude 4 Sonnet, Gemini 2.5 Flash) produced items under a naive strategy using only public EPA descriptors, while GPT-4o additionally produced items under a guided strategy that incorporated proprietary blueprints, item-writing guidelines, and exemplar items, yielding 160 total items. Question stems and options were encoded using PubMedBERT and BioBERT, and intra- and inter-strategy cosine similarity coefficients were calculated. Results showed high internal consistency within each prompting strategy, while cross-strategy similarity was lower overall. However, several domain model pairs, particularly in narrowly defined areas such as viral pneumonia and hypertension, exceeded the 0.65 threshold, indicating convergence between naive and guided pipelines. These findings suggest that while proprietary resources impart distinctiveness, LLMs prompted only with public information can still generate items closely resembling guided outputs in constrained clinical domains, thereby heightening risks of item exposure. Safeguarding the integrity of high stakes examinations will require human-first, AI-assisted item development, strict separation of formative and summative item pools, and systematic similarity surveillance to balance innovation with security. - oai:arXiv.org:2512.23729v1 - cs.CY - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ting Wang, Caroline Prendergast, Susan Lottridge - - - When in Doubt, Deliberate: Confidence-Based Routing to Expert Debate for Sexism Detection - https://arxiv.org/abs/2512.23732 - arXiv:2512.23732v1 Announce Type: new -Abstract: Sexist content online increasingly appears in subtle, context-dependent forms that evade traditional detection methods. Its interpretation often depends on overlapping linguistic, psychological, legal, and cultural dimensions, which produce mixed and sometimes contradictory signals, even in annotated datasets. These inconsistencies, combined with label scarcity and class imbalance, result in unstable decision boundaries and cause fine-tuned models to overlook subtler, underrepresented forms of harm. Together, these limitations point to the need for a design that explicitly addresses the combined effects of (i) underrepresentation, (ii) noise, and (iii) conceptual ambiguity in both data and model predictions. To address these challenges, we propose a two-stage framework that unifies (i) targeted training procedures to adapt supervision to scarce and noisy data with (ii) selective, reasoning-based inference to handle ambiguous or borderline cases. Our training setup applies class-balanced focal loss, class-aware batching, and post-hoc threshold calibration to mitigate label imbalance and noisy supervision. At inference time, a dynamic routing mechanism classifies high-confidence cases directly and escalates uncertain instances to a novel \textit{Collaborative Expert Judgment} (CEJ) module, which prompts multiple personas and consolidates their reasoning through a judge model. Our approach achieves state-of-the-art results across several benchmarks, with a +2.72\% improvement in F1 on the EXIST 2025 Task 1.1, and a gains of +4.48\% and +1.30\% on the EDOS Tasks A and B, respectively. - oai:arXiv.org:2512.23732v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Anwar Alajmi, Gabriele Pergola - - - Biochemical Computing Mode for Sequential Logic - https://arxiv.org/abs/2512.23734 - arXiv:2512.23734v1 Announce Type: new -Abstract: Recent years have witnessed the growing scholarly interest in the next-generation general-purpose computers. Various innovative computing modes have been proposed, such as optical, quantum phenomena, and DNA-based modes. Sequential logic circuits are a critical factor that enables these modes to function as general-purpose computers, given their essential role in facilitating continuous computation and memory storage through their ability to store states. However, compared to computability, it is often overlooked due to the difficulty of its implementation. In this paper, we first demonstrate sequential mapping, a crucial necessary condition for electronic computers to realize sequential logic circuits, and highlight this distinctive property of general-purpose computers in the context of logic gate circuits. To achieve computational functionalities comparable to those of electronic computers, we utilize the control effect of enzymes on enzymatic reactions to design a logic gate model that is composed of small molecules and driven by enzymes, subsequently propose a biochemical computing mode. Furthermore, we mathematically analyze the static and dynamic input-output properties of biochemical logic gate components and prove that the biochemical computing mode satisfies sequential mapping similar to electronic computers. When combined with the storage characteristics of NOT-AND gates, it can realize sequential logic circuits. The findings can serve as a theoretical foundation for developing general-purpose biochemical computers. - oai:arXiv.org:2512.23734v1 - cs.ET - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Han Huang, Chengzhi Ma, Yuxin Zhao, Qingyao Wang, Xinglong Xiao, Xiulin Shu, Zhifeng Hao - - - Ovonic switches enable energy-efficient dendrite-like computing - https://arxiv.org/abs/2512.23736 - arXiv:2512.23736v1 Announce Type: new -Abstract: Over the last decade, dendrites within individual biological neurons, which were previously thought to generally perform information pooling and networking, have now been shown to express complex temporal dynamics, Boolean-like logic, arithmetic, signal discrimination, and edge detection for image and sound recognition. Mimicking this rich functional density could offer a powerful primitive for neuromorphic computing, which has sought to replace the aging digital computing paradigms using biological inspirations. Here, using electrically driven Ovonic threshold switching in Sb-Te-doped GeSe, we demonstrate a single two-terminal component capable of self-sustained dynamics and universal Boolean logic, in addition to XOR operations (which is traditionally thought to require a network of active components). We then employ logic-driven dynamics in a single component to detect and estimate the gradients of edges in images, a task that otherwise requires elaborate circuits. A network of Ovonic switches exhibits properties of a half adder and a full adder, in addition to discriminative logic accommodating inhibitory and excitatory signals. We show that this computational primitive is not only seemingly simpler, but also offers many orders of magnitude improved energy efficiency compared to prevailing digital solutions. As such, this work paves the path for potentially emulating dendrites for efficient post-digital neuromorphic computing. - oai:arXiv.org:2512.23736v1 - cs.ET - cond-mat.mtrl-sci - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1021/acs.nanolett.5c04348 - Unhyeon Kang, Jaesang Lee, Seungmin Oh, Hanchan Song, Jongkil Park, Jaewook Kim, Seongsik Park, Hyun Jae Jang, Sangbum Kim, Su-in Yi, Suhas Kumar, Suyoun Lee - - - Governing Cloud Data Pipelines with Agentic AI - https://arxiv.org/abs/2512.23737 - arXiv:2512.23737v1 Announce Type: new -Abstract: Cloud data pipelines increasingly operate under dynamic workloads, evolving schemas, cost constraints, and strict governance requirements. Despite advances in cloud-native orchestration frameworks, most production pipelines rely on static configurations and reactive operational practices, resulting in prolonged recovery times, inefficient resource utilization, and high manual overhead. This paper presents Agentic Cloud Data Engineering, a policy-aware control architecture that integrates bounded AI agents into the governance and control plane of cloud data pipelines. In Agentic Cloud Data Engineering platform, specialized agents analyze pipeline telemetry and metadata, reason over declarative cost and compliance policies, and propose constrained operational actions such as adaptive resource reconfiguration, schema reconciliation, and automated failure recovery. All agent actions are validated against governance policies to ensure predictable and auditable behavior. We evaluate Agentic Cloud Data Engineering platform using representative batch and streaming analytics workloads constructed from public enterprise-style datasets. Experimental results show that Agentic Cloud Data Engineering platform reduces mean pipeline recovery time by up to 45%, lowers operational cost by approximately 25%, and decreases manual intervention events by over 70% compared to static orchestration, while maintaining data freshness and policy compliance. These results demonstrate that policy-bounded agentic control provides an effective and practical approach for governing cloud data pipelines in enterprise environments. - oai:arXiv.org:2512.23737v1 - cs.DC - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.5281/zenodo.18048728 - International Journal of Computer Science Trends and Technology (IJCST), Volume 13 Issue 6,Dec 2025 - Aswathnarayan Muthukrishnan Kirubakaran, Adithya Parthasarathy, Nitin Saksena, Ram Sekhar Bodala, Akshay Deshpande, Suhas Malempati, Shiva Carimireddy, Abhirup Mazumder - - - Enforcing Temporal Constraints for LLM Agents - https://arxiv.org/abs/2512.23738 - arXiv:2512.23738v1 Announce Type: new -Abstract: LLM-based agents are deployed in safety-critical applications, yet current guardrail systems fail to prevent violations of temporal safety policies, requirements that govern the ordering and sequencing of agent actions. For instance, agents may access sensitive data before authenticating users or process refunds to unauthorized payment methods, violations that require reasoning about sequences of action rather than an individual action. Existing guardrails rely on imprecise natural language instructions or post-hoc monitoring, and provide no formal guarantees that agents will satisfy temporal constraints. We present Agent-C, a novel framework that provides run-time guarantees ensuring LLM agents adhere to formal temporal safety properties. Agent-C introduces a domain-specific language for expressing temporal properties (e.g., authenticate before accessing data), translates specifications to first-order logic, and uses SMT solving to detect non-compliant agent actions during token generation. When the LLM attempts to generate a non-compliant tool call, Agent-C leverages constrained generation techniques to ensure that every action generated by the LLM complies with the specification, and to generate a compliant alternative to a non-compliant agent action. We evaluate Agent-C across two real-world applications: retail customer service and airline ticket reservation system, and multiple language models (open and closed-source). Our results demonstrate that Agent-C achieves perfect safety (100% conformance, 0% harm), while improving task utility compared to state-of-the-art guardrails and unrestricted agents. On SoTA closed-source models, Agent-C improves conformance (77.4% to 100% for Claude Sonnet 4.5 and 83.7% to 100% for GPT-5), while simultaneously increasing utility (71.8% to 75.2% and 66.1% to 70.6%, respectively), representing a new SoTA frontier for reliable agentic reasoning. - oai:arXiv.org:2512.23738v1 - cs.PL - cs.AI - cs.FL - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Adharsh Kamath, Sishen Zhang, Calvin Xu, Shubham Ugare, Gagandeep Singh, Sasa Misailovic - - - Break Out the Silverware -- Semantic Understanding of Stored Household Items - https://arxiv.org/abs/2512.23739 - arXiv:2512.23739v1 Announce Type: new -Abstract: ``Bring me a plate.'' For domestic service robots, this simple command reveals a complex challenge: inferring where everyday items are stored, often out of sight in drawers, cabinets, or closets. Despite advances in vision and manipulation, robots still lack the commonsense reasoning needed to complete this task. We introduce the Stored Household Item Challenge, a benchmark task for evaluating service robots' cognitive capabilities: given a household scene and a queried item, predict its most likely storage location. - Our benchmark includes two datasets: (1) a real-world evaluation set of 100 item-image pairs with human-annotated ground truth from participants' kitchens, and (2) a development set of 6,500 item-image pairs annotated with storage polygons over public kitchen images. These datasets support realistic modeling of household organization and enable comparative evaluation across agent architectures. - To begin tackling this challenge, we introduce NOAM (Non-visible Object Allocation Model), a hybrid agent pipeline that combines structured scene understanding with large language model inference. NOAM converts visual input into natural language descriptions of spatial context and visible containers, then prompts a language model (e.g., GPT-4) to infer the most likely hidden storage location. This integrated vision-language agent exhibits emergent commonsense reasoning and is designed for modular deployment within broader robotic systems. - We evaluate NOAM against baselines including random selection, vision-language pipelines (Grounding-DINO + SAM), leading multimodal models (e.g., Gemini, GPT-4o, Kosmos-2, LLaMA, Qwen), and human performance. NOAM significantly improves prediction accuracy and approaches human-level results, highlighting best practices for deploying cognitively capable agents in domestic environments. - oai:arXiv.org:2512.23739v1 - cs.CL - cs.AI - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Michaela Levi-Richter, Reuth Mirsky, Oren Glickman - - - Towards representation agnostic probabilistic programming - https://arxiv.org/abs/2512.23740 - arXiv:2512.23740v1 Announce Type: new -Abstract: Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of their underlying representation. This enables representation-agnostic probabilistic programming where users can freely mix different representations (e.g. discrete tables, Gaussians distributions, sample-based approaches) within a single unified framework, allowing practical inference in complex hybrid models that current toolkits cannot adequately express. - oai:arXiv.org:2512.23740v1 - cs.PL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ole Fenske, Maximilian Popko, Sebastian Bader, Thomas Kirste - - - AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization - https://arxiv.org/abs/2512.23742 - arXiv:2512.23742v1 Announce Type: new -Abstract: With the continued scaling of advanced technology nodes, the design-technology co-optimization (DTCO) paradigm has become increasingly critical, rendering efficient device design and optimization essential. In the domain of TCAD simulation, however, the scarcity of open-source resources hinders language models from generating valid TCAD code. To overcome this limitation, we construct an open-source TCAD dataset curated by experts and fine-tune a domain-specific model for TCAD code generation. Building on this foundation, we propose AgenticTCAD, a natural language - driven multi-agent framework that enables end-to-end automated device design and optimization. Validation on a 2 nm nanosheet FET (NS-FET) design shows that AgenticTCAD achieves the International Roadmap for Devices and Systems (IRDS)-2024 device specifications within 4.2 hours, whereas human experts required 7.1 days with commercial tools. - oai:arXiv.org:2512.23742v1 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Guangxi Fan, Tianliang Ma, Xuguang Sun, Xun Wang, Kain Lu Low, Leilai Shao - - - Hybrid-Code: A Privacy-Preserving, Redundant Multi-Agent Framework for Reliable Local Clinical Coding - https://arxiv.org/abs/2512.23743 - arXiv:2512.23743v1 Announce Type: new -Abstract: Clinical coding automation using cloud-based Large Language Models (LLMs) poses privacy risks and latency bottlenecks, rendering them unsuitable for on-premise healthcare deployment. We introduce Hybrid-Code, a hybrid neuro-symbolic multi-agent framework for local clinical coding that ensures production reliability through redundancy and verification. Our system comprises two agents: a Coder that attempts language model-based semantic reasoning using BioMistral-7B but falls back to deterministic keyword matching when model output is unreliable, ensuring pipeline completion; and an Auditor that verifies codes against a 257-code knowledge base and clinical evidence. Evaluating on 1,000 MIMIC-III discharge summaries, we demonstrate no hallucinated codes among accepted outputs within the knowledge base, 24.47% verification rate, and 34.11% coverage (95% CI: 31.2%--37.0%) with 86%+ language model utilization. The Auditor filtered invalid format codes and provided evidence-based quality control (75.53% rejection rate) while ensuring no patient data leaves the hospital firewall. The hybrid architecture -- combining language model semantic understanding (when successful), deterministic fallback (when the model fails), and symbolic verification (always active) -- ensures both reliability and privacy preservation, addressing critical barriers to AI adoption in healthcare. Our key finding is that reliability through redundancy is more valuable than pure model performance in production healthcare systems, where system failures are unacceptable. - oai:arXiv.org:2512.23743v1 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yunguo Yu - - - A Comprehensive Study of Deep Learning Model Fixing Approaches - https://arxiv.org/abs/2512.23745 - arXiv:2512.23745v1 Announce Type: new -Abstract: Deep Learning (DL) has been widely adopted in diverse industrial domains, including autonomous driving, intelligent healthcare, and aided programming. Like traditional software, DL systems are also prone to faults, whose malfunctioning may expose users to significant risks. Consequently, numerous approaches have been proposed to address these issues. In this paper, we conduct a large-scale empirical study on 16 state-of-the-art DL model fixing approaches, spanning model-level, layer-level, and neuron-level categories, to comprehensively evaluate their performance. We assess not only their fixing effectiveness (their primary purpose) but also their impact on other critical properties, such as robustness, fairness, and backward compatibility. To ensure comprehensive and fair evaluation, we employ a diverse set of datasets, model architectures, and application domains within a uniform experimental setup for experimentation. We summarize several key findings with implications for both industry and academia. For example, model-level approaches demonstrate superior fixing effectiveness compared to others. No single approach can achieve the best fixing performance while improving accuracy and maintaining all other properties. Thus, academia should prioritize research on mitigating these side effects. These insights highlight promising directions for future exploration in this field. - oai:arXiv.org:2512.23745v1 - cs.LG - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hanmo You, Zan Wang, Zishuo Dong, Luanqi Mo, Jianjun Zhao, Junjie Chen - - - DEFT: Differentiable Automatic Test Pattern Generation - https://arxiv.org/abs/2512.23746 - arXiv:2512.23746v1 Announce Type: new -Abstract: Modern IC complexity drives test pattern growth, with the majority of patterns targeting a small set of hard-to-detect (HTD) faults. This motivates new ATPG algorithms to improve test effectiveness specifically for HTD faults. This paper presents DEFT (Differentiable Automatic Test Pattern Generation), a new ATPG approach that reformulates the discrete ATPG problem as a continuous optimization task. DEFT introduces a mathematically grounded reparameterization that aligns the expected continuous objective with discrete fault-detection semantics, enabling reliable gradient-based pattern generation. To ensure scalability and stability on deep circuit graphs, DEFT integrates a custom CUDA kernel for efficient forward-backward propagation and applies gradient normalization to mitigate vanishing gradients. Compared to a leading commercial tool on two industrial benchmarks, DEFT improves HTD fault detection by 21.1% and 48.9% on average under the same pattern budget and comparable runtime. DEFT also supports practical ATPG settings such as partial assignment pattern generation, producing patterns with 19.3% fewer 0/1 bits while still detecting 35% more faults. These results indicate DEFT is a promising and effective ATPG engine, offering a valuable complement to existing heuristic. - oai:arXiv.org:2512.23746v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wei Li, Yan Zou, Yixin Liang, Jos\'e Moura, Shawn Blanton - - - State-of-the-art Small Language Coder Model: Mify-Coder - https://arxiv.org/abs/2512.23747 - arXiv:2512.23747v1 Announce Type: new -Abstract: We present Mify-Coder, a 2.5B-parameter code model trained on 4.2T tokens using a compute-optimal strategy built on the Mify-2.5B foundation model. Mify-Coder achieves comparable accuracy and safety while significantly outperforming much larger baseline models on standard coding and function-calling benchmarks, demonstrating that compact models can match frontier-grade models in code generation and agent-driven workflows. Our training pipeline combines high-quality curated sources with synthetic data generated through agentically designed prompts, refined iteratively using enterprise-grade evaluation datasets. LLM-based quality filtering further enhances data density, enabling frugal yet effective training. Through disciplined exploration of CPT-SFT objectives, data mixtures, and sampling dynamics, we deliver frontier-grade code intelligence within a single continuous training trajectory. Empirical evidence shows that principled data and compute discipline allow smaller models to achieve competitive accuracy, efficiency, and safety compliance. Quantized variants of Mify-Coder enable deployment on standard desktop environments without requiring specialized hardware. - oai:arXiv.org:2512.23747v1 - cs.SE - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Abhinav Parmar, Abhisek Panigrahi, Abhishek Kumar Dwivedi, Abhishek Bhattacharya, Adarsh Ramachandra, Aditya Choudhary, Aditya Garg, Aditya Raj, Alankrit Bhatt, Alpesh Yadav, Anant Vishnu, Ananthu Pillai, Ankush Kumar, Aryan Patnaik, Aswatha Narayanan S, Avanish Raj Singh, Bhavya Shree Gadda, Brijesh Pankajbhai Kachhadiya, Buggala Jahnavi, Chidurala Nithin Krishna, Chintan Shah, Chunduru Akshaya, Debarshi Banerjee, Debrup Dey, Deepa R., Deepika B G, Faiz ur Rahman, Gagan Gayari, Gudhi Jagadeesh Kumar Naidu, Gursimar Singh, Harshal Tyagi, Harshini K, James Mani Vathalloor, Jayarama Nettar, Jayashree Gajjam, Joe Walter Sugil George, Kamalakara Sri Krishna Tadepalli, Kamalkumar Rathinasamy, Karan Chaurasia, Karthikeyan S, Kashish Arora, Kaushal Desai, Khushboo Buwade, Kiran Manjrekar, Malikireddy Venkata Sai Likhitha, Manjunath A, Mitali Mahavir Bedmutha, Mohammed Rafee Tarafdar, Nikhil Tiwari, Nikitha K Gigi, Pavan Ravikumar, Pendyala Swarnanjali, Piyush Anand, Prakash Chandrasekar, Prasanna Bhalchandra Gawade, Prasanth Sivan, Preeti Khurana, Priyanshi Babbar, Rajab Ali Mondal, Rajesh Kumar Vissapragada, Rajeshwari Ganesan, Rajeswari Koppisetti, Ramjee R., Ramkumar Thiruppathisamy, Rani G. S., S Reka, Samarth Gupta, Sandeep Reddy Kothakota, Sarathy K, Sathyanarayana Sampath Kumar, Saurabh Kumar, Shashank Khasare, Shenbaga Devi Venkatesh Kumar, Shiva Rama Krishna Parvatham, Shoeb Shaikh, Shrishanmathi A, Shubham Pathak, Sree Samhita Koppaka, Sreenivasa Raghavan K S, Sreeram Venkatasubramanian, Suprabha Desai Bojja, Swetha R, Syed Ahmed, Chinmai Harshitha Thota, Tushar Yadav, Veeravelly Kusumitha, V V S S Prasanth Patnaik, Vidya Sri Sesetti, Vijayakeerthi K, Vikram Raj Bakshi, Vinay K K, Vinoth Kumar Loganathan, Vipin Tiwari, Vivek Kumar Shrivastav, V Venkata Sri Datta Charan, Wasim Akhtar Khan - - - A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios - https://arxiv.org/abs/2512.23748 - arXiv:2512.23748v1 Announce Type: new -Abstract: For complex simulation problems, inferring parameters of scientific interest often precludes the use of classical likelihood-based techniques due to intractable likelihood functions. Simulation-based inference (SBI) methods forego the need for explicit likelihoods by directly utilizing samples from the simulator to learn posterior distributions over parameters $\mathbf{\theta}$ given observed data $\mathbf{x}_{\text{o}}$. Recent work has brought attention to diffusion models -- a type of generative model rooted in score matching and reverse-time stochastic dynamics -- as a flexible framework SBI tasks. This article reviews diffusion-based SBI from first principles to applications in practice. We first recall the mathematical foundations of diffusion modeling (forward noising, reverse-time SDE/ODE, probability flow, and denoising score matching) and explain how conditional scores enable likelihood-free posterior sampling. We then examine where diffusion models address pain points of normalizing flows in neural posterior/likelihood estimation and where they introduce new trade-offs (e.g., iterative sampling costs). The key theme of this review is robustness of diffusion-based SBI in non-ideal conditions common to scientific data: misspecification (mismatch between simulated training data and reality), unstructured or infinite-dimensional observations, and missingness. We synthesize methods spanning foundations drawing from Schrodinger-bridge formulations, conditional and sequential posterior samplers, amortized architectures for unstructured data, and inference-time prior adaptation. Throughout, we adopt consistent notation and emphasize conditions and caveats required for accurate posteriors. The review closes with a discussion of open problems with an eye toward applications of uncertainty quantification for probabilistic geophysical models that may benefit from diffusion-based SBI. - oai:arXiv.org:2512.23748v1 - cs.LG - math.PR - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haley Rosso, Talea Mayo - - - Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents - https://arxiv.org/abs/2512.23749 - arXiv:2512.23749v1 Announce Type: new -Abstract: Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more effectively. \vspace*{6pt} - Contribution: In this paper, we present the Coordinate Matrix Machine (CM$^2$). This purpose-built small model augments human intelligence by learning document structures and using this information to classify documents. While modern "Red AI" trends rely on massive pre-training and energy-intensive GPU infrastructure, CM$^2$ is designed as a Green AI solution. It achieves human-level concept learning by identifying only the structural "important features" a human would consider, allowing it to classify very similar documents using only one sample per class. - Advantage: Our algorithm outperforms traditional vectorizers and complex deep learning models that require larger datasets and significant compute. By focusing on structural coordinates rather than exhaustive semantic vectors, CM$^2$ offers: 1. High accuracy with minimal data (one-shot learning) 2. Geometric and structural intelligence 3. Green AI and environmental sustainability 4. Optimized for CPU-only environments 5. Inherent explainability (glass-box model) 6. Faster computation and low latency 7. Robustness against unbalanced classes 8. Economic viability 9. Generic, expandable, and extendable - oai:arXiv.org:2512.23749v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Amin Sadri, M Maruf Hossain - - - Geometric Scaling of Bayesian Inference in LLMs - https://arxiv.org/abs/2512.23752 - arXiv:2512.23752v1 Announce Type: new -Abstract: Recent work has shown that small transformers trained in controlled "wind-tunnel'' settings can implement exact Bayesian inference, and that their training dynamics produce a geometric substrate -- low-dimensional value manifolds and progressively orthogonal keys -- that encodes posterior structure. We investigate whether this geometric signature persists in production-grade language models. Across Pythia, Phi-2, Llama-3, and Mistral families, we find that last-layer value representations organize along a single dominant axis whose position strongly correlates with predictive entropy, and that domain-restricted prompts collapse this structure into the same low-dimensional manifolds observed in synthetic settings. - To probe the role of this geometry, we perform targeted interventions on the entropy-aligned axis of Pythia-410M during in-context learning. Removing or perturbing this axis selectively disrupts the local uncertainty geometry, whereas matched random-axis interventions leave it intact. However, these single-layer manipulations do not produce proportionally specific degradation in Bayesian-like behavior, indicating that the geometry is a privileged readout of uncertainty rather than a singular computational bottleneck. Taken together, our results show that modern language models preserve the geometric substrate that enables Bayesian inference in wind tunnels, and organize their approximate Bayesian updates along this substrate. - oai:arXiv.org:2512.23752v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Naman Aggarwal, Siddhartha R. Dalal, Vishal Misra - - - Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation - https://arxiv.org/abs/2512.23753 - arXiv:2512.23753v1 Announce Type: new -Abstract: Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requiring specific activation functions whose geometric properties can induce activation-dependent learning-freeze behavior: a regime where gradients become extremely small for samples mapped into low-evidence regions. We theoretically characterize this behavior and analyze how different evidential activations influence learning dynamics. Building on this analysis, we design a general family of activation functions and corresponding evidential regularizers that provide an alternative pathway for consistent evidence updates across activation regimes. Extensive experiments on four benchmark classification problems (MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet), two few-shot classification problems, and blind face restoration problem empirically validate the developed theory and demonstrate the effectiveness of the proposed generalized regularized evidential models. - oai:arXiv.org:2512.23753v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Deep Shankar Pandey, Hyomin Choi, Qi Yu - - - HINTS: Extraction of Human Insights from Time-Series Without External Sources - https://arxiv.org/abs/2512.23755 - arXiv:2512.23755v1 Announce Type: new -Abstract: Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series forecasting models leverage external sources (e.g., news and social media) to capture human factors, but these approaches incur high data dependency costs in terms of financial, computational, and practical implications. In this study, we propose HINTS, a self-supervised learning framework that extracts these latent factors endogenously from time series residuals without external data. HINTS leverages the Friedkin-Johnsen (FJ) opinion dynamics model as a structural inductive bias to model evolving social influence, memory, and bias patterns. The extracted human factors are integrated into a state-of-the-art backbone model as an attention map. Experimental results using nine real-world and benchmark datasets demonstrate that HINTS consistently improves forecasting accuracy. Furthermore, multiple case studies and ablation studies validate the interpretability of HINTS, demonstrating strong semantic alignment between the extracted factors and real-world events, demonstrating the practical utility of HINTS. - oai:arXiv.org:2512.23755v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sheo Yon Jhin, Noseong Park - - - Sparse Random Matrices for Dimensionality Reduction - https://arxiv.org/abs/2512.23756 - arXiv:2512.23756v1 Announce Type: new -Abstract: The Johnson-Lindenstrauss (JL) theorem states that a set of points in high-dimensional space can be embedded into a lower-dimensional space while approximately preserving pairwise distances with high probability Johnson and Lindenstrauss (1984). The standard JL theorem uses dense random matrices with Gaussian entries. However, for some applications, sparse random matrices are preferred as they allow for faster matrix-vector multiplication. I outline the constructions and proofs introduced by Achlioptas (2003) and the contemporary standard by Kane and Nelson (2014). Further, I implement and empirically compare these sparse constructions with standard Gaussian JL matrices. - oai:arXiv.org:2512.23756v1 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Pierre Mackenzie - - - Audited Skill-Graph Self-Improvement for Agentic LLMs via Verifiable Rewards, Experience Synthesis, and Continual Memory - https://arxiv.org/abs/2512.23760 - arXiv:2512.23760v1 Announce Type: new -Abstract: Reinforcement learning is increasingly used to transform large language models into agentic systems that act over long horizons, invoke tools, and manage memory under partial observability. While recent work has demonstrated performance gains through tool learning, verifiable rewards, and continual training, deployed self-improving agents raise unresolved security and governance challenges: optimization pressure can incentivize reward hacking, behavioral drift is difficult to audit or reproduce, and improvements are often entangled in opaque parameter updates rather than reusable, verifiable artifacts. - This paper proposes Audited Skill-Graph Self-Improvement (ASG-SI), a framework that treats self-improvement as iterative compilation of an agent into a growing, auditable skill graph. Each candidate improvement is extracted from successful trajectories, normalized into a skill with an explicit interface, and promoted only after passing verifier-backed replay and contract checks. Rewards are decomposed into reconstructible components derived from replayable evidence, enabling independent audit of promotion decisions and learning signals. ASG-SI further integrates experience synthesis for scalable stress testing and continual memory control to preserve long-horizon performance under bounded context. - We present a complete system architecture, threat model, and security analysis, and provide a fully runnable reference implementation that demonstrates verifier-backed reward construction, skill compilation, audit logging, and measurable improvement under continual task streams. ASG-SI reframes agentic self-improvement as accumulation of verifiable, reusable capabilities, offering a practical path toward reproducible evaluation and operational governance of self-improving AI agents. - oai:arXiv.org:2512.23760v1 - cs.CR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ken Huang, Jerry Huang - - - Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data - https://arxiv.org/abs/2512.23761 - arXiv:2512.23761v1 Announce Type: new -Abstract: Advances in data acquisition and computational methods have accelerated the use of differential equation based modelling for complex systems. Such systems are often described by coupled (or more) variables, yet governing equation is typically available for one variable, while the remaining variable can be accessed only through data. This mismatch between known physics and observed data poses a fundamental challenge for existing physics-informed machine learning approaches, which generally assume either complete knowledge of the governing equations or full data availability across all variables. In this paper, we introduce MUSIC (Multitask Learning Under Sparse and Incomplete Constraints), a sparsity induced multitask neural network framework that integrates partial physical constraints with data-driven learning to recover full-dimensional solutions of coupled systems when physics-constrained and data-informed variables are mutually exclusive. MUSIC employs mesh-free (random) sampling of training data and sparsity regularization, yielding highly compressed models with improved training and evaluation efficiency. We demonstrate that MUSIC accurately learns solutions (shock wave solutions, discontinuous solutions, pattern formation solutions) to complex coupled systems under data-scarce and noisy conditions, consistently outperforming non-sparse formulations. These results highlight MUSIC as a flexible and effective approach for modeling partially observed systems with incomplete physical knowledge. - oai:arXiv.org:2512.23761v1 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Esha Saha, Hao Wang - - - Drift-Based Dataset Stability Benchmark - https://arxiv.org/abs/2512.23762 - arXiv:2512.23762v1 Announce Type: new -Abstract: Machine learning (ML) represents an efficient and popular approach for network traffic classification. However, network traffic classification is a challenging domain, and trained models may degrade soon after deployment due to the obsolete datasets and quick evolution of computer networks as new or updated protocols appear. Moreover, significant change in the behavior of a traffic type (and, therefore, the underlying features representing the traffic) can produce a large and sudden performance drop of the deployed model, known as a data or concept drift. In most cases, complete retraining is performed, often without further investigation of root causes, as good dataset quality is assumed. However, this is not always the case and further investigation must be performed. This paper proposes a novel methodology to evaluate the stability of datasets and a benchmark workflow that can be used to compare datasets. - The proposed framework is based on a concept drift detection method that also uses ML feature weights to boost the detection performance. The benefits of this work are demonstrated on CESNET-TLS-Year22 dataset. We provide the initial dataset stability benchmark that is used to describe dataset stability and weak points to identify the next steps for optimization. Lastly, using the proposed benchmarking methodology, we show the optimization impact on the created dataset variants. - oai:arXiv.org:2512.23762v1 - cs.LG - cs.AI - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Dominik Soukup, Richard Pln\'y, Daniel Va\v{s}ata, Tom\'a\v{s} \v{C}ejka - - - Neural Optimal Design of Experiment for Inverse Problems - https://arxiv.org/abs/2512.23763 - arXiv:2512.23763v1 Announce Type: new -Abstract: We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed-budget set of continuous design variables representing sensor locations, sampling times, or measurement angles, within a single optimization loop. By optimizing measurement locations directly rather than weighting a dense grid of candidates, the proposed approach enforces sparsity by design, eliminates the need for l1 tuning, and substantially reduces computational complexity. We validate NODE on an analytically tractable exponential growth benchmark, on MNIST image sampling, and illustrate its effectiveness on a real world sparse view X ray CT example. In all cases, NODE outperforms baseline approaches, demonstrating improved reconstruction accuracy and task-specific performance. - oai:arXiv.org:2512.23763v1 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - John E. Darges, Babak Maboudi Afkham, Matthias Chung - - - Exploring Cumulative Effects in Survival Data Using Deep Learning Networks - https://arxiv.org/abs/2512.23764 - arXiv:2512.23764v1 Announce Type: new -Abstract: In epidemiological research, modeling the cumulative effects of time-dependent exposures on survival outcomes presents a challenge due to their intricate temporal dynamics. Conventional spline-based statistical methods, though effective, require repeated data transformation for each spline parameter tuning, with survival analysis computations relying on the entire dataset, posing difficulties for large datasets. Meanwhile, existing neural network-based survival analysis methods focus on accuracy but often overlook the interpretability of cumulative exposure patterns. To bridge this gap, we introduce CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data. Evaluated on two diverse real-world datasets, CENNSurv revealed a multi-year lagged association between chronic environmental exposure and a critical survival outcome, as well as a critical short-term behavioral shift prior to subscription lapse. This demonstrates CENNSurv's ability to model complex temporal patterns with improved scalability. CENNSurv provides researchers studying cumulative effects a practical tool with interpretable insights. - oai:arXiv.org:2512.23764v1 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Kang-Chung Yang, Shinsheng Yuan - - - Entropy-Aware Speculative Decoding Toward Improved LLM Reasoning - https://arxiv.org/abs/2512.23765 - arXiv:2512.23765v1 Announce Type: new -Abstract: Speculative decoding (SD) accelerates large language model (LLM) reasoning by using a small draft model to generate candidate tokens, which the target LLM either accepts directly or regenerates upon rejection. However, excessive alignment between the draft and target models constrains SD to the performance of the target LLM. To address this limitation, we propose Entropy-Aware Speculative Decoding (EASD), a training-free enhancement. Building on standard SD, EASD incorporates a dynamic entropy-based penalty. At each decoding step, we employ the entropy of the sampling distribution to quantify model uncertainty. When both models exhibit high entropy with substantial overlap among their top-N predictions, the corresponding token is rejected and re-sampled by the target LLM. This penalty prevents low-confidence errors from propagating. By incorporating draft-model verification, EASD enables the possibility of surpassing the target model's inherent performance. Experiments across multiple reasoning benchmarks demonstrate that EASD consistently outperforms existing SD methods and, in most cases, surpasses the target LLM itself. We further prove that the efficiency of EASD is comparable to that of SD. The code can be found in the Supplementary Materials. - oai:arXiv.org:2512.23765v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Tiancheng Su, Meicong Zhang, Guoxiu He - - - A Granular Grassmannian Clustering Framework via the Schubert Variety of Best Fit - https://arxiv.org/abs/2512.23766 - arXiv:2512.23766v1 Announce Type: new -Abstract: In many classification and clustering tasks, it is useful to compute a geometric representative for a dataset or a cluster, such as a mean or median. When datasets are represented by subspaces, these representatives become points on the Grassmann or flag manifold, with distances induced by their geometry, often via principal angles. We introduce a subspace clustering algorithm that replaces subspace means with a trainable prototype defined as a Schubert Variety of Best Fit (SVBF) - a subspace that comes as close as possible to intersecting each cluster member in at least one fixed direction. Integrated in the Linde-Buzo-Grey (LBG) pipeline, this SVBF-LBG scheme yields improved cluster purity on synthetic, image, spectral, and video action data, while retaining the mathematical structure required for downstream analysis. - oai:arXiv.org:2512.23766v1 - cs.LG - cs.CG - cs.CV - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Karim Salta, Michael Kirby, Chris Peterson - - - Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics - https://arxiv.org/abs/2512.23767 - arXiv:2512.23767v1 Announce Type: new -Abstract: Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recovery (MR), which identifies governing equations from sensor data, is a key primitive for safe and explainable monitoring in mission-critical autonomous systems operating under strict latency, compute, and power constraints. However, state-of-the-art MR methods (e.g., EMILY and PINN+SR) rely on Neural ODE formulations that require iterative solvers and are difficult to accelerate efficiently on edge hardware. We present \textbf{MERINDA} (Model Recovery in Reconfigurable Dynamic Architecture), an FPGA-accelerated MR framework designed to make physical AI practical on resource-constrained devices. MERINDA replaces expensive Neural ODE components with a hardware-friendly formulation that combines (i) GRU-based discretized dynamics, (ii) dense inverse-ODE layers, (iii) sparsity-driven dropout, and (iv) lightweight ODE solvers. The resulting computation is structured for streaming parallelism, enabling critical kernels to be fully parallelized on the FPGA. Across four benchmark nonlinear dynamical systems, MERINDA delivers substantial gains over GPU implementations: \textbf{114$\times$ lower energy} (434~J vs.\ 49{,}375~J), \textbf{28$\times$ smaller memory footprint} (214~MB vs.\ 6{,}118~MB), and \textbf{1.68$\times$ faster training}, while matching state-of-the-art model-recovery accuracy. These results demonstrate that MERINDA can bring accurate, explainable MR to the edge for real-time monitoring of autonomous systems. - oai:arXiv.org:2512.23767v1 - cs.LG - cs.AI - cs.AR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Bin Xu, Ayan Banerjee, Sandeep Gupta - - - VGC: A High-Performance Zone-Based Garbage Collector Architecture for Python with Partitioning and Parallel Execution - https://arxiv.org/abs/2512.23768 - arXiv:2512.23768v1 Announce Type: new -Abstract: The Virtual Garbage Collector (VGC) introduces a novel memory management framework designed to optimize performance across diverse systems, ranging from resource constrained embedded devices to high performance parallel architectures. Unlike conventional garbage collectors, VGC employs a dual layer architecture consisting of Active VGC and Passive VGC to enable efficient, low overhead memory management. Active VGC dynamically manages runtime objects using a concurrent mark and sweep strategy tailored for parallel workloads, reducing pause times by up to 30 percent compared to generational collectors in multithreaded benchmarks. Passive VGC operates at compile time and optimizes static object allocation through predictive memory mapping, minimizing fragmentation by aligning objects to cache boundaries. This separation of responsibilities ensures predictable memory access patterns, reduces total memory usage by up to 25 percent, and improves scalability for modern parallel applications. By integrating compile time and runtime optimizations, VGC provides a robust and adaptable solution for memory intensive systems across both low level and high level programming environments. - oai:arXiv.org:2512.23768v1 - cs.PL - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/publicdomain/zero/1.0/ - Abdulla M - - - Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations - https://arxiv.org/abs/2512.23769 - arXiv:2512.23769v1 Announce Type: new -Abstract: Fairness in algorithmic decision-making is often framed in terms of individual fairness, which requires that similar individuals receive similar outcomes. A system violates individual fairness if there exists a pair of inputs differing only in protected attributes (such as race or gender) that lead to significantly different outcomes-for example, one favorable and the other unfavorable. While this notion highlights isolated instances of unfairness, it fails to capture broader patterns of systematic or clustered discrimination that may affect entire subgroups. We introduce and motivate the concept of discrimination clustering, a generalization of individual fairness violations. Rather than detecting single counterfactual disparities, we seek to uncover regions of the input space where small perturbations in protected features lead to k-significantly distinct clusters of outcomes. That is, for a given input, we identify a local neighborhood-differing only in protected attributes-whose members' outputs separate into many distinct clusters. These clusters reveal significant arbitrariness in treatment solely based on protected attributes that help expose patterns of algorithmic bias that elude pairwise fairness checks. We present HyFair, a hybrid technique that combines formal symbolic analysis (via SMT and MILP solvers) to certify individual fairness with randomized search to discover discriminatory clusters. This combination enables both formal guarantees-when no counterexamples exist-and the detection of severe violations that are computationally challenging for symbolic methods alone. Given a set of inputs exhibiting high k-unfairness, we introduce a novel explanation method to generate interpretable, decision-tree-style artifacts. Our experiments demonstrate that HyFair outperforms state-of-the-art fairness verification and local explanation methods. - oai:arXiv.org:2512.23769v1 - cs.SE - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ranit Debnath Akash (Gary), Ashish Kumar (Gary), Verya Monjezi (Gary), Ashutosh Trivedi (Gary), Gang (Gary), Tan, Saeid Tizpaz-Niari - - - Safety-Biased Policy Optimisation: Towards Hard-Constrained Reinforcement Learning via Trust Regions - https://arxiv.org/abs/2512.23770 - arXiv:2512.23770v1 Announce Type: new -Abstract: Reinforcement learning (RL) in safety-critical domains requires agents to maximise rewards while strictly adhering to safety constraints. Existing approaches, such as Lagrangian and projection-based methods, often either fail to ensure near-zero safety violations or sacrifice reward performance in the face of hard constraints. We propose Safety-Biased Trust Region Policy Optimisation (SB-TRPO), a new trust-region algorithm for hard-constrained RL. SB-TRPO adaptively biases policy updates towards constraint satisfaction while still seeking reward improvement. Concretely, it performs trust-region updates using a convex combination of the natural policy gradients of cost and reward, ensuring a fixed fraction of optimal cost reduction at each step. We provide a theoretical guarantee of local progress towards safety, with reward improvement when gradients are suitably aligned. Experiments on standard and challenging Safety Gymnasium tasks show that SB-TRPO consistently achieves the best balance of safety and meaningful task completion compared to state-of-the-art methods. - oai:arXiv.org:2512.23770v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ankit Kanwar, Dominik Wagner, Luke Ong - - - FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading - https://arxiv.org/abs/2512.23773 - arXiv:2512.23773v1 Announce Type: new -Abstract: Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage and liquidity and, therefore, thrive in the Crypto market. RL has been widely applied in various quantitative tasks. However, most methods focus on the spot and could not be directly applied to the futures market with high leverage because of 2 challenges. First, high leverage amplifies reward fluctuations, making training stochastic and difficult to converge. Second, prior works lacked self-awareness of capability boundaries, exposing them to the risk of significant loss when encountering new market state (e.g.,a black swan event like COVID-19). To tackle these challenges, we propose the Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading (FineFT), a novel three-stage ensemble RL framework with stable training and proper risk management. In stage I, ensemble Q learners are selectively updated by ensemble TD errors to improve convergence. In stage II, we filter the Q-learners based on their profitabilities and train VAEs on market states to identify the capability boundaries of the learners. In stage III, we choose from the filtered ensemble and a conservative policy, guided by trained VAEs, to maintain profitability and mitigate risk with new market states. Through extensive experiments on crypto futures in a high-frequency trading environment with high fidelity and 5x leverage, we demonstrate that FineFT outperforms 12 SOTA baselines in 6 financial metrics, reducing risk by more than 40% while achieving superior profitability compared to the runner-up. Visualization of the selective update mechanism shows that different agents specialize in distinct market dynamics, and ablation studies certify routing with VAEs reduces maximum drawdown effectively, and selective update improves convergence and performance. - oai:arXiv.org:2512.23773v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Molei Qin, Xinyu Cai, Yewen Li, Haochong Xia, Chuqiao Zong, Shuo Sun, Xinrun Wang, Bo An - - - Secure and Governed API Gateway Architectures for Multi-Cluster Cloud Environments - https://arxiv.org/abs/2512.23774 - arXiv:2512.23774v1 Announce Type: new -Abstract: API gateways serve as critical enforcement points for security, governance, and traffic management in cloud-native systems. As organizations increasingly adopt multi-cluster and hybrid cloud deployments, maintaining consistent policy enforcement, predictable performance, and operational stability across heterogeneous gateway environments becomes challenging. Existing approaches typically manage security, governance, and performance as loosely coupled concerns, leading to configuration drift, delayed policy propagation, and unstable runtime behavior under dynamic workloads. This paper presents a governance-aware, intent-driven architecture for coordinated API gateway management in multi-cluster cloud environments. The proposed approach expresses security, governance, and performance objectives as high-level declarative intents, which are systematically translated into enforceable gateway configurations and continuously validated through policy verification and telemetry-driven feedback. By decoupling intent specification from enforcement while enabling bounded, policy-compliant adaptation, the architecture supports heterogeneous gateway implementations without compromising governance guarantees or service-level objectives. A prototype implementation across multiple Kubernetes clusters demonstrates the effectiveness of the proposed design. Experimental results show up to a 42% reduction in policy drift, a 31% improvement in configuration propagation time, and sustained p95 latency overhead below 6% under variable workloads, compared to manual and declarative baseline approaches. These results indicate that governance-aware, intent-driven gateway orchestration provides a scalable and reliable foundation for secure, consistent, and performance-predictable cloud-native platforms. - oai:arXiv.org:2512.23774v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Vinoth Punniyamoorthy, Kabilan Kannan, Akshay Deshpande, Lokesh Butra, Akash Kumar Agarwal, Adithya Parthasarathy, Suhas Malempati, Bikesh Kumar - - - A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms - https://arxiv.org/abs/2512.23777 - arXiv:2512.23777v1 Announce Type: new -Abstract: This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs),focusing on the effectiveness of various models. By analyzing prevalent fraudulent activities, the research highlights and compares the existing work related to fraud detection which can be useful when addressing fraudulent incidents within the online ride hailing platforms. Also, the paper highlights addressing class imbalance and fraudulent camouflage. It also outlines a structured overview of GNN architectures and methodologies applied to anomaly detection, identifying significant methodological progress and gaps. The paper calls for further exploration into real-world applicability and technical improvements to enhance fraud detection strategies in the rapidly evolving ride-hailing industry. - oai:arXiv.org:2512.23777v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/ICAIBD62003.2024.10604597 - 2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 168-179 - Kanishka Hewageegana, Janani Harischandra, Nipuna Senanayake, Gihan Danansuriya, Kavindu Hapuarachchi, Pooja Illangarathne - - - SyncGait: Robust Long-Distance Authentication for Drone Delivery via Implicit Gait Behaviors - https://arxiv.org/abs/2512.23778 - arXiv:2512.23778v1 Announce Type: new -Abstract: In recent years, drone delivery, which utilizes unmanned aerial vehicles (UAVs) for package delivery and pickup, has gradually emerged as a crucial method in logistics. Since delivery drones are expensive and may carry valuable packages, they must maintain a safe distance from individuals until user-drone mutual authentication is confirmed. Despite numerous authentication schemes being developed, existing solutions are limited in authentication distance and lack resilience against sophisticated attacks. To this end, we introduce SyncGait, an implicit gait-based mutual authentication system for drone delivery. SyncGait leverages the user's unique arm swing as he walks toward the drone to achieve mutual authentication without requiring additional hardware or specific authentication actions. We conducted extensive experiments on 14 datasets collected from 31 subjects. The results demonstrate that SyncGait achieves an average accuracy of 99.84\% at a long distance ($>18m$) and exhibits strong resilience against various spoofing attacks, making it a robust, secure, and user-friendly solution in real-world scenarios. - oai:arXiv.org:2512.23778v1 - cs.CR - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zijian Ling, Man Zhou, Hongda Zhai, Yating Huang, Lingchen Zhao, Qi Li, Chao Shen, Qian Wang - - - Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark - https://arxiv.org/abs/2512.23779 - arXiv:2512.23779v1 Announce Type: new -Abstract: Large language models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This degrades answer quality, inflates latency and cost, and can be weaponized as a denial-of-service (DoS) attack. Recent work has begun to study DoS-style prompt attacks, but typically focuses on a single attack algorithm or assumes white-box access, without an attack-side benchmark that compares prompt-based attackers in a black-box, query-only regime with a known tokenizer. We introduce such a benchmark and study two prompt-only attackers. The first is Evolutionary Over-Generation Prompt Search (EOGen), which searches the token space for prefixes that suppress EOS and induce long continuations. The second is a goal-conditioned reinforcement learning attacker (RL-GOAL) that trains a network to generate prefixes conditioned on a target length. To characterize behavior, we introduce Over-Generation Factor (OGF), the ratio of produced tokens to a model's context window, along with stall and latency summaries. Our evolutionary attacker achieves mean OGF = 1.38 +/- 1.15 and Success@OGF >= 2 of 24.5 percent on Phi-3. RL-GOAL is stronger: across victims it achieves higher mean OGF (up to 2.81 +/- 1.38). - oai:arXiv.org:2512.23779v1 - cs.CR - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Manu, Yi Guo, Jo Plested, Tim Lynar, Kanchana Thilakarathna, Nirhoshan Sivaroopan, Jack Yang, Wangli Yang - - - Test Case Specification Techniques and System Testing Tools in the Automotive Industry: A Review - https://arxiv.org/abs/2512.23780 - arXiv:2512.23780v1 Announce Type: new -Abstract: The automotive domain is shifting to software-centric development to meet regulation, market pressure, and feature velocity. This shift increases embedded systems' complexity and strains testing capacity. Despite relevant standards, a coherent system-testing methodology that spans heterogeneous, legacy-constrained toolchains remains elusive, and practice often depends on individual expertise rather than a systematic strategy. We derive challenges and requirements from a systematic literature review (SLR), complemented by industry experience and practice. We map them to test case specification techniques and testing tools, evaluating their suitability for automotive testing using PRISMA. Our contribution is a curated catalog that supports technique/tool selection and can inform future testing frameworks and improvements. We synthesize nine recurring challenge areas across the life cycle, such as requirements quality and traceability, variability management, and toolchain fragmentation. We then provide a prioritized criteria catalog that recommends model-based planning, interoperable and traceable toolchains, requirements uplift, pragmatic automation and virtualization, targeted AI and formal methods, actionable metrics, and lightweight organizational practices. - oai:arXiv.org:2512.23780v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Denesa Zyberaj, Pascal Hirmer, Marco Aiello, Stefan Wagner - - - Personalized Promotions in Practice: Dynamic Allocation and Reference Effects - https://arxiv.org/abs/2512.23781 - arXiv:2512.23781v1 Announce Type: new -Abstract: Partnering with a large online retailer, we consider the problem of sending daily personalized promotions to a userbase of over 20 million customers. We propose an efficient policy for determining, every day, the promotion that each customer should receive (10%, 12%, 15%, 17%, or 20% off), while respecting global allocation constraints. This policy was successfully deployed to see a 4.5% revenue increase during an A/B test, by better targeting promotion-sensitive customers and also learning intertemporal patterns across customers. - We also consider theoretically modeling the intertemporal state of the customer. The data suggests a simple new combinatorial model of pricing with reference effects, where the customer remembers the best promotion they saw over the past $\ell$ days as the "reference value", and is more likely to purchase if this value is poor. We tightly characterize the structure of optimal policies for maximizing long-run average revenue under this model -- they cycle between offering poor promotion values $\ell$ times and offering good values once. - oai:arXiv.org:2512.23781v1 - cs.GT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jackie Baek, Will Ma, Dmitry Mitrofanov - - - A Systematic Mapping on Software Fairness: Focus, Trends and Industrial Context - https://arxiv.org/abs/2512.23782 - arXiv:2512.23782v1 Announce Type: new -Abstract: Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of research solutions for ensuring fairness in software systems remains challenging. Objectives: This paper presents a systematic literature mapping to explore and categorize current advancements in fairness solutions within software engineering, focusing on three key dimensions: research trends, research focus, and viability in industrial contexts. Methods: We develop a classification framework to organize research on software fairness from a fresh perspective, applying it to 95 selected studies and analyzing their potential for industrial adoption. Results: Our findings reveal that software fairness research is expanding, yet it remains heavily focused on methods and algorithms. It primarily focuses on post-processing and group fairness, with less emphasis on early-stage interventions, individual fairness metrics, and understanding bias root causes. Additionally fairness research remains largely academic, with limited industry collaboration and low to medium Technology Readiness Level (TRL), indicating that industrial transferability remains distant. Conclusion: Our results underscore the need to incorporate fairness considerations across all stages of the software development life-cycle and to foster greater collaboration between academia and industry. This analysis provides a comprehensive overview of the field, offering a foundation to guide future research and practical applications of fairness in software systems. - oai:arXiv.org:2512.23782v1 - cs.SE - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kessia Nepomuceno, Fabio Petrillo - - - Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey - https://arxiv.org/abs/2512.23785 - arXiv:2512.23785v1 Announce Type: new -Abstract: Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks in cybersecurity, which rely on data-dependent power variations in a system to extract sensitive information. While there are related surveys, they primarily focus on power side-channel attacks on cryptographic implementations. In recent years, power-side channel attacks have been explored in diverse application domains, including key extraction from cryptographic implementations, reverse engineering of machine learning models, user behavior data exploitation, and instruction-level disassembly. In this paper, we provide a comprehensive survey of power side-channel attacks and their countermeasures in different application domains. Specifically, this survey aims to classify recent power side-channel attacks and provide a comprehensive comparison based on application-specific considerations. - oai:arXiv.org:2512.23785v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sahan Sanjaya, Aruna Jayasena, Prabhat Mishra - - - Leveraging Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments - https://arxiv.org/abs/2512.23786 - arXiv:2512.23786v1 Announce Type: new -Abstract: Accurate Monocular Depth Estimation (MDE) is critical for robotic surgery but remains fragile in specular, fluid-filled endoscopic environments. Existing self-supervised methods, typically relying on foundation models trained with noisy real-world pseudo-labels, often suffer from boundary collapse on thin surgical tools and transparent surfaces. In this work, we address this by leveraging the high-fidelity synthetic priors of the Depth Anything V2 architecture, which inherently captures precise geometric details of thin structures. We efficiently adapt these priors to the medical domain using Dynamic Vector Low-Rank Adaptation (DV-LORA), minimizing the parameter budget while bridging the synthetic-to-real gap. Additionally, we introduce a physically-stratified evaluation protocol on the SCARED dataset to rigorously quantify performance in high-specularity regimes often masked by aggregate metrics. Our approach establishes a new state-of-the-art, achieving an accuracy (< 1.25) of 98.1% and reducing Squared Relative Error by over 17% compared to established baselines, demonstrating superior robustness in adverse surgical lighting. - oai:arXiv.org:2512.23786v1 - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ankan Aich, Yangming Lee - - - TabMixNN: A Unified Deep Learning Framework for Structural Mixed Effects Modeling on Tabular Data - https://arxiv.org/abs/2512.23787 - arXiv:2512.23787v1 Announce Type: new -Abstract: We present TabMixNN, a flexible PyTorch-based deep learning framework that synthesizes classical mixed-effects modeling with modern neural network architectures for tabular data analysis. TabMixNN addresses the growing need for methods that can handle hierarchical data structures while supporting diverse outcome types including regression, classification, and multitask learning. The framework implements a modular three-stage architecture: (1) a mixed-effects encoder with variational random effects and flexible covariance structures, (2) backbone architectures including Generalized Structural Equation Models (GSEM) and spatial-temporal manifold networks, and (3) outcome-specific prediction heads supporting multiple outcome families. Key innovations include an R-style formula interface for accessibility, support for directed acyclic graph (DAG) constraints for causal structure learning, Stochastic Partial Differential Equation (SPDE) kernels for spatial modeling, and comprehensive interpretability tools including SHAP values and variance decomposition. We demonstrate the framework's flexibility through applications to longitudinal data analysis, genomic prediction, and spatial-temporal modeling. TabMixNN provides a unified interface for researchers to leverage deep learning while maintaining the interpretability and theoretical grounding of classical mixed-effects models. - oai:arXiv.org:2512.23787v1 - cs.LG - stat.CO - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Deniz Akdemir - - - VBSF: A Visual-Based Spam Filtering Technique for Obfuscated Emails - https://arxiv.org/abs/2512.23788 - arXiv:2512.23788v1 Announce Type: new -Abstract: Recent spam email techniques exploit visual effects in text messages, such as poisoning text, obfuscating words, and hidden text salting techniques. These effects were able to evade spam detection techniques based on the text. In this paper, we overcome this limitation by introducing a novel visual-based spam detection architecture, denoted as visual-based spam filter (VBSF). The multi-step process mimics the human eye's natural way of processing visual information, automatically rendering incoming emails and capturing their content as it appears on a user screen. Then, two different processing pipelines are applied in parallel. The first pipeline pertains to the perceived textual content, as it includes optical character recognition (OCR) to extract rendered textual content, followed by naive Bayes (NB) and decision tree (DT) content classifiers. The second pipeline focuses on the appearance of the email, as it analyzes and classifies the images of rendered emails through a specific convolutional neural network. Lastly, a meta classifier integrates text- and image-based classifier outputs, exploiting the stacking ensemble learning method. The performance of the proposed VBSF is assessed, showing that it achieves an accuracy of more than 98%, which is higher than the compared existing techniques on the designed dataset. - oai:arXiv.org:2512.23788v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.5220/0013133700003899 - Ali Hossary, Stefano Tomasin - - - A note on the space-time variational formulation for the wave equation with source term in $L^2(Q)$ - https://arxiv.org/abs/2512.23807 - arXiv:2512.23807v1 Announce Type: new -Abstract: We derive a variational formulation for the scalar wave equation in the second-order formulation on bounded Lipschitz domains and homogeneous initial conditions. We investigate a variational framework in a bounded space-time cylinder $Q$ with a new solution space and the test space $L^2(Q)$ for source terms in $L^2(Q)$. Using existence and uniqueness results in $H^1(Q)$, we prove that this variational setting fits the inf-sup theory, including an isomorphism as solution operator. Moreover, we show that the new solution space is not a subspace of $H^2(Q)$. This new uniqueness and solvability result is not only crucial for discretizations using space-time methods, including least-squares approaches, but also important for regularity results and the analysis of related space-time boundary integral equations, which form the basis for space-time boundary element methods. - oai:arXiv.org:2512.23807v1 - math.NA - cs.NA - math.AP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Marco Zank - - - MiMo-Audio: Audio Language Models are Few-Shot Learners - https://arxiv.org/abs/2512.23808 - arXiv:2512.23808v1 Announce Type: new -Abstract: Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio. - oai:arXiv.org:2512.23808v1 - cs.CL - cs.SD - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Core Team, Dong Zhang, Gang Wang, Jinlong Xue, Kai Fang, Liang Zhao, Rui Ma, Shuhuai Ren, Shuo Liu, Tao Guo, Weiji Zhuang, Xin Zhang, Xingchen Song, Yihan Yan, Yongzhe He, Cici, Bowen Shen, Chengxuan Zhu, Chong Ma, Chun Chen, Heyu Chen, Jiawei Li, Lei Li, Menghang Zhu, Peidian Li, Qiying Wang, Sirui Deng, Weimin Xiong, Wenshan Huang, Wenyu Yang, Yilin Jiang, Yixin Yang, Yuanyuan Tian, Yue Ma, Yue Yu, Zihan Zhang, Zihao Yue, Bangjun Xiao, Bingquan Xia, Bofei Gao, Bowen Ye, Can Cai, Chang Liu, Chenhong He, Chunan Li, Dawei Zhu, Duo Zhang, Fengyuan Shi, Guoan Wang, Hailin Zhang, Hanglong Lv, Hanyu Li, Hao Tian, Heng Qu, Hongshen Xu, Houbin Zhang, Huaqiu Liu, Jiangshan Duo, Jianguang Zuo, Jianyu Wei, Jiebao Xiao, Jinhao Dong, Jun Shi, Junhao Hu, Kainan Bao, Kang Zhou, Linghao Zhang, Meng Chen, Nuo Chen, Peng Zhang, Qianli Chen, Qiantong Wang, Rang Li, Shaohui Liu, Shengfan Wang, Shicheng Li, Shihua Yu, Shijie Cao, Shimao Chen, Shuhao Gu, Weikun Wang, Wenhan Ma, Xiangwei Deng, Xing Yong, Xing Zhang, Xu Wang, Yifan Song, Yihao Zhao, Yingbo Zhao, Yizhao Gao, Yu Cheng, Yu Tu, Yudong Wang, Zhaojun Huang, Zhengju Tang, Zhenru Lin, Zhichao Song, Zhipeng Xu, Zhixian Zheng, Zihan Jiang - - - Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems - https://arxiv.org/abs/2512.23809 - arXiv:2512.23809v1 Announce Type: new -Abstract: Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight urgent security gaps in Industrial IoT (IIoT) deployments. While Federated Learning (FL) enables privacy-preserving collaborative intrusion detection, existing frameworks remain vulnerable to Byzantine poisoning attacks and lack robust agent authentication. We propose Zero-Trust Agentic Federated Learning (ZTA-FL), a defense in depth framework combining: (1) TPM-based cryptographic attestation achieving less than 0.0000001 false acceptance rate, (2) a novel SHAP-weighted aggregation algorithm providing explainable Byzantine detection under non-IID conditions with theoretical guarantees, and (3) privacy-preserving on-device adversarial training. Comprehensive experiments across three IDS benchmarks (Edge-IIoTset, CIC-IDS2017, UNSW-NB15) demonstrate that ZTA-FL achieves 97.8 percent detection accuracy, 93.2 percent accuracy under 30 percent Byzantine attacks (outperforming FLAME by 3.1 percent, p less than 0.01), and 89.3 percent adversarial robustness while reducing communication overhead by 34 percent. We provide theoretical analysis, failure mode characterization, and release code for reproducibility. - oai:arXiv.org:2512.23809v1 - cs.LG - cs.AI - cs.CR - cs.DC - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Samaresh Kumar Singh, Joyjit Roy, Martin So - - - StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection - https://arxiv.org/abs/2512.23813 - arXiv:2512.23813v1 Announce Type: new -Abstract: The prevalence of chronic stress represents a significant public health concern, with social media platforms like Twitter serving as important venues for individuals to share their experiences. This paper introduces StressRoBERTa, a cross-condition transfer learning approach for automatic detection of self-reported chronic stress in English tweets. The investigation examines whether continual training on clinically related conditions (depression, anxiety, PTSD), disorders with high comorbidity with chronic stress, improves stress detection compared to general language models and broad mental health models. RoBERTa is continually trained on the Stress-SMHD corpus (108M words from users with self-reported diagnoses of depression, anxiety, and PTSD) and fine-tuned on the SMM4H 2022 Task 8 dataset. StressRoBERTa achieves 82% F1-score, outperforming the best shared task system (79% F1) by 3 percentage points. The results demonstrate that focused cross-condition transfer from stress-related disorders (+1% F1 over vanilla RoBERTa) provides stronger representations than general mental health training. Evaluation on Dreaddit (81% F1) further demonstrates transfer from clinical mental health contexts to situational stress discussions. - oai:arXiv.org:2512.23813v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Amal Alqahtani, Efsun Kayi, Mona Diab - - - Greedy Rational Approximation for Frequency-Domain Model Reduction of Parametric LTI Systems - https://arxiv.org/abs/2512.23814 - arXiv:2512.23814v1 Announce Type: new -Abstract: We investigate model reduction of parametric linear time-invariant (LTI) dynamical systems. When posed in the frequency domain, this problem can be formulated as seeking a low-order rational function approximation of a high-order rational function. We propose to use a standard reduced basis method (RBM) to construct this low-order rational function. Algorithmically, this procedure is an iterative greedy approach, where the greedy objective is evaluated through an error estimator that exploits the linearity of the frequency domain representation. The greedy framework is motivated through theoretical results of rational approximability of functions. This framework provides a principled approach to rational compression of high-order rational functions, and provides a computational pathway for model reduction of parametric LTI systems. - oai:arXiv.org:2512.23814v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Filip B\v{e}l\'ik, Yanlai Chen, Akil Narayan - - - Improved Bounds for Private and Robust Alignment - https://arxiv.org/abs/2512.23816 - arXiv:2512.23816v1 Announce Type: new -Abstract: In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the suboptimality gap in both offline and online settings. We consider preference labels subject to privacy constraints and/or adversarial corruption, and analyze two distinct interplays between them: privacy-first and corruption-first. For the privacy-only setting, we show that log loss with an MLE-style algorithm achieves near-optimal rates, in contrast to conventional wisdom. For the joint privacy-and-corruption setting, we first demonstrate that existing offline algorithms in fact provide stronger guarantees -- simultaneously in terms of corruption level and privacy parameters -- than previously known, which further yields improved bounds in the corruption-only regime. In addition, we also present the first set of results for private and robust online alignment. Our results are enabled by new uniform convergence guarantees for log loss and square loss under privacy and corruption, which we believe have broad applicability across learning theory and statistics. - oai:arXiv.org:2512.23816v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Wenqian Weng, Yi He, Xingyu Zhou - - - Video-Based Performance Evaluation for ECR Drills in Synthetic Training Environments - https://arxiv.org/abs/2512.23819 - arXiv:2512.23819v1 Announce Type: new -Abstract: Effective urban warfare training requires situational awareness and muscle memory, developed through repeated practice in realistic yet controlled environments. A key drill, Enter and Clear the Room (ECR), demands threat assessment, coordination, and securing confined spaces. The military uses Synthetic Training Environments that offer scalable, controlled settings for repeated exercises. However, automatic performance assessment remains challenging, particularly when aiming for objective evaluation of cognitive, psychomotor, and teamwork skills. Traditional methods often rely on costly, intrusive sensors or subjective human observation, limiting scalability and accuracy. This paper introduces a video-based assessment pipeline that derives performance analytics from training videos without requiring additional hardware. By utilizing computer vision models, the system extracts 2D skeletons, gaze vectors, and movement trajectories. From these data, we develop task-specific metrics that measure psychomotor fluency, situational awareness, and team coordination. These metrics feed into an extended Cognitive Task Analysis (CTA) hierarchy, which employs a weighted combination to generate overall performance scores for teamwork and cognition. We demonstrate the approach with a case study of real-world ECR drills, providing actionable, domain specific metrics that capture individual and team performance. We also discuss how these insights can support After Action Reviews with interactive dashboards within Gamemaster and the Generalized Intelligent Framework for Tutoring (GIFT), providing intuitive and understandable feedback. We conclude by addressing limitations, including tracking difficulties, ground-truth validation, and the broader applicability of our approach. Future work includes expanding analysis to 3D video data and leveraging video analysis to enable scalable evaluation within STEs. - oai:arXiv.org:2512.23819v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Surya Rayala, Marcos Quinones-Grueiro, Naveeduddin Mohammed, Ashwin T S, Benjamin Goldberg, Randall Spain, Paige Lawton, Gautam Biswas - - - MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling - https://arxiv.org/abs/2512.23824 - arXiv:2512.23824v1 Announce Type: new -Abstract: State-space models (SSMs) have recently attention as an efficient alternative to computationally expensive attention-based models for sequence modeling. They rely on linear recurrences to integrate information over time, enabling fast inference, parallelizable training, and control over recurrence stability. However, traditional SSMs often suffer from limited effective memory, requiring larger state sizes for improved recall. Moreover, existing SSMs struggle to capture multi-scale dependencies, which are essential for modeling complex structures in time series, images, and natural language. This paper introduces a multi-scale SSM framework that addresses these limitations by representing sequence dynamics across multiple resolution and processing each resolution with specialized state-space dynamics. By capturing both fine-grained, high-frequency patterns and coarse, global trends, MS-SSM enhances memory efficiency and long-range modeling. We further introduce an input-dependent scale-mixer, enabling dynamic information fusion across resolutions. The proposed approach significantly improves sequence modeling, particularly in long-range and hierarchical tasks, while maintaining computational efficiency. Extensive experiments on benchmarks, including Long Range Arena, hierarchical reasoning, time series classification, and image recognition, demonstrate that MS-SSM consistently outperforms prior SSM-based models, highlighting the benefits of multi-resolution processing in state-space architectures. - oai:arXiv.org:2512.23824v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mahdi Karami, Ali Behrouz, Peilin Zhong, Razvan Pascanu, Vahab Mirrokni - - - Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations - https://arxiv.org/abs/2512.23829 - arXiv:2512.23829v1 Announce Type: new -Abstract: Inverse problems are important mathematical problems that seek to recover model parameters from noisy data. Since inverse problems are often ill-posed, they require regularization or incorporation of prior information about the underlying model or unknown variables. Proximal operators, ubiquitous in nonsmooth optimization, are central to this because they provide a flexible and convenient way to encode priors and build efficient iterative algorithms. They have also recently become key to modern machine learning methods, e.g., for plug-and-play methods for learned denoisers and deep neural architectures for learning priors of proximal operators. The latter was developed partly due to recent work characterizing proximal operators of nonconvex priors as subdifferential of convex potentials. In this work, we propose to leverage connections between proximal operators and Hamilton-Jacobi partial differential equations (HJ PDEs) to develop novel deep learning architectures for learning the prior. In contrast to other existing methods, we learn the prior directly without recourse to inverting the prior after training. We present several numerical results that demonstrate the efficiency of the proposed method in high dimensions. - oai:arXiv.org:2512.23829v1 - math.NA - cs.LG - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Oluwatosin Akande, Gabriel P. Langlois, Akwum Onwunta - - - Exploiting the Prior of Generative Time Series Imputation - https://arxiv.org/abs/2512.23832 - arXiv:2512.23832v1 Announce Type: new -Abstract: Time series imputation, i.e., filling the missing values of a time recording, finds various applications in electricity, finance, and weather modelling. Previous methods have introduced generative models such as diffusion probabilistic models and Schrodinger bridge models to conditionally generate the missing values from Gaussian noise or directly from linear interpolation results. However, as their prior is not informative to the ground-truth target, their generation process inevitably suffer increased burden and limited imputation accuracy. In this work, we present Bridge-TS, building a data-to-data generation process for generative time series imputation and exploiting the design of prior with two novel designs. Firstly, we propose expert prior, leveraging a pretrained transformer-based module as an expert to fill the missing values with a deterministic estimation, and then taking the results as the prior of ground truth target. Secondly, we explore compositional priors, utilizing several pretrained models to provide different estimation results, and then combining them in the data-to-data generation process to achieve a compositional priors-to-target imputation process. Experiments conducted on several benchmark datasets such as ETT, Exchange, and Weather show that Bridge-TS reaches a new record of imputation accuracy in terms of mean square error and mean absolute error, demonstrating the superiority of improving prior for generative time series imputation. - oai:arXiv.org:2512.23832v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - YuYang Miao, Chang Li, Zehua Chen - - - Artificial Intelligence for All? Brazilian Teachers on Ethics, Equity, and the Everyday Challenges of AI in Education - https://arxiv.org/abs/2512.23834 - arXiv:2512.23834v1 Announce Type: new -Abstract: This study examines the perceptions of Brazilian K-12 education teachers regarding the use of AI in education, specifically General Purpose AI. This investigation employs a quantitative analysis approach, extracting information from a questionnaire completed by 346 educators from various regions of Brazil regarding their AI literacy and use. Educators vary in their educational level, years of experience, and type of educational institution. The analysis of the questionnaires shows that although most educators had only basic or limited knowledge of AI (80.3\%), they showed a strong interest in its application, particularly for the creation of interactive content (80.6%), lesson planning (80.2%), and personalized assessment (68.6%). The potential of AI to promote inclusion and personalized learning is also widely recognized (65.5%). The participants emphasized the importance of discussing ethics and digital citizenship, reflecting on technological dependence, biases, transparency, and responsible use of AI, aligning with critical education and the development of conscious students. Despite enthusiasm for the pedagogical potential of AI, significant structural challenges were identified, including a lack of training (43.4%), technical support (41.9%), and limitations of infrastructure, such as low access to computers, reliable Internet connections, and multimedia resources in schools. The study shows that Brazil is still in a bottom-up model for AI integration, missing official curricula to guide its implementation and structured training for teachers and students. Furthermore, effective implementation of AI depends on integrated public policies, adequate teacher training, and equitable access to technology, promoting ethical, inclusive, and contextually grounded adoption of AI in Brazilian K-12 education. - oai:arXiv.org:2512.23834v1 - cs.CY - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Bruno Florentino, Camila Sestito, Wellington Cruz, Andr\'e de Carvalho, Robson Bonidia - - - Explaining News Bias Detection: A Comparative SHAP Analysis of Transformer Model Decision Mechanisms - https://arxiv.org/abs/2512.23835 - arXiv:2512.23835v1 Announce Type: new -Abstract: Automated bias detection in news text is heavily used to support journalistic analysis and media accountability, yet little is known about how bias detection models arrive at their decisions or why they fail. In this work, we present a comparative interpretability study of two transformer-based bias detection models: a bias detector fine-tuned on the BABE dataset and a domain-adapted pre-trained RoBERTa model fine-tuned on the BABE dataset, using SHAP-based explanations. We analyze word-level attributions across correct and incorrect predictions to characterize how different model architectures operationalize linguistic bias. Our results show that although both models attend to similar categories of evaluative language, they differ substantially in how these signals are integrated into predictions. The bias detector model assigns stronger internal evidence to false positives than to true positives, indicating a misalignment between attribution strength and prediction correctness and contributing to systematic over-flagging of neutral journalistic content. In contrast, the domain-adaptive model exhibits attribution patterns that better align with prediction outcomes and produces 63\% fewer false positives. We further demonstrate that model errors arise from distinct linguistic mechanisms, with false positives driven by discourse-level ambiguity rather than explicit bias cues. These findings highlight the importance of interpretability-aware evaluation for bias detection systems and suggest that architectural and training choices critically affect both model reliability and deployment suitability in journalistic contexts. - oai:arXiv.org:2512.23835v1 - cs.CL - cs.AI - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Himel Ghosh - - - Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance? - https://arxiv.org/abs/2512.23836 - arXiv:2512.23836v1 Announce Type: new -Abstract: The success of expanded context windows in Large Language Models (LLMs) has driven increased use of broader context in retrieval-augmented generation. We investigate the use of LLMs for retrieval augmented question answering. While longer contexts make it easier to incorporate targeted knowledge, they introduce more irrelevant information that hinders the model's generation process and degrades its performance. To address the issue, we design an adaptive prompting strategy which involves splitting the retrieved information into smaller chunks and sequentially prompting a LLM to answer the question using each chunk. Adjusting the chunk size allows a trade-off between incorporating relevant information and reducing irrelevant information. Experimental results on three open-domain question answering datasets demonstrate that the adaptive strategy matches the performance of standard prompting while using fewer tokens. Our analysis reveals that when encountering insufficient information, the LLM often generates incorrect answers instead of declining to respond, which constitutes a major source of error. This finding highlights the need for further research into enhancing LLMs' ability to effectively decline requests when faced with inadequate information. - oai:arXiv.org:2512.23836v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Dingmin Wang, Ji Ma, Shankar Kumar - - - Adversarial Lens: Exploiting Attention Layers to Generate Adversarial Examples for Evaluation - https://arxiv.org/abs/2512.23837 - arXiv:2512.23837v1 Announce Type: new -Abstract: Recent advances in mechanistic interpretability suggest that intermediate attention layers encode token-level hypotheses that are iteratively refined toward the final output. In this work, we exploit this property to generate adversarial examples directly from attention-layer token distributions. Unlike prompt-based or gradient-based attacks, our approach leverages model-internal token predictions, producing perturbations that are both plausible and internally consistent with the model's own generation process. We evaluate whether tokens extracted from intermediate layers can serve as effective adversarial perturbations for downstream evaluation tasks. We conduct experiments on argument quality assessment using the ArgQuality dataset, with LLaMA-3.1-Instruct-8B serving as both the generator and evaluator. Our results show that attention-based adversarial examples lead to measurable drops in evaluation performance while remaining semantically similar to the original inputs. However, we also observe that substitutions drawn from certain layers and token positions can introduce grammatical degradation, limiting their practical effectiveness. Overall, our findings highlight both the promise and current limitations of using intermediate-layer representations as a principled source of adversarial examples for stress-testing LLM-based evaluation pipelines. - oai:arXiv.org:2512.23837v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Kaustubh Dhole - - - From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering - https://arxiv.org/abs/2512.23844 - arXiv:2512.23844v1 Announce Type: new -Abstract: As Large Language Models (LLMs) evolve from code generators into collaborative partners for software engineers, our methods for evaluation are lagging. Current benchmarks, focused on code correctness, fail to capture the nuanced, interactive behaviors essential for successful human-AI partnership. To bridge this evaluation gap, this paper makes two core contributions. First, we present a foundational taxonomy of desirable agent behaviors for enterprise software engineering, derived from an analysis of 91 sets of user-defined agent rules. This taxonomy defines four key expectations of agent behavior: Adhere to Standards and Processes, Ensure Code Quality and Reliability, Solving Problems Effectively, and Collaborating with the User. - Second, recognizing that these expectations are not static, we introduce the Context-Adaptive Behavior (CAB) Framework. This emerging framework reveals how behavioral expectations shift along two empirically-derived axes: the Time Horizon (from immediate needs to future ideals), established through interviews with 15 expert engineers, and the Type of Work (from enterprise production to rapid prototyping, for example), identified through a prompt analysis of a prototyping agent. Together, these contributions offer a human-centered foundation for designing and evaluating the next generation of AI agents, moving the field's focus from the correctness of generated code toward the dynamics of true collaborative intelligence. - oai:arXiv.org:2512.23844v1 - cs.SE - cs.AI - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tao Dong, Harini Sampath, Ja Young Lee, Sherry Y. Shi, Andrew Macvean - - - Integrating Domain Knowledge for Financial QA: A Multi-Retriever RAG Approach with LLMs - https://arxiv.org/abs/2512.23848 - arXiv:2512.23848v1 Announce Type: new -Abstract: This research project addresses the errors of financial numerical reasoning Question Answering (QA) tasks due to the lack of domain knowledge in finance. Despite recent advances in Large Language Models (LLMs), financial numerical questions remain challenging because they require specific domain knowledge in finance and complex multi-step numeric reasoning. We implement a multi-retriever Retrieval Augmented Generators (RAG) system to retrieve both external domain knowledge and internal question contexts, and utilize the latest LLM to tackle these tasks. Through comprehensive ablation experiments and error analysis, we find that domain-specific training with the SecBERT encoder significantly contributes to our best neural symbolic model surpassing the FinQA paper's top model, which serves as our baseline. This suggests the potential superior performance of domain-specific training. Furthermore, our best prompt-based LLM generator achieves the state-of-the-art (SOTA) performance with significant improvement (>7%), yet it is still below the human expert performance. This study highlights the trade-off between hallucinations loss and external knowledge gains in smaller models and few-shot examples. For larger models, the gains from external facts typically outweigh the hallucination loss. Finally, our findings confirm the enhanced numerical reasoning capabilities of the latest LLM, optimized for few-shot learning. - oai:arXiv.org:2512.23848v1 - cs.CL - cs.CE - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yukun Zhang, Stefan Elbl Droguett, Samyak Jain - - - Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense - https://arxiv.org/abs/2512.23849 - arXiv:2512.23849v1 Announce Type: new -Abstract: Detection-based security fails against sophisticated attackers using encryption, stealth, and low-rate techniques, particularly in IoT/edge environments where resource constraints preclude ML-based intrusion detection. We present Economic Denial Security (EDS), a detection-independent framework that makes attacks economically infeasible by exploiting a fundamental asymmetry: defenders control their environment while attackers cannot. EDS composes four mechanisms adaptive computational puzzles, decoy-driven interaction entropy, temporal stretching, and bandwidth taxation achieving provably superlinear cost amplification. We formalize EDS as a Stackelberg game, deriving closed-form equilibria for optimal parameter selection (Theorem 1) and proving that mechanism composition yields 2.1x greater costs than the sum of individual mechanisms (Theorem 2). EDS requires < 12KB memory, enabling deployment on ESP32 class microcontrollers. Evaluation on a 20-device heterogeneous IoT testbed across four attack scenarios (n = 30 trials, p < 0.001) demonstrates: 32-560x attack slowdown, 85-520:1 cost asymmetry, 8-62% attack success reduction, < 20ms latency overhead, and close to 0% false positives. Validation against IoT-23 malware (Mirai, Torii, Hajime) shows 88% standalone mitigation; combined with ML-IDS, EDS achieves 94% mitigation versus 67% for IDS alone a 27% improvement. EDS provides detection-independent protection suitable for resource-constrained environments where traditional approaches fail. The ability to detect and mitigate the malware samples tested was enhanced; however, the benefits provided by EDS were realized even without the inclusion of an IDS. Overall, the implementation of EDS serves to shift the economic balance in favor of the defender and provides a viable method to protect IoT and edge systems methodologies. - oai:arXiv.org:2512.23849v1 - cs.CR - cs.AI - cs.DC - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Samaresh Kumar Singh, Joyjit Roy - - - The Drill-Down and Fabricate Test (DDFT): A Protocol for Measuring Epistemic Robustness in Language Models - https://arxiv.org/abs/2512.23850 - arXiv:2512.23850v1 Announce Type: new -Abstract: Current language model evaluations measure what models know under ideal conditions but not how robustly they know it under realistic stress. Static benchmarks like MMLU and TruthfulQA cannot distinguish a model that lacks knowledge from one whose verification mechanisms collapse when information degrades or adversaries probe for weaknesses. We introduce the Drill-Down and Fabricate Test (DDFT), a protocol that measures epistemic robustness: a model's ability to maintain factual accuracy under progressive semantic compression and adversarial fabrication. We propose a two-system cognitive model comprising a Semantic System that generates fluent text and an Epistemic Verifier that validates factual accuracy. Our findings, based on evaluating 9 frontier models across 8 knowledge domains at 5 compression levels (1,800 turn-level evaluations), reveal that epistemic robustness is orthogonal to conventional design paradigms. Neither parameter count (r=0.083, p=0.832) nor architectural type (r=0.153, p=0.695) significantly predicts robustness, suggesting it emerges from training methodology and verification mechanisms distinct from current approaches. Error detection capability strongly predicts overall robustness (rho=-0.817, p=0.007), indicating this is the critical bottleneck. We find that flagship models exhibit brittleness despite their scale, while smaller models can achieve robust performance, challenging assumptions about the relationship between model size and reliability. The DDFT framework provides both theoretical foundation and practical tools for assessing epistemic robustness before deployment in critical applications. - oai:arXiv.org:2512.23850v1 - cs.AI - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Rahul Baxi - - - Pretraining Frame Preservation in Autoregressive Video Memory Compression - https://arxiv.org/abs/2512.23851 - arXiv:2512.23851v1 Announce Type: new -Abstract: We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temporal positions. The baseline model can compress a 20-second video into a context at about 5k length, where random frames can be retrieved with perceptually preserved appearances. Such pretrained models can be directly fine-tuned as memory encoders for autoregressive video models, enabling long history memory with low context cost and relatively low fidelity loss. We evaluate the framework with ablative settings and discuss the trade-offs of possible neural architecture designs. - oai:arXiv.org:2512.23851v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Lvmin Zhang, Shengqu Cai, Muyang Li, Chong Zeng, Beijia Lu, Anyi Rao, Song Han, Gordon Wetzstein, Maneesh Agrawala - - - Trellis: Learning to Compress Key-Value Memory in Attention Models - https://arxiv.org/abs/2512.23852 - arXiv:2512.23852v1 Announce Type: new -Abstract: Transformers, while powerful, suffer from quadratic computational complexity and the ever-growing Key-Value (KV) cache of the attention mechanism. This paper introduces Trellis, a novel Transformer architecture with bounded memory that learns how to compress its key-value memory dynamically at test time. Trellis replaces the standard KV cache with a fixed-size memory and train a two-pass recurrent compression mechanism to store new keys and values into memory. To achieve this, it leverages an online gradient descent procedure with a forget gate, enabling the compressed memory to be updated recursively while learning to retain important contextual information from incoming tokens at test time. Extensive experiments on language modeling, common-sense reasoning, recall-intensive tasks, and time series show that the proposed architecture outperforms strong baselines. Notably, its performance gains increase as the sequence length grows, highlighting its potential for long-context applications. - oai:arXiv.org:2512.23852v1 - cs.LG - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mahdi Karami, Ali Behrouz, Praneeth Kacham, Vahab Mirrokni - - - Flow Matching Neural Processes - https://arxiv.org/abs/2512.23853 - arXiv:2512.23853v1 Announce Type: new -Abstract: Neural processes (NPs) are a class of models that learn stochastic processes directly from data and can be used for inference, sampling and conditional sampling. We introduce a new NP model based on flow matching, a generative modeling paradigm that has demonstrated strong performance on various data modalities. Following the NP training framework, the model provides amortized predictions of conditional distributions over any arbitrary points in the data. Compared to previous NP models, our model is simple to implement and can be used to sample from conditional distributions using an ODE solver, without requiring auxiliary conditioning methods. In addition, the model provides a controllable tradeoff between accuracy and running time via the number of steps in the ODE solver. We show that our model outperforms previous state-of-the-art neural process methods on various benchmarks including synthetic 1D Gaussian processes data, 2D images, and real-world weather data. - oai:arXiv.org:2512.23853v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Hussen Abu Hamad, Dan Rosenbaum - - - Simultaneous Extrinsic Contact and In-Hand Pose Estimation via Distributed Tactile Sensing - https://arxiv.org/abs/2512.23856 - arXiv:2512.23856v1 Announce Type: new -Abstract: Prehensile autonomous manipulation, such as peg insertion, tool use, or assembly, require precise in-hand understanding of the object pose and the extrinsic contacts made during interactions. Providing accurate estimation of pose and contacts is challenging. Tactile sensors can provide local geometry at the sensor and force information about the grasp, but the locality of sensing means resolving poses and contacts from tactile alone is often an ill-posed problem, as multiple configurations can be consistent with the observations. Adding visual feedback can help resolve ambiguities, but can suffer from noise and occlusions. In this work, we propose a method that pairs local observations from sensing with the physical constraints of contact. We propose a set of factors that ensure local consistency with tactile observations as well as enforcing physical plausibility, namely, that the estimated pose and contacts must respect the kinematic and force constraints of quasi-static rigid body interactions. We formalize our problem as a factor graph, allowing for efficient estimation. In our experiments, we demonstrate that our method outperforms existing geometric and contact-informed estimation pipelines, especially when only tactile information is available. Video results can be found at https://tacgraph.github.io/. - oai:arXiv.org:2512.23856v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mark Van der Merwe, Kei Ota, Dmitry Berenson, Nima Fazeli, Devesh K. Jha - - - Yggdrasil: Bridging Dynamic Speculation and Static Runtime for Latency-Optimal Tree-Based LLM Decoding - https://arxiv.org/abs/2512.23858 - arXiv:2512.23858v1 Announce Type: new -Abstract: Speculative decoding improves LLM inference by generating and verifying multiple tokens in parallel, but existing systems suffer from suboptimal performance due to a mismatch between dynamic speculation and static runtime assumptions. We present Yggdrasil, a co-designed system that enables latency-optimal speculative decoding through context-aware tree drafting and compiler-friendly execution. Yggdrasil introduces an equal-growth tree structure for static graph compatibility, a latency-aware optimization objective for draft selection, and stage-based scheduling to reduce overhead. Yggdrasil supports unmodified LLMs and achieves up to $3.98\times$ speedup over state-of-the-art baselines across multiple hardware setups. - oai:arXiv.org:2512.23858v1 - cs.LG - cs.PL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yue Guan, Changming Yu, Shihan Fang, Weiming Hu, Zaifeng Pan, Zheng Wang, Zihan Liu, Yangjie Zhou, Yufei Ding, Minyi Guo, Jingwen Leng - - - Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis - https://arxiv.org/abs/2512.23859 - arXiv:2512.23859v1 Announce Type: new -Abstract: Online, people often recount their experiences turning to conversational AI agents (e.g., ChatGPT, Claude, Copilot) for mental health support -- going so far as to replace their therapists. These anecdotes suggest that AI agents have great potential to offer accessible mental health support. However, it's unclear how to meet this potential in extreme mental health crisis use cases. In this work, we explore the first-person experience of turning to a conversational AI agent in a mental health crisis. From a testimonial survey (n = 53) of lived experiences, we find that people use AI agents to fill the in-between spaces of human support; they turn to AI due to lack of access to mental health professionals or fears of burdening others. At the same time, our interviews with mental health experts (n = 16) suggest that human-human connection is an essential positive action when managing a mental health crisis. Using the stages of change model, our results suggest that a responsible AI crisis intervention is one that increases the user's preparedness to take a positive action while de-escalating any intended negative action. We discuss the implications of designing conversational AI agents as bridges towards human-human connection rather than ends in themselves. - oai:arXiv.org:2512.23859v1 - cs.HC - cs.AI - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Leah Hope Ajmani, Arka Ghosh, Benjamin Kaveladze, Eugenia Kim, Keertana Namuduri, Theresa Nguyen, Ebele Okoli, Jessica Schleider, Denae Ford, Jina Suh - - - Lifelong Domain Adaptive 3D Human Pose Estimation - https://arxiv.org/abs/2512.23860 - arXiv:2512.23860v1 Announce Type: new -Abstract: 3D Human Pose Estimation (3D HPE) is vital in various applications, from person re-identification and action recognition to virtual reality. However, the reliance on annotated 3D data collected in controlled environments poses challenges for generalization to diverse in-the-wild scenarios. Existing domain adaptation (DA) paradigms like general DA and source-free DA for 3D HPE overlook the issues of non-stationary target pose datasets. To address these challenges, we propose a novel task named lifelong domain adaptive 3D HPE. To our knowledge, we are the first to introduce the lifelong domain adaptation to the 3D HPE task. In this lifelong DA setting, the pose estimator is pretrained on the source domain and subsequently adapted to distinct target domains. Moreover, during adaptation to the current target domain, the pose estimator cannot access the source and all the previous target domains. The lifelong DA for 3D HPE involves overcoming challenges in adapting to current domain poses and preserving knowledge from previous domains, particularly combating catastrophic forgetting. We present an innovative Generative Adversarial Network (GAN) framework, which incorporates 3D pose generators, a 2D pose discriminator, and a 3D pose estimator. This framework effectively mitigates domain shifts and aligns original and augmented poses. Moreover, we construct a novel 3D pose generator paradigm, integrating pose-aware, temporal-aware, and domain-aware knowledge to enhance the current domain's adaptation and alleviate catastrophic forgetting on previous domains. Our method demonstrates superior performance through extensive experiments on diverse domain adaptive 3D HPE datasets. - oai:arXiv.org:2512.23860v1 - cs.CV - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Qucheng Peng, Hongfei Xue, Pu Wang, Chen Chen - - - Probing the Limits of Compressive Memory: A Study of Infini-Attention in Small-Scale Pretraining - https://arxiv.org/abs/2512.23862 - arXiv:2512.23862v1 Announce Type: new -Abstract: This study investigates small-scale pretraining for Small Language Models (SLMs) to enable efficient use of limited data and compute, improve accessibility in low-resource settings and reduce costs. To enhance long-context extrapolation in compact models, we focus on Infini-attention, which builds a compressed memory from past segments while preserving local attention. In our work, we conduct an empirical study using 300M-parameter LLaMA models pretrained with Infini-attention. The model demonstrates training stability and outperforms the baseline in long-context retrieval. We identify the balance factor as a key part of the model performance, and we found that retrieval accuracy drops with repeated memory compressions over long sequences. Even so, Infini-attention still effectively compensates for the SLM's limited parameters. Particularly, despite performance degradation at a 16,384-token context, the Infini-attention model achieves up to 31% higher accuracy than the baseline. Our findings suggest that achieving robust long-context capability in SLMs benefits from architectural memory like Infini-attention. - oai:arXiv.org:2512.23862v1 - cs.LG - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Ruizhe Huang, Kexuan Zhang, Yihao Fang, Baifeng Yu - - - Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation - https://arxiv.org/abs/2512.23864 - arXiv:2512.23864v1 Announce Type: new -Abstract: Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain blind to physical contact. Consequently, they struggle with contact-rich manipulation tasks that require reasoning about force, texture, and slip. While some approaches incorporate low-dimensional tactile signals, they fail to capture the high-resolution dynamics essential for such interactions. To address this limitation, we introduce DreamTacVLA, a framework that grounds VLA models in contact physics by learning to feel the future. Our model adopts a hierarchical perception scheme in which high-resolution tactile images serve as micro-vision inputs coupled with wrist-camera local vision and third-person macro vision. To reconcile these multi-scale sensory streams, we first train a unified policy with a Hierarchical Spatial Alignment (HSA) loss that aligns tactile tokens with their spatial counterparts in the wrist and third-person views. To further deepen the model's understanding of fine-grained contact dynamics, we finetune the system with a tactile world model that predicts future tactile signals. To mitigate tactile data scarcity and the wear-prone nature of tactile sensors, we construct a hybrid large-scale dataset sourced from both high-fidelity digital twin and real-world experiments. By anticipating upcoming tactile states, DreamTacVLA acquires a rich model of contact physics and conditions its actions on both real observations and imagined consequences. Across contact-rich manipulation tasks, it outperforms state-of-the-art VLA baselines, achieving up to 95% success, highlighting the importance of understanding physical contact for robust, touch-aware robotic agents. - oai:arXiv.org:2512.23864v1 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Guo Ye, Zexi Zhang, Xu Zhao, Shang Wu, Haoran Lu, Shihan Lu, Han Liu - - - Improving Reliability of Human Trafficking Alerts in Airports - https://arxiv.org/abs/2512.23865 - arXiv:2512.23865v1 Announce Type: new -Abstract: This paper investigates the latter scenario of individual emergency alerts in airports by applying two existing benchmark delay tolerant network protocols and evaluating their performance of delivery ratio and latency. First, the paper provides a background on Mobile Ad Hoc Networks (MANETs) and Delay Tolerant Networks (DTNs), as well as Vehicular Ad Hoc Networks (VANETs) as a subset of MANETs. Next, the scenario is simulated using the Opportunistic Network Environment (ONE) simulator and runs the DTN protocols applying Spray and Wait and Epidemic. The study discusses the results, highlighting the advantages and limitations of each protocol within the scenario and addressing constraints of the simulation or experimental setup. A wider discussion then considers related research on technologies that combat human trafficking and the potential role of DTN networks in improving this global issue for the better. - oai:arXiv.org:2512.23865v1 - cs.NI - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Nana Oye Akrofi Quarcoo, Milena Radenkovic - - - Max-Entropy Reinforcement Learning with Flow Matching and A Case Study on LQR - https://arxiv.org/abs/2512.23870 - arXiv:2512.23870v1 Announce Type: new -Abstract: Soft actor-critic (SAC) is a popular algorithm for max-entropy reinforcement learning. In practice, the energy-based policies in SAC are often approximated using simple policy classes for efficiency, sacrificing the expressiveness and robustness. In this paper, we propose a variant of the SAC algorithm that parameterizes the policy with flow-based models, leveraging their rich expressiveness. In the algorithm, we evaluate the flow-based policy utilizing the instantaneous change-of-variable technique and update the policy with an online variant of flow matching developed in this paper. This online variant, termed importance sampling flow matching (ISFM), enables policy update with only samples from a user-specified sampling distribution rather than the unknown target distribution. We develop a theoretical analysis of ISFM, characterizing how different choices of sampling distributions affect the learning efficiency. Finally, we conduct a case study of our algorithm on the max-entropy linear quadratic regulator problems, demonstrating that the proposed algorithm learns the optimal action distribution. - oai:arXiv.org:2512.23870v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yuyang Zhang, Yang Hu, Bo Dai, Na Li - - - Hierarchical Quasi-cyclic Codes from Reed-Solomon and Polynomial Evaluation Codes - https://arxiv.org/abs/2512.23872 - arXiv:2512.23872v1 Announce Type: new -Abstract: We introduce the first example of algebraically constructed hierarchical quasi-cyclic codes. These codes are built from Reed-Solomon codes using a 1964 construction of superimposed codes by Kautz and Singleton. We show both the number of levels in the hierarchy and the index of these Reed-Solomon derived codes are determined by the field size. We show that this property also holds for certain additional classes of polynomial evaluation codes. - We provide explicit code parameters and properties as well as some additional bounds on parameters such as rank and distance. In particular, starting with Reed-Solomon codes of dimension $k=2$ yields hierarchical quasi-cyclic codes with Tanner graphs of girth 6. - We present a table of small code parameters and note that some of these codes meet the best known minimum distance for binary codes, with the additional hierarchical quasi-cyclic structure. We draw connections to similar constructions in the literature, but importantly, while existing literature on related codes is largely simulation-based, we present a novel algebraic approach to determining new bounds on parameters of these codes. - oai:arXiv.org:2512.23872v1 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Emily McMillon, Kathryn Haymaker - - - From Illusion to Insight: Change-Aware File-Level Software Defect Prediction Using Agentic AI - https://arxiv.org/abs/2512.23875 - arXiv:2512.23875v1 Announce Type: new -Abstract: Much of the reported progress in file-level software defect prediction (SDP) is, in reality, nothing but an illusion of accuracy. Over the last decades, machine learning and deep learning models have reported increasing performance across software versions. However, since most files persist across releases and retain their defect labels, standard evaluation rewards label-persistence bias rather than reasoning about code changes. To address this issue, we reformulate SDP as a change-aware prediction task, in which models reason over code changes of a file within successive project versions, rather than relying on static file snapshots. Building on this formulation, we propose an LLM-driven, change-aware, multi-agent debate framework. Our experiments on multiple PROMISE projects show that traditional models achieve inflated F1, while failing on rare but critical defect-transition cases. In contrast, our change-aware reasoning and multi-agent debate framework yields more balanced performance across evolution subsets and significantly improves sensitivity to defect introductions. These results highlight fundamental flaws in current SDP evaluation practices and emphasize the need for change-aware reasoning in practical defect prediction. The source code is publicly available. - oai:arXiv.org:2512.23875v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mohsen Hesamolhokama, Behnam Rohani, Amirahmad Shafiee, MohammadAmin Fazli, Jafar Habibi - - - CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution - https://arxiv.org/abs/2512.23880 - arXiv:2512.23880v1 Announce Type: new -Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search and code extraction, and self-reflection via introspection and knowledge graph exploration, among others. We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-agent collaboration and memory consolidation, CASCADE accumulates executable skills that can be shared across agents and scientists, moving toward scalable AI-assisted scientific research. - oai:arXiv.org:2512.23880v1 - cs.AI - cond-mat.mtrl-sci - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xu Huang, Junwu Chen, Yuxing Fei, Zhuohan Li, Philippe Schwaller, Gerbrand Ceder - - - Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack - https://arxiv.org/abs/2512.23881 - arXiv:2512.23881v1 Announce Type: new -Abstract: Audio-language models combine audio encoders with large language models to enable multimodal reasoning, but they also introduce new security vulnerabilities. We propose a universal targeted latent space attack, an encoder-level adversarial attack that manipulates audio latent representations to induce attacker-specified outputs in downstream language generation. Unlike prior waveform-level or input-specific attacks, our approach learns a universal perturbation that generalizes across inputs and speakers and does not require access to the language model. Experiments on Qwen2-Audio-7B-Instruct demonstrate consistently high attack success rates with minimal perceptual distortion, revealing a critical and previously underexplored attack surface at the encoder level of multimodal systems. - oai:arXiv.org:2512.23881v1 - cs.SD - cs.AI - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Roee Ziv, Raz Lapid, Moshe Sipper - - - Institutional cooperations in Austrian research: An analysis of shared researchers - https://arxiv.org/abs/2512.23882 - arXiv:2512.23882v1 Announce Type: new -Abstract: Multiple organisational affiliations are an increasingly common feature of research systems, yet their implications for organisational performance had received limited systematic attention. We developed a scalable, network-based analytical framework that represents simultaneous researcher affiliations as relational links between organisations and applied it to bibliometric data from Austria. Using harmonised publication and affiliation metadata, we constructed two complementary co-affiliation networks: a complete network capturing all simultaneous affiliations and a temporally filtered network retaining only organisational pairs that recurred over time. Network regression analyses showed that geographical proximity remained an important determinant of co-affiliation formation, with spatial distance consistently reducing shared appointments. Clear sectoral differences emerged beyond geography. Universities formed a dense and persistent core of co-affiliations, whereas ties involving medical institutions, government, non-profit and private-sector organisations were often short-lived and attenuated under temporal filtering. Among crosssector links, co-affiliations between universities and research institutes were notably resilient, indicating a more structurally embedded form of organisational integration. We assessed the effect of concurrent affiliations on organisational citation impact across organisational types using field- and year-normalised indicators. Research institutes and universities consistently exhibited higher citation impact than organisations from other sectors, and persistent co-affiliations were associated with greater and more stable scientific visibility. - oai:arXiv.org:2512.23882v1 - cs.DL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Christoph Schlager, Lutz Bornmann, Gerald Schweiger - - - How Large Language Models Systematically Misrepresent American Climate Opinions - https://arxiv.org/abs/2512.23889 - arXiv:2512.23889v1 Announce Type: new -Abstract: Federal agencies and researchers increasingly use large language models to analyze and simulate public opinion. When AI mediates between the public and policymakers, accuracy across intersecting identities becomes consequential; inaccurate group-level estimates can mislead outreach, consultation, and policy design. While research examines intersectionality in LLM outputs, no study has compared these outputs against real human responses across intersecting identities. Climate policy is one such domain, and this is particularly urgent for climate change, where opinion is contested and diverse. We investigate how LLMs represent intersectional patterns in U.S. climate opinions. We prompted six LLMs with profiles of 978 respondents from a nationally representative U.S. climate opinion survey and compared AI-generated responses to actual human answers across 20 questions. We find that LLMs appear to compress the diversity of American climate opinions, predicting less-concerned groups as more concerned and vice versa. This compression is intersectional: LLMs apply uniform gender assumptions that match reality for White and Hispanic Americans but misrepresent Black Americans, where actual gender patterns differ. These patterns, which may be invisible to standard auditing approaches, could undermine equitable climate governance. - oai:arXiv.org:2512.23889v1 - cs.CY - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Sola Kim, Jieshu Wang, Marco A. Janssen, John M. Anderies - - - MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework - https://arxiv.org/abs/2512.23894 - arXiv:2512.23894v1 Announce Type: new -Abstract: Quantifying normative pediatric cranial development and suture ossification is crucial for diagnosing and treating growth-related cephalic disorders. Computed tomography (CT) is widely used to evaluate cranial and sutural deformities; however, its ionizing radiation is contraindicated in children without significant abnormalities. Magnetic resonance imaging (MRI) offers radiation free scans with superior soft tissue contrast, but unlike CT, MRI cannot elucidate cranial sutures, estimate skull bone density, or assess cranial vault growth. This study proposes a deep learning driven pipeline for transforming T1 weighted MRIs of children aged 0.2 to 2 years into synthetic CTs (sCTs), predicting detailed cranial bone segmentation, generating suture probability heatmaps, and deriving direct suture segmentation from the heatmaps. With our in-house pediatric data, sCTs achieved 99% structural similarity and a Frechet inception distance of 1.01 relative to real CTs. Skull segmentation attained an average Dice coefficient of 85% across seven cranial bones, and sutures achieved 80% Dice. Equivalence of skull and suture segmentation between sCTs and real CTs was confirmed using two one sided tests (TOST p < 0.05). To our knowledge, this is the first pediatric cranial CT synthesis framework to enable suture segmentation on sCTs derived from MRI, despite MRI's limited depiction of bone and sutures. By combining robust, domain specific variational autoencoders, our method generates perceptually indistinguishable cranial sCTs from routine pediatric MRIs, bridging critical gaps in non invasive cranial evaluation. - oai:arXiv.org:2512.23894v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Krithika Iyer, Austin Tapp, Athelia Paulli, Gabrielle Dickerson, Syed Muhammad Anwar, Natasha Lepore, Marius George Linguraru - - - Wireless Multimodal Foundation Model (WMFM): Integrating Vision and Communication Modalities for 6G ISAC Systems - https://arxiv.org/abs/2512.23897 - arXiv:2512.23897v1 Announce Type: new -Abstract: The emergence of multimodal foundation models has revolutionized learning paradigms by enabling joint understanding across diverse data types. In the context of next-generation wireless networks, integrating sensing and communication modalities presents a unique opportunity to develop generalizable and data-efficient models. In this work, we introduce the contrastive learning based Wireless Multimodal Foundation Model (WMFM), a large-scale framework that jointly learns from wireless channel coefficients and visual imagery. The WMFM is pretrained using contrastive learning, a self-supervised learning technique that aligns embeddings of camera and channel data without requiring explicit labels. The pretrained encoders are then frozen and employed as feature extractors, with lightweight task-specific heads, fine-tuned for downstream tasks, including user localization and LoS/nLoS classification. Extensive experiments on the DeepVerse6G dataset demonstrate that the proposed WMFM achieves a 17% improvement in balanced accuracy for LoS/nLoS classification and a 48.5% reduction in localization error compared to the end-to-end (E2E) benchmark, while reducing training time by up to 90-fold. Even when trained with as little as 20% of the data, the WMFM-based heads outperform the fully supervised E2E model, underscoring their robustness and data-efficient learning. The proposed approach establishes a foundation for scalable, multimodal learning in Integrated Sensing and Communication (ISAC) systems, paving the way for intelligent and adaptive 6G networks. - oai:arXiv.org:2512.23897v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mohammad Farzanullah, Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci - - - Efficient Deep Learning for Short-Term Solar Irradiance Time Series Forecasting: A Benchmark Study in Ho Chi Minh City - https://arxiv.org/abs/2512.23898 - arXiv:2512.23898v1 Announce Type: new -Abstract: Reliable forecasting of Global Horizontal Irradiance (GHI) is essential for mitigating the variability of solar energy in power grids. This study presents a comprehensive benchmark of ten deep learning architectures for short-term (1-hour ahead) GHI time series forecasting in Ho Chi Minh City, leveraging high-resolution NSRDB satellite data (2011-2020) to compare established baselines (e.g. LSTM, TCN) against emerging state-of-the-art architectures, including Transformer, Informer, iTransformer, TSMixer, and Mamba. Experimental results identify the Transformer as the superior architecture, achieving the highest predictive accuracy with an R^2 of 0.9696. The study further utilizes SHAP analysis to contrast the temporal reasoning of these architectures, revealing that Transformers exhibit a strong "recency bias" focused on immediate atmospheric conditions, whereas Mamba explicitly leverages 24-hour periodic dependencies to inform predictions. Furthermore, we demonstrate that Knowledge Distillation can compress the high-performance Transformer by 23.5% while surprisingly reducing error (MAE: 23.78 W/m^2), offering a proven pathway for deploying sophisticated, low-latency forecasting on resource-constrained edge devices. - oai:arXiv.org:2512.23898v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tin Hoang - - - Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations - https://arxiv.org/abs/2512.23900 - arXiv:2512.23900v1 Announce Type: new -Abstract: Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions. - oai:arXiv.org:2512.23900v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1109/ICC52391.2025.11161258 - ICC 2025 - IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 4383-4388 - Hesam Khoshkbari, Georges Kaddoum, Bassant Selim, Omid Abbasi, Halim Yanikomeroglu - - - Road Rules for Radio: Why Your Wi-Fi Got Better - https://arxiv.org/abs/2512.23901 - arXiv:2512.23901v1 Announce Type: new -Abstract: WiFi allows for the connection of devices and people around the globe. It has proven to be a monumental and revolutionary tool that keeps the world connected. However, recent WiFi advancements are numerous and at times confusing. WiFi has grown significantly over the years, yet few understand the scope and scale of WiFi progression as a whole. This paper tackles that problem, providing a broad literature review on the advancements of key WiFi features to date. This paper will center on seven key areas of focus: (1) bandwidth, (2) battery life, (3) traffic collisions, (4) interference, (5) data-intensive transmissions, (6) numerous devices, and (7) peak throughput/modulation. Each section will focus on WiFi's problems, how those problems were fixed, as well as the limitations of existing solutions. Moreover, the paper explains the role of new unreleased technologies in these seven areas. This includes exploring the upcoming WiFi 8 standard based on the IEEE 802.11bn "Ultra High Reliability" (UHR) specification and how it builds upon current specifications. Compared to previous specifications, WiFi 8 marks a stronger and more significant shift toward prioritizing reliability over pure data rates. Beyond a sole literature review, this paper uses a novel analogy. A road/highway analogy will be integrated throughout the paper to facilitate understanding of networking mechanisms. This paper is approachable and is written such that someone with very little WiFi knowledge should come away with a strong understanding of WiFi. As is typical of literature review papers, technical claims will be grounded in prior work. - oai:arXiv.org:2512.23901v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bradley Fang, Michael Roger - - - Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale - https://arxiv.org/abs/2512.23903 - arXiv:2512.23903v1 Announce Type: new -Abstract: We explore the scaling behaviors of artificial intelligence to establish practical techniques for training foundation models on high-resolution electro-optical (EO) datasets that exceed the current state-of-the-art scale by orders of magnitude. Modern multimodal machine learning (ML) applications, such as generative artificial intelligence (GenAI) systems for image captioning, search, and reasoning, depend on robust, domain-specialized encoders for non-text modalities. In natural-image domains where internet-scale data is plentiful, well-established scaling laws help optimize the joint scaling of model capacity, training compute, and dataset size. Unfortunately, these relationships are much less well-understood in high-value domains like remote sensing (RS). Using over a quadrillion pixels of commercial satellite EO data and the MITRE Federal AI Sandbox, we train progressively larger vision transformer (ViT) backbones, report success and failure modes observed at petascale, and analyze implications for bridging domain gaps across additional RS modalities. We observe that even at this scale, performance is consistent with a data limited regime rather than a model parameter-limited one. These practical insights are intended to inform data-collection strategies, compute budgets, and optimization schedules that advance the future development of frontier-scale RS foundation models. - oai:arXiv.org:2512.23903v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Charith Wickrema, Eliza Mace, Hunter Brown, Heidys Cabrera, Nick Krall, Matthew O'Neill, Shivangi Sarkar, Lowell Weissman, Eric Hughes, Guido Zarrella - - - Rethinking Dense Linear Transformations: Stagewise Pairwise Mixing (SPM) for Near-Linear Training in Neural Networks - https://arxiv.org/abs/2512.23905 - arXiv:2512.23905v1 Announce Type: new -Abstract: Dense linear layers are a dominant source of computational and parametric cost in modern machine learning models, despite their quadratic complexity and often being misaligned with the compositional structure of learned representations. We introduce Stagewise Pairwise Mixers (SPM), a structured linear operator that replaces dense matrices with a composition of sparse pairwise-mixing stages. An SPM layer implements a global linear transformation in $O(nL)$ time with $O(nL)$ parameters, where $L$ is typically constant or $log_2n$, and admits exact closed-form forward and backward computations. SPM is designed as a drop-in replacement for dense linear layers in feedforward networks, recurrent architectures, attention mechanisms, etc. We derive complete forward and backward expressions for two parameterizations: an orthogonal norm-preserving rotation-based variant and a fully general $2 \times 2$ mixing variant. Beyond computational savings, the stagewise structure of SPM induces an explicit compositional inductive bias that constrains model capacity and improves generalization when aligned with task structure. We present proof-of-concept experiments demonstrating substantial reductions in wall-clock cost and improved accuracy on structured learning problems, while retaining competitive performance on real-world benchmarks. - oai:arXiv.org:2512.23905v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Peter Farag - - - Deletion Considered Harmful - https://arxiv.org/abs/2512.23907 - arXiv:2512.23907v1 Announce Type: new -Abstract: In a world of information overload, understanding how we can most effectively manage information is crucial to success. We set out to understand how people view deletion, the removal of material no longer needed: does it help by reducing clutter and improving the signal to noise ratio, or does the effort required to decide to delete something make it not worthwhile? How does deletion relate to other strategies like filing; do people who spend extensive time in filing also prune their materials too? We studied the behaviour of 51 knowledge workers though a series of questionnaires and interviews to evaluate a range of tactics they used aimed at organizing, filing, and retrieving digital resources. Our study reveals that deletion is consistently under-adopted compared to other tactics such as Filing, Coverage, Ontology, and Timeliness. Moreover, the empirical data indicate that deletion is actually detrimental to retrieval success and satisfaction. In this paper, we examine the practice of deletion, review the related literature, and present detailed statistical results and clustering outcomes that underscore its adverse effects. - oai:arXiv.org:2512.23907v1 - cs.HC - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - 10.14236/ewic/BCSHCI2025.19 - Proceedings of the 38th International BCS Human-Computer Interaction Conference, pp 212-221, 2025 - Paul Englefield, Russell Beale - - - Hardware Acceleration for Neural Networks: A Comprehensive Survey - https://arxiv.org/abs/2512.23914 - arXiv:2512.23914v1 Announce Type: new -Abstract: Neural networks have become a dominant computational workload across cloud and edge platforms, but rapid growth in model size and deployment diversity has exposed hardware bottlenecks increasingly dominated by memory movement, communication, and irregular operators rather than peak arithmetic throughput. This survey reviews the technology landscape for hardware acceleration of deep learning, spanning GPUs and tensor-core architectures; domain-specific accelerators (e.g., TPUs/NPUs); FPGA-based designs; ASIC inference engines; and emerging LLM-serving accelerators such as LPUs (language processing units), alongside in-/near-memory computing and neuromorphic/analog approaches. We organize the space using a unified taxonomy across (i) workloads (CNNs, RNNs, GNNs, and Transformers/LLMs), (ii) execution settings (training vs.\ inference; datacenter vs.\ edge), and (iii) optimization levers (reduced precision, sparsity and pruning, operator fusion, compilation and scheduling, and memory-system/interconnect design). We synthesize key architectural ideas including systolic arrays, vector and SIMD engines, specialized attention and softmax kernels, quantization-aware datapaths, and high-bandwidth memory, and we discuss how software stacks and compilers bridge model semantics to hardware. Finally, we highlight open challenges -- including efficient long-context LLM inference (KV-cache management), robust support for dynamic and sparse workloads, energy- and security-aware deployment, and fair benchmarking -- and point to promising directions for the next generation of neural acceleration. - oai:arXiv.org:2512.23914v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Bin Xu, Ayan Banerjee, Sandeep Gupta - - - In Memorium: The Academic Journal - https://arxiv.org/abs/2512.23915 - arXiv:2512.23915v1 Announce Type: new -Abstract: We reflect on the life and influence of the academic journal, charting their history and contributions, discussing how their influence changed society, and examining how in death they will be mourned for what they initially stood for but in the end had moved so far from that they will less missed than they might have been. - oai:arXiv.org:2512.23915v1 - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/MC.2025.3581466 - IEEE Computer Volume 58, Issue 9, September 2025, pp 123-126 - Russell Beale - - - Constraint Breeds Generalization: Temporal Dynamics as an Inductive Bias - https://arxiv.org/abs/2512.23916 - arXiv:2512.23916v1 Announce Type: new -Abstract: Conventional deep learning prioritizes unconstrained optimization, yet biological systems operate under strict metabolic constraints. We propose that these physical constraints shape dynamics to function not as limitations, but as a temporal inductive bias that breeds generalization. Through a phase-space analysis of signal propagation, we reveal a fundamental asymmetry: expansive dynamics amplify noise, whereas proper dissipative dynamics compress phase space that aligns with the network's spectral bias, compelling the abstraction of invariant features. This condition can be imposed externally via input encoding, or intrinsically through the network's own temporal dynamics. Both pathways require architectures capable of temporal integration and proper constraints to decode induced invariants, whereas static architectures fail to capitalize on temporal structure. Through comprehensive evaluations across supervised classification, unsupervised reconstruction, and zero-shot reinforcement learning, we demonstrate that a critical "transition" regime maximizes generalization capability. These findings establish dynamical constraints as a distinct class of inductive bias, suggesting that robust AI development requires not only scaling and removing limitations, but computationally mastering the temporal characteristics that naturally promote generalization. - oai:arXiv.org:2512.23916v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xia Chen - - - Analysis of Collaboration in CS Prizewinning with a Nobel-Turing Comparison - https://arxiv.org/abs/2512.23919 - arXiv:2512.23919v1 Announce Type: new -Abstract: In the scientific community, prizes play a pivotal role in shaping research trajectories by conferring credibility and offering financial incentives to researchers. Yet, we know little about the relationship between academic collaborations and prizewinning. By analyzing over 100 scientific prizes and the collaboration behaviors of over 5,000 prizewinners in CS, we find that prizewinners collaborate earlier and more frequently with other prizewinners than researchers who have not yet received similar recognition. Moreover, CS researchers across age groups collaborate more with prizewinners after winning their first prize, and collaborating with prizewinners after their first win increases the likelihood of the collaborator winning an award. We find that recipients of general CS prizes collaborate more than recipients of more specialized prizes, who collaborate less frequently. With Coarsened Exact Matching (CEM) and regression, we find an increase in prizewinning odds with strength of prizewinner collaboration. We examine the context of recent Nobel Prizes going to CS researchers by showing how an increasing share of Physics awards go to Physics-CS collaborations, and contrast Nobel-Turing winning author's trajectories. Our findings shed light on the relationship between prizewinning and collaboration. - oai:arXiv.org:2512.23919v1 - cs.SI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Boleslaw K. Szymanski (Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA), Yongtao Zhang (Zhejiang University, Hangzhou, China), Brian Uzzi (Kellogg School of Management, Northwestern University, IL, USA), Mohammed Shahid Modi (Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA) - - - Learning to learn skill assessment for fetal ultrasound scanning - https://arxiv.org/abs/2512.23920 - arXiv:2512.23920v1 Announce Type: new -Abstract: Traditionally, ultrasound skill assessment has relied on expert supervision and feedback, a process known for its subjectivity and time-intensive nature. Previous works on quantitative and automated skill assessment have predominantly employed supervised learning methods, often limiting the analysis to predetermined or assumed factors considered influential in determining skill levels. In this work, we propose a novel bi-level optimisation framework that assesses fetal ultrasound skills by how well a task is performed on the acquired fetal ultrasound images, without using manually predefined skill ratings. The framework consists of a clinical task predictor and a skill predictor, which are optimised jointly by refining the two networks simultaneously. We validate the proposed method on real-world clinical ultrasound videos of scanning the fetal head. The results demonstrate the feasibility of predicting ultrasound skills by the proposed framework, which quantifies optimised task performance as a skill indicator. - oai:arXiv.org:2512.23920v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yipei Wang, Qianye Yang, Lior Drukker, Aris T. Papageorghiou, Yipeng Hu, J. Alison Noble - - - Interactive Machine Learning: From Theory to Scale - https://arxiv.org/abs/2512.23924 - arXiv:2512.23924v1 Announce Type: new -Abstract: Machine learning has achieved remarkable success across a wide range of applications, yet many of its most effective methods rely on access to large amounts of labeled data or extensive online interaction. In practice, acquiring high-quality labels and making decisions through trial-and-error can be expensive, time-consuming, or risky, particularly in large-scale or high-stakes settings. This dissertation studies interactive machine learning, in which the learner actively influences how information is collected or which actions are taken, using past observations to guide future interactions. We develop new algorithmic principles and establish fundamental limits for interactive learning along three dimensions: active learning with noisy data and rich model classes, sequential decision making with large action spaces, and model selection under partial feedback. Our results include the first computationally efficient active learning algorithms achieving exponential label savings without low-noise assumptions; the first efficient, general-purpose contextual bandit algorithms whose guarantees are independent of the size of the action space; and the first tight characterizations of the fundamental cost of model selection in sequential decision making. Overall, this dissertation advances the theoretical foundations of interactive learning by developing algorithms that are statistically optimal and computationally efficient, while also providing principled guidance for deploying interactive learning methods in large-scale, real-world settings. - oai:arXiv.org:2512.23924v1 - cs.LG - cs.AI - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yinglun Zhu - - - Hojabr: Towards a Theory of Everything for AI and Data Analytics - https://arxiv.org/abs/2512.23925 - arXiv:2512.23925v1 Announce Type: new -Abstract: Modern data analytics pipelines increasingly combine relational queries, graph processing, and tensor computation within a single application, but existing systems remain fragmented across paradigms, execution models, and research communities. This fragmentation results in repeated optimization efforts, limited interoperability, and strict separation between logical abstractions and physical execution strategies. - We propose Hojabr as a unified declarative intermediate language to address this problem. Hojabr integrates relational algebra, tensor algebra, and constraint-based reasoning within a single higher-order algebraic framework, in which joins, aggregations, tensor contractions, and recursive computations are expressed uniformly. Physical choices, such as join algorithms, execution models, and sparse versus dense tensor representations, are handled as constraint-specialization decisions rather than as separate formalisms. Hojabr supports bidirectional translation with existing declarative languages, enabling programs to be both lowered into Hojabr for analysis and optimization and lifted back into their original declarative form. By making semantic, structural, and algebraic properties explicit, and by supporting extensibility across the compilation stack, Hojabr enables systematic reasoning and reuse of optimization techniques across database systems, machine learning frameworks, and compiler infrastructures. - oai:arXiv.org:2512.23925v1 - cs.DB - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Amir Shaikhha - - - Identification of fixations and saccades in eye-tracking data using adaptive threshold-based method - https://arxiv.org/abs/2512.23926 - arXiv:2512.23926v1 Announce Type: new -Abstract: Properties of ocular fixations and saccades are highly stochastic during many experimental tasks, and their statistics are often used as proxies for various aspects of cognition. Although distinguishing saccades from fixations is not trivial, experimentalists generally use common ad-hoc thresholds in detection algorithms. This neglects inter-task and inter-individual variability in oculomotor dynamics, and potentially biases the resulting statistics. In this article, we introduce and evaluate an adaptive method based on a Markovian approximation of eye-gaze dynamics, using saccades and fixations as states such that the optimal threshold minimizes state transitions. Applying this to three common threshold-based algorithms (velocity, angular velocity, and dispersion), we evaluate the overall accuracy against a multi-threshold benchmark as well as robustness to noise. We find that a velocity threshold achieves the highest baseline accuracy (90-93\%) across both free-viewing and visual search tasks. However, velocity-based methods degrade rapidly under noise when thresholds remain fixed, with accuracy falling below 20% at high noise levels. Adaptive threshold optimization via K-ratio minimization substantially improves performance under noisy conditions for all algorithms. Adaptive dispersion thresholds demonstrate superior noise robustness, maintaining accuracy above 81% even at extreme noise levels ({\sigma} = 50 px), though a precision-recall trade-off emerges that favors fixation detection at the expense of saccade identification. In addition to demonstrating our parsimonious adaptive thresholding method, these findings provide practical guidance for selecting and tuning classification algorithms based on data quality and analytical priorities. - oai:arXiv.org:2512.23926v1 - cs.NE - nlin.CD - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Charles Oriioma, Josef Krivan, Rujeena Mathema, Pedro G. Lind, Alexander Szorkovszky, Shailendra Bhandari - - - SRM at 30: Lessons from Early Data-Centric Networking and Their Impact on Named Data Networking - https://arxiv.org/abs/2512.23928 - arXiv:2512.23928v1 Announce Type: new -Abstract: A 1995 SIGCOMM paper, "A Reliable Multicast Framework for Light-weight Sessions and Application-Level Framing", commonly known as SRM, explored a fundamentally new approach to reliable multiparty data delivery. Rather than adapting established sender-driven reliable unicast mechanisms to multicast, as most contemporaneous proposals did, SRM introduced a data-centric model in which data receivers recover losses by explicitly requesting missing data. Thirty years later, we revisit the SRM framework, examining the challenges it faced, the lessons learned, and its influence on the later development of Named Data Networking (NDN). Experimentations with SRM revealed a fundamental semantic mismatch between its data-centric framework and IP's address-based delivery; while the application layer named data, the network layer remained 'blind' to those names, resulting in inefficient loss recovery. NDN resolves this architectural friction by aligning network delivery with the data-retrieval model and by securing data directly rather than securing communication channels. This retrospective highlights how early insights from SRM informed key design decisions in NDN and illustrates how NDN's design emerged from the cumulative insights gained over decades of networking research and development. - oai:arXiv.org:2512.23928v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Tianyuan Yu, Adam Thieme, Junxiao Shi, Lan Wang, Lixia Zhang - - - A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming - https://arxiv.org/abs/2512.23932 - arXiv:2512.23932v1 Announce Type: new -Abstract: Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-quality knowledge bases. This work introduces McCoy, a framework that combines Large Language Models (LLMs) with Answer Set Programming (ASP) to overcome this barrier. McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis. This integration yields a robust, interpretable prediction framework that leverages the strengths of both paradigms. Preliminary results show McCoy has strong performance on small-scale disease diagnosis tasks. - oai:arXiv.org:2512.23932v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ioanna Gemou, Evangelos Lamprou - - - MGML: A Plug-and-Play Meta-Guided Multi-Modal Learning Framework for Incomplete Multimodal Brain Tumor Segmentation - https://arxiv.org/abs/2512.23936 - arXiv:2512.23936v1 Announce Type: new -Abstract: Leveraging multimodal information from Magnetic Resonance Imaging (MRI) plays a vital role in lesion segmentation, especially for brain tumors. However, in clinical practice, multimodal MRI data are often incomplete, making it challenging to fully utilize the available information. Therefore, maximizing the utilization of this incomplete multimodal information presents a crucial research challenge. We present a novel meta-guided multi-modal learning (MGML) framework that comprises two components: meta-parameterized adaptive modality fusion and consistency regularization module. The meta-parameterized adaptive modality fusion (Meta-AMF) enables the model to effectively integrate information from multiple modalities under varying input conditions. By generating adaptive soft-label supervision signals based on the available modalities, Meta-AMF explicitly promotes more coherent multimodal fusion. In addition, the consistency regularization module enhances segmentation performance and implicitly reinforces the robustness and generalization of the overall framework. Notably, our approach does not alter the original model architecture and can be conveniently integrated into the training pipeline for end-to-end model optimization. We conducted extensive experiments on the public BraTS2020 and BraTS2023 datasets. Compared to multiple state-of-the-art methods from previous years, our method achieved superior performance. On BraTS2020, for the average Dice scores across fifteen missing modality combinations, building upon the baseline, our method obtained scores of 87.55, 79.36, and 62.67 for the whole tumor (WT), the tumor core (TC), and the enhancing tumor (ET), respectively. We have made our source code publicly available at https://github.com/worldlikerr/MGML. - oai:arXiv.org:2512.23936v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yulong Zou, Bo Liu, Cun-Jing Zheng, Yuan-ming Geng, Siyue Li, Qiankun Zuo, Shuihua Wang, Yudong Zhang, Jin Hong - - - Learnable Query Aggregation with KV Routing for Cross-view Geo-localisation - https://arxiv.org/abs/2512.23938 - arXiv:2512.23938v1 Announce Type: new -Abstract: Cross-view geo-localisation (CVGL) aims to estimate the geographic location of a query image by matching it with images from a large-scale database. However, the significant view-point discrepancies present considerable challenges for effective feature aggregation and alignment. To address these challenges, we propose a novel CVGL system that incorporates three key improvements. Firstly, we leverage the DINOv2 backbone with a convolution adapter fine-tuning to enhance model adaptability to cross-view variations. Secondly, we propose a multi-scale channel reallocation module to strengthen the diversity and stability of spatial representations. Finally, we propose an improved aggregation module that integrates a Mixture-of-Experts (MoE) routing into the feature aggregation process. Specifically, the module dynamically selects expert subspaces for the keys and values in a cross-attention framework, enabling adaptive processing of heterogeneous input domains. Extensive experiments on the University-1652 and SUES-200 datasets demonstrate that our method achieves competitive performance with fewer trained parameters. - oai:arXiv.org:2512.23938v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hualin Ye, Bingxi Liu, Jixiang Du, Yu Qin, Ziyi Chen, Hong Zhang - - - Disentangling Learning from Judgment: Representation Learning for Open Response Analytics - https://arxiv.org/abs/2512.23941 - arXiv:2512.23941v1 Announce Type: new -Abstract: Open-ended responses are central to learning, yet automated scoring often conflates what students wrote with how teachers grade. We present an analytics-first framework that separates content signals from rater tendencies, making judgments visible and auditable via analytics. Using de-identified ASSISTments mathematics responses, we model teacher histories as dynamic priors and derive text representations from sentence embeddings, incorporating centering and residualization to mitigate prompt and teacher confounds. Temporally-validated linear models quantify the contributions of each signal, and a projection surfaces model disagreements for qualitative inspection. Results show that teacher priors heavily influence grade predictions; the strongest results arise when priors are combined with content embeddings (AUC~0.815), while content-only models remain above chance but substantially weaker (AUC~0.626). Adjusting for rater effects sharpens the residual content representation, retaining more informative embedding dimensions and revealing cases where semantic evidence supports understanding as opposed to surface-level differences in how students respond. The contribution presents a practical pipeline that transforms embeddings from mere features into learning analytics for reflection, enabling teachers and researchers to examine where grading practices align (or conflict) with evidence of student reasoning and learning. - oai:arXiv.org:2512.23941v1 - cs.CL - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1145/3785022.3785042 - Conrad Borchers, Manit Patel, Seiyon M. Lee, Anthony F. Botelho - - - Kinematic-Based Assessment of Surgical Actions in Microanastomosis - https://arxiv.org/abs/2512.23942 - arXiv:2512.23942v1 Announce Type: new -Abstract: Proficiency in microanastomosis is a critical surgical skill in neurosurgery, where the ability to precisely manipulate fine instruments is crucial to successful outcomes. These procedures require sustained attention, coordinated hand movements, and highly refined motor skills, underscoring the need for objective and systematic methods to evaluate and enhance microsurgical training. Conventional assessment approaches typically rely on expert raters supervising the procedures or reviewing surgical videos, which is an inherently subjective process prone to inter-rater variability, inconsistency, and significant time investment. These limitations highlight the necessity for automated and scalable solutions. To address this challenge, we introduce a novel AI-driven framework for automated action segmentation and performance assessment in microanastomosis procedures, designed to operate efficiently on edge computing platforms. The proposed system comprises three main components: (1) an object tip tracking and localization module based on YOLO and DeepSORT; (2) an action segmentation module leveraging self-similarity matrix for action boundary detection and unsupervised clustering; and (3) a supervised classification module designed to evaluate surgical gesture proficiency. Experimental validation on a dataset of 58 expert-rated microanastomosis videos demonstrates the effectiveness of our approach, achieving a frame-level action segmentation accuracy of 92.4% and an overall skill classification accuracy of 85.5% in replicating expert evaluations. These findings demonstrate the potential of the proposed method to provide objective, real-time feedback in microsurgical education, thereby enabling more standardized, data-driven training protocols and advancing competency assessment in high-stakes surgical environments. - oai:arXiv.org:2512.23942v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yan Meng, Daniel Donoho, Marcelle Altshuler, Omar Arnaout - - - Statistical Guarantees in the Search for Less Discriminatory Algorithms - https://arxiv.org/abs/2512.23943 - arXiv:2512.23943v1 Announce Type: new -Abstract: Recent scholarship has argued that firms building data-driven decision systems in high-stakes domains like employment, credit, and housing should search for "less discriminatory algorithms" (LDAs) (Black et al., 2024). That is, for a given decision problem, firms considering deploying a model should make a good-faith effort to find equally performant models with lower disparate impact across social groups. Evidence from the literature on model multiplicity shows that randomness in training pipelines can lead to multiple models with the same performance, but meaningful variations in disparate impact. This suggests that developers can find LDAs simply by randomly retraining models. Firms cannot continue retraining forever, though, which raises the question: What constitutes a good-faith effort? In this paper, we formalize LDA search via model multiplicity as an optimal stopping problem, where a model developer with limited information wants to produce strong evidence that they have sufficiently explored the space of models. Our primary contribution is an adaptive stopping algorithm that yields a high-probability upper bound on the gains achievable from a continued search, allowing the developer to certify (e.g., to a court) that their search was sufficient. We provide a framework under which developers can impose stronger assumptions about the distribution of models, yielding correspondingly stronger bounds. We validate the method on real-world credit, employment and housing datasets. - oai:arXiv.org:2512.23943v1 - cs.CY - cs.LG - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Chris Hays, Ben Laufer, Solon Barocas, Manish Raghavan - - - Decoupling Constraint from Two Direction in Evolutionary Constrained Multi-objective Optimization - https://arxiv.org/abs/2512.23945 - arXiv:2512.23945v1 Announce Type: new -Abstract: Real-world Constrained Multi-objective Optimization Problems (CMOPs) often contain multiple constraints, and understanding and utilizing the coupling between these constraints is crucial for solving CMOPs. However, existing Constrained Multi-objective Evolutionary Algorithms (CMOEAs) typically ignore these couplings and treat all constraints as a single aggregate, which lacks interpretability regarding the specific geometric roles of constraints. To address this limitation, we first analyze how different constraints interact and show that the final Constrained Pareto Front (CPF) depends not only on the Pareto fronts of individual constraints but also on the boundaries of infeasible regions. This insight implies that CMOPs with different coupling types must be solved from different search directions. Accordingly, we propose a novel algorithm named Decoupling Constraint from Two Directions (DCF2D). This method periodically detects constraint couplings and spawns an auxiliary population for each relevant constraint with an appropriate search direction. Extensive experiments on seven challenging CMOP benchmark suites and on a collection of real-world CMOPs demonstrate that DCF2D outperforms five state-of-the-art CMOEAs, including existing decoupling-based methods. - oai:arXiv.org:2512.23945v1 - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ruiqing Sun, Dawei Feng, Xing Zhou, Lianghao Li, Sheng Qi, Bo Ding, Yijie Wang, Rui Wang, Huaimin Wang - - - Improved Balanced Classification with Theoretically Grounded Loss Functions - https://arxiv.org/abs/2512.23947 - arXiv:2512.23947v1 Announce Type: new -Abstract: The balanced loss is a widely adopted objective for multi-class classification under class imbalance. By assigning equal importance to all classes, regardless of their frequency, it promotes fairness and ensures that minority classes are not overlooked. However, directly minimizing the balanced classification loss is typically intractable, which makes the design of effective surrogate losses a central question. This paper introduces and studies two advanced surrogate loss families: Generalized Logit-Adjusted (GLA) loss functions and Generalized Class-Aware weighted (GCA) losses. GLA losses generalize Logit-Adjusted losses, which shift logits based on class priors, to the broader general cross-entropy loss family. GCA loss functions extend the standard class-weighted losses, which scale losses inversely by class frequency, by incorporating class-dependent confidence margins and extending them to the general cross-entropy family. We present a comprehensive theoretical analysis of consistency for both loss families. We show that GLA losses are Bayes-consistent, but only $H$-consistent for complete (i.e., unbounded) hypothesis sets. Moreover, their $H$-consistency bounds depend inversely on the minimum class probability, scaling at least as $1/\mathsf p_{\min}$. In contrast, GCA losses are $H$-consistent for any hypothesis set that is bounded or complete, with $H$-consistency bounds that scale more favorably as $1/\sqrt{\mathsf p_{\min}}$, offering significantly stronger theoretical guarantees in imbalanced settings. We report the results of experiments demonstrating that, empirically, both the GCA losses with calibrated class-dependent confidence margins and GLA losses can greatly outperform straightforward class-weighted losses as well as the LA losses. GLA generally performs slightly better in common benchmarks, whereas GCA exhibits a slight edge in highly imbalanced settings. - oai:arXiv.org:2512.23947v1 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Corinna Cortes, Mehryar Mohri, Yutao Zhong - - - DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attacks - https://arxiv.org/abs/2512.23948 - arXiv:2512.23948v1 Announce Type: new -Abstract: Convolutional Neural Networks (CNNs) and their quantized counterparts are vulnerable to extraction attacks, posing a significant threat of IP theft. Yet, the robustness of quantized models against these attacks is little studied compared to large models. Previous defenses propose to inject calculated noise into the prediction probabilities. However, these defenses are limited since they are not incorporated during the model design and are only added as an afterthought after training. Additionally, most defense techniques are computationally expensive and often have unrealistic assumptions about the victim model that are not feasible in edge device implementations and do not apply to quantized models. In this paper, we propose DivQAT, a novel algorithm to train quantized CNNs based on Quantization Aware Training (QAT) aiming to enhance their robustness against extraction attacks. To the best of our knowledge, our technique is the first to modify the quantization process to integrate a model extraction defense into the training process. Through empirical validation on benchmark vision datasets, we demonstrate the efficacy of our technique in defending against model extraction attacks without compromising model accuracy. Furthermore, combining our quantization technique with other defense mechanisms improves their effectiveness compared to traditional QAT. - oai:arXiv.org:2512.23948v1 - cs.LG - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Kacem Khaled, Felipe Gohring de Magalh\~aes, Gabriela Nicolescu - - - U-Net-Like Spiking Neural Networks for Single Image Dehazing - https://arxiv.org/abs/2512.23950 - arXiv:2512.23950v1 Announce Type: new -Abstract: Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN. - oai:arXiv.org:2512.23950v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/IJCNN64981.2025.11228757 - 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, pp. 1-9, 2025 - Huibin Li, Haoran Liu, Mingzhe Liu, Yulong Xiao, Peng Li, Guibin Zan - - - Squeezing Edge Performance: A Sensitivity-Aware Container Management for Heterogeneous Tasks - https://arxiv.org/abs/2512.23952 - arXiv:2512.23952v1 Announce Type: new -Abstract: Edge computing enables latency-critical applications to process data close to end devices, yet task heterogeneity and limited resources pose significant challenges to efficient orchestration. This paper presents a measurement-driven, container-based resource management framework for intra-node optimization on a single edge server hosting multiple heterogeneous applications. Extensive profiling experiments are conducted to derive a nonlinear fitting model that characterizes the relationship among CPU/memory allocations and processing latency across diverse workloads, enabling reliable estimation of performance under varying configurations and providing quantitative support for subsequent optimization. Using this model and a queueing-based delay formulation, we formulate a mixed-integer nonlinear programming (MINLP) problem to jointly minimize system latency and power consumption, which is shown to be NP-hard. The problem is decomposed into tractable convex subproblems and solved through a two-stage container-based resource management scheme (CRMS) combining convex optimization and greedy refinement. The proposed scheme achieves polynomial-time complexity and supports quasi-dynamic execution under global resource constraints. Simulation results demonstrate that CRMS reduces latency by over 14\% and improves energy efficiency compared with heuristic and search-based baselines, offering a practical and scalable solution for heterogeneous edge environments with dynamic workload characteristics. - oai:arXiv.org:2512.23952v1 - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yongmin Zhang, Pengyu Huang, Mingyi Dong, Jing Yao - - - T2VAttack: Adversarial Attack on Text-to-Video Diffusion Models - https://arxiv.org/abs/2512.23953 - arXiv:2512.23953v1 Announce Type: new -Abstract: The rapid evolution of Text-to-Video (T2V) diffusion models has driven remarkable advancements in generating high-quality, temporally coherent videos from natural language descriptions. Despite these achievements, their vulnerability to adversarial attacks remains largely unexplored. In this paper, we introduce T2VAttack, a comprehensive study of adversarial attacks on T2V diffusion models from both semantic and temporal perspectives. Considering the inherently dynamic nature of video data, we propose two distinct attack objectives: a semantic objective to evaluate video-text alignment and a temporal objective to assess the temporal dynamics. To achieve an effective and efficient attack process, we propose two adversarial attack methods: (i) T2VAttack-S, which identifies semantically or temporally critical words in prompts and replaces them with synonyms via greedy search, and (ii) T2VAttack-I, which iteratively inserts optimized words with minimal perturbation to the prompt. By combining these objectives and strategies, we conduct a comprehensive evaluation on the adversarial robustness of several state-of-the-art T2V models, including ModelScope, CogVideoX, Open-Sora, and HunyuanVideo. Our experiments reveal that even minor prompt modifications, such as the substitution or insertion of a single word, can cause substantial degradation in semantic fidelity and temporal dynamics, highlighting critical vulnerabilities in current T2V diffusion models. - oai:arXiv.org:2512.23953v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Changzhen Li, Yuecong Min, Jie Zhang, Zheng Yuan, Shiguang Shan, Xilin Chen - - - Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling - https://arxiv.org/abs/2512.23959 - arXiv:2512.23959v1 Announce Type: new -Abstract: Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Many RAG systems incorporate a working memory module to consolidate retrieved information. However, existing memory designs function primarily as passive storage that accumulates isolated facts for the purpose of condensing the lengthy inputs and generating new sub-queries through deduction. This static nature overlooks the crucial high-order correlations among primitive facts, the compositions of which can often provide stronger guidance for subsequent steps. Therefore, their representational strength and impact on multi-step reasoning and knowledge evolution are limited, resulting in fragmented reasoning and weak global sense-making capacity in extended contexts. We introduce HGMem, a hypergraph-based memory mechanism that extends the concept of memory beyond simple storage into a dynamic, expressive structure for complex reasoning and global understanding. In our approach, memory is represented as a hypergraph whose hyperedges correspond to distinct memory units, enabling the progressive formation of higher-order interactions within memory. This mechanism connects facts and thoughts around the focal problem, evolving into an integrated and situated knowledge structure that provides strong propositions for deeper reasoning in subsequent steps. We evaluate HGMem on several challenging datasets designed for global sense-making. Extensive experiments and in-depth analyses show that our method consistently improves multi-step RAG and substantially outperforms strong baseline systems across diverse tasks. - oai:arXiv.org:2512.23959v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Chulun Zhou, Chunkang Zhang, Guoxin Yu, Fandong Meng, Jie Zhou, Wai Lam, Mo Yu - - - An Comparative Analysis about KYC on a Recommendation System Toward Agentic Recommendation System - https://arxiv.org/abs/2512.23961 - arXiv:2512.23961v1 Announce Type: new -Abstract: This research presents a cutting-edge recommendation system utilizing agentic AI for KYC (Know Your Customer in the financial domain), and its evaluation across five distinct content verticals: Advertising (Ad), News, Gossip, Sharing (User-Generated Content), and Technology (Tech). The study compares the performance of four experimental groups, grouping by the intense usage of KYC, benchmarking them against the Normalized Discounted Cumulative Gain (nDCG) metric at truncation levels of $k=1$, $k=3$, and $k=5$. By synthesizing experimental data with theoretical frameworks and industry benchmarks from platforms such as Baidu and Xiaohongshu, this research provides insight by showing experimental results for engineering a large-scale agentic recommendation system. - oai:arXiv.org:2512.23961v1 - cs.IR - cs.AI - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Junjie H. Xu - - - Physics-informed Graph Neural Networks for Operational Flood Modeling - https://arxiv.org/abs/2512.23964 - arXiv:2512.23964v1 Announce Type: new -Abstract: Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical flood models are accurate, their substantial computational cost limits their use in operational settings where rapid predictions are essential. Models designed with graph neural networks (GNNs) provide both speed and accuracy while having the ability to process unstructured spatial domains. Given its flexible input and architecture, GNNs can be leveraged alongside physics-informed techniques with ease, significantly improving interpretability. This study introduces a novel flood GNN architecture, DUALFloodGNN, which embeds physical constraints at both global and local scales through explicit loss terms. The model jointly predicts water volume at nodes and flow along edges through a shared message-passing framework. To improve performance for autoregressive inference, model training is conducted with a multi-step loss enhanced with dynamic curriculum learning. Compared with standard GNN architectures and state-of-the-art GNN flood models, DUALFloodGNN achieves substantial improvements in predicting multiple hydrologic variables while maintaining high computational efficiency. The model is open-sourced at https://github.com/acostacos/dual_flood_gnn. - oai:arXiv.org:2512.23964v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Carlo Malapad Acosta, Herath Mudiyanselage Viraj Vidura Herath, Jia Yu Lim, Abhishek Saha, Sanka Rasnayaka, Lucy Marshall - - - Multimodal sampling via Schr\"odinger-F\"ollmer samplers with temperatures - https://arxiv.org/abs/2512.23965 - arXiv:2512.23965v1 Announce Type: new -Abstract: Generating samples from complex and high-dimensional distributions is ubiquitous in various scientific fields of statistical physics, Bayesian inference, scientific computing and machine learning. Very recently, Huang et al. (IEEE Trans. Inform. Theory, 2025) proposed new Schr\"odinger-F\"ollmer samplers (SFS), based on the Euler discretization of the Schr\"odinger-F\"ollmer diffusion evolving on the unit interval $[0, 1]$. There, a convergence rate of order $\mathcal{O}(\sqrt{h})$ in the $L^2$-Wasserstein distance was obtained for the Euler discretization with a uniform time step-size $h>0$. - By incorporating a temperature parameter, different samplers are introduced in this paper, based on the Euler discretization of the Schr\"odinger-F\"ollmer process with temperatures. As revealed by numerical experiments, high temperatures are vital, particularly in sampling from multimodal distributions. Further, a novel approach of error analysis is developed for the time discretization and an enhanced convergence rate of order ${ \mathcal{O}(h)}$ is obtained in the $L^2$-Wasserstein distance, under certain smoothness conditions on the drift. This significantly improves the existing order-half convergence in the aforementioned paper. Unlike Langevin samplers, SFS is of gradient-free, works in a unit interval $[0, 1]$ and does not require any ergodicity. Numerical experiments confirm the convergence rate and show that, the SFS substantially outperforms vanilla Langevin samplers, particularly in sampling from multimodal distributions. - oai:arXiv.org:2512.23965v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xiaojie Wang, Xiaoyan Zhang - - - Efficient Context Scaling with LongCat ZigZag Attention - https://arxiv.org/abs/2512.23966 - arXiv:2512.23966v1 Announce Type: new -Abstract: We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited compute budget. In long-context scenarios, LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases. Specifically, by applying LoZA to LongCat-Flash during mid-training, we serve LongCat-Flash-Exp as a long-context foundation model that can swiftly process up to 1 million tokens, enabling efficient long-term reasoning and long-horizon agentic capabilities. - oai:arXiv.org:2512.23966v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chen Zhang, Yang Bai, Jiahuan Li, Anchun Gui, Keheng Wang, Feifan Liu, Guanyu Wu, Yuwei Jiang, Defei Bu, Li Wei, Haihang Jing, Hongyin Tang, Xin Chen, Xiangzhou Huang, Fengcun Li, Rongxiang Weng, Yulei Qian, Yifan Lu, Yerui Sun, Jingang Wang, Yuchen Xie, Xunliang Cai - - - HERO-Sign: Hierarchical Tuning and Efficient Compiler-Time GPU Optimizations for SPHINCS+ Signature Generation - https://arxiv.org/abs/2512.23969 - arXiv:2512.23969v1 Announce Type: new -Abstract: SPHINCS+ is a stateless hash-based signature scheme that provides strong post quantum security, but its signature generation is slow due to intensive hash computations. GPUs offer massive parallelism that can potentially accelerate SPHINCS+ signatures. However, existing GPU-based optimizations either fail to fully exploit the inherent parallelism of SPHINCS+'s Merkle tree structure or lack fine-grained, compiler-level customization across its diverse computational kernels. This paper proposes HERO Sign, a GPU-accelerated SPHINCS+ implementation that adopts hierarchical tuning and efficient compiler time optimizations. HERO Sign reexamines the parallelization opportunities enabled by data independence across SPHINCS+ components, including FORS, MSS, and WOTS+. It introduces a Tree Fusion strategy for FORS, which contains a large number of independent branches. The fusion strategy is guided by an automated Tree Tuning search algorithm that adapts fusion schemes to different GPU architectures. To further improve performance, HERO Sign employs an adaptive compilation strategy that accounts for the varying effectiveness of compiler optimizations across SPHINCS+ kernels such as FORS Sign, TREE Sign, and WOTS+ Sign. During compilation, the strategy automatically selects between PTX and native code paths to maximize efficiency. For batched signature generation, HERO Sign optimizes kernel-level overlapping using a task graph-based construction to reduce multi-stream idle time and kernel launch overhead. Experimental results show that, compared to state of the art GPU implementations, HERO Sign achieves throughput improvements of 1.28-3.13, 1.28-2.92, and 1.24-2.60 under the SPHINCS+ 128f, 192f, and 256f parameter sets on RTX 4090. Similar gains are observed on A100, H100, and GTX 2080, along with a two orders of magnitude reduction in kernel launch latency. - oai:arXiv.org:2512.23969v1 - cs.AR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yaoyun Zhou, Qian Wang - - - CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards - https://arxiv.org/abs/2512.23971 - arXiv:2512.23971v1 Announce Type: new -Abstract: Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on costly annotations. We introduce CEC-Zero, a zero-supervision reinforcement learning framework that addresses this by enabling LLMs to correct their own mistakes. CEC-Zero synthesizes errorful inputs from clean text, computes cluster-consensus rewards via semantic similarity and candidate agreement, and optimizes the policy with PPO. It outperforms supervised baselines by 10--13 F$_1$ points and strong LLM fine-tunes by 5--8 points across 9 benchmarks, with theoretical guarantees of unbiased rewards and convergence. CEC-Zero establishes a label-free paradigm for robust, scalable CSC, unlocking LLM potential in noisy text pipelines. - oai:arXiv.org:2512.23971v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhiming Lin, Kai Zhao, Sophie Zhang, Peilai Yu, Canran Xiao - - - SHIELD: Spherical-Projection Hybrid-Frontier Integration for Efficient LiDAR-based Drone Exploration - https://arxiv.org/abs/2512.23972 - arXiv:2512.23972v1 Announce Type: new -Abstract: This paper introduces SHIELD, a Spherical-Projection Hybrid-Frontier Integration for Efficient LiDAR-based Drone exploration method. Although laser LiDAR offers the advantage of a wide field of view, its application in UAV exploration still faces several challenges. The observation quality of LiDAR point clouds is generally inferior to that of depth cameras. Traditional frontier methods based on known and unknown regions impose a heavy computational burden, especially when handling the wide field of view of LiDAR. In addition, regions without point cloud are also difficult to classify as free space through raycasting. To address these problems, the SHIELD is proposed. It maintains an observation-quality occupancy map and performs ray-casting on this map to address the issue of inconsistent point-cloud quality during exploration. A hybrid frontier method is used to tackle both the computational burden and the limitations of point-cloud quality exploration. In addition, an outward spherical-projection ray-casting strategy is proposed to jointly ensure flight safety and exploration efficiency in open areas. Simulations and flight experiments prove the effectiveness of SHIELD. This work will be open-sourced to contribute to the research community. - oai:arXiv.org:2512.23972v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Liangtao Feng, Zhenchang Liu, Feng Zhang, Xuefeng Ren - - - A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence - https://arxiv.org/abs/2512.23973 - arXiv:2512.23973v1 Announce Type: new -Abstract: Influence Maximization (IM) seeks to identify a small set of seed nodes in a social network to maximize expected information spread under a diffusion model. While community-based approaches improve scalability by exploiting modular structure, they typically assume independence between communities, overlooking inter-community influence$\unicode{x2014}$a limitation that reduces effectiveness in real-world networks. We introduce Community-IM++, a scalable framework that explicitly models cross-community diffusion through a principled heuristic based on community-based diffusion degree (CDD) and a progressive budgeting strategy. The algorithm partitions the network, computes CDD to prioritize bridging nodes, and allocates seeds adaptively across communities using lazy evaluation to minimize redundant computations. Experiments on large real-world social networks under different edge weight models show that Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime, while outperforming Community-IM and degree heuristics across budgets and structural conditions. These results demonstrate the practicality of Community-IM++ for large-scale applications such as viral marketing, misinformation control, and public health campaigns, where efficiency and cross-community reach are critical. - oai:arXiv.org:2512.23973v1 - cs.SI - cs.AI - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Eliot W. Robson, Abhishek K. Umrawal - - - Exploring the Potential of Spiking Neural Networks in UWB Channel Estimation - https://arxiv.org/abs/2512.23975 - arXiv:2512.23975v1 Announce Type: new -Abstract: Although existing deep learning-based Ultra-Wide Band (UWB) channel estimation methods achieve high accuracy, their computational intensity clashes sharply with the resource constraints of low-cost edge devices. Motivated by this, this letter explores the potential of Spiking Neural Networks (SNNs) for this task and develops a fully unsupervised SNN solution. To enable a comprehensive performance analysis, we devise an extensive set of comparative strategies and evaluate them on a compelling public benchmark. Experimental results show that our unsupervised approach still attains 80% test accuracy, on par with several supervised deep learning-based strategies. Moreover, compared with complex deep learning methods, our SNN implementation is inherently suited to neuromorphic deployment and offers a drastic reduction in model complexity, bringing significant advantages for future neuromorphic practice. - oai:arXiv.org:2512.23975v1 - cs.ET - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Youdong Zhang, Xu He, Xiaolin Meng - - - Causify DataFlow: A Framework For High-performance Machine Learning Stream Computing - https://arxiv.org/abs/2512.23977 - arXiv:2512.23977v1 Announce Type: new -Abstract: We present DataFlow, a computational framework for building, testing, and deploying high-performance machine learning systems on unbounded time-series data. Traditional data science workflows assume finite datasets and require substantial reimplementation when moving from batch prototypes to streaming production systems. This gap introduces causality violations, batch boundary artifacts, and poor reproducibility of real-time failures. - DataFlow resolves these issues through a unified execution model based on directed acyclic graphs (DAGs) with point-in-time idempotency: outputs at any time t depend only on a fixed-length context window preceding t. This guarantee ensures that models developed in batch mode execute identically in streaming production without code changes. The framework enforces strict causality by automatically tracking knowledge time across all transformations, eliminating future-peeking bugs. - DataFlow supports flexible tiling across temporal and feature dimensions, allowing the same model to operate at different frequencies and memory profiles via configuration alone. It integrates natively with the Python data science stack and provides fit/predict semantics for online learning, caching and incremental computation, and automatic parallelization through DAG-based scheduling. We demonstrate its effectiveness across domains including financial trading, IoT, fraud detection, and real-time analytics. - oai:arXiv.org:2512.23977v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Giacinto Paolo Saggese, Paul Smith - - - Assured Autonomy: How Operations Research Powers and Orchestrates Generative AI Systems - https://arxiv.org/abs/2512.23978 - arXiv:2512.23978v1 Announce Type: new -Abstract: Generative artificial intelligence (GenAI) is shifting from conversational assistants toward agentic systems -- autonomous decision-making systems that sense, decide, and act within operational workflows. This shift creates an autonomy paradox: as GenAI systems are granted greater operational autonomy, they should, by design, embody more formal structure, more explicit constraints, and stronger tail-risk discipline. We argue stochastic generative models can be fragile in operational domains unless paired with mechanisms that provide verifiable feasibility, robustness to distribution shift, and stress testing under high-consequence scenarios. To address this challenge, we develop a conceptual framework for assured autonomy grounded in operations research (OR), built on two complementary approaches. First, flow-based generative models frame generation as deterministic transport characterized by an ordinary differential equation, enabling auditability, constraint-aware generation, and connections to optimal transport, robust optimization, and sequential decision control. Second, operational safety is formulated through an adversarial robustness lens: decision rules are evaluated against worst-case perturbations within uncertainty or ambiguity sets, making unmodeled risks part of the design. This framework clarifies how increasing autonomy shifts OR's role from solver to guardrail to system architect, with responsibility for control logic, incentive protocols, monitoring regimes, and safety boundaries. These elements define a research agenda for assured autonomy in safety-critical, reliability-sensitive operational domains. - oai:arXiv.org:2512.23978v1 - cs.LG - math.OC - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Tinglong Dai, David Simchi-Levi, Michelle Xiao Wu, Yao Xie - - - Information-Theoretic Quality Metric of Low-Dimensional Embeddings - https://arxiv.org/abs/2512.23981 - arXiv:2512.23981v1 Announce Type: new -Abstract: In this work we study the quality of low-dimensional embeddings from an explicitly information-theoretic perspective. We begin by noting that classical evaluation metrics such as stress, rank-based neighborhood criteria, or Local Procrustes quantify distortions in distances or in local geometries, but do not directly assess how much information is preserved when projecting high-dimensional data onto a lower-dimensional space. To address this limitation, we introduce the Entropy Rank Preservation Measure (ERPM), a local metric based on the Shannon entropy of the singular-value spectrum of neighborhood matrices and on the stable rank, which quantifies changes in uncertainty between the original representation and its reduced projection, providing neighborhood-level indicators and a global summary statistic. To validate the results of the metric, we compare its outcomes with the Mean Relative Rank Error (MRRE), which is distance-based, and with Local Procrustes, which is based on geometric properties, using a financial time series and a manifold commonly studied in the literature. We observe that distance-based criteria exhibit very low correlation with geometric and spectral measures, while ERPM and Local Procrustes show strong average correlation but display significant discrepancies in local regimes, leading to the conclusion that ERPM complements existing metrics by identifying neighborhoods with severe information loss, thereby enabling a more comprehensive assessment of embeddings, particularly in information-sensitive applications such as the construction of early-warning indicators. - oai:arXiv.org:2512.23981v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sebasti\'an Guti\'errez-Bernal (Tecnol\'ogico de Monterrey, Monterrey, N.L., Mexico), Hector Medel Cobaxin (Tecnol\'ogico de Monterrey, Monterrey, N.L., Mexico), Abiel Galindo Gonz\'alez (Tecnol\'ogico de Monterrey, Monterrey, N.L., Mexico) - - - Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education - https://arxiv.org/abs/2512.23982 - arXiv:2512.23982v1 Announce Type: new -Abstract: Recent advances in large language models (LLMs) have introduced new paradigms in software development, including vibe coding, AI-assisted coding, and agentic coding, fundamentally reshaping how software is designed, implemented, and maintained. Prior research has primarily examined AI-based coding at the individual level or in educational settings, leaving industrial practitioners' perspectives underexplored. This paper addresses this gap by investigating how LLM coding tools are used in professional practice, the associated concerns and risks, and the resulting transformations in development workflows, with particular attention to implications for computing education. We conducted a qualitative analysis of 57 curated YouTube videos published between late 2024 and 2025, capturing reflections and experiences shared by practitioners. Following a filtering and quality assessment process, the selected sources were analyzed to compare LLM-based and traditional programming, identify emerging risks, and characterize evolving workflows. Our findings reveal definitions of AI-based coding practices, notable productivity gains, and lowered barriers to entry. Practitioners also report a shift in development bottlenecks toward code review and concerns regarding code quality, maintainability, security vulnerabilities, ethical issues, erosion of foundational problem-solving skills, and insufficient preparation of entry-level engineers. Building on these insights, we discuss implications for computer science and software engineering education and argue for curricular shifts toward problem-solving, architectural thinking, code review, and early project-based learning that integrates LLM tools. This study offers an industry-grounded perspective on AI-based coding and provides guidance for aligning educational practices with rapidly evolving professional realities. - oai:arXiv.org:2512.23982v1 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Hung-Fu Chang, MohammadShokrolah Shirazi, Lizhou Cao, Supannika Koolmanojwong Mobasser - - - DriveExplorer: Images-Only Decoupled 4D Reconstruction with Progressive Restoration for Driving View Extrapolation - https://arxiv.org/abs/2512.23983 - arXiv:2512.23983v1 Announce Type: new -Abstract: This paper presents an effective solution for view extrapolation in autonomous driving scenarios. Recent approaches focus on generating shifted novel view images from given viewpoints using diffusion models. However, these methods heavily rely on priors such as LiDAR point clouds, 3D bounding boxes, and lane annotations, which demand expensive sensors or labor-intensive labeling, limiting applicability in real-world deployment. In this work, with only images and optional camera poses, we first estimate a global static point cloud and per-frame dynamic point clouds, fusing them into a unified representation. We then employ a deformable 4D Gaussian framework to reconstruct the scene. The initially trained 4D Gaussian model renders degraded and pseudo-images to train a video diffusion model. Subsequently, progressively shifted Gaussian renderings are iteratively refined by the diffusion model,and the enhanced results are incorporated back as training data for 4DGS. This process continues until extrapolation reaches the target viewpoints. Compared with baselines, our method produces higher-quality images at novel extrapolated viewpoints. - oai:arXiv.org:2512.23983v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuang Jia, Jinlong Wang, Jiayi Zhao, Chunlam Li, Shunzhou Wang, Wei Gao - - - Anomaly detection in satellite imagery through temporal inpainting - https://arxiv.org/abs/2512.23986 - arXiv:2512.23986v1 Announce Type: new -Abstract: Detecting surface changes from satellite imagery is critical for rapid disaster response and environmental monitoring, yet remains challenging due to the complex interplay between atmospheric noise, seasonal variations, and sensor artifacts. Here we show that deep learning can leverage the temporal redundancy of satellite time series to detect anomalies at unprecedented sensitivity, by learning to predict what the surface should look like in the absence of change. We train an inpainting model built upon the SATLAS foundation model to reconstruct the last frame of a Sentinel-2 time series from preceding acquisitions, using globally distributed training data spanning diverse climate zones and land cover types. When applied to regions affected by sudden surface changes, the discrepancy between prediction and observation reveals anomalies that traditional change detection methods miss. We validate our approach on earthquake-triggered surface ruptures from the 2023 Turkey-Syria earthquake sequence, demonstrating detection of a rift feature in Tepehan with higher sensitivity and specificity than temporal median or Reed-Xiaoli anomaly detectors. Our method reaches detection thresholds approximately three times lower than baseline approaches, providing a path towards automated, global-scale monitoring of surface changes from freely available multi-spectral satellite data. - oai:arXiv.org:2512.23986v1 - cs.CV - physics.geo-ph - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bertrand Rouet-Leduc, Claudia Hulbert - - - MeLeMaD: Adaptive Malware Detection via Chunk-wise Feature Selection and Meta-Learning - https://arxiv.org/abs/2512.23987 - arXiv:2512.23987v1 Announce Type: new -Abstract: Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The paper introduces Meta Learning Malware Detection (MeLeMaD), a novel framework leveraging the adaptability and generalization capabilities of Model-Agnostic Meta-Learning (MAML) for malware detection. MeLeMaD incorporates a novel feature selection technique, Chunk-wise Feature Selection based on Gradient Boosting (CFSGB), tailored for handling large-scale, high-dimensional malware datasets, significantly enhancing the detection efficiency. Two benchmark malware datasets (CIC-AndMal2020 and BODMAS) and a custom dataset (EMBOD) were used for rigorously validating the MeLeMaD, achieving a remarkable performance in terms of key evaluation measures, including accuracy, precision, recall, F1-score, MCC, and AUC. With accuracies of 98.04\% on CIC-AndMal2020 and 99.97\% on BODMAS, MeLeMaD outperforms the state-of-the-art approaches. The custom dataset, EMBOD, also achieves a commendable accuracy of 97.85\%. The results underscore the MeLeMaD's potential to address the challenges of robustness, adaptability, and large-scale, high-dimensional datasets in malware detection, paving the way for more effective and efficient cybersecurity solutions. - oai:arXiv.org:2512.23987v1 - cs.CR - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ajvad Haneef K, Karan Kuwar Singh, Madhu Kumar S D - - - Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process - https://arxiv.org/abs/2512.23988 - arXiv:2512.23988v1 Announce Type: new -Abstract: Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches often rely on human-defined concepts (e.g., overthinking, reflection) at the word level to analyze reasoning in a supervised manner. However, such methods are limited, as it is infeasible to capture the full spectrum of potential reasoning behaviors, many of which are difficult to define in token space. In this work, we propose an unsupervised framework (namely, RISE: Reasoning behavior Interpretability via Sparse auto-Encoder) for discovering reasoning vectors, which we define as directions in the activation space that encode distinct reasoning behaviors. By segmenting chain-of-thought traces into sentence-level 'steps' and training sparse auto-encoders (SAEs) on step-level activations, we uncover disentangled features corresponding to interpretable behaviors such as reflection and backtracking. Visualization and clustering analyses show that these behaviors occupy separable regions in the decoder column space. Moreover, targeted interventions on SAE-derived vectors can controllably amplify or suppress specific reasoning behaviors, altering inference trajectories without retraining. Beyond behavior-specific disentanglement, SAEs capture structural properties such as response length, revealing clusters of long versus short reasoning traces. More interestingly, SAEs enable the discovery of novel behaviors beyond human supervision. We demonstrate the ability to control response confidence by identifying confidence-related vectors in the SAE decoder space. These findings underscore the potential of unsupervised latent discovery for both interpreting and controllably steering reasoning in LLMs. - oai:arXiv.org:2512.23988v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhenyu Zhang, Shujian Zhang, John Lambert, Wenxuan Zhou, Zhangyang Wang, Mingqing Chen, Andrew Hard, Rajiv Mathews, Lun Wang - - - Bisplit graphs -- A Structural and algorithmic study - https://arxiv.org/abs/2512.23989 - arXiv:2512.23989v1 Announce Type: new -Abstract: A dominating set $S$ of a graph $G(V,E)$ is called a \textit{secure dominating set} if each vertex $u \in V(G) \setminus S$ is adjacent to a vertex $v \in S$ such that $(S \setminus \{v\}) \cup \{u\}$ is a dominating set of $G$. The \textit{secure domination number} $\gamma_s(G)$ of $G$ is the minimum cardinality of a secure dominating set of $G$. The \textit{Minimum Secure Domination problem} is to find a secure dominating set of a graph $G$ of cardinality $\gamma_s(G)$. In this paper, the computational complexity of the secure domination problem on several graph classes is investigated. The decision version of secure domination problem was shown to be NP-complete on star(comb) convex split graphs and bisplit graphs. So we further focus on complexity analysis of secure domination problem under additional structural restrictions on bisplit graphs. In particular, by imposing chordality as a parameter, we analyse its impact on the computational status of the problem on bisplit graphs. We establish the P versus NP-C dichotomy status of secure domination problem under restrictions on cycle length within bisplit graphs. In addition, we establish that the problem is polynomial-time solvable in chain graphs. We also prove that the secure domination problem cannot be approximated for a bisplit graph within a factor of $(1-\epsilon)~ln~|V|$ for any $\epsilon > 0$, unless $NP \subseteq DTIME(|V|^{O(log~log~|V|)})$. - oai:arXiv.org:2512.23989v1 - cs.DM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Swathi D, N Sadagopan - - - GCA-ResUNet: Medical Image Segmentation Using Grouped Coordinate Attention - https://arxiv.org/abs/2512.23990 - arXiv:2512.23990v1 Announce Type: new -Abstract: Accurate segmentation of heterogeneous anatomical structures is pivotal for computer-aided diagnosis and subsequent clinical decision-making. Although U-Net based convolutional neural networks have achieved remarkable progress, their intrinsic locality and largely homogeneous attention formulations often limit the modeling of long-range contextual dependencies, especially in multi-organ scenarios and low-contrast regions. Transformer-based architectures mitigate this issue by leveraging global self-attention, but they usually require higher computational resources and larger training data, which may impede deployment in resource-constrained clinical environments.In this paper, we propose GCA-ResUNet, an efficient medical image segmentation framework equipped with a lightweight and plug-and-play Grouped Coordinate Attention (GCA) module. The proposed GCA decouples channel-wise context modeling into multiple groups to explicitly account for semantic heterogeneity across channels, and integrates direction-aware coordinate encoding to capture structured spatial dependencies along horizontal and vertical axes. This design enhances global representation capability while preserving the efficiency advantages of CNN backbones. Extensive experiments on two widely used benchmarks, Synapse and ACDC, demonstrate that GCA-ResUNet achieves Dice scores of 86.11% and 92.64%, respectively, outperforming a range of representative CNN and Transformer-based methods, including Swin-UNet and TransUNet. In particular, GCA-ResUNet yields consistent improvements in delineating small anatomical structures with complex boundaries. These results indicate that the proposed approach provides a favorable trade-off between segmentation accuracy and computational efficiency, offering a practical and scalable solution for clinical deployment. - oai:arXiv.org:2512.23990v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jun Ding, Shang Gao - - - PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation - https://arxiv.org/abs/2512.23994 - arXiv:2512.23994v1 Announce Type: new -Abstract: Text-to-audio-video (T2AV) generation underpins a wide range of applications demanding realistic audio-visual content, including virtual reality, world modeling, gaming, and filmmaking. However, existing T2AV models remain incapable of generating physically plausible sounds, primarily due to their limited understanding of physical principles. To situate current research progress, we present PhyAVBench, a challenging audio physics-sensitivity benchmark designed to systematically evaluate the audio physics grounding capabilities of existing T2AV models. PhyAVBench comprises 1,000 groups of paired text prompts with controlled physical variables that implicitly induce sound variations, enabling a fine-grained assessment of models' sensitivity to changes in underlying acoustic conditions. We term this evaluation paradigm the Audio-Physics Sensitivity Test (APST). Unlike prior benchmarks that primarily focus on audio-video synchronization, PhyAVBench explicitly evaluates models' understanding of the physical mechanisms underlying sound generation, covering 6 major audio physics dimensions, 4 daily scenarios (music, sound effects, speech, and their mix), and 50 fine-grained test points, ranging from fundamental aspects such as sound diffraction to more complex phenomena, e.g., Helmholtz resonance. Each test point consists of multiple groups of paired prompts, where each prompt is grounded by at least 20 newly recorded or collected real-world videos, thereby minimizing the risk of data leakage during model pre-training. Both prompts and videos are iteratively refined through rigorous human-involved error correction and quality control to ensure high quality. We argue that only models with a genuine grasp of audio-related physical principles can generate physically consistent audio-visual content. We hope PhyAVBench will stimulate future progress in this critical yet largely unexplored domain. - oai:arXiv.org:2512.23994v1 - cs.SD - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tianxin Xie, Wentao Lei, Guanjie Huang, Pengfei Zhang, Kai Jiang, Chunhui Zhang, Fengji Ma, Haoyu He, Han Zhang, Jiangshan He, Jinting Wang, Linghan Fang, Lufei Gao, Orkesh Ablet, Peihua Zhang, Ruolin Hu, Shengyu Li, Weilin Lin, Xiaoyang Feng, Xinyue Yang, Yan Rong, Yanyun Wang, Zihang Shao, Zelin Zhao, Chenxing Li, Shan Yang, Wenfu Wang, Meng Yu, Dong Yu, Li Liu - - - RepetitionCurse: Measuring and Understanding Router Imbalance in Mixture-of-Experts LLMs under DoS Stress - https://arxiv.org/abs/2512.23995 - arXiv:2512.23995v1 Announce Type: new -Abstract: Mixture-of-Experts architectures have become the standard for scaling large language models due to their superior parameter efficiency. To accommodate the growing number of experts in practice, modern inference systems commonly adopt expert parallelism to distribute experts across devices. However, the absence of explicit load balancing constraints during inference allows adversarial inputs to trigger severe routing concentration. We demonstrate that out-of-distribution prompts can manipulate the routing strategy such that all tokens are consistently routed to the same set of top-$k$ experts, which creates computational bottlenecks on certain devices while forcing others to idle. This converts an efficiency mechanism into a denial-of-service attack vector, leading to violations of service-level agreements for time to first token. We propose RepetitionCurse, a low-cost black-box strategy to exploit this vulnerability. By identifying a universal flaw in MoE router behavior, RepetitionCurse constructs adversarial prompts using simple repetitive token patterns in a model-agnostic manner. On widely deployed MoE models like Mixtral-8x7B, our method increases end-to-end inference latency by 3.063x, degrading service availability significantly. - oai:arXiv.org:2512.23995v1 - cs.CR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ruixuan Huang, Qingyue Wang, Hantao Huang, Yudong Gao, Dong Chen, Shuai Wang, Wei Wang - - - State Space Estimation for DPOR-based Model Checkers - https://arxiv.org/abs/2512.23996 - arXiv:2512.23996v1 Announce Type: new -Abstract: We study the estimation problem for concurrent programs: given a bounded program $P$, estimate the number of Mazurkiewicz trace-equivalence classes induced by its interleavings. This quantity informs two practical questions for enumeration-based model checking: how long a model checking run is likely to take, and what fraction of the search space has been covered so far. We first show the counting problem is #P-hard even for restricted programs and, unless $P=NP$, inapproximable within any subexponential factor, ruling out efficient exact or randomized approximation algorithms. We give a Monte Carlo approach to obtain a poly-time unbiased estimator: we convert a stateless optimal DPOR algorithm into an unbiased estimator by viewing its exploration as a bounded-depth, bounded-width tree whose leaves are the maximal Mazurkiewicz traces. A classical estimator by Knuth, when run on this tree, yields an unbiased estimate. To control the variance, we apply stochastic enumeration by maintaining a small population of partial paths per depth whose evolution is coupled. We have implemented our estimator in the JMC model checker and evaluated it on shared-memory benchmarks. With modest budgets, our estimator yields stable estimates, typically within a 20% band, within a few hundred trials, even when the state space has $10^5$--$10^6$ classes. We also show how the same machinery estimates model-checking cost by weighting all explored graphs, not only complete traces. Our algorithms provide the first provable poly-time unbiased estimators for counting traces, a problem of considerable importance when allocating model checking resources. - oai:arXiv.org:2512.23996v1 - cs.PL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - A. R. Balasubramanian, Mohammad Hossein Khoshechin Jorshari, Rupak Majumdar, Umang Mathur, Minjian Zhang - - - Bridging Structure and Appearance: Topological Features for Robust Self-Supervised Segmentation - https://arxiv.org/abs/2512.23997 - arXiv:2512.23997v1 Announce Type: new -Abstract: Self-supervised semantic segmentation methods often fail when faced with appearance ambiguities. We argue that this is due to an over-reliance on unstable, appearance-based features such as shadows, glare, and local textures. We propose \textbf{GASeg}, a novel framework that bridges appearance and geometry by leveraging stable topological information. The core of our method is Differentiable Box-Counting (\textbf{DBC}) module, which quantifies multi-scale topological statistics from two parallel streams: geometric-based features and appearance-based features. To force the model to learn these stable structural representations, we introduce Topological Augmentation (\textbf{TopoAug}), an adversarial strategy that simulates real-world ambiguities by applying morphological operators to the input images. A multi-objective loss, \textbf{GALoss}, then explicitly enforces cross-modal alignment between geometric-based and appearance-based features. Extensive experiments demonstrate that GASeg achieves state-of-the-art performance on four benchmarks, including COCO-Stuff, Cityscapes, and PASCAL, validating our approach of bridging geometry and appearance via topological information. - oai:arXiv.org:2512.23997v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haotang Li, Zhenyu Qi, Hao Qin, Huanrui Yang, Sen He, Kebin Peng - - - Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge - https://arxiv.org/abs/2512.23998 - arXiv:2512.23998v1 Announce Type: new -Abstract: This work presents a novel pipeline to recover the 3D structure of an unknown target spacecraft from a sequence of images captured during Rendezvous and Proximity Operations (RPO) in space. The target's geometry and appearance are represented as a 3D Gaussian Splatting (3DGS) model. However, learning 3DGS requires static scenes, an assumption in contrast to dynamic lighting conditions encountered in spaceborne imagery. The trained 3DGS model can also be used for camera pose estimation through photometric optimization. Therefore, in addition to recovering a geometrically accurate 3DGS model, the photometric accuracy of the rendered images is imperative to downstream pose estimation tasks during the RPO process. This work proposes to incorporate the prior knowledge of the Sun's position, estimated and maintained by the servicer spacecraft, into the training pipeline for improved photometric quality of 3DGS rasterization. Experimental studies demonstrate the effectiveness of the proposed solution, as 3DGS models trained on a sequence of images learn to adapt to rapidly changing illumination conditions in space and reflect global shadowing and self-occlusion. - oai:arXiv.org:2512.23998v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tae Ha Park, Simone D'Amico - - - WISE: Web Information Satire and Fakeness Evaluation - https://arxiv.org/abs/2512.24000 - arXiv:2512.24000v1 Announce Type: new -Abstract: Distinguishing fake or untrue news from satire or humor poses a unique challenge due to their overlapping linguistic features and divergent intent. This study develops WISE (Web Information Satire and Fakeness Evaluation) framework which benchmarks eight lightweight transformer models alongside two baseline models on a balanced dataset of 20,000 samples from Fakeddit, annotated as either fake news or satire. Using stratified 5-fold cross-validation, we evaluate models across comprehensive metrics including accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, MCC, Brier score, and Expected Calibration Error. Our evaluation reveals that MiniLM, a lightweight model, achieves the highest accuracy (87.58%) among all models, while RoBERTa-base achieves the highest ROC-AUC (95.42%) and strong accuracy (87.36%). DistilBERT offers an excellent efficiency-accuracy trade-off with 86.28\% accuracy and 93.90\% ROC-AUC. Statistical tests confirm significant performance differences between models, with paired t-tests and McNemar tests providing rigorous comparisons. Our findings highlight that lightweight models can match or exceed baseline performance, offering actionable insights for deploying misinformation detection systems in real-world, resource-constrained settings. - oai:arXiv.org:2512.24000v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury - - - Tracing the Heart's Pathways: ECG Representation Learning from a Cardiac Conduction Perspective - https://arxiv.org/abs/2512.24002 - arXiv:2512.24002v1 Announce Type: new -Abstract: The multi-lead electrocardiogram (ECG) stands as a cornerstone of cardiac diagnosis. Recent strides in electrocardiogram self-supervised learning (eSSL) have brightened prospects for enhancing representation learning without relying on high-quality annotations. Yet earlier eSSL methods suffer a key limitation: they focus on consistent patterns across leads and beats, overlooking the inherent differences in heartbeats rooted in cardiac conduction processes, while subtle but significant variations carry unique physiological signatures. Moreover, representation learning for ECG analysis should align with ECG diagnostic guidelines, which progress from individual heartbeats to single leads and ultimately to lead combinations. This sequential logic, however, is often neglected when applying pre-trained models to downstream tasks. To address these gaps, we propose CLEAR-HUG, a two-stage framework designed to capture subtle variations in cardiac conduction across leads while adhering to ECG diagnostic guidelines. In the first stage, we introduce an eSSL model termed Conduction-LEAd Reconstructor (CLEAR), which captures both specific variations and general commonalities across heartbeats. Treating each heartbeat as a distinct entity, CLEAR employs a simple yet effective sparse attention mechanism to reconstruct signals without interference from other heartbeats. In the second stage, we implement a Hierarchical lead-Unified Group head (HUG) for disease diagnosis, mirroring clinical workflow. Experimental results across six tasks show a 6.84% improvement, validating the effectiveness of CLEAR-HUG. This highlights its ability to enhance representations of cardiac conduction and align patterns with expert diagnostic guidelines. - oai:arXiv.org:2512.24002v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tan Pan, Yixuan Sun, Chen Jiang, Qiong Gao, Rui Sun, Xingmeng Zhang, Zhenqi Yang, Limei Han, Yixiu Liang, Yuan Cheng, Kaiyu Guo - - - TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems - https://arxiv.org/abs/2512.24007 - arXiv:2512.24007v1 Announce Type: new -Abstract: Simulation optimization (SO) is frequently challenged by noisy evaluations, high computational costs, and complex, multimodal search landscapes. This paper introduces Tabu-Enhanced Simulation Optimization (TESO), a novel metaheuristic framework integrating adaptive search with memory-based strategies. TESO leverages a short-term Tabu List to prevent cycling and encourage diversification, and a long-term Elite Memory to guide intensification by perturbing high-performing solutions. An aspiration criterion allows overriding tabu restrictions for exceptional candidates. This combination facilitates a dynamic balance between exploration and exploitation in stochastic environments. We demonstrate TESO's effectiveness and reliability using an queue optimization problem, showing improved performance compared to benchmarks and validating the contribution of its memory components. Source code and data are available at: https://github.com/bulentsoykan/TESO. - oai:arXiv.org:2512.24007v1 - cs.NE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bulent Soykan, Sean Mondesire, Ghaith Rabadi - - - SPARK: Search Personalization via Agent-Driven Retrieval and Knowledge-sharing - https://arxiv.org/abs/2512.24008 - arXiv:2512.24008v1 Announce Type: new -Abstract: Personalized search demands the ability to model users' evolving, multi-dimensional information needs; a challenge for systems constrained by static profiles or monolithic retrieval pipelines. We present SPARK (Search Personalization via Agent-Driven Retrieval and Knowledge-sharing), a framework in which coordinated persona-based large language model (LLM) agents deliver task-specific retrieval and emergent personalization. SPARK formalizes a persona space defined by role, expertise, task context, and domain, and introduces a Persona Coordinator that dynamically interprets incoming queries to activate the most relevant specialized agents. Each agent executes an independent retrieval-augmented generation process, supported by dedicated long- and short-term memory stores and context-aware reasoning modules. Inter-agent collaboration is facilitated through structured communication protocols, including shared memory repositories, iterative debate, and relay-style knowledge transfer. Drawing on principles from cognitive architectures, multi-agent coordination theory, and information retrieval, SPARK models how emergent personalization properties arise from distributed agent behaviors governed by minimal coordination rules. The framework yields testable predictions regarding coordination efficiency, personalization quality, and cognitive load distribution, while incorporating adaptive learning mechanisms for continuous persona refinement. By integrating fine-grained agent specialization with cooperative retrieval, SPARK provides insights for next-generation search systems capable of capturing the complexity, fluidity, and context sensitivity of human information-seeking behavior. - oai:arXiv.org:2512.24008v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury - - - Bridging the Perception-Cognition Gap:Re-engineering SAM2 with Hilbert-Mamba for Robust VLM-based Medical Diagnosis - https://arxiv.org/abs/2512.24013 - arXiv:2512.24013v1 Announce Type: new -Abstract: Recent studies suggest that Visual Language Models (VLMs) hold great potential for tasks such as automated medical diagnosis. However, processing complex three-dimensional (3D) multimodal medical images poses significant challenges - specifically, the effective integration of complementary information and the occasional oversight of subtle yet critical pathological features. To address these issues, we present a novel two-stage fusion framework termed Hilbert-VLM. This framework leverages the HilbertMed-SAM module for precise lesion segmentation, with the generated multimodal enhanced prompts then guiding the VLM toward accurate disease classification. Our key innovation lies in the systematic redesign of the Segment Anything Model 2 (SAM2) architecture: we incorporate Hilbert space-filling curves into the scanning mechanism of the Mamba State Space Model (SSM) to maximize the preservation of spatial locality in 3D data, a property critical for medical image analysis. We also introduce a novel Hilbert-Mamba Cross-Attention (HMCA) mechanism and a scale-aware decoder to capture fine-grained details. Meanwhile, the prompt enhancement module unifies segmentation masks and their corresponding textual attributes into an information-dense prompt to support VLM inference. Extensive experiments were conducted to validate the effectiveness of the Hilbert-VLM model. On the BraTS2021 segmentation benchmark, it achieves a Dice score of 82.35 percent, with a diagnostic classification accuracy (ACC) of 78.85 percent. These results demonstrate that the proposed model offers substantial potential to improve the accuracy and reliability of medical VLM-based analysis. - oai:arXiv.org:2512.24013v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Hao Wu, Hui Li, Yiyun Su - - - iCLP: Large Language Model Reasoning with Implicit Cognition Latent Planning - https://arxiv.org/abs/2512.24014 - arXiv:2512.24014v1 Announce Type: new -Abstract: Large language models (LLMs), when guided by explicit textual plans, can perform reliable step-by-step reasoning during problem-solving. However, generating accurate and effective textual plans remains challenging due to LLM hallucinations and the high diversity of task-specific questions. To address this, we draw inspiration from human Implicit Cognition (IC), the subconscious process by which decisions are guided by compact, generalized patterns learned from past experiences without requiring explicit verbalization. We propose iCLP, a novel framework that enables LLMs to adaptively generate latent plans (LPs), which are compact encodings of effective reasoning instructions. iCLP first distills explicit plans from existing step-by-step reasoning trajectories. It then learns discrete representations of these plans via a vector-quantized autoencoder coupled with a codebook. Finally, by fine-tuning LLMs on paired latent plans and corresponding reasoning steps, the models learn to perform implicit planning during reasoning. Experimental results on mathematical reasoning and code generation tasks demonstrate that, with iCLP, LLMs can plan in latent space while reasoning in language space. This approach yields significant improvements in both accuracy and efficiency and, crucially, demonstrates strong cross-domain generalization while preserving the interpretability of chain-of-thought reasoning. - oai:arXiv.org:2512.24014v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sijia Chen, Di Niu - - - On Exact Editing of Flow-Based Diffusion Models - https://arxiv.org/abs/2512.24015 - arXiv:2512.24015v1 Announce Type: new -Abstract: Recent methods in flow-based diffusion editing have enabled direct transformations between source and target image distribution without explicit inversion. However, the latent trajectories in these methods often exhibit accumulated velocity errors, leading to semantic inconsistency and loss of structural fidelity. We propose Conditioned Velocity Correction (CVC), a principled framework that reformulates flow-based editing as a distribution transformation problem driven by a known source prior. CVC rethinks the role of velocity in inter-distribution transformation by introducing a dual-perspective velocity conversion mechanism. This mechanism explicitly decomposes the latent evolution into two components: a structure-preserving branch that remains consistent with the source trajectory, and a semantically-guided branch that drives a controlled deviation toward the target distribution. The conditional velocity field exhibits an absolute velocity error relative to the true underlying distribution trajectory, which inherently introduces potential instability and trajectory drift in the latent space. To address this quantifiable deviation and maintain fidelity to the true flow, we apply a posterior-consistent update to the resulting conditional velocity field. This update is derived from Empirical Bayes Inference and Tweedie correction, which ensures a mathematically grounded error compensation over time. Our method yields stable and interpretable latent dynamics, achieving faithful reconstruction alongside smooth local semantic conversion. Comprehensive experiments demonstrate that CVC consistently achieves superior fidelity, better semantic alignment, and more reliable editing behavior across diverse tasks. - oai:arXiv.org:2512.24015v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zixiang Li, Yue Song, Jianing Peng, Ting Liu, Jun Huang, Xiaochao Qu, Luoqi Liu, Wei Wang, Yao Zhao, Yunchao Wei - - - FitControler: Toward Fit-Aware Virtual Try-On - https://arxiv.org/abs/2512.24016 - arXiv:2512.24016v1 Announce Type: new -Abstract: Realistic virtual try-on (VTON) concerns not only faithful rendering of garment details but also coordination of the style. Prior art typically pursues the former, but neglects a key factor that shapes the holistic style -- garment fit. Garment fit delineates how a garment aligns with the body of a wearer and is a fundamental element in fashion design. In this work, we introduce fit-aware VTON and present FitControler, a learnable plug-in that can seamlessly integrate into modern VTON models to enable customized fit control. To achieve this, we highlight two challenges: i) how to delineate layouts of different fits and ii) how to render the garment that matches the layout. FitControler first features a fit-aware layout generator to redraw the body-garment layout conditioned on a set of delicately processed garment-agnostic representations, and a multi-scale fit injector is then used to deliver layout cues to enable layout-driven VTON. In particular, we build a fit-aware VTON dataset termed Fit4Men, including 13,000 body-garment pairs of different fits, covering both tops and bottoms, and featuring varying camera distances and body poses. Two fit consistency metrics are also introduced to assess the fitness of generations. Extensive experiments show that FitControler can work with various VTON models and achieve accurate fit control. Code and data will be released. - oai:arXiv.org:2512.24016v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Lu Yang, Yicheng Liu, Yanan Li, Xiang Bai, Hao Lu - - - Structure-Guided Allocation of 2D Gaussians for Image Representation and Compression - https://arxiv.org/abs/2512.24018 - arXiv:2512.24018v1 Announce Type: new -Abstract: Recent advances in 2D Gaussian Splatting (2DGS) have demonstrated its potential as a compact image representation with millisecond-level decoding. However, existing 2DGS-based pipelines allocate representation capacity and parameter precision largely oblivious to image structure, limiting their rate-distortion (RD) efficiency at low bitrates. To address this, we propose a structure-guided allocation principle for 2DGS, which explicitly couples image structure with both representation capacity and quantization precision, while preserving native decoding speed. First, we introduce a structure-guided initialization that assigns 2D Gaussians according to spatial structural priors inherent in natural images, yielding a localized and semantically meaningful distribution. Second, during quantization-aware fine-tuning, we propose adaptive bitwidth quantization of covariance parameters, which grants higher precision to small-scale Gaussians in complex regions and lower precision elsewhere, enabling RD-aware optimization, thereby reducing redundancy without degrading edge quality. Third, we impose a geometry-consistent regularization that aligns Gaussian orientations with local gradient directions to better preserve structural details. Extensive experiments demonstrate that our approach substantially improves both the representational power and the RD performance of 2DGS while maintaining over 1000 FPS decoding. Compared with the baseline GSImage, we reduce BD-rate by 43.44% on Kodak and 29.91% on DIV2K. - oai:arXiv.org:2512.24018v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Huanxiong Liang, Yunuo Chen, Yicheng Pan, Sixian Wang, Jincheng Dai, Guo Lu, Wenjun Zhang - - - FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensing - https://arxiv.org/abs/2512.24022 - arXiv:2512.24022v1 Announce Type: new -Abstract: Large vision-language models (VLMs) exhibit strong performance across various tasks. However, these VLMs encounter significant challenges when applied to the remote sensing domain due to the inherent differences between remote sensing images and natural images. Existing remote sensing VLMs often fail to extract fine-grained visual features and suffer from visual forgetting during deep language processing. To address this, we introduce MF-RSVLM, a Multi-Feature Fusion Remote Sensing Vision--Language Model that effectively extracts and fuses visual features for RS understanding. MF-RSVLM learns multi-scale visual representations and combines global context with local details, improving the capture of small and complex structures in RS scenes. A recurrent visual feature injection scheme ensures the language model remains grounded in visual evidence and reduces visual forgetting during generation. Extensive experiments on diverse RS benchmarks show that MF-RSVLM achieves state-of-the-art or highly competitive performance across remote sensing classification, image captioning, and VQA tasks. Our code is publicly available at https://github.com/Yunkaidang/RSVLM. - oai:arXiv.org:2512.24022v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yunkai Dang, Donghao Wang, Jiacheng Yang, Yifan Jiang, Meiyi Zhu, Yuekun Yang, Cong Wang, Qi Fan, Wenbin Li, Yang Gao - - - RSAgent: Learning to Reason and Act for Text-Guided Segmentation via Multi-Turn Tool Invocations - https://arxiv.org/abs/2512.24023 - arXiv:2512.24023v1 Announce Type: new -Abstract: Text-guided object segmentation requires both cross-modal reasoning and pixel grounding abilities. Most recent methods treat text-guided segmentation as one-shot grounding, where the model predicts pixel prompts in a single forward pass to drive an external segmentor, which limits verification, refocusing and refinement when initial localization is wrong. To address this limitation, we propose RSAgent, an agentic Multimodal Large Language Model (MLLM) which interleaves reasoning and action for segmentation via multi-turn tool invocations. RSAgent queries a segmentation toolbox, observes visual feedback, and revises its spatial hypothesis using historical observations to re-localize targets and iteratively refine masks. We further build a data pipeline to synthesize multi-turn reasoning segmentation trajectories, and train RSAgent with a two-stage framework: cold-start supervised fine-tuning followed by agentic reinforcement learning with fine-grained, task-specific rewards. Extensive experiments show that RSAgent achieves a zero-shot performance of 66.5% gIoU on ReasonSeg test, improving over Seg-Zero-7B by 9%, and reaches 81.5% cIoU on RefCOCOg, demonstrating state-of-the-art performance on both in-domain and out-of-domain benchmarks. - oai:arXiv.org:2512.24023v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xingqi He, Yujie Zhang, Shuyong Gao, Wenjie Li, Lingyi Hong, Mingxi Chen, Kaixun Jiang, Jiyuan Fu, Wenqiang Zhang - - - PipeFlow: Pipelined Processing and Motion-Aware Frame Selection for Long-Form Video Editing - https://arxiv.org/abs/2512.24026 - arXiv:2512.24026v1 Announce Type: new -Abstract: Long-form video editing poses unique challenges due to the exponential increase in the computational cost from joint editing and Denoising Diffusion Implicit Models (DDIM) inversion across extended sequences. To address these limitations, we propose PipeFlow, a scalable, pipelined video editing method that introduces three key innovations: First, based on a motion analysis using Structural Similarity Index Measure (SSIM) and Optical Flow, we identify and propose to skip editing of frames with low motion. Second, we propose a pipelined task scheduling algorithm that splits a video into multiple segments and performs DDIM inversion and joint editing in parallel based on available GPU memory. Lastly, we leverage a neural network-based interpolation technique to smooth out the border frames between segments and interpolate the previously skipped frames. Our method uniquely scales to longer videos by dividing them into smaller segments, allowing PipeFlow's editing time to increase linearly with video length. In principle, this enables editing of infinitely long videos without the growing per-frame computational overhead encountered by other methods. PipeFlow achieves up to a 9.6X speedup compared to TokenFlow and a 31.7X speedup over Diffusion Motion Transfer (DMT). - oai:arXiv.org:2512.24026v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Mustafa Munir, Md Mostafijur Rahman, Kartikeya Bhardwaj, Paul Whatmough, Radu Marculescu - - - Evaluation of Impression Difference of a Domestic Mobile Manipulator with Autonomous and/or Remote Control in Fetch-and-Carry Tasks - https://arxiv.org/abs/2512.24029 - arXiv:2512.24029v1 Announce Type: new -Abstract: A single service robot can present two distinct agencies: its onboard autonomy and an operator-mediated agency, yet users experience them through one physical body. We formalize this dual-agency structure as a User-Robot-Operator triad in an autonomous remote-control setting that combines autonomous execution with remote human support. Prior to the recent surge of language-based and multimodal interfaces, we developed and evaluated an early-stage prototype in 2020 that combined natural-language text chat with freehand sketch annotations over the robot's live camera view to support remote intervention. We evaluated three modes - autonomous, remote, and hybrid - in controlled fetch-and-carry tasks using a domestic mobile manipulator (HSR) on a World Robot Summit 2020 rule-compliant test field. The results show systematic mode-dependent differences in user-rated affinity and additional insights on perceived security, indicating that switching or blending agency within one robot measurably shapes human impressions. These findings provide empirical guidance for designing human-in-the-loop mobile manipulation in domestic physical tasks. - oai:arXiv.org:2512.24029v1 - cs.RO - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1080/01691864.2020.1780152 - Advanced Robotics, 34(20):1291-1308, 2020 - Takashi Yamamoto, Hiroaki Yaguchi, Shohei Kato, Hiroyuki Okada - - - Reinforced Diffusion: Learning to Push the Limits of Anisotropic Diffusion for Image Denoising - https://arxiv.org/abs/2512.24035 - arXiv:2512.24035v1 Announce Type: new -Abstract: Image denoising is an important problem in low-level vision and serves as a critical module for many image recovery tasks. Anisotropic diffusion is a wide family of image denoising approaches with promising performance. However, traditional anisotropic diffusion approaches use explicit diffusion operators which are not well adapted to complex image structures. As a result, their performance is limited compared to recent learning-based approaches. In this work, we describe a trainable anisotropic diffusion framework based on reinforcement learning. By modeling the denoising process as a series of naive diffusion actions with order learned by deep Q-learning, we propose an effective diffusion-based image denoiser. The diffusion actions selected by deep Q-learning at different iterations indeed composite a stochastic anisotropic diffusion process with strong adaptivity to different image structures, which enjoys improvement over the traditional ones. The proposed denoiser is applied to removing three types of often-seen noise. The experiments show that it outperforms existing diffusion-based methods and competes with the representative deep CNN-based methods. - oai:arXiv.org:2512.24035v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xinran Qin, Yuhui Quan, Ruotao Xu, Hui Ji - - - Kidney Exchange: Faster Parameterized Algorithms and Tighter Lower Bounds - https://arxiv.org/abs/2512.24037 - arXiv:2512.24037v1 Announce Type: new -Abstract: The kidney exchange mechanism allows many patient-donor pairs who are otherwise incompatible with each other to come together and exchange kidneys along a cycle. However, due to infrastructure and legal constraints, kidney exchange can only be performed in small cycles in practice. In reality, there are also some altruistic donors who do not have any paired patients. This allows us to also perform kidney exchange along paths that start from some altruistic donor. Unfortunately, the computational task is NP-complete. To overcome this computational barrier, an important line of research focuses on designing faster algorithms, both exact and using the framework of parameterized complexity. - The standard parameter for the kidney exchange problem is the number $t$ of patients that receive a healthy kidney. The current fastest known deterministic FPT algorithm for this problem, parameterized by $t$, is $O^\star\left(14^t\right)$. In this work, we improve this by presenting a deterministic FPT algorithm that runs in time $O^\star\left((4e)^t\right)\approx O^\star\left(10.88^t\right)$. This problem is also known to be W[1]-hard parameterized by the treewidth of the underlying undirected graph. A natural question here is whether the kidney exchange problem admits an FPT algorithm parameterized by the pathwidth of the underlying undirected graph. We answer this negatively in this paper by proving that this problem is W[1]-hard parameterized by the pathwidth of the underlying undirected graph. We also present some parameterized intractability results improving the current understanding of the problem under the framework of parameterized complexity. - oai:arXiv.org:2512.24037v1 - cs.DS - cs.AI - cs.CC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Aritra Banik, Sujoy Bhore, Palash Dey, Abhishek Sahu - - - A precise proof of the n-variable Bekic principle - https://arxiv.org/abs/2512.24038 - arXiv:2512.24038v1 Announce Type: new -Abstract: We provide a proof of the $n$-ary Beki\v{c} principle, which states that a vectorial fixpoint of size $n$ can be written in terms of nested fixpoints in each coordinate according to lexicographic order. The proof is inductive. - oai:arXiv.org:2512.24038v1 - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Jun Xu - - - Continuous Angular Power Spectrum Recovery From Channel Covariance via Chebyshev Polynomials - https://arxiv.org/abs/2512.24039 - arXiv:2512.24039v1 Announce Type: new -Abstract: This paper proposes a Chebyshev polynomial expansion framework for the recovery of a continuous angular power spectrum (APS) from channel covariance. By exploiting the orthogonality of Chebyshev polynomials in a transformed domain, we derive an exact series representation of the covariance and reformulate the inherently ill-posed APS inversion as a finite-dimensional linear regression problem via truncation. The associated approximation error is directly controlled by the tail of the APS's Chebyshev series and decays rapidly with increasing angular smoothness. Building on this representation, we derive an exact semidefinite characterization of nonnegative APS and introduce a derivative-based regularizer that promotes smoothly varying APS profiles while preserving transitions of clusters. Simulation results show that the proposed Chebyshev-based framework yields accurate APS reconstruction, and enables reliable downlink (DL) covariance prediction from uplink (UL) measurements in a frequency division duplex (FDD) setting. These findings indicate that jointly exploiting smoothness and nonnegativity in a Chebyshev domain provides an effective tool for covariance-domain processing in multi-antenna systems. - oai:arXiv.org:2512.24039v1 - cs.IT - eess.SP - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shengsong Luo, Ruilin Wu, Chongbin Xu, Junjie Ma, Xiaojun Yuan, Xin Wang - - - ROAD: Reflective Optimization via Automated Debugging for Zero-Shot Agent Alignment - https://arxiv.org/abs/2512.24040 - arXiv:2512.24040v1 Announce Type: new -Abstract: Automatic Prompt Optimization (APO) has emerged as a critical technique for enhancing Large Language Model (LLM) performance, yet current state-of-the-art methods typically rely on large, labeled gold-standard development sets to compute fitness scores for evolutionary or Reinforcement Learning (RL) approaches. In real-world software engineering, however, such curated datasets are rarely available during the initial cold start of agent development, where engineers instead face messy production logs and evolving failure modes. We present ROAD (Reflective Optimization via Automated Debugging), a novel framework that bypasses the need for refined datasets by treating optimization as a dynamic debugging investigation rather than a stochastic search. Unlike traditional mutation strategies, ROAD utilizes a specialized multi-agent architecture, comprising an Analyzer for root-cause analysis, an Optimizer for pattern aggregation, and a Coach for strategy integration, to convert unstructured failure logs into robust, structured Decision Tree Protocols. We evaluated ROAD across both a standardized academic benchmark and a live production Knowledge Management engine. Experimental results demonstrate that ROAD is highly sample-efficient, achieving a 5.6 percent increase in success rate (73.6 percent to 79.2 percent) and a 3.8 percent increase in search accuracy within just three automated iterations. Furthermore, on complex reasoning tasks in the retail domain, ROAD improved agent performance by approximately 19 percent relative to the baseline. These findings suggest that mimicking the human engineering loop of failure analysis and patching offers a viable, data-efficient alternative to resource-intensive RL training for deploying reliable LLM agents. - oai:arXiv.org:2512.24040v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Natchaya Temyingyong, Daman Jain, Neeraj Kumarsahu, Prabhat Kumar, Rachata Phondi, Wachiravit Modecrua, Krittanon Kaewtawee, Krittin Pachtrachai, Touchapon Kraisingkorn - - - Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race? - https://arxiv.org/abs/2512.24044 - arXiv:2512.24044v1 Announce Type: new -Abstract: As large language models (LLMs) are increasingly deployed, ensuring their safe use is paramount. Jailbreaking, adversarial prompts that bypass model alignment to trigger harmful outputs, present significant risks, with existing studies reporting high success rates in evading common LLMs. However, previous evaluations have focused solely on the models, neglecting the full deployment pipeline, which typically incorporates additional safety mechanisms like content moderation filters. To address this gap, we present the first systematic evaluation of jailbreak attacks targeting LLM safety alignment, assessing their success across the full inference pipeline, including both input and output filtering stages. Our findings yield two key insights: first, nearly all evaluated jailbreak techniques can be detected by at least one safety filter, suggesting that prior assessments may have overestimated the practical success of these attacks; second, while safety filters are effective in detection, there remains room to better balance recall and precision to further optimize protection and user experience. We highlight critical gaps and call for further refinement of detection accuracy and usability in LLM safety systems. - oai:arXiv.org:2512.24044v1 - cs.CR - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yuan Xin, Dingfan Chen, Linyi Yang, Michael Backes, Xiao Zhang - - - Beyond Dedicated-Active: A General Reliability Provisioning Framework for SFC Placement in Fog Computing - https://arxiv.org/abs/2512.24049 - arXiv:2512.24049v1 Announce Type: new -Abstract: The explosive growth of Internet of Things (IoT) devices has strained traditional cloud infrastructures, highlighting the need for low-latency and energy-efficient alternatives. Fog computing addresses this by placing computation near the network edge. However, limited and heterogeneous fog resources pose reliability challenges, especially for mission-critical applications. On the other hand, to improve flexibility, applications are deployed as Service Function Chains (SFCs), where each function runs as a Virtual Network Function (VNF). While scalable, this approach is more failure-prone than monolithic deployments, necessitating intelligent redundancy and placement strategies. This paper addresses the reliability-aware SFC placement problem over heterogeneous fog servers through the lens of reliability theory. We explore four redundancy strategies, combining shared vs. dedicated and active vs. standby modes, and propose a general framework to minimize latency and cost while meeting reliability and deadline constraints. The problem is formulated as an Integer Non-Linear Program (INLP), and two genetic algorithm (GA)-based solutions are developed. Simulation results show that shared-standby redundancy outperforms the conventional dedicated-active approach by up to 84%. - oai:arXiv.org:2512.24049v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Negin Doostar, Mohammad Reza Heidarpour, Amir Khorsandi - - - AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives - https://arxiv.org/abs/2512.24052 - arXiv:2512.24052v1 Announce Type: new -Abstract: Although Large Audio-Language Models (LALMs) deliver state-of-the-art (SOTA) performance, they frequently suffer from hallucinations, e.g. generating text not grounded in the audio input. We analyze these grounding failures and identify a distinct taxonomy: Event Omission, False Event Identity, Temporal Relation Error, and Quantitative Temporal Error. To address this, we introduce the AHA (Audio Hallucination Alignment) framework. By leveraging counterfactual hard negative mining, our pipeline constructs a high-quality preference dataset that forces models to distinguish strict acoustic evidence from linguistically plausible fabrications. Additionally, we establish AHA-Eval, a diagnostic benchmark designed to rigorously test these fine-grained temporal reasoning capabilities. We apply this data to align Qwen2.5-Omni. The resulting model, Qwen-Audio-AHA, achieves a 13.7% improvement on AHA-Eval. Crucially, this benefit generalizes beyond our diagnostic set. Our model shows substantial gains on public benchmarks, including 1.3% on MMAU-Test and 1.6% on MMAR, outperforming latest SOTA methods. - oai:arXiv.org:2512.24052v1 - cs.SD - cs.AI - cs.CL - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yanxi Chen, Wenhui Zhu, Xiwen Chen, Zhipeng Wang, Xin Li, Peijie Qiu, Hao Wang, Xuanzhao Dong, Yujian Xiong, Anderson Schneider, Yuriy Nevmyvaka, Yalin Wang - - - Beyond Hallucinations: A Composite Score for Measuring Reliability in Open-Source Large Language Models - https://arxiv.org/abs/2512.24058 - arXiv:2512.24058v1 Announce Type: new -Abstract: Large Language Models (LLMs) like LLaMA, Mistral, and Gemma are increasingly used in decision-critical domains such as healthcare, law, and finance, yet their reliability remains uncertain. They often make overconfident errors, degrade under input shifts, and lack clear uncertainty estimates. Existing evaluations are fragmented, addressing only isolated aspects. We introduce the Composite Reliability Score (CRS), a unified framework that integrates calibration, robustness, and uncertainty quantification into a single interpretable metric. Through experiments on ten leading open-source LLMs across five QA datasets, we assess performance under baselines, perturbations, and calibration methods. CRS delivers stable model rankings, uncovers hidden failure modes missed by single metrics, and highlights that the most dependable systems balance accuracy, robustness, and calibrated uncertainty. - oai:arXiv.org:2512.24058v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Rohit Kumar Salla, Manoj Saravanan, Shrikar Reddy Kota - - - Hyperspherical Graph Representation Learning via Adaptive Neighbor-Mean Alignment and Uniformity - https://arxiv.org/abs/2512.24062 - arXiv:2512.24062v1 Announce Type: new -Abstract: Graph representation learning (GRL) aims to encode structural and semantic dependencies of graph-structured data into low-dimensional embeddings. However, existing GRL methods often rely on surrogate contrastive objectives or mutual information maximization, which typically demand complex architectures, negative sampling strategies, and sensitive hyperparameter tuning. These design choices may induce over-smoothing, over-squashing, and training instability. In this work, we propose HyperGRL, a unified framework for hyperspherical graph representation learning via adaptive neighbor-mean alignment and sampling-free uniformity. HyperGRL embeds nodes on a unit hypersphere through two adversarially coupled objectives: neighbor-mean alignment and sampling-free uniformity. The alignment objective uses the mean representation of each node's local neighborhood to construct semantically grounded, stable targets that capture shared structural and feature patterns. The uniformity objective formulates dispersion via an L2-based hyperspherical regularization, encouraging globally uniform embedding distributions while preserving discriminative information. To further stabilize training, we introduce an entropy-guided adaptive balancing mechanism that dynamically regulates the interplay between alignment and uniformity without requiring manual tuning. Extensive experiments on node classification, node clustering, and link prediction demonstrate that HyperGRL delivers superior representation quality and generalization across diverse graph structures, achieving average improvements of 1.49%, 0.86%, and 0.74% over the strongest existing methods, respectively. These findings highlight the effectiveness of geometrically grounded, sampling-free contrastive objectives for graph representation learning. - oai:arXiv.org:2512.24062v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rui Chen, Junjun Guo, Hongbin Wang, Yan Xiang, Yantuan Xian, Zhengtao Yu - - - How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns - https://arxiv.org/abs/2512.24063 - arXiv:2512.24063v1 Announce Type: new -Abstract: Large Language Models (LLMs) display strikingly different generalization behaviors: supervised fine-tuning (SFT) often narrows capability, whereas reinforcement-learning (RL) tuning tends to preserve it. The reasons behind this divergence remain unclear, as prior studies have largely relied on coarse accuracy metrics. We address this gap by introducing a novel benchmark that decomposes reasoning into atomic core skills such as calculation, fact retrieval, simulation, enumeration, and diagnostic, providing a concrete framework for addressing the fundamental question of what constitutes reasoning in LLMs. By isolating and measuring these core skills, the benchmark offers a more granular view of how specific cognitive abilities emerge, transfer, and sometimes collapse during post-training. Combined with analyses of low-level statistical patterns such as distributional divergence and parameter statistics, it enables a fine-grained study of how generalization evolves under SFT and RL across mathematical, scientific reasoning, and non-reasoning tasks. Our meta-probing framework tracks model behavior at different training stages and reveals that RL-tuned models maintain more stable behavioral profiles and resist collapse in reasoning skills, whereas SFT models exhibit sharper drift and overfit to surface patterns. This work provides new insights into the nature of reasoning in LLMs and points toward principles for designing training strategies that foster broad, robust generalization. - oai:arXiv.org:2512.24063v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haoyue Bai, Yiyou Sun, Wenjie Hu, Shi Qiu, Maggie Ziyu Huan, Peiyang Song, Robert Nowak, Dawn Song - - - Neighbor-aware Instance Refining with Noisy Labels for Cross-Modal Retrieval - https://arxiv.org/abs/2512.24064 - arXiv:2512.24064v1 Announce Type: new -Abstract: In recent years, Cross-Modal Retrieval (CMR) has made significant progress in the field of multi-modal analysis. However, since it is time-consuming and labor-intensive to collect large-scale and well-annotated data, the annotation of multi-modal data inevitably contains some noise. This will degrade the retrieval performance of the model. To tackle the problem, numerous robust CMR methods have been developed, including robust learning paradigms, label calibration strategies, and instance selection mechanisms. Unfortunately, they often fail to simultaneously satisfy model performance ceilings, calibration reliability, and data utilization rate. To overcome the limitations, we propose a novel robust cross-modal learning framework, namely Neighbor-aware Instance Refining with Noisy Labels (NIRNL). Specifically, we first propose Cross-modal Margin Preserving (CMP) to adjust the relative distance between positive and negative pairs, thereby enhancing the discrimination between sample pairs. Then, we propose Neighbor-aware Instance Refining (NIR) to identify pure subset, hard subset, and noisy subset through cross-modal neighborhood consensus. Afterward, we construct different tailored optimization strategies for this fine-grained partitioning, thereby maximizing the utilization of all available data while mitigating error propagation. Extensive experiments on three benchmark datasets demonstrate that NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates. - oai:arXiv.org:2512.24064v1 - cs.CV - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yizhi Liu, Ruitao Pu, Shilin Xu, Yingke Chen, Quan-Hui Liu, Yuan Sun - - - Pathology Context Recalibration Network for Ocular Disease Recognition - https://arxiv.org/abs/2512.24066 - arXiv:2512.24066v1 Announce Type: new -Abstract: Pathology context and expert experience play significant roles in clinical ocular disease diagnosis. Although deep neural networks (DNNs) have good ocular disease recognition results, they often ignore exploring the clinical pathology context and expert experience priors to improve ocular disease recognition performance and decision-making interpretability. To this end, we first develop a novel Pathology Recalibration Module (PRM) to leverage the potential of pathology context prior via the combination of the well-designed pixel-wise context compression operator and pathology distribution concentration operator; then this paper applies a novel expert prior Guidance Adapter (EPGA) to further highlight significant pixel-wise representation regions by fully mining the expert experience prior. By incorporating PRM and EPGA into the modern DNN, the PCRNet is constructed for automated ocular disease recognition. Additionally, we introduce an Integrated Loss (IL) to boost the ocular disease recognition performance of PCRNet by considering the effects of sample-wise loss distributions and training label frequencies. The extensive experiments on three ocular disease datasets demonstrate the superiority of PCRNet with IL over state-of-the-art attention-based networks and advanced loss methods. Further visualization analysis explains the inherent behavior of PRM and EPGA that affects the decision-making process of DNNs. - oai:arXiv.org:2512.24066v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1007/s11633-025-1631-8 - Machine Intelligence Research 2025 - Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Jiang Liu - - - Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning - https://arxiv.org/abs/2512.24069 - arXiv:2512.24069v1 Announce Type: new -Abstract: We consider the design of mixing matrices to minimize the operation cost for decentralized federated learning (DFL) in wireless networks, with focus on minimizing the maximum per-node energy consumption. As a critical hyperparameter for DFL, the mixing matrix controls both the convergence rate and the needs of agent-to-agent communications, and has thus been studied extensively. However, existing designs mostly focused on minimizing the communication time, leaving open the minimization of per-node energy consumption that is critical for energy-constrained devices. This work addresses this gap through a theoretically-justified solution for mixing matrix design that aims at minimizing the maximum per-node energy consumption until convergence, while taking into account the broadcast nature of wireless communications. Based on a novel convergence theorem that allows arbitrarily time-varying mixing matrices, we propose a multi-phase design framework that activates time-varying communication topologies under optimized budgets to trade off the per-iteration energy consumption and the convergence rate while balancing the energy consumption across nodes. Our evaluations based on real data have validated the efficacy of the proposed solution in combining the low energy consumption of sparse mixing matrices and the fast convergence of dense mixing matrices. - oai:arXiv.org:2512.24069v1 - cs.LG - cs.DC - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xusheng Zhang, Tuan Nguyen, Ting He - - - CPePC: Cooperative and Predictive Popularity based Caching for Named Data Networks - https://arxiv.org/abs/2512.24073 - arXiv:2512.24073v1 Announce Type: new -Abstract: Caching content is an inherent feature of Named Data Networks. Limited cache capacity of routers warrants that the choice of content being cached is judiciously done. Existing techniques resort to caching popular content to maximize utilization. However, these methods experience significant overhead for coordinating and estimating the popularity of content. To address this issue, in this paper, we present CPePC, which is a cooperative caching technique designed to improve performance. It accomplishes this through a combination of two factors. First, CPePC enhances efficiency by minimizing the overhead of popularity estimation. Second, it forecasts a parameter that governs caching decisions. Efficiency in popularity estimation is achieved by dividing the network into several non-overlapping communities using a community estimation algorithm and selecting a leader node to coordinate this on behalf of all the nodes in the community. CPePC bases its caching decisions by predicting a parameter whose value is estimated using current cache occupancy and the popularity of the content into account. We present algorithms for community detection, leader selection, content popularity estimation, and caching decisions made by the CPePC method. We evaluate and compare it with six other state-of-the-art caching techniques, with simulations performed using a discrete event simulator to show that it outperforms others. - oai:arXiv.org:2512.24073v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Pankaj Chaudhary, Neminath Hubballi, Sameer G. Kulkarni - - - Balanced Hierarchical Contrastive Learning with Decoupled Queries for Fine-grained Object Detection in Remote Sensing Images - https://arxiv.org/abs/2512.24074 - arXiv:2512.24074v1 Announce Type: new -Abstract: Fine-grained remote sensing datasets often use hierarchical label structures to differentiate objects in a coarse-to-fine manner, with each object annotated across multiple levels. However, embedding this semantic hierarchy into the representation learning space to improve fine-grained detection performance remains challenging. Previous studies have applied supervised contrastive learning at different hierarchical levels to group objects under the same parent class while distinguishing sibling subcategories. Nevertheless, they overlook two critical issues: (1) imbalanced data distribution across the label hierarchy causes high-frequency classes to dominate the learning process, and (2) learning semantic relationships among categories interferes with class-agnostic localization. To address these issues, we propose a balanced hierarchical contrastive loss combined with a decoupled learning strategy within the detection transformer (DETR) framework. The proposed loss introduces learnable class prototypes and equilibrates gradients contributed by different classes at each hierarchical level, ensuring that each hierarchical class contributes equally to the loss computation in every mini-batch. The decoupled strategy separates DETR's object queries into classification and localization sets, enabling task-specific feature extraction and optimization. Experiments on three fine-grained datasets with hierarchical annotations demonstrate that our method outperforms state-of-the-art approaches. - oai:arXiv.org:2512.24074v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jingzhou Chen, Dexin Chen, Fengchao Xiong, Yuntao Qian, Liang Xiao - - - Multi-Scenario Highway Lane-Change Intention Prediction: A Temporal Physics-Informed Multi-Modal Framework - https://arxiv.org/abs/2512.24075 - arXiv:2512.24075v1 Announce Type: new -Abstract: Lane-change intention prediction is safety-critical for autonomous driving and ADAS, but remains difficult in naturalistic traffic due to noisy kinematics, severe class imbalance, and limited generalization across heterogeneous highway scenarios. We propose Temporal Physics-Informed AI (TPI-AI), a hybrid framework that fuses deep temporal representations with physics-inspired interaction cues. A two-layer bidirectional LSTM (Bi-LSTM) encoder learns compact embeddings from multi-step trajectory histories; we concatenate these embeddings with kinematics-, safety-, and interaction-aware features (e.g., headway, TTC, and safe-gap indicators) and train a LightGBM classifier for three-class intention recognition (No-LC, Left-LC, Right-LC). To improve minority-class reliability, we apply imbalance-aware optimization including resampling/weighting and fold-wise threshold calibration. Experiments on two large-scale drone-based datasets, highD (straight highways) and exiD (ramp-rich environments), use location-based splits and evaluate prediction horizons T = 1, 2, 3 s. TPI-AI outperforms standalone LightGBM and Bi-LSTM baselines, achieving macro-F1 of 0.9562, 0.9124, 0.8345 on highD and 0.9247, 0.8197, 0.7605 on exiD at T = 1, 2, 3 s, respectively. These results show that combining physics-informed interaction features with learned temporal embeddings yields robust multi-scenario lane-change intention prediction. - oai:arXiv.org:2512.24075v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jiazhao Shi, Ziyu Wang, Yichen Lin, Shoufeng Lu - - - LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm - https://arxiv.org/abs/2512.24077 - arXiv:2512.24077v1 Announce Type: new -Abstract: The transition from static Large Language Models (LLMs) to self-improving agents is hindered by the lack of structured reasoning in traditional evolutionary approaches. Existing methods often struggle with premature convergence and inefficient exploration in high-dimensional code spaces. To address these challenges, we introduce LoongFlow, a self-evolving agent framework that achieves state-of-the-art solution quality with significantly reduced computational costs. Unlike "blind" mutation operators, LoongFlow integrates LLMs into a cognitive "Plan-Execute-Summarize" (PES) paradigm, effectively mapping the evolutionary search to a reasoning-heavy process. To sustain long-term architectural coherence, we incorporate a hybrid evolutionary memory system. By synergizing Multi-Island models with MAP-Elites and adaptive Boltzmann selection, this system theoretically balances the exploration-exploitation trade-off, maintaining diverse behavioral niches to prevent optimization stagnation. We instantiate LoongFlow with a General Agent for algorithmic discovery and an ML Agent for pipeline optimization. Extensive evaluations on the AlphaEvolve benchmark and Kaggle competitions demonstrate that LoongFlow outperforms leading baselines (e.g., OpenEvolve, ShinkaEvolve) by up to 60% in evolutionary efficiency while discovering superior solutions. LoongFlow marks a substantial step forward in autonomous scientific discovery, enabling the generation of expert-level solutions with reduced computational overhead. - oai:arXiv.org:2512.24077v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chunhui Wan, Xunan Dai, Zhuo Wang, Minglei Li, Yanpeng Wang, Yinan Mao, Yu Lan, Zhiwen Xiao - - - High-dimensional Regret Minimization - https://arxiv.org/abs/2512.24078 - arXiv:2512.24078v1 Announce Type: new -Abstract: Multi-criteria decision making in large databases is very important in real world applications. Recently, an interactive query has been studied extensively in the database literature with the advantage of both the top-k query (with limited output size) and the skyline query (which does not require users to explicitly specify their preference function). This approach iteratively asks the user to select the one preferred within a set of options. Based on rounds of feedback, the query learns the implicit preference and returns the most favorable as a recommendation. - However, many modern applications in areas like housing or financial product markets feature datasets with hundreds of attributes. Existing interactive algorithms either fail to scale or require excessive user interactions (often exceeding 1000 rounds). Motivated by this, we propose FHDR (Fast High-Dimensional Reduction), a novel framework that takes less than 0.01s with fewer than 30 rounds of interaction. It is considered a breakthrough in the field of interactive queries since most, if not all, existing studies are not scalable to high-dimensional datasets. - Extensive experiments demonstrate that FHDR outperforms the best-known algorithms by at least an order of magnitude in execution time and up to several orders of magnitude in terms of the number of interactions required, establishing a new state of the art for scalable interactive regret minimization. - oai:arXiv.org:2512.24078v1 - cs.DB - cs.CG - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Junyu Liao, Ashwin Lall, Mitsunori Ogihara, Raymond Wong - - - RainFusion2.0: Temporal-Spatial Awareness and Hardware-Efficient Block-wise Sparse Attention - https://arxiv.org/abs/2512.24086 - arXiv:2512.24086v1 Announce Type: new -Abstract: In video and image generation tasks, Diffusion Transformer (DiT) models incur extremely high computational costs due to attention mechanisms, which limits their practical applications. Furthermore, with hardware advancements, a wide range of devices besides graphics processing unit (GPU), such as application-specific integrated circuit (ASIC), have been increasingly adopted for model inference. Sparse attention, which leverages the inherent sparsity of attention by skipping computations for insignificant tokens, is an effective approach to mitigate computational costs. However, existing sparse attention methods have two critical limitations: the overhead of sparse pattern prediction and the lack of hardware generality, as most of these methods are designed for GPU. To address these challenges, this study proposes RainFusion2.0, which aims to develop an online adaptive, hardware-efficient, and low-overhead sparse attention mechanism to accelerate both video and image generative models, with robust performance across diverse hardware platforms. Key technical insights include: (1) leveraging block-wise mean values as representative tokens for sparse mask prediction; (2) implementing spatiotemporal-aware token permutation; and (3) introducing a first-frame sink mechanism specifically designed for video generation scenarios. Experimental results demonstrate that RainFusion2.0 can achieve 80% sparsity while achieving an end-to-end speedup of 1.5~1.8x without compromising video quality. Moreover, RainFusion2.0 demonstrates effectiveness across various generative models and validates its generalization across diverse hardware platforms. - oai:arXiv.org:2512.24086v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Aiyue Chen, Yaofu Liu, Junjian Huang, Guang Lian, Yiwu Yao, Wangli Lan, Jing Lin, Zhixin Ma, Tingting Zhou, Harry Yang - - - Random Multiplexing - https://arxiv.org/abs/2512.24087 - arXiv:2512.24087v1 Announce Type: new -Abstract: As wireless communication applications evolve from traditional multipath environments to high-mobility scenarios like unmanned aerial vehicles, multiplexing techniques have advanced accordingly. Traditional single-carrier frequency-domain equalization (SC-FDE) and orthogonal frequency-division multiplexing (OFDM) have given way to emerging orthogonal time-frequency space (OTFS) and affine frequency-division multiplexing (AFDM). These approaches exploit specific channel structures to diagonalize or sparsify the effective channel, thereby enabling low-complexity detection. However, their reliance on these structures significantly limits their robustness in dynamic, real-world environments. To address these challenges, this paper studies a random multiplexing technique that is decoupled from the physical channels, enabling its application to arbitrary norm-bounded and spectrally convergent channel matrices. Random multiplexing achieves statistical fading-channel ergodicity for transmitted signals by constructing an equivalent input-isotropic channel matrix in the random transform domain. It guarantees the asymptotic replica MAP bit-error rate (BER) optimality of AMP-type detectors for linear systems with arbitrary norm-bounded, spectrally convergent channel matrices and signaling configurations, under the unique fixed point assumption. A low-complexity cross-domain memory AMP (CD-MAMP) detector is considered, leveraging the sparsity of the time-domain channel and the randomness of the equivalent channel. Optimal power allocations are derived to minimize the replica MAP BER and maximize the replica constrained capacity of random multiplexing systems. The optimal coding principle and replica constrained-capacity optimality of CD-MAMP detector are investigated for random multiplexing systems. Additionally, the versatility of random multiplexing in diverse wireless applications is explored. - oai:arXiv.org:2512.24087v1 - cs.IT - cs.AI - cs.LG - eess.SP - math.IT - math.ST - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Lei Liu, Yuhao Chi, Shunqi Huang, Zhaoyang Zhang - - - FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detection - https://arxiv.org/abs/2512.24088 - arXiv:2512.24088v1 Announce Type: new -Abstract: This work implements a lightweight Transformer model for IDS in the domain of Connected and Autonomous Vehicles - oai:arXiv.org:2512.24088v1 - cs.CR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Devika S, Pratik Narang, Tejasvi Alladi - - - HY-MT1.5 Technical Report - https://arxiv.org/abs/2512.24092 - arXiv:2512.24092v1 Announce Type: new -Abstract: In this report, we introduce our latest translation models, HY-MT1.5-1.8B and HY-MT1.5-7B, a new family of machine translation models developed through a holistic training framework tailored for high-performance translation. Our methodology orchestrates a multi-stage pipeline that integrates general and MT-oriented pre-training, supervised fine-tuning, on-policy distillation, and reinforcement learning. HY-MT1.5-1.8B, the 1.8B-parameter model demonstrates remarkable parameter efficiency, comprehensively outperforming significantly larger open-source baselines (e.g., Tower-Plus-72B, Qwen3-32B) and mainstream commercial APIs (e.g., Microsoft Translator, Doubao Translator) in standard Chinese-foreign and English-foreign tasks. It achieves approximately 90% of the performance of ultra-large proprietary models such as Gemini-3.0-Pro, while marginally trailing Gemini-3.0-Pro on WMT25 and Mandarin-minority language benchmarks, it maintains a substantial lead over other competing models. Furthermore, HY-MT1.5-7B establishes a new state-of-the-art for its size class, achieving 95% of Gemini-3.0-Pro's performance on Flores-200 and surpassing it on the challenging WMT25 and Mandarin-minority language test sets. Beyond standard translation, the HY-MT1.5 series supports advanced constraints, including terminology intervention, context-aware translation, and format preservation. Extensive empirical evaluations confirm that both models offer highly competitive, robust solutions for general and specialized translation tasks within their respective parameter scales. - oai:arXiv.org:2512.24092v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mao Zheng, Zheng Li, Tao Chen, Mingyang Song, Di Wang - - - Factorized Learning for Temporally Grounded Video-Language Models - https://arxiv.org/abs/2512.24097 - arXiv:2512.24097v1 Announce Type: new -Abstract: Recent video-language models have shown great potential for video understanding, but still struggle with accurate temporal grounding for event-level perception. We observe that two main factors in video understanding (i.e., temporal grounding and textual response) form a logical hierarchy: accurate temporal evidence grounding lays the foundation for reliable textual response. However, existing works typically handle these two tasks in a coupled manner without a clear logical structure, leading to sub-optimal objectives. We address this from a factorized learning perspective. We first propose D$^2$VLM, a framework that decouples the learning of these two tasks while also emphasizing their inherent dependency. We adopt a "grounding then answering with evidence referencing" paradigm and introduce evidence tokens for evidence grounding, which emphasize event-level visual semantic capture beyond the focus on timestamp representation in existing works. To further facilitate the learning of these two tasks, we introduce a novel factorized preference optimization (FPO) algorithm. Unlike standard preference optimization, FPO explicitly incorporates probabilistic temporal grounding modeling into the optimization objective, enabling preference learning for both temporal grounding and textual response. We also construct a synthetic dataset to address the lack of suitable datasets for factorized preference learning with explicit temporal grounding. Experiments on various tasks demonstrate the clear advantage of our approach. Our source code is available at https://github.com/nusnlp/d2vlm. - oai:arXiv.org:2512.24097v1 - cs.CV - cs.AI - cs.CL - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Wenzheng Zeng, Difei Gao, Mike Zheng Shou, Hwee Tou Ng - - - Training a Huggingface Model on AWS Sagemaker (Without Tears) - https://arxiv.org/abs/2512.24098 - arXiv:2512.24098v1 Announce Type: new -Abstract: The development of Large Language Models (LLMs) has primarily been driven by resource-rich research groups and industry partners. Due to the lack of on-premise computing resources required for increasingly complex models, many researchers are turning to cloud services like AWS SageMaker to train Hugging Face models. However, the steep learning curve of cloud platforms often presents a barrier for researchers accustomed to local environments. Existing documentation frequently leaves knowledge gaps, forcing users to seek fragmented information across the web. This demo paper aims to democratize cloud adoption by centralizing the essential information required for researchers to successfully train their first Hugging Face model on AWS SageMaker from scratch. - oai:arXiv.org:2512.24098v1 - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Liling Tan - - - Think Before You Move: Latent Motion Reasoning for Text-to-Motion Generation - https://arxiv.org/abs/2512.24100 - arXiv:2512.24100v1 Announce Type: new -Abstract: Current state-of-the-art paradigms predominantly treat Text-to-Motion (T2M) generation as a direct translation problem, mapping symbolic language directly to continuous poses. While effective for simple actions, this System 1 approach faces a fundamental theoretical bottleneck we identify as the Semantic-Kinematic Impedance Mismatch: the inherent difficulty of grounding semantically dense, discrete linguistic intent into kinematically dense, high-frequency motion data in a single shot. In this paper, we argue that the solution lies in an architectural shift towards Latent System 2 Reasoning. Drawing inspiration from Hierarchical Motor Control in cognitive science, we propose Latent Motion Reasoning (LMR) that reformulates generation as a two-stage Think-then-Act decision process. Central to LMR is a novel Dual-Granularity Tokenizer that disentangles motion into two distinct manifolds: a compressed, semantically rich Reasoning Latent for planning global topology, and a high-frequency Execution Latent for preserving physical fidelity. By forcing the model to autoregressively reason (plan the coarse trajectory) before it moves (instantiates the frames), we effectively bridge the ineffability gap between language and physics. We demonstrate LMR's versatility by implementing it for two representative baselines: T2M-GPT (discrete) and MotionStreamer (continuous). Extensive experiments show that LMR yields non-trivial improvements in both semantic alignment and physical plausibility, validating that the optimal substrate for motion planning is not natural language, but a learned, motion-aligned concept space. Codes and demos can be found in \hyperlink{https://chenhaoqcdyq.github.io/LMR/}{https://chenhaoqcdyq.github.io/LMR/} - oai:arXiv.org:2512.24100v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yijie Qian, Juncheng Wang, Yuxiang Feng, Chao Xu, Wang Lu, Yang Liu, Baigui Sun, Yiqiang Chen, Yong Liu, Shujun Wang - - - Economic and Technical Feasibility of V2G in Non-Road Mobile Machinery sector - https://arxiv.org/abs/2512.24101 - arXiv:2512.24101v1 Announce Type: new -Abstract: This paper investigates the economic and technical feasibility of integrating Vehicle-to-Grid (V2G) technology in the Non-Road Mobile Machinery (NRMM) sector. These often-idling assets, with their substantial battery capacities, present a unique opportunity to participate in energy markets, providing grid services and generating additional revenue. A novel methodology is introduced that integrates Bayesian Optimization (BO) to optimize the energy infrastructure together with an operating strategy optimization to reduce the electricity costs while enhancing grid interaction. While the focus lies on the methodology, the financial opportunities for the use-case of an electric NRMM rental service will be presented. However, the study is limited by the availability of real-world data on the usage of electric NRMM and does not address regulatory challenges of V2G. Further research is needed to extend the model accuracy and validate these findings. - oai:arXiv.org:2512.24101v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - R\"o{\ss}ler Nicolas, Khan Irfan, Schade Thomas, Wellmann Christoph, Cao Xinyuan, Kopynske Milan, Xia Feihong, Savelsberg Rene, Andert Jakob - - - Autoregressivity in the Latent Space of a GP-VAE Language Model: An Empirical Ablation Study - https://arxiv.org/abs/2512.24102 - arXiv:2512.24102v1 Announce Type: new -Abstract: This paper provides an ablation-based analysis of latent autoregression in GP-VAE models, building upon our previous work introducing the architecture. Language models typically rely on an autoregressive factorization over tokens. In contrast, our prior work proposed shifting sequential structure to the latent space through a causal Gaussian process, while using a non-autoregressive decoder. Here, we conduct a systematic ablation study of the role played by latent autoregression. We compare (i) a full GP-VAE model with autoregressive latent dynamics, (ii) a non-autoregressive ablation in which latent variables are independent, and (iii) a standard token-level autoregressive Transformer. Our results show that, within the considered regime (medium-scale corpora and short training contexts), latent autoregression induces latent trajectories that are significantly more compatible with the Gaussian-process prior and exhibit greater long-horizon stability. In contrast, removing autoregression leads to degraded latent structure and unstable long-range behavior. These findings highlight the role of latent autoregression as an effective mechanism for organizing long-range structure, while remaining complementary to token-level autoregressive modeling. They should be interpreted as an empirical analysis of representational structure rather than as a proposal for a new architecture. - oai:arXiv.org:2512.24102v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yves Ruffenach - - - Enhancing LLM Planning Capabilities through Intrinsic Self-Critique - https://arxiv.org/abs/2512.24103 - arXiv:2512.24103v1 Announce Type: new -Abstract: We demonstrate an approach for LLMs to critique their \emph{own} answers with the goal of enhancing their performance that leads to significant improvements over established planning benchmarks. Despite the findings of earlier research that has cast doubt on the effectiveness of LLMs leveraging self critique methods, we show significant performance gains on planning datasets in the Blocksworld domain through intrinsic self-critique, without external source such as a verifier. We also demonstrate similar improvements on Logistics and Mini-grid datasets, exceeding strong baseline accuracies. We employ a few-shot learning technique and progressively extend it to a many-shot approach as our base method and demonstrate that it is possible to gain substantial improvement on top of this already competitive approach by employing an iterative process for correction and refinement. We illustrate how self-critique can significantly boost planning performance. Our empirical results present new state-of-the-art on the class of models considered, namely LLM model checkpoints from October 2024. Our primary focus lies on the method itself, demonstrating intrinsic self-improvement capabilities that are applicable regardless of the specific model version, and we believe that applying our method to more complex search techniques and more capable models will lead to even better performance. - oai:arXiv.org:2512.24103v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Bernd Bohnet, Pierre-Alexandre Kamienny, Hanie Sedghi, Dilan Gorur, Pranjal Awasthi, Aaron Parisi, Kevin Swersky, Rosanne Liu, Azade Nova, Noah Fiedel - - - Multilevel Fair Allocation - https://arxiv.org/abs/2512.24105 - arXiv:2512.24105v1 Announce Type: new -Abstract: We introduce the concept of multilevel fair allocation of resources with tree-structured hierarchical relations among agents. While at each level it is possible to consider the problem locally as an allocation of an agent to its children, the multilevel allocation can be seen as a trace capturing the fact that the process is iterated until the leaves of the tree. In principle, each intermediary node may have its own local allocation mechanism. The main challenge is then to design algorithms which can retain good fairness and efficiency properties. In this paper we propose two original algorithms under the assumption that leaves of the tree have matroid-rank utility functions and the utility of any internal node is the sum of the utilities of its children. The first one is a generic polynomial-time sequential algorithm that comes with theoretical guarantees in terms of efficiency and fairness. It operates in a top-down fashion -- as commonly observed in real-world applications -- and is compatible with various local algorithms. The second one extends the recently proposed General Yankee Swap to the multilevel setting. This extension comes with efficiency guarantees only, but we show that it preserves excellent fairness properties in practice. - oai:arXiv.org:2512.24105v1 - cs.GT - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Maxime Lucet, Nawal Benabbou, Aur\'elie Beynier, Nicolas Maudet - - - When Wires Can't Keep Up: Reconfigurable AI Data Centers Empowered by Terahertz Wireless Communications - https://arxiv.org/abs/2512.24110 - arXiv:2512.24110v1 Announce Type: new -Abstract: The explosive growth of artificial intelligence (AI) workloads in modern data centers demands a radical transformation of interconnect architectures. Traditional copper and optical wiring face fundamental challenges in latency, power consumption, and rigidity, constraining the scalability of distributed AI clusters. This article introduces a vision for Terahertz (THz) Wireless Data Center (THz-WDC) that combines ultra-broadband capacity, one-hop low-latency communication, and energy efficiency in the short-to-medium range (1-100m). Performance and technical requirements are first articulated, including up to 1 Tbps per link, aggregate throughput up to 10 Tbps via spatial multiplexing, sub-50 ns single-hop latency, and sub-10 pJ/bit energy efficiency over 20m. To achieve these ambitious goals, key enabling technologies are explored, including digital-twin-based orchestration, low-complexity beam manipulation technologies, all-silicon THz transceivers, and low-complexity analog baseband architectures. Moreover, as future data centers shift toward quantum and chiplet-based modular architectures, THz wireless links provide a flexible mechanism for interconnecting, testing, and reconfiguring these modules. Finally, numerical analysis is presented on the latency and power regimes of THz versus optical and copper interconnects, identifying the specific distance and throughput domains where THz links can surpass conventional wired solutions. The article concludes with a roadmap toward wireless-defined, reconfigurable, and sustainable AI data centers. - oai:arXiv.org:2512.24110v1 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chong Han, Mingjie Zhu, Wenqi Zhao, Ziming Yu, Guolong Huang, Guangjian Wang, Wen Tong, Wenjun Zhang - - - Guided Diffusion-based Generation of Adversarial Objects for Real-World Monocular Depth Estimation Attacks - https://arxiv.org/abs/2512.24111 - arXiv:2512.24111v1 Announce Type: new -Abstract: Monocular Depth Estimation (MDE) serves as a core perception module in autonomous driving systems, but it remains highly susceptible to adversarial attacks. Errors in depth estimation may propagate through downstream decision making and influence overall traffic safety. Existing physical attacks primarily rely on texture-based patches, which impose strict placement constraints and exhibit limited realism, thereby reducing their effectiveness in complex driving environments. To overcome these limitations, this work introduces a training-free generative adversarial attack framework that generates naturalistic, scene-consistent adversarial objects via a diffusion-based conditional generation process. The framework incorporates a Salient Region Selection module that identifies regions most influential to MDE and a Jacobian Vector Product Guidance mechanism that steers adversarial gradients toward update directions supported by the pre-trained diffusion model. This formulation enables the generation of physically plausible adversarial objects capable of inducing substantial adversarial depth shifts. Extensive digital and physical experiments demonstrate that our method significantly outperforms existing attacks in effectiveness, stealthiness, and physical deployability, underscoring its strong practical implications for autonomous driving safety assessment. - oai:arXiv.org:2512.24111v1 - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Yongtao Chen, Yanbo Wang, Wentao Zhao, Guole Shen, Tianchen Deng, Jingchuan Wang - - - RflyUT-Sim: A Simulation Platform for Development and Testing of Complex Low-Altitude Traffic Control - https://arxiv.org/abs/2512.24112 - arXiv:2512.24112v1 Announce Type: new -Abstract: Significant challenges are posed by simulation and testing in the field of low-altitude unmanned aerial vehicle (UAV) traffic due to the high costs associated with large-scale UAV testing and the complexity of establishing low-altitude traffic test scenarios. Stringent safety requirements make high fidelity one of the key metrics for simulation platforms. Despite advancements in simulation platforms for low-altitude UAVs, there is still a shortage of platforms that feature rich traffic scenarios, high-precision UAV and scenario simulators, and comprehensive testing capabilities for low-altitude traffic. Therefore, this paper introduces an integrated high-fidelity simulation platform for low-altitude UAV traffic. This platform simulates all components of the UAV traffic network, including the control system, the traffic management system, the UAV system, the communication network , the anomaly and fault modules, etc. Furthermore, it integrates RflySim/AirSim and Unreal Engine 5 to develop full-state models of UAVs and 3D maps that model the real world using the oblique photogrammetry technique. Additionally, the platform offers a wide range of interfaces, and all models and scenarios can be customized with a high degree of flexibility. The platform's source code has been released, making it easier to conduct research related to low-altitude traffic. - oai:arXiv.org:2512.24112v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zonghan Li, Tianwen Tao, Rao Fu, Liang Wang, Dongyuan Zhang, Quan Quan - - - CogRec: A Cognitive Recommender Agent Fusing Large Language Models and Soar for Explainable Recommendation - https://arxiv.org/abs/2512.24113 - arXiv:2512.24113v1 Announce Type: new -Abstract: Large Language Models (LLMs) have demonstrated a remarkable capacity in understanding user preferences for recommendation systems. However, they are constrained by several critical challenges, including their inherent "Black-Box" characteristics, susceptibility to knowledge hallucination, and limited online learning capacity. These factors compromise their trustworthiness and adaptability. Conversely, cognitive architectures such as Soar offer structured and interpretable reasoning processes, yet their knowledge acquisition is notoriously laborious. To address these complementary challenges, we propose a novel cognitive recommender agent called CogRec which synergizes the strengths of LLMs with the Soar cognitive architecture. CogRec leverages Soar as its core symbolic reasoning engine and leverages an LLM for knowledge initialization to populate its working memory with production rules. The agent operates on a Perception-Cognition-Action(PCA) cycle. Upon encountering an impasse, it dynamically queries the LLM to obtain a reasoned solution. This solution is subsequently transformed into a new symbolic production rule via Soar's chunking mechanism, thereby enabling robust online learning. This learning paradigm allows the agent to continuously evolve its knowledge base and furnish highly interpretable rationales for its recommendations. Extensive evaluations conducted on three public datasets demonstrate that CogRec demonstrates significant advantages in recommendation accuracy, explainability, and its efficacy in addressing the long-tail problem. - oai:arXiv.org:2512.24113v1 - cs.AI - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jiaxin Hu, Tao Wang, Bingsan Yang, Hongrun Wang - - - GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation - https://arxiv.org/abs/2512.24119 - arXiv:2512.24119v1 Announce Type: new -Abstract: Geometric problem solving constitutes a critical branch of mathematical reasoning, requiring precise analysis of shapes and spatial relationships. Current evaluations of geometric reasoning in vision-language models (VLMs) face limitations, including the risk of test data contamination from textbook-based benchmarks, overemphasis on final answers over reasoning processes, and insufficient diagnostic granularity. To address these issues, we present GeoBench, a hierarchical benchmark featuring four reasoning levels in geometric problem-solving: Visual Perception, Goal-Oriented Planning, Rigorous Theorem Application, and Self-Reflective Backtracking. Through six formally verified tasks generated via TrustGeoGen, we systematically assess capabilities ranging from attribute extraction to logical error correction. Experiments reveal that while reasoning models like OpenAI-o3 outperform general MLLMs, performance declines significantly with increasing task complexity. Key findings demonstrate that sub-goal decomposition and irrelevant premise filtering critically influence final problem-solving accuracy, whereas Chain-of-Thought prompting unexpectedly degrades performance in some tasks. These findings establish GeoBench as a comprehensive benchmark while offering actionable guidelines for developing geometric problem-solving systems. - oai:arXiv.org:2512.24119v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yuan Feng, Yue Yang, Xiaohan He, Jiatong Zhao, Jianlong Chen, Zijun Chen, Daocheng Fu, Qi Liu, Renqiu Xia, Bo Zhang, Junchi Yan - - - Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design - https://arxiv.org/abs/2512.24120 - arXiv:2512.24120v1 Announce Type: new -Abstract: Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints require both effective architectures and efficient search methods. Large Language Models (LLMs) present a promising alternative to computationally intensive Neural Architecture Search (NAS), but their application to architecture generation in computer vision has not been systematically studied, particularly regarding prompt engineering and validation strategies. Building on the task-agnostic NNGPT/LEMUR framework, this work introduces and validates two key contributions for computer vision. First, we present Few-Shot Architecture Prompting (FSAP), the first systematic study of the number of supporting examples (n = 1, 2, 3, 4, 5, 6) for LLM-based architecture generation. We find that using n = 3 examples best balances architectural diversity and context focus for vision tasks. Second, we introduce Whitespace-Normalized Hash Validation, a lightweight deduplication method (less than 1 ms) that provides a 100x speedup over AST parsing and prevents redundant training of duplicate computer vision architectures. In large-scale experiments across seven computer vision benchmarks (MNIST, CIFAR-10, CIFAR-100, CelebA, ImageNette, SVHN, Places365), we generated 1,900 unique architectures. We also introduce a dataset-balanced evaluation methodology to address the challenge of comparing architectures across heterogeneous vision tasks. These contributions provide actionable guidelines for LLM-based architecture search in computer vision and establish rigorous evaluation practices, making automated design more accessible to researchers with limited computational resources. - oai:arXiv.org:2512.24120v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chandini Vysyaraju, Raghuvir Duvvuri, Avi Goyal, Dmitry Ignatov, Radu Timofte - - - High order numerical discretizations of the Einstein-Euler equations in the Generalized Harmonic formulation - https://arxiv.org/abs/2512.24121 - arXiv:2512.24121v1 Announce Type: new -Abstract: We propose two new alternative numerical schemes to solve the coupled Einstein-Euler equations in the Generalized Harmonic formulation. The first one is a finite difference (FD) Central Weighted Essentially Non-Oscillatory (CWENO) scheme on a traditional Cartesian mesh, while the second one is an ADER (Arbitrary high order Derivatives) discontinuous Galerkin (DG) scheme on 2D unstructured polygonal meshes. The latter, in particular, represents a preliminary step in view of a full 3D numerical relativity calculation on moving meshes. Both schemes are equipped with a well-balancing (WB) property, which allows to preserve the equilibrium of a priori known stationary solutions exactly at the discrete level. We validate our numerical approaches by successfully reproducing standard vacuum test cases, such as the robust stability, the linearized wave, and the gauge wave tests, as well as achieving long-term stable evolutions of stationary black holes, including Kerr black holes with extreme spin. Concerning the coupling with matter, modeled by the relativistic Euler equations, we perform a classical test of spherical accretion onto a Schwarzschild black hole, as well as an evolution of a perturbed non-rotating neutron star, demonstrating the capability of our schemes to operate also on the full Einstein-Euler system. Altogether, these results provide a solid foundation for addressing more complex and challenging simulations of astrophysical sources through DG schemes on unstructured 3D meshes. - oai:arXiv.org:2512.24121v1 - math.NA - cs.NA - gr-qc - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Stefano Muzzolon, Michael Dumbser, Olindo Zanotti, Elena Gaburro - - - OptRot: Mitigating Weight Outliers via Data-Free Rotations for Post-Training Quantization - https://arxiv.org/abs/2512.24124 - arXiv:2512.24124v1 Announce Type: new -Abstract: The presence of outliers in Large Language Models (LLMs) weights and activations makes them difficult to quantize. Recent work has leveraged rotations to mitigate these outliers. In this work, we propose methods that learn fusible rotations by minimizing principled and cheap proxy objectives to the weight quantization error. We primarily focus on GPTQ as the quantization method. Our main method is OptRot, which reduces weight outliers simply by minimizing the element-wise fourth power of the rotated weights. We show that OptRot outperforms both Hadamard rotations and more expensive, data-dependent methods like SpinQuant and OSTQuant for weight quantization. It also improves activation quantization in the W4A8 setting. We also propose a data-dependent method, OptRot$^{+}$, that further improves performance by incorporating information on the activation covariance. In the W4A4 setting, we see that both OptRot and OptRot$^{+}$ perform worse, highlighting a trade-off between weight and activation quantization. - oai:arXiv.org:2512.24124v1 - cs.LG - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Advait Gadhikar, Riccardo Grazzi, James Hensman - - - Unified Embodied VLM Reasoning with Robotic Action via Autoregressive Discretized Pre-training - https://arxiv.org/abs/2512.24125 - arXiv:2512.24125v1 Announce Type: new -Abstract: General-purpose robotic systems operating in open-world environments must achieve both broad generalization and high-precision action execution, a combination that remains challenging for existing Vision-Language-Action (VLA) models. While large Vision-Language Models (VLMs) improve semantic generalization, insufficient embodied reasoning leads to brittle behavior, and conversely, strong reasoning alone is inadequate without precise control. To provide a decoupled and quantitative assessment of this bottleneck, we introduce Embodied Reasoning Intelligence Quotient (ERIQ), a large-scale embodied reasoning benchmark in robotic manipulation, comprising 6K+ question-answer pairs across four reasoning dimensions. By decoupling reasoning from execution, ERIQ enables systematic evaluation and reveals a strong positive correlation between embodied reasoning capability and end-to-end VLA generalization. To bridge the gap from reasoning to precise execution, we propose FACT, a flow-matching-based action tokenizer that converts continuous control into discrete sequences while preserving high-fidelity trajectory reconstruction. The resulting GenieReasoner jointly optimizes reasoning and action in a unified space, outperforming both continuous-action and prior discrete-action baselines in real-world tasks. Together, ERIQ and FACT provide a principled framework for diagnosing and overcoming the reasoning-precision trade-off, advancing robust, general-purpose robotic manipulation. - oai:arXiv.org:2512.24125v1 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yi Liu, Sukai Wang, Dafeng Wei, Xiaowei Cai, Linqing Zhong, Jiange Yang, Guanghui Ren, Jinyu Zhang, Maoqing Yao, Chuankang Li, Xindong He, Liliang Chen, Jianlan Luo - - - Structure-preserving schemes for nonlinear symmetric hyperbolic and thermodynamically compatible systems of partial differential equations - https://arxiv.org/abs/2512.24127 - arXiv:2512.24127v1 Announce Type: new -Abstract: This paper aims at developing exactly energy-conservative and structure-preserving finite volume schemes for the discretisation of first-order symmetric-hyperbolic and thermodynamically compatible (SHTC) systems of partial differential equations in continuum physics. Due to their thermodynamic compatibility the class of SHTC systems satisfies an additional conservation law for the total energy and many PDE in this class of equations also satisfy stationary differential constraints (involutions). First, we propose a simple semi-discrete cell-centered HTC finite volume scheme that employs collocated grids and that is compatible with the total energy conservation law, but which does not satisfy the involutions. Second, we develop a fully discrete semi-implicit finite volume scheme that conserves total energy and which can be proven to satisfy also the involution constraints exactly at the discrete level. This method is a vertex-based staggered semi-implicit scheme that preserves the basic vector calculus identities $\nabla \cdot \nabla \times A = 0$ and $\nabla \times \nabla \phi = 0$ for any vector and scalar field, respectively, exactly at the discrete level and which is also exactly totally energy conservative. The main key ingredient of the proposed implicit scheme is the fact that it uses a discrete version of the symmetric-hyperbolic Godunov-form of the governing PDE system. This leads naturally to sequences of symmetric and positive definite linear algebraic systems to be solved inside an iterative fixed-point method used in each time step. We apply our new schemes to three different SHTC systems. In particular, we consider the equations of nonlinear acoustics, the nonlinear Maxwell equations in the absence of charges and a nonlinear version of the Maxwell-GLM system. We also show some numerical results to provide evidence of the stated properties of the proposed schemes. - oai:arXiv.org:2512.24127v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Alessia Lucca, Michael Dumbser - - - ROBOPOL: Social Robotics Meets Vehicular Communications for Cooperative Automated Driving - https://arxiv.org/abs/2512.24129 - arXiv:2512.24129v1 Announce Type: new -Abstract: On the way towards full autonomy, sharing roads between automated vehicles and human actors in so-called mixed traffic is unavoidable. Moreover, even if all vehicles on the road were autonomous, pedestrians would still be crossing the streets. We propose social robots as moderators between autonomous vehicles and vulnerable road users (VRU). To this end, we identify four enablers requiring integration: (1) advanced perception, allowing the robot to see the environment; (2) vehicular communications allowing connected vehicles to share intentions and the robot to send guiding commands; (3) social human-robot interaction allowing the robot to effectively communicate with VRUs and drivers; (4) formal specification allowing the robot to understand traffic and plan accordingly. This paper presents an overview of the key enablers and report on a first proof-of-concept integration of the first three enablers envisioning a social robot advising pedestrians in scenarios with a cooperative automated e-bike. - oai:arXiv.org:2512.24129v1 - cs.RO - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Manuel Bied, John Arockiasamy, Andy Comeca, Maximilian Schrapel, Victoria Yang, Alexey Rolich, Barbara Bruno, Maike Schwammberger, Dieter Fiems, Alexey Vinel - - - From artificial to circular intelligence to support the well-being of our habitat - https://arxiv.org/abs/2512.24131 - arXiv:2512.24131v1 Announce Type: new -Abstract: The proliferation of machine learning and artificial intelligence redefines the interaction between the anthropogenic and natural elements of our habitat.The use of monitoring tools, processing facilities and the internet of things supports the assessment of planetary health at any given time through automation. However, these data, natural resources and infrastructure intensive technologies are not neutral on the Earth. As the community of AI practitioners works on the creation of tools with minimal socio-environmental impacts, we contribute to the these efforts by proposing a novel conceptual and procedural framework which we call Circular Intelligence or CIntel. CIntel leverages a bottom-up and community-driven approach to learn from the ability of nature to regenerate and adapt. CIntel incorporates ethical principles in its technical design to preserve the stability of the habitat, while also increasing the well-being of its inhabitants by design. - oai:arXiv.org:2512.24131v1 - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Francesca Larosa, Daniel Depellegrin, Andrea Conte, Marco Molinari, Silvia Santato, Adam Wickberg, Fermin Mallor, Anna Sperotto - - - From FPT Decision to FPT Enumeration - https://arxiv.org/abs/2512.24137 - arXiv:2512.24137v1 Announce Type: new -Abstract: Fixed-parameter tractable (FPT) algorithms have been successfully applied to many intractable problems -- with a focus on decision and optimization problems. Their aim is to confine the exponential explosion to some parameter, while the time complexity only depends polynomially on the instance size. In contrast, intractable enumeration problems have received comparatively little attention so far. The goal of this work is to study how FPT decision algorithms could be turned into FPT enumeration algorithms. We thus inspect several fundamental approaches for designing FPT decision or optimization algorithms and we present ideas how they can be extended to FPT enumeration algorithms. - oai:arXiv.org:2512.24137v1 - cs.CC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Nadia Creignou, Timo Camillo Merkl, Reinhard Pichler, Daniel Unterberger - - - GARDO: Reinforcing Diffusion Models without Reward Hacking - https://arxiv.org/abs/2512.24138 - arXiv:2512.24138v1 Announce Type: new -Abstract: Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-truth objective for visual tasks remains challenging, the models are often optimized using a proxy reward that only partially captures the true goal. This mismatch often leads to reward hacking, where proxy scores increase while real image quality deteriorates and generation diversity collapses. While common solutions add regularization against the reference policy to prevent reward hacking, they compromise sample efficiency and impede the exploration of novel, high-reward regions, as the reference policy is usually sub-optimal. To address the competing demands of sample efficiency, effective exploration, and mitigation of reward hacking, we propose Gated and Adaptive Regularization with Diversity-aware Optimization (GARDO), a versatile framework compatible with various RL algorithms. Our key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty. To address the exploration challenge, GARDO introduces an adaptive regularization mechanism wherein the reference model is periodically updated to match the capabilities of the online policy, ensuring a relevant regularization target. To address the mode collapse issue in RL, GARDO amplifies the rewards for high-quality samples that also exhibit high diversity, encouraging mode coverage without destabilizing the optimization process. Extensive experiments across diverse proxy rewards and hold-out unseen metrics consistently show that GARDO mitigates reward hacking and enhances generation diversity without sacrificing sample efficiency or exploration, highlighting its effectiveness and robustness. - oai:arXiv.org:2512.24138v1 - cs.LG - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Haoran He, Yuxiao Ye, Jie Liu, Jiajun Liang, Zhiyong Wang, Ziyang Yuan, Xintao Wang, Hangyu Mao, Pengfei Wan, Ling Pan - - - Colorful Pinball: Density-Weighted Quantile Regression for Conditional Guarantee of Conformal Prediction - https://arxiv.org/abs/2512.24139 - arXiv:2512.24139v1 Announce Type: new -Abstract: While conformal prediction provides robust marginal coverage guarantees, achieving reliable conditional coverage for specific inputs remains challenging. Although exact distribution-free conditional coverage is impossible with finite samples, recent work has focused on improving the conditional coverage of standard conformal procedures. Distinct from approaches that target relaxed notions of conditional coverage, we directly minimize the mean squared error of conditional coverage by refining the quantile regression components that underpin many conformal methods. Leveraging a Taylor expansion, we derive a sharp surrogate objective for quantile regression: a density-weighted pinball loss, where the weights are given by the conditional density of the conformity score evaluated at the true quantile. We propose a three-headed quantile network that estimates these weights via finite differences using auxiliary quantile levels at \(1-\alpha \pm \delta\), subsequently fine-tuning the central quantile by optimizing the weighted loss. We provide a theoretical analysis with exact non-asymptotic guarantees characterizing the resulting excess risk. Extensive experiments on diverse high-dimensional real-world datasets demonstrate remarkable improvements in conditional coverage performance. - oai:arXiv.org:2512.24139v1 - cs.LG - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qianyi Chen, Bo Li - - - Environmental Sound Deepfake Detection Challenge: An Overview - https://arxiv.org/abs/2512.24140 - arXiv:2512.24140v1 Announce Type: new -Abstract: Recent progress in audio generation models has made it possible to create highly realistic and immersive soundscapes, which are now widely used in film and virtual-reality-related applications. However, these audio generators also raise concerns about potential misuse, such as producing deceptive audio for fabricated videos or spreading misleading information. Therefore, it is essential to develop effective methods for detecting fake environmental sounds. Existing datasets for environmental sound deepfake detection (ESDD) remain limited in both scale and the diversity of sound categories they cover. To address this gap, we introduced EnvSDD, the first large-scale curated dataset designed for ESDD. Based on EnvSDD, we launched the ESDD Challenge, recognized as one of the ICASSP 2026 Grand Challenges. This paper presents an overview of the ESDD Challenge, including a detailed analysis of the challenge results. - oai:arXiv.org:2512.24140v1 - cs.SD - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Han Yin, Yang Xiao, Rohan Kumar Das, Jisheng Bai, Ting Dang - - - Activation Steering for Masked Diffusion Language Models - https://arxiv.org/abs/2512.24143 - arXiv:2512.24143v1 Announce Type: new -Abstract: Masked diffusion language models (MDLMs) generate text through an iterative denoising process. They have recently gained attention due to mask-parallel decoding and competitive performance with autoregressive large language models. However, effective mechanisms for inference-time control and steering in MDLMs remain largely unexplored. We present an activation-steering framework for MDLMs that computes layer-wise steering vectors from a single forward pass using contrastive examples, without simulating the denoising trajectory. These directions are applied at every reverse-diffusion step, yielding an efficient inference-time control mechanism. Experiments on LLaDA-8B-Instruct demonstrate reliable modulation of high-level attributes, with ablations examining the effects of steering across transformer sub-modules and token scope (prompt vs.\ response). - oai:arXiv.org:2512.24143v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Adi Shnaidman, Erin Feiglin, Osher Yaari, Efrat Mentel, Amit Levi, Raz Lapid - - - Paired Seed Evaluation: Statistical Reliability for Learning-Based Simulators - https://arxiv.org/abs/2512.24145 - arXiv:2512.24145v1 Announce Type: new -Abstract: Machine learning systems appear stochastic but are deterministically random, as seeded pseudorandom number generators produce identical realisations across executions. Learning-based simulators are widely used to compare algorithms, design choices, and interventions under such dynamics, yet evaluation outcomes often exhibit high variance due to random initialisation and learning stochasticity. We analyse the statistical structure of comparative evaluation in these settings and show that standard independent evaluation designs fail to exploit shared sources of randomness across alternatives. We formalise a paired seed evaluation design in which competing systems are evaluated under identical random seeds, inducing matched realisations of stochastic components and strict variance reduction whenever outcomes are positively correlated at the seed level. This yields tighter confidence intervals, higher statistical power, and effective sample size gains at fixed computational budgets. Empirically, seed-level correlations are typically large and positive, producing order-of-magnitude efficiency gains. Paired seed evaluation is weakly dominant in practice, improving statistical reliability when correlation is present and reducing to independent evaluation without loss of validity when it is not. - oai:arXiv.org:2512.24145v1 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Udit Sharma - - - Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning - https://arxiv.org/abs/2512.24146 - arXiv:2512.24146v1 Announce Type: new -Abstract: Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, while existing methods achieve high scores on automated reward metrics, they often lead to Preference Mode Collapse (PMC)-a specific form of reward hacking where models converge on narrow, high-scoring outputs (e.g., images with monolithic styles or pervasive overexposure), severely degrading generative diversity. In this work, we introduce and quantify this phenomenon, proposing DivGenBench, a novel benchmark designed to measure the extent of PMC. We posit that this collapse is driven by over-optimization along the reward model's inherent biases. Building on this analysis, we propose Directional Decoupling Alignment (D$^2$-Align), a novel framework that mitigates PMC by directionally correcting the reward signal. Specifically, our method first learns a directional correction within the reward model's embedding space while keeping the model frozen. This correction is then applied to the reward signal during the optimization process, preventing the model from collapsing into specific modes and thereby maintaining diversity. Our comprehensive evaluation, combining qualitative analysis with quantitative metrics for both quality and diversity, reveals that D$^2$-Align achieves superior alignment with human preference. - oai:arXiv.org:2512.24146v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chubin Chen, Sujie Hu, Jiashu Zhu, Meiqi Wu, Jintao Chen, Yanxun Li, Nisha Huang, Chengyu Fang, Jiahong Wu, Xiangxiang Chu, Xiu Li - - - Large Emotional World Model - https://arxiv.org/abs/2512.24149 - arXiv:2512.24149v1 Announce Type: new -Abstract: World Models serve as tools for understanding the current state of the world and predicting its future dynamics, with broad application potential across numerous fields. As a key component of world knowledge, emotion significantly influences human decision-making. While existing Large Language Models (LLMs) have shown preliminary capability in capturing world knowledge, they primarily focus on modeling physical-world regularities and lack systematic exploration of emotional factors. In this paper, we first demonstrate the importance of emotion in understanding the world by showing that removing emotionally relevant information degrades reasoning performance. Inspired by theory of mind, we further propose a Large Emotional World Model (LEWM). Specifically, we construct the Emotion-Why-How (EWH) dataset, which integrates emotion into causal relationships and enables reasoning about why actions occur and how emotions drive future world states. Based on this dataset, LEWM explicitly models emotional states alongside visual observations and actions, allowing the world model to predict both future states and emotional transitions. Experimental results show that LEWM more accurately predicts emotion-driven social behaviors while maintaining comparable performance to general world models on basic tasks. - oai:arXiv.org:2512.24149v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Changhao Song, Yazhou Zhang, Hui Gao, Chang Yang, Peng Zhang - - - Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks - https://arxiv.org/abs/2512.24156 - arXiv:2512.24156v1 Announce Type: new -Abstract: We present a training-free graph-based approach for solving interactive reasoning tasks in the ARC-AGI-3 benchmark. ARC-AGI-3 comprises game-like tasks where agents must infer task mechanics through limited interactions, and adapt to increasing complexity as levels progress. Success requires forming hypotheses, testing them, and tracking discovered mechanics. The benchmark has revealed that state-of-the-art LLMs are currently incapable of reliably solving these tasks. Our method combines vision-based frame processing with systematic state-space exploration using graph-structured representations. It segments visual frames into meaningful components, prioritizes actions based on visual salience, and maintains a directed graph of explored states and transitions. By tracking visited states and tested actions, the agent prioritizes actions that provide the shortest path to untested state-action pairs. On the ARC-AGI-3 Preview Challenge, this structured exploration strategy solves a median of 30 out of 52 levels across six games and ranks 3rd on the private leaderboard, substantially outperforming frontier LLM-based agents. These results demonstrate that explicit graph-structured exploration, even without learning, can serve as a strong baseline for interactive reasoning and underscore the importance of systematic state tracking and action prioritization in sparse-feedback environments where current LLMs fail to capture task dynamics. The code is open source and available at https://github.com/dolphin-in-a-coma/arc-agi-3-just-explore. - oai:arXiv.org:2512.24156v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - AAAI 2026 Workshop on AI for Scientific Research - Evgenii Rudakov, Jonathan Shock, Benjamin Ultan Cowley - - - Training Report of TeleChat3-MoE - https://arxiv.org/abs/2512.24157 - arXiv:2512.24157v1 Announce Type: new -Abstract: TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one trillion,trained end-to-end on Ascend NPU cluster. This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes. We detail systematic methodologies for operator-level and end-to-end numerical accuracy verification, ensuring consistency across hardware platforms and distributed parallelism strategies. Furthermore, we introduce a suite of performance optimizations, including interleaved pipeline scheduling, attention-aware data scheduling for long-sequence training,hierarchical and overlapped communication for expert parallelism, and DVM-based operator fusion. A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations. Additionally, we present methodological approaches to cluster-level optimizations, addressing host- and device-bound bottlenecks during large-scale training tasks. These infrastructure advancements yield significant throughput improvements and near-linear scaling on clusters comprising thousands of devices, providing a robust foundation for large-scale language model development on hardware ecosystems. - oai:arXiv.org:2512.24157v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xinzhang Liu, Chao Wang, Zhihao Yang, Zhuo Jiang, Xuncheng Zhao, Haoran Wang, Lei Li, Dongdong He, Luobin Liu, Kaizhe Yuan, Han Gao, Zihan Wang, Yitong Yao, Sishi Xiong, Wenmin Deng, Haowei He, Kaidong Yu, Yu Zhao, Ruiyu Fang, Yuhao Jiang, Yingyan Li, Xiaohui Hu, Xi Yu, Jingqi Li, Yanwei Liu, Qingli Li, Xinyu Shi, Junhao Niu, Chengnuo Huang, Yao Xiao, Ruiwen Wang, Fengkai Li, Luwen Pu, Kaipeng Jia, Fubei Yao, Yuyao Huang, Xuewei He, Zhuoru Jiang, Ruiting Song, Rui Xue, Qiyi Xie, Jie Zhang, Zilu Huang, Zhaoxi Zhang, Zhilong Lu, Yanhan Zhang, Yin Zhang, Yanlei Xue, Zhu Yuan, Teng Su, Xin Jiang, Shuangyong Song, Yongxiang Li, Xuelong Li - - - Developing controlled natural language for formal specification patterns using AI assistants - https://arxiv.org/abs/2512.24159 - arXiv:2512.24159v1 Announce Type: new -Abstract: Using an AI assistant, we developed a method for systematically constructing controlled natural language for requirements based on formal specification patterns containing logical attributes. The method involves three stages: 1) compiling a generalized natural language requirement pattern that utilizes all attributes of the formal specification template; 2) generating, using the AI assistant, a corpus of natural language requirement patterns, reduced by partially evaluating attributes (the developed prompt utilizes the generalized template, attribute definitions, and specific formal semantics of the requirement patterns); and 3) formalizing the syntax of the controlled natural language based on an analysis of the grammatical structure of the resulting patterns. The method has been tested for event-driven temporal requirements. - oai:arXiv.org:2512.24159v1 - cs.SE - cs.AI - cs.FL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Natalia Garanina, Vladimir Zyubin, Igor Anureev - - - Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset - https://arxiv.org/abs/2512.24160 - arXiv:2512.24160v1 Announce Type: new -Abstract: We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence. - oai:arXiv.org:2512.24160v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - TsaiChing Ni, ZhenQi Chen, YuanFu Yang - - - Bayesian Self-Distillation for Image Classification - https://arxiv.org/abs/2512.24162 - arXiv:2512.24162v1 Announce Type: new -Abstract: Supervised training of deep neural networks for classification typically relies on hard targets, which promote overconfidence and can limit calibration, generalization, and robustness. Self-distillation methods aim to mitigate this by leveraging inter-class and sample-specific information present in the model's own predictions, but often remain dependent on hard targets, reducing their effectiveness. With this in mind, we propose Bayesian Self-Distillation (BSD), a principled method for constructing sample-specific target distributions via Bayesian inference using the model's own predictions. Unlike existing approaches, BSD does not rely on hard targets after initialization. BSD consistently yields higher test accuracy (e.g. +1.4% for ResNet-50 on CIFAR-100) and significantly lower Expected Calibration Error (ECE) (-40% ResNet-50, CIFAR-100) than existing architecture-preserving self-distillation methods for a range of deep architectures and datasets. Additional benefits include improved robustness against data corruptions, perturbations, and label noise. When combined with a contrastive loss, BSD achieves state-of-the-art robustness under label noise for single-stage, single-network methods. - oai:arXiv.org:2512.24162v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Anton Adel\"ow, Matteo Gamba, Atsuto Maki - - - DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models - https://arxiv.org/abs/2512.24165 - arXiv:2512.24165v1 Announce Type: new -Abstract: While recent Multimodal Large Language Models (MLLMs) have attained significant strides in multimodal reasoning, their reasoning processes remain predominantly text-centric, leading to suboptimal performance in complex long-horizon, vision-centric tasks. In this paper, we establish a novel Generative Multimodal Reasoning paradigm and introduce DiffThinker, a diffusion-based reasoning framework. Conceptually, DiffThinker reformulates multimodal reasoning as a native generative image-to-image task, achieving superior logical consistency and spatial precision in vision-centric tasks. We perform a systematic comparison between DiffThinker and MLLMs, providing the first in-depth investigation into the intrinsic characteristics of this paradigm, revealing four core properties: efficiency, controllability, native parallelism, and collaboration. Extensive experiments across four domains (sequential planning, combinatorial optimization, constraint satisfaction, and spatial configuration) demonstrate that DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2\%) and Gemini-3-Flash (+111.6\%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0\%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning. - oai:arXiv.org:2512.24165v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zefeng He, Xiaoye Qu, Yafu Li, Tong Zhu, Siyuan Huang, Yu Cheng - - - External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation - https://arxiv.org/abs/2512.24166 - arXiv:2512.24166v1 Announce Type: new -Abstract: Increasing autonomous vehicles (AVs) in transportation systems makes effective interactions between AVs and pedestrians indispensable. External human--machine interface (eHMI), which employs visual or auditory cues to explicitly convey vehicle behaviors can compensate for the loss of human-like interactions and enhance AV--pedestrian cooperation. To facilitate faster intent convergence between pedestrian and AVs, this study incorporates an adaptive interaction mechanism into eHMI based on pedestrian intent recognition, namely IR-eHMI. IR-eHMI dynamically detects and infers the behavioral intentions of both pedestrians and AVs through identifying their cooperation states. The proposed interaction framework is implemented and evaluated on a virtual reality (VR) experimental platform to demonstrate its effectiveness through statistical analysis. Experimental results show that IR-eHMI significantly improves crossing efficiency, reduces gaze distraction while maintaining interaction safety compared to traditional fixed-distance eHMI. This adaptive and explicit interaction mode introduces an innovative procedural paradigm for AV--pedestrian cooperation. - oai:arXiv.org:2512.24166v1 - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Boya Sun, Haotian Shi, Ying Ni, Shaocheng Jia, Haoyang Liang - - - Hybrid Voltage and Current Control Method for Harmonic Mitigation of Single-Phase AC Loads in DC Microgrids - https://arxiv.org/abs/2512.24170 - arXiv:2512.24170v1 Announce Type: new -Abstract: DC microgrids provide an efficient framework for the interconnection of DC distributed energy resources (DERs) and DC loads. To continue to supply legacy single-phase AC loads, DC/AC converters can be integrated in the DC microgrid. The oscillatory instantaneous power of the single-phase AC load translates into a harmonic current on the converter's DC side, which increases the losses and causes unwanted voltage harmonics in the DC microgrid. To mitigate this issue, this paper proposes a hybrid voltage and current control method (HCM) for DERs. This scheme consists of an inner current control loop and an outer control layer which determines the reference for the inner loop. The outer control layer combines the DC voltage control loop with an output harmonic current control loop. This hybrid structure enables simultaneous regulation of the DC components of the DER output voltage and control of the harmonic component of the DER output current in accordance with the local single-phase AC load's demand. Frequency-domain analysis of the proposed method is presented to demonstrate the DC voltage and harmonic current loops are decoupled and there is no unwanted interaction between them. Additionally, time-domain response of the proposed scheme is validated through hardware-in-the-loop test results. - oai:arXiv.org:2512.24170v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mehdi Baharizadeh, Mohammad Sadegh Golsorkhi, Neda Keshavarzi, Thomas Ebel - - - Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges - https://arxiv.org/abs/2512.24172 - arXiv:2512.24172v1 Announce Type: new -Abstract: Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory. While foundation models pre-trained on large remote sensing datasets show promise, their learned representations often fail to transfer to domain-specific applications like close-range agricultural monitoring where spectral signatures, spatial scales, and semantic targets differ fundamentally. This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation that learns global clustering structure from local patch observations without pre-training. DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware while maintaining constant memory usage. On a leaf disease dataset, DGC achieves background-tissue separation (mean IoU 0.925) and demonstrates unsupervised disease detection through navigable semantic granularity. However, the framework suffers from optimization instability rooted in multi-objective loss balancing: meaningful representations emerge rapidly but degrade due to cluster over-merging in feature space. We position this work as intellectual scaffolding - the design philosophy has merit, but stable implementation requires principled approaches to dynamic loss balancing. Code and data are available at https://github.com/b05611038/HSI_global_clustering. - oai:arXiv.org:2512.24172v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yu-Tang Chang, Pin-Wei Chen, Shih-Fang Chen - - - Guiding a Diffusion Transformer with the Internal Dynamics of Itself - https://arxiv.org/abs/2512.24176 - arXiv:2512.24176v1 Announce Type: new -Abstract: The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover low-probability areas, the model will be penalized for failing to generate high-quality images corresponding to these areas. To achieve better generation quality, guidance strategies such as classifier free guidance (CFG) can guide the samples to the high-probability areas during the sampling stage. However, the standard CFG often leads to over-simplified or distorted samples. On the other hand, the alternative line of guiding diffusion model with its bad version is limited by carefully designed degradation strategies, extra training and additional sampling steps. In this paper, we proposed a simple yet effective strategy Internal Guidance (IG), which introduces an auxiliary supervision on the intermediate layer during training process and extrapolates the intermediate and deep layer's outputs to obtain generative results during sampling process. This simple strategy yields significant improvements in both training efficiency and generation quality on various baselines. On ImageNet 256x256, SiT-XL/2+IG achieves FID=5.31 and FID=1.75 at 80 and 800 epochs. More impressively, LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19. - oai:arXiv.org:2512.24176v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xingyu Zhou, Qifan Li, Xiaobin Hu, Hai Chen, Shuhang Gu - - - Now or Never: Continuous Surveillance AIoT System for Ephemeral Events in Intermittent Sensor Networks - https://arxiv.org/abs/2512.24179 - arXiv:2512.24179v1 Announce Type: new -Abstract: Wilderness monitoring tasks, such as poaching surveillance and forest fire detection, require pervasive and high-accuracy sensing. While AIoT offers a promising path, covering vast, inaccessible regions necessitates the massive deployment of maintenance-free, battery-less nodes with limited computational resources. However, these constraints create a critical `Availability Gap.' Conventional intermittent operations prioritize computation throughput, forcing sensors to sleep during energy buffering. Consequently, systems miss ephemeral, `now-or-never' events (e.g., Vocalizations of natural monuments or Fire), which is fatal for detecting rare but high-stakes anomalies. To address this, we propose an Energy-aware Elastic Split Computing Algorithm that prioritizes continuous sensing by dynamically offloading tasks to energy-rich neighbors. Preliminary results demonstrate stable monitoring of an additional $2,496\;\text{m}^2$ and the capture of approximately 103 more critical events per day. Ultimately, this algorithm establishes a robust foundation for building resilient, fail-safe surveillance systems even on resource-constrained nodes. - oai:arXiv.org:2512.24179v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Joonhee Lee, Kichang Lee, Jeonggil Ko - - - MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring - https://arxiv.org/abs/2512.24181 - arXiv:2512.24181v1 Announce Type: new -Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant promise in clinical diagnosis. However, current models struggle to emulate the iterative, diagnostic hypothesis-driven reasoning of real clinical scenarios. Specifically, current LLMs suffer from three critical limitations: (1) generating hallucinated medical content due to weak grounding in verified knowledge, (2) asking redundant or inefficient questions rather than discriminative ones that hinder diagnostic progress, and (3) losing coherence over multi-turn dialogues, leading to contradictory or inconsistent conclusions. To address these challenges, we propose MedKGI, a diagnostic framework grounded in clinical practices. MedKGI integrates a medical knowledge graph (KG) to constrain reasoning to validated medical ontologies, selects questions based on information gain to maximize diagnostic efficiency, and adopts an OSCE-format structured state to maintain consistent evidence tracking across turns. Experiments on clinical benchmarks show that MedKGI outperforms strong LLM baselines in both diagnostic accuracy and inquiry efficiency, improving dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy. - oai:arXiv.org:2512.24181v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qipeng Wang, Rui Sheng, Yafei Li, Huamin Qu, Yushi Sun, Min Zhu - - - CoHalLo: code hallucination localization via probing hidden layer vector - https://arxiv.org/abs/2512.24183 - arXiv:2512.24183v1 Announce Type: new -Abstract: The localization of code hallucinations aims to identify specific lines of code containing hallucinations, helping developers to improve the reliability of AI-generated code more efficiently. Although recent studies have adopted several methods to detect code hallucination, most of these approaches remain limited to coarse-grained detection and lack specialized techniques for fine-grained hallucination localization. This study introduces a novel method, called CoHalLo, which achieves line-level code hallucination localization by probing the hidden-layer vectors from hallucination detection models. CoHalLo uncovers the key syntactic information driving the model's hallucination judgments and locates the hallucinating code lines accordingly. Specifically, we first fine-tune the hallucination detection model on manually annotated datasets to ensure that it learns features pertinent to code syntactic information. Subsequently, we designed a probe network that projects high-dimensional latent vectors onto a low-dimensional syntactic subspace, generating vector tuples and reconstructing the predicted abstract syntax tree (P-AST). By comparing P-AST with the original abstract syntax tree (O-AST) extracted from the input AI-generated code, we identify the key syntactic structures associated with hallucinations. This information is then used to pinpoint hallucinated code lines. To evaluate CoHalLo's performance, we manually collected a dataset of code hallucinations. The experimental results show that CoHalLo achieves a Top-1 accuracy of 0.4253, Top-3 accuracy of 0.6149, Top-5 accuracy of 0.7356, Top-10 accuracy of 0.8333, IFA of 5.73, Recall@1% Effort of 0.052721, and Effort@20% Recall of 0.155269, which outperforms the baseline methods. - oai:arXiv.org:2512.24183v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Nan Jia, Wangchao Sang, Pengfei Lin, Xiangping Chen, Yuan Huang, Yi Liu, Mingliang Li - - - SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents - https://arxiv.org/abs/2512.24189 - arXiv:2512.24189v1 Announce Type: new -Abstract: We introduce SCP: the Science Context Protocol, an open-source standard designed to accelerate discovery by enabling a global network of autonomous scientific agents. SCP is built on two foundational pillars: (1) Unified Resource Integration: At its core, SCP provides a universal specification for describing and invoking scientific resources, spanning software tools, models, datasets, and physical instruments. This protocol-level standardization enables AI agents and applications to discover, call, and compose capabilities seamlessly across disparate platforms and institutional boundaries. (2) Orchestrated Experiment Lifecycle Management: SCP complements the protocol with a secure service architecture, which comprises a centralized SCP Hub and federated SCP Servers. This architecture manages the complete experiment lifecycle (registration, planning, execution, monitoring, and archival), enforces fine-grained authentication and authorization, and orchestrates traceable, end-to-end workflows that bridge computational and physical laboratories. Based on SCP, we have constructed a scientific discovery platform that offers researchers and agents a large-scale ecosystem of more than 1,600 tool resources. Across diverse use cases, SCP facilitates secure, large-scale collaboration between heterogeneous AI systems and human researchers while significantly reducing integration overhead and enhancing reproducibility. By standardizing scientific context and tool orchestration at the protocol level, SCP establishes essential infrastructure for scalable, multi-institution, agent-driven science. - oai:arXiv.org:2512.24189v1 - cs.AI - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yankai Jiang, Wenjie Lou, Lilong Wang, Zhenyu Tang, Shiyang Feng, Jiaxuan Lu, Haoran Sun, Yaning Pan, Shuang Gu, Haoyang Su, Feng Liu, Wangxu Wei, Pan Tan, Dongzhan Zhou, Fenghua Ling, Cheng Tan, Bo Zhang, Xiaosong Wang, Lei Bai, Bowen Zhou - - - PointRAFT: 3D deep learning for high-throughput prediction of potato tuber weight from partial point clouds - https://arxiv.org/abs/2512.24193 - arXiv:2512.24193v1 Announce Type: new -Abstract: Potato yield is a key indicator for optimizing cultivation practices in agriculture. Potato yield can be estimated on harvesters using RGB-D cameras, which capture three-dimensional (3D) information of individual tubers moving along the conveyor belt. However, point clouds reconstructed from RGB-D images are incomplete due to self-occlusion, leading to systematic underestimation of tuber weight. To address this, we introduce PointRAFT, a high-throughput point cloud regression network that directly predicts continuous 3D shape properties, such as tuber weight, from partial point clouds. Rather than reconstructing full 3D geometry, PointRAFT infers target values directly from raw 3D data. Its key architectural novelty is an object height embedding that incorporates tuber height as an additional geometric cue, improving weight prediction under practical harvesting conditions. PointRAFT was trained and evaluated on 26,688 partial point clouds collected from 859 potato tubers across four cultivars and three growing seasons on an operational harvester in Japan. On a test set of 5,254 point clouds from 172 tubers, PointRAFT achieved a mean absolute error of 12.0 g and a root mean squared error of 17.2 g, substantially outperforming a linear regression baseline and a standard PointNet++ regression network. With an average inference time of 6.3 ms per point cloud, PointRAFT supports processing rates of up to 150 tubers per second, meeting the high-throughput requirements of commercial potato harvesters. Beyond potato weight estimation, PointRAFT provides a versatile regression network applicable to a wide range of 3D phenotyping and robotic perception tasks. The code, network weights, and a subset of the dataset are publicly available at https://github.com/pieterblok/pointraft.git. - oai:arXiv.org:2512.24193v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Pieter M. Blok, Haozhou Wang, Hyun Kwon Suh, Peicheng Wang, James Burridge, Wei Guo - - - CorGi: Contribution-Guided Block-Wise Interval Caching for Training-Free Acceleration of Diffusion Transformers - https://arxiv.org/abs/2512.24195 - arXiv:2512.24195v1 Announce Type: new -Abstract: Diffusion transformer (DiT) achieves remarkable performance in visual generation, but its iterative denoising process combined with larger capacity leads to a high inference cost. Recent works have demonstrated that the iterative denoising process of DiT models involves substantial redundant computation across steps. To effectively reduce the redundant computation in DiT, we propose CorGi (Contribution-Guided Block-Wise Interval Caching), training-free DiT inference acceleration framework that selectively reuses the outputs of transformer blocks in DiT across denoising steps. CorGi caches low-contribution blocks and reuses them in later steps within each interval to reduce redundant computation while preserving generation quality. For text-to-image tasks, we further propose CorGi+, which leverages per-block cross-attention maps to identify salient tokens and applies partial attention updates to protect important object details. Evaluation on the state-of-the-art DiT models demonstrates that CorGi and CorGi+ achieve up to 2.0x speedup on average, while preserving high generation quality. - oai:arXiv.org:2512.24195v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yonglak Son, Suhyeok Kim, Seungryong Kim, Young Geun Kim - - - PartMotionEdit: Fine-Grained Text-Driven 3D Human Motion Editing via Part-Level Modulation - https://arxiv.org/abs/2512.24200 - arXiv:2512.24200v1 Announce Type: new -Abstract: Existing text-driven 3D human motion editing methods have demonstrated significant progress, but are still difficult to precisely control over detailed, part-specific motions due to their global modeling nature. In this paper, we propose PartMotionEdit, a novel fine-grained motion editing framework that operates via part-level semantic modulation. The core of PartMotionEdit is a Part-aware Motion Modulation (PMM) module, which builds upon a predefined five-part body decomposition. PMM dynamically predicts time-varying modulation weights for each body part, enabling precise and interpretable editing of local motions. To guide the training of PMM, we also introduce a part-level similarity curve supervision mechanism enhanced with dual-layer normalization. This mechanism assists PMM in learning semantically consistent and editable distributions across all body parts. Furthermore, we design a Bidirectional Motion Interaction (BMI) module. It leverages bidirectional cross-modal attention to achieve more accurate semantic alignment between textual instructions and motion semantics. Extensive quantitative and qualitative evaluations on a well-known benchmark demonstrate that PartMotionEdit outperforms the state-of-the-art methods. - oai:arXiv.org:2512.24200v1 - cs.GR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yujie Yang, Zhichao Zhang, Jiazhou Chen, Zichao Wu - - - BATISNet: Instance Segmentation of Tooth Point Clouds with Boundary Awareness - https://arxiv.org/abs/2512.24201 - arXiv:2512.24201v1 Announce Type: new -Abstract: Accurate segmentation of the tooth point cloud is of great significance for diagnosis clinical assisting and treatment planning. Existing methods mostly employ semantic segmentation, focusing on the semantic feature between different types of teeth. However, due to the tightly packed structure of teeth, unclear boundaries, and the diversity of complex cases such as missing teeth, malposed teeth, semantic segmentation often struggles to achieve satisfactory results when dealing with complex dental cases. To address these issues, this paper propose BATISNet, a boundary-aware instance network for tooth point cloud segmentation. This network model consists of a feature extraction backbone and an instance segmentation module. It not only focuses on extracting the semantic features of different types of teeth but also learns the instance features of individual teeth. It helps achieve more robust and accurate tooth instance segmentation in complex clinical scenarios such as missing teeth and malposed teeth. Additionally, to further enhance the completeness and accuracy of tooth boundary segmentation, a boundary-aware loss function is designed to specifically supervise the boundary segmentation between instances. It mitigates effectively tooth adhesion and boundary ambiguity issues. Extensive experimental results show that BATISNet outperforms existing methods in tooth integrity segmentation, providing more reliable and detailed data support for practical clinical applications. - oai:arXiv.org:2512.24201v1 - cs.GR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yating Cai, Yanghui Xu, Zehua Hu, Jiazhou Chen, Jing Huang - - - Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma - https://arxiv.org/abs/2512.24205 - arXiv:2512.24205v1 Announce Type: new -Abstract: Plasma kinetic equations exhibit pronounced sensitivity to microscopic perturbations in model parameters and data, making reliable and efficient uncertainty quantification (UQ) essential for predictive simulations. However, the cost of uncertainty sampling, the high-dimensional phase space, and multiscale stiffness pose severe challenges to both computational efficiency and error control in traditional numerical methods. These aspects are further emphasized in presence of collisions where the high-dimensional nonlocal collision integrations and conservation properties pose severe constraints. To overcome this, we present a variance-reduced Monte Carlo framework for UQ in the Vlasov--Poisson--Landau (VPL) system, in which neural network surrogates replace the multiple costly evaluations of the Landau collision term. The method couples a high-fidelity, asymptotic-preserving VPL solver with inexpensive, strongly correlated surrogates based on the Vlasov--Poisson--Fokker--Planck (VPFP) and Euler--Poisson (EP) equations. For the surrogate models, we introduce a generalization of the separable physics-informed neural network (SPINN), developing a class of tensor neural networks based on an anisotropic micro-macro decomposition, to reduce velocity-moment costs, model complexity, and the curse of dimensionality. To further increase correlation with VPL, we calibrate the VPFP model and design an asymptotic-preserving SPINN whose small- and large-Knudsen limits recover the EP and VP systems, respectively. Numerical experiments show substantial variance reduction over standard Monte Carlo, accurate statistics with far fewer high-fidelity samples, and lower wall-clock time, while maintaining robustness to stochastic dimension. - oai:arXiv.org:2512.24205v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wei Chen, Giacomo Dimarco, Lorenzo Pareschi - - - GR-Dexter Technical Report - https://arxiv.org/abs/2512.24210 - arXiv:2512.24210v1 Announce Type: new -Abstract: Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to bimanual robots with high degree-of-freedom (DoF) dexterous hands remains challenging due to the expanded action space, frequent hand-object occlusions, and the cost of collecting real-robot data. We present GR-Dexter, a holistic hardware-model-data framework for VLA-based generalist manipulation on a bimanual dexterous-hand robot. Our approach combines the design of a compact 21-DoF robotic hand, an intuitive bimanual teleoperation system for real-robot data collection, and a training recipe that leverages teleoperated robot trajectories together with large-scale vision-language and carefully curated cross-embodiment datasets. Across real-world evaluations spanning long-horizon everyday manipulation and generalizable pick-and-place, GR-Dexter achieves strong in-domain performance and improved robustness to unseen objects and unseen instructions. We hope GR-Dexter serves as a practical step toward generalist dexterous-hand robotic manipulation. - oai:arXiv.org:2512.24210v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ruoshi Wen, Guangzeng Chen, Zhongren Cui, Min Du, Yang Gou, Zhigang Han, Liqun Huang, Mingyu Lei, Yunfei Li, Zhuohang Li, Wenlei Liu, Yuxiao Liu, Xiao Ma, Hao Niu, Yutao Ouyang, Zeyu Ren, Haixin Shi, Wei Xu, Haoxiang Zhang, Jiajun Zhang, Xiao Zhang, Liwei Zheng, Weiheng Zhong, Yifei Zhou, Zhengming Zhu, Hang Li - - - RANGER: A Monocular Zero-Shot Semantic Navigation Framework through Contextual Adaptation - https://arxiv.org/abs/2512.24212 - arXiv:2512.24212v1 Announce Type: new -Abstract: Efficiently finding targets in complex environments is fundamental to real-world embodied applications. While recent advances in multimodal foundation models have enabled zero-shot object goal navigation, allowing robots to search for arbitrary objects without fine-tuning, existing methods face two key limitations: (1) heavy reliance on precise depth and pose information provided by simulators, which restricts applicability in real-world scenarios; and (2) lack of in-context learning (ICL) capability, making it difficult to quickly adapt to new environments, as in leveraging short videos. To address these challenges, we propose RANGER, a novel zero-shot, open-vocabulary semantic navigation framework that operates using only a monocular camera. Leveraging powerful 3D foundation models, RANGER eliminates the dependency on depth and pose while exhibiting strong ICL capability. By simply observing a short video of a new environment, the system can also significantly improve task efficiency without requiring architectural modifications or fine-tuning. The framework integrates several key components: keyframe-based 3D reconstruction, semantic point cloud generation, vision-language model (VLM)-driven exploration value estimation, high-level adaptive waypoint selection, and low-level action execution. Experiments on the HM3D benchmark and real-world environments demonstrate that RANGER achieves competitive performance in terms of navigation success rate and exploration efficiency, while showing superior ICL adaptability, with no previous 3D mapping of the environment required. - oai:arXiv.org:2512.24212v1 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ming-Ming Yu, Yi Chen, B\"orje F. Karlsson, Wenjun Wu - - - Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19 - https://arxiv.org/abs/2512.24214 - arXiv:2512.24214v1 Announce Type: new -Abstract: The challenge of imbalanced data is prominent in medical image classification. This challenge arises when there is a significant disparity in the number of images belonging to a particular class, such as the presence or absence of a specific disease, as compared to the number of images belonging to other classes. This issue is especially notable during pandemics, which may result in an even more significant imbalance in the dataset. Researchers have employed various approaches in recent years to detect COVID-19 infected individuals accurately and quickly, with artificial intelligence and machine learning algorithms at the forefront. However, the lack of sufficient and balanced data remains a significant obstacle to these methods. This study addresses the challenge by proposing a progressive generative adversarial network to generate synthetic data to supplement the real ones. The proposed method suggests a weighted approach to combine synthetic data with real ones before inputting it into a deep network classifier. A multi-objective meta-heuristic population-based optimization algorithm is employed to optimize the hyper-parameters of the classifier. The proposed model exhibits superior cross-validated metrics compared to existing methods when applied to a large and imbalanced chest X-ray image dataset of COVID-19. The proposed model achieves 95.5% and 98.5% accuracy for 4-class and 2-class imbalanced classification problems, respectively. The successful experimental outcomes demonstrate the effectiveness of the proposed model in classifying medical images using imbalanced data during pandemics. - oai:arXiv.org:2512.24214v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sina Jahromi, Farshid Hajati, Alireza Rezaee, Javaher Nourian - - - Efficient Decoding of Twisted GRS Codes and Roth--Lempel Codes - https://arxiv.org/abs/2512.24217 - arXiv:2512.24217v1 Announce Type: new -Abstract: MDS codes play a central role in practice due to their broad applications. To date, most known MDS codes are generalized Reed-Solomon (GRS) codes, leaving codes that are not equivalent to GRS codes comparatively less understood. Studying this non-GRS regime is therefore of intrinsic theoretical interest, and is also practically relevant since the strong algebraic structure of GRS codes can be undesirable in cryptographic settings. Among the known non-GRS codes, twisted generalized Reed-Solomon (TGRS) codes and Roth-Lempel codes are two representative families of non-GRS codes that have attracted significant attention. Though substantial work has been devoted to the construction and structural analysis of TGRS and Roth-Lempel codes, comparatively little attention has been paid to their decoding, and many problems remain open. In this paper, we propose list and unique decoding algorithms for TGRS codes and Roth-Lempel codes based on the Guruswami-Sudan algorithm. Under suitable parameter conditions, our algorithms achieve near-linear running time in the code length, improving upon the previously best-known quadratic-time complexity. Our TGRS decoder supports fixed-rate TGRS codes with up to O(n^2) twists, substantially extending prior work that only handled the single-twist case. For Roth-Lempel codes, we provide what appears to be the first efficient decoder. Moreover, our list decoders surpass the classical unique-decoding radius for a broad range of parameters. Finally, we incorporate algebraic manipulation detection (AMD) codes into the list-decoding framework, enabling recovery of the correct message from the output list with high probability. - oai:arXiv.org:2512.24217v1 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Runtian Zhu, Lingfei Jin - - - ARM: A Learnable, Plug-and-Play Module for CLIP-based Open-vocabulary Semantic Segmentation - https://arxiv.org/abs/2512.24224 - arXiv:2512.24224v1 Announce Type: new -Abstract: Open-vocabulary semantic segmentation (OVSS) is fundamentally hampered by the coarse, image-level representations of CLIP, which lack precise pixel-level details. Existing training-free methods attempt to resolve this by either importing priors from costly external foundation models (e.g., SAM, DINO) or by applying static, hand-crafted heuristics to CLIP's internal features. These approaches are either computationally expensive or sub-optimal. We propose the Attention Refinement Module (ARM), a lightweight, learnable module that effectively unlocks and refines CLIP's internal potential. Unlike static-fusion methods, ARM learns to adaptively fuse hierarchical features. It employs a semantically-guided cross-attention block, using robust deep features (K, V) to select and refine detail-rich shallow features (Q), followed by a self-attention block. The key innovation lies in a ``train once, use anywhere" paradigm. Trained once on a general-purpose dataset (e.g., COCO-Stuff), ARM acts as a universal plug-and-play post-processor for diverse training-free frameworks. Extensive experiments show that ARM consistently boosts baseline performance on multiple benchmarks with negligible inference overhead, establishing an efficient and effective paradigm for training-free OVSS. - oai:arXiv.org:2512.24224v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ziquan Liu, Zhewei Zhu, Xuyang Shi - - - Mirage: One-Step Video Diffusion for Photorealistic and Coherent Asset Editing in Driving Scenes - https://arxiv.org/abs/2512.24227 - arXiv:2512.24227v1 Announce Type: new -Abstract: Vision-centric autonomous driving systems rely on diverse and scalable training data to achieve robust performance. While video object editing offers a promising path for data augmentation, existing methods often struggle to maintain both high visual fidelity and temporal coherence. In this work, we propose \textbf{Mirage}, a one-step video diffusion model for photorealistic and coherent asset editing in driving scenes. Mirage builds upon a text-to-video diffusion prior to ensure temporal consistency across frames. However, 3D causal variational autoencoders often suffer from degraded spatial fidelity due to compression, and directly passing 3D encoder features to decoder layers breaks temporal causality. To address this, we inject temporally agnostic latents from a pretrained 2D encoder into the 3D decoder to restore detail while preserving causal structures. Furthermore, because scene objects and inserted assets are optimized under different objectives, their Gaussians exhibit a distribution mismatch that leads to pose misalignment. To mitigate this, we introduce a two-stage data alignment strategy combining coarse 3D alignment and fine 2D refinement, thereby improving alignment and providing cleaner supervision. Extensive experiments demonstrate that Mirage achieves high realism and temporal consistency across diverse editing scenarios. Beyond asset editing, Mirage can also generalize to other video-to-video translation tasks, serving as a reliable baseline for future research. Our code is available at https://github.com/wm-research/mirage. - oai:arXiv.org:2512.24227v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Shuyun Wang, Haiyang Sun, Bing Wang, Hangjun Ye, Xin Yu - - - MotivNet: Evolving Meta-Sapiens into an Emotionally Intelligent Foundation Model - https://arxiv.org/abs/2512.24231 - arXiv:2512.24231v1 Announce Type: new -Abstract: In this paper, we introduce MotivNet, a generalizable facial emotion recognition model for robust real-world application. Current state-of-the-art FER models tend to have weak generalization when tested on diverse data, leading to deteriorated performance in the real world and hindering FER as a research domain. Though researchers have proposed complex architectures to address this generalization issue, they require training cross-domain to obtain generalizable results, which is inherently contradictory for real-world application. Our model, MotivNet, achieves competitive performance across datasets without cross-domain training by using Meta-Sapiens as a backbone. Sapiens is a human vision foundational model with state-of-the-art generalization in the real world through large-scale pretraining of a Masked Autoencoder. We propose MotivNet as an additional downstream task for Sapiens and define three criteria to evaluate MotivNet's viability as a Sapiens task: benchmark performance, model similarity, and data similarity. Throughout this paper, we describe the components of MotivNet, our training approach, and our results showing MotivNet is generalizable across domains. We demonstrate that MotivNet can be benchmarked against existing SOTA models and meets the listed criteria, validating MotivNet as a Sapiens downstream task, and making FER more incentivizing for in-the-wild application. The code is available at https://github.com/OSUPCVLab/EmotionFromFaceImages. - oai:arXiv.org:2512.24231v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Rahul Medicharla, Alper Yilmaz - - - SC-LDPC Codes Over $\mathbb{F}_q$: Minimum Distance, Decoding Analysis and Threshold Saturation - https://arxiv.org/abs/2512.24232 - arXiv:2512.24232v1 Announce Type: new -Abstract: We investigate random spatially coupled low-density parity-check (SC-LDPC) code ensembles over finite fields. Under different variable-node edge-spreading rules, the random Tanner graphs of several coupled ensembles are defined by multiple independent, uniformly random monomial maps. The two main coupled ensembles considered are referred to as the standard coupled ensemble and the improved coupled ensemble. We prove that both coupled ensembles exhibit asymptotically good minimum distance and minimum stopping set size. Theoretical and numerical results show that the improved coupled ensemble can achieve better distance performance than the standard coupled ensemble. We introduce the essential preliminaries and analytical tools needed to analyze the iterative decoding threshold of coupled ensembles over any finite field. We consider a class of memoryless channels with special symmetry, termed q-ary input memoryless symmetric channels (QMSCs), and show that, for these channels, the distribution of channel messages (in form of probability vectors) likewise exhibits this symmetry. Consequently, we define symmetric probability measures and their reference measures on a finite-dimensional probability simplex, analyze their foundational properties and those of their linear functionals, endow their respective spaces with metric topologies, and conduct an in-depth study of their degradation theory. Based on our analytical framework, we establish a universal threshold saturation result for both of the coupled ensembles over a q-ary finite field on QMSCs. Specifically, as the coupling parameters increase, the belief-propagation threshold of a coupled system saturates to a well-defined threshold that depends only on the underlying ensemble and the channel family. - oai:arXiv.org:2512.24232v1 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jiaxin Lyu, Guanghui He - - - LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring - https://arxiv.org/abs/2512.24235 - arXiv:2512.24235v1 Announce Type: new -Abstract: Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems. - oai:arXiv.org:2512.24235v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - May Bashendy, Walid Massoud, Sohaila Eltanbouly, Salam Albatarni, Marwan Sayed, Abrar Abir, Houda Bouamor, Tamer Elsayed - - - A Framing and Analysis of Applicative Tangible Interfaces - https://arxiv.org/abs/2512.24237 - arXiv:2512.24237v1 Announce Type: new -Abstract: The investigation of tangible user interfaces commenced approximately thirty years ago. Questions on its commercial potential become more pressing as the field becomes mature. To take the field one step further -- as the emergence of components contributed to the commercial development of graphical user interfaces -- this article suggests that applicative tangible user interfaces could also be split into components. These components are composed of the aggregation, combination, or coupling of physical items and fulfil four roles that are described through a new interaction model. This article successfully distributed among these four components' roles all of the 159 physical items from a representative collection of 35 applications. Further examination of these applicative tangible interfaces coincides with four research phases in the field and identifies three main paths for future research to fully realize the potential of tangible user interfaces. - oai:arXiv.org:2512.24237v1 - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Guillaume Riviere - - - Spatial Discretization for Fine-Grain Zone Checks with STARKs - https://arxiv.org/abs/2512.24238 - arXiv:2512.24238v1 Announce Type: new -Abstract: Many location-based services rely on a point-in-polygon test (PiP), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive in zero-knowledge proofs, privately performing the PiP test is challenging. In this paper, we answer the research questions of how different ways of encoding zones affect accuracy and proof cost by exploiting gridbased lookup tables under a fixed STARK execution model. Beyond a Boolean grid-based baseline that marks cells as in- or outside, we explore a distance-aware encoding approach that stores how far each cell is from a zone boundary and uses interpolation to reason within a cell. Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids (max. 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4x), making zone encoding the key lever for efficient zero-knowledge spatial checks. - oai:arXiv.org:2512.24238v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Sungmin Lee, Kichang Lee, Gyeongmin Han, JeongGil Ko - - - The Uncanny Valley in medical simulation-based training: a visual summary - https://arxiv.org/abs/2512.24240 - arXiv:2512.24240v1 Announce Type: new -Abstract: The purpose of this review article is to provide a bibliographical as well as evidence-based visual guide regarding the effect of ``Uncanny Valley'' (UV) and how it profoundly influences medical virtual reality simulation-based training. The phenomenon, where increasingly realistic virtual humans elicit discomfort due to subtle imperfections, is crucial to understand and address in the context of medical training, where realism and immersion are key to effective learning. - Our research team, consisting of experts in computer graphics, virtual reality, and medical education, brings a diverse and multidisciplinary perspective to this subject. Our collective experience spans developing advanced computer graphics systems, VR character simulation, and innovative educational technologies. We have collaborated across institutions and industries to push the boundaries of VR applications in medical training. - oai:arXiv.org:2512.24240v1 - cs.GR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Eleni Grigoriou, Manos Kamarianakis, George Papagiannakis - - - MambaSeg: Harnessing Mamba for Accurate and Efficient Image-Event Semantic Segmentation - https://arxiv.org/abs/2512.24243 - arXiv:2512.24243v1 Announce Type: new -Abstract: Semantic segmentation is a fundamental task in computer vision with wide-ranging applications, including autonomous driving and robotics. While RGB-based methods have achieved strong performance with CNNs and Transformers, their effectiveness degrades under fast motion, low-light, or high dynamic range conditions due to limitations of frame cameras. Event cameras offer complementary advantages such as high temporal resolution and low latency, yet lack color and texture, making them insufficient on their own. To address this, recent research has explored multimodal fusion of RGB and event data; however, many existing approaches are computationally expensive and focus primarily on spatial fusion, neglecting the temporal dynamics inherent in event streams. In this work, we propose MambaSeg, a novel dual-branch semantic segmentation framework that employs parallel Mamba encoders to efficiently model RGB images and event streams. To reduce cross-modal ambiguity, we introduce the Dual-Dimensional Interaction Module (DDIM), comprising a Cross-Spatial Interaction Module (CSIM) and a Cross-Temporal Interaction Module (CTIM), which jointly perform fine-grained fusion along both spatial and temporal dimensions. This design improves cross-modal alignment, reduces ambiguity, and leverages the complementary properties of each modality. Extensive experiments on the DDD17 and DSEC datasets demonstrate that MambaSeg achieves state-of-the-art segmentation performance while significantly reducing computational cost, showcasing its promise for efficient, scalable, and robust multimodal perception. - oai:arXiv.org:2512.24243v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Fuqiang Gu, Yuanke Li, Xianlei Long, Kangping Ji, Chao Chen, Qingyi Gu, Zhenliang Ni - - - Time-Aware Adaptive Side Information Fusion for Sequential Recommendation - https://arxiv.org/abs/2512.24246 - arXiv:2512.24246v1 Announce Type: new -Abstract: Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, despite significant advancements, current models are generally limited by three key challenges: they often overlook the fine-grained temporal dynamics inherent in timestamps, exhibit vulnerability to noise in user interaction sequences, and rely on computationally expensive fusion architectures. To systematically address these challenges, we propose the Time-Aware Adaptive Side Information Fusion (TASIF) framework. TASIF integrates three synergistic components: (1) a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns; (2) an adaptive frequency filter that leverages a learnable gate to denoise feature sequences adaptively, thereby providing higher-quality inputs for subsequent fusion modules; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture, where attributes guide the attention mechanism without being mixed into the content-representing item embeddings, ensuring deep interaction while ensuring computational efficiency. Extensive experiments on four public datasets demonstrate that TASIF significantly outperforms state-of-the-art baselines while maintaining excellent efficiency in training. Our source code is available at https://github.com/jluo00/TASIF. - oai:arXiv.org:2512.24246v1 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jie Luo, Wenyu Zhang, Xinming Zhang, Yuan Fang - - - Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking - https://arxiv.org/abs/2512.24249 - arXiv:2512.24249v1 Announce Type: new -Abstract: Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrotor systems. To address these challenges, we propose HBO-PID, a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization (HBO) framework with the classical PID controller to achieve accurate and robust trajectory tracking. By explicitly modeling input-dependent noise variance, the proposed method can better adapt to dynamic and complex environments, and therefore improve the accuracy and robustness of trajectory tracking. To accelerate the convergence of optimization, we adopt a two-stage optimization strategy that allow us to more efficiently find the optimal controller parameters. Through experiments in both simulation and real-world scenarios, we demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%. - oai:arXiv.org:2512.24249v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1109/IROS60139.2025.11247487 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Fuqiang Gu, Jiangshan Ai, Xu Lu, Xianlei Long, Yan Li, Tao Jiang, Chao Chen, Huidong Liu - - - Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem - https://arxiv.org/abs/2512.24251 - arXiv:2512.24251v1 Announce Type: new -Abstract: The Fleet Size and Mix Vehicle Routing Problem (FSMVRP) is a prominent variant of the Vehicle Routing Problem (VRP), extensively studied in operations research and computational science. FSMVRP requires simultaneous decisions on fleet composition and routing, making it highly applicable to real-world scenarios such as short-term vehicle rental and on-demand logistics. However, these requirements also increase the complexity of FSMVRP, posing significant challenges, particularly in large-scale and time-constrained environments. In this paper, we propose a deep reinforcement learning (DRL)-based approach for solving FSMVRP, capable of generating near-optimal solutions within a few seconds. Specifically, we formulate the problem as a Markov Decision Process (MDP) and develop a novel policy network, termed FRIPN, that seamlessly integrates fleet composition and routing decisions. Our method incorporates specialized input embeddings designed for distinctdecision objectives, including a remaining graph embedding to facilitate effective vehicle employment decisions. Comprehensive experiments are conducted on both randomly generated instances and benchmark datasets. The experimental results demonstrate that our method exhibits notable advantages in terms of computational efficiency and scalability, particularly in large-scale and time-constrained scenarios. These strengths highlight the potential of our approach for practical applications and provide valuable inspiration for extending DRL-based techniques to other variants of VRP. - oai:arXiv.org:2512.24251v1 - cs.AI - cs.LG - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Pengfu Wan, Jiawei Chen, Gangyan Xu - - - Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm - https://arxiv.org/abs/2512.24253 - arXiv:2512.24253v1 Announce Type: new -Abstract: Sepsis, characterized by a dysregulated immune response to infection, results in significant mortality, morbidity, and healthcare costs. The timely prediction of sepsis progression is crucial for reducing adverse outcomes through early intervention. Despite the development of numerous models for Intensive Care Unit (ICU) patients, there remains a notable gap in approaches for the early detection of sepsis in non-ward settings. This research introduces and evaluates four novel machine learning algorithms designed for predicting the onset of sepsis on wearable devices by analyzing heart rate data. The architecture of these models was refined through a genetic algorithm, optimizing for performance, computational complexity, and memory requirements. Performance metrics were subsequently extracted for each model to evaluate their feasibility for implementation on wearable devices capable of accurate heart rate monitoring. The models were initially tailored for a prediction window of one hour, later extended to four hours through transfer learning. The encouraging outcomes of this study suggest the potential for wearable technology to facilitate early sepsis detection outside ICU and ward environments. - oai:arXiv.org:2512.24253v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Alireza Rafiei, Farshid Hajati, Alireza Rezaee, Amirhossien Panahi, Shahadat Uddin - - - How Would Oblivious Memory Boost Graph Analytics on Trusted Processors? - https://arxiv.org/abs/2512.24255 - arXiv:2512.24255v1 Announce Type: new -Abstract: Trusted processors provide a way to perform joint computations while preserving data privacy. To overcome the performance degradation caused by data-oblivious algorithms to prevent information leakage, we explore the benefits of oblivious memory (OM) integrated in processors, to which the accesses are unobservable by adversaries. We focus on graph analytics, an important application vulnerable to access-pattern attacks. With a co-design between storage structure and algorithms, our prototype system is 100x faster than baselines given an OM sized around the per-core cache which can be implemented on existing processors with negligible overhead. This gives insights into equipping trusted processors with OM. - oai:arXiv.org:2512.24255v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jiping Yu, Xiaowei Zhu, Kun Chen, Guanyu Feng, Yunyi Chen, Xiaoyu Fan, Wenguang Chen - - - Tracing the Flow of Knowledge From Science to Technology Using Deep Learning - https://arxiv.org/abs/2512.24259 - arXiv:2512.24259v1 Announce Type: new -Abstract: We develop a language similarity model suitable for working with patents and scientific publications at the same time. In a horse race-style evaluation, we subject eight language (similarity) models to predict credible Patent-Paper Citations. We find that our Pat-SPECTER model performs best, which is the SPECTER2 model fine-tuned on patents. In two real-world scenarios (separating patent-paper-pairs and predicting patent-paper-pairs) we demonstrate the capabilities of the Pat-SPECTER. We finally test the hypothesis that US patents cite papers that are semantically less similar than in other large jurisdictions, which we posit is because of the duty of candor. The model is open for the academic community and practitioners alike. - oai:arXiv.org:2512.24259v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Michael E. Rose, Mainak Ghosh, Sebastian Erhardt, Cheng Li, Erik Buunk, Dietmar Harhoff - - - Physically-Grounded Manifold Projection with Foundation Priors for Metal Artifact Reduction in Dental CBCT - https://arxiv.org/abs/2512.24260 - arXiv:2512.24260v1 Announce Type: new -Abstract: Metal artifacts in Dental CBCT severely obscure anatomical structures, hindering diagnosis. Current deep learning for Metal Artifact Reduction (MAR) faces limitations: supervised methods suffer from spectral blurring due to "regression-to-the-mean", while unsupervised ones risk structural hallucinations. Denoising Diffusion Models (DDPMs) offer realism but rely on slow, stochastic iterative sampling, unsuitable for clinical use. To resolve this, we propose the Physically-Grounded Manifold Projection (PGMP) framework. First, our Anatomically-Adaptive Physics Simulation (AAPS) pipeline synthesizes high-fidelity training pairs via Monte Carlo spectral modeling and patient-specific digital twins, bridging the synthetic-to-real gap. Second, our DMP-Former adapts the Direct x-Prediction paradigm, reformulating restoration as a deterministic manifold projection to recover clean anatomy in a single forward pass, eliminating stochastic sampling. Finally, a Semantic-Structural Alignment (SSA) module anchors the solution using priors from medical foundation models (MedDINOv3), ensuring clinical plausibility. Experiments on synthetic and multi-center clinical datasets show PGMP outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability. Code and data: https://github.com/ricoleehduu/PGMP - oai:arXiv.org:2512.24260v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Zhi Li, Yaqi Wang, Bingtao Ma, Yifan Zhang, Huiyu Zhou, Shuai Wang - - - Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment - https://arxiv.org/abs/2512.24263 - arXiv:2512.24263v1 Announce Type: new -Abstract: When fine-tuning pre-trained Language Models (LMs) to exhibit desired behaviors, maintaining control over risk is critical for ensuring both safety and trustworthiness. Most existing safety alignment methods, such as Safe RLHF and SACPO, typically operate under a risk-neutral paradigm that is insufficient to address the risks arising from deviations from the reference policy and offers limited robustness against rare but potentially catastrophic harmful behaviors. To address this limitation, we propose Risk-aware Stepwise Alignment (RSA), a novel alignment method that explicitly incorporates risk awareness into the policy optimization process by leveraging a class of nested risk measures. Specifically, RSA formulates safety alignment as a token-level risk-aware constrained policy optimization problem and solves it through a stepwise alignment procedure that yields token-level policy updates derived from the nested risk measures. This design offers two key benefits: (1) it mitigates risks induced by excessive model shift away from a reference policy, and (2) it explicitly suppresses low-probability yet high-impact harmful behaviors. Moreover, we provide theoretical analysis on policy optimality under mild assumptions. Experimental results demonstrate that our method achieves high levels of helpfulness while ensuring strong safety and significantly suppresses tail risks, namely low-probability yet high-impact unsafe responses. - oai:arXiv.org:2512.24263v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Lijun Zhang, Lin Li, Wei Wei, Yajie Qi, Huizhong Song, Jun Wang, Yaodong Yang, Jiye Liang - - - Joint Selection for Large-Scale Pre-Training Data via Policy Gradient-based Mask Learning - https://arxiv.org/abs/2512.24265 - arXiv:2512.24265v1 Announce Type: new -Abstract: A fine-grained data recipe is crucial for pre-training large language models, as it can significantly enhance training efficiency and model performance. One important ingredient in the recipe is to select samples based on scores produced by defined rules, LLM judgment, or statistical information in embeddings, which can be roughly categorized into quality and diversity metrics. Due to the high computational cost when applied to trillion-scale token pre-training datasets such as FineWeb and DCLM, these two or more types of metrics are rarely considered jointly in a single selection process. However, in our empirical study, selecting samples based on quality metrics exhibit severe diminishing returns during long-term pre-training, while selecting on diversity metrics removes too many valuable high-quality samples, both of which limit pre-trained LLMs' capabilities. Therefore, we introduce DATAMASK, a novel and efficient joint learning framework designed for large-scale pre-training data selection that can simultaneously optimize multiple types of metrics in a unified process, with this study focusing specifically on quality and diversity metrics. DATAMASK approaches the selection process as a mask learning problem, involving iterative sampling of data masks, computation of policy gradients based on predefined objectives with sampled masks, and updating of mask sampling logits. Through policy gradient-based optimization and various acceleration enhancements, it significantly reduces selection time by 98.9% compared to greedy algorithm, enabling our study to explore joint learning within trillion-scale tokens. With DATAMASK, we select a subset of about 10% from the 15 trillion-token FineWeb dataset, termed FineWeb-Mask. Evaluated across 12 diverse tasks, we achieves significant improvements of 3.2% on a 1.5B dense model and 1.9% on a 7B MoE model. - oai:arXiv.org:2512.24265v1 - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ziqing Fan, Yuqiao Xian, Yan Sun, Li Shen - - - RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation - https://arxiv.org/abs/2512.24268 - arXiv:2512.24268v1 Announce Type: new -Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models (LLMs) with external knowledge, reducing hallucinations and compensating for outdated information. However, recent studies have exposed a critical vulnerability in RAG pipelines corpus poisoning where adversaries inject malicious documents into the retrieval corpus to manipulate model outputs. In this work, we propose two complementary retrieval-stage defenses: RAGPart and RAGMask. Our defenses operate directly on the retriever, making them computationally lightweight and requiring no modification to the generation model. RAGPart leverages the inherent training dynamics of dense retrievers, exploiting document partitioning to mitigate the effect of poisoned points. In contrast, RAGMask identifies suspicious tokens based on significant similarity shifts under targeted token masking. Across two benchmarks, four poisoning strategies, and four state-of-the-art retrievers, our defenses consistently reduce attack success rates while preserving utility under benign conditions. We further introduce an interpretable attack to stress-test our defenses. Our findings highlight the potential and limitations of retrieval-stage defenses, providing practical insights for robust RAG deployments. - oai:arXiv.org:2512.24268v1 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Pankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang, Furong Huang - - - Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation - https://arxiv.org/abs/2512.24271 - arXiv:2512.24271v1 Announce Type: new -Abstract: Multimodal Large Language Models (MLLMs) have made remarkable progress in video understanding. However, they suffer from a critical vulnerability: an over-reliance on language priors, which can lead to visual ungrounded hallucinations, especially when processing counterfactual videos that defy common sense. This limitation, stemming from the intrinsic data imbalance between text and video, is challenging to address due to the substantial cost of collecting and annotating counterfactual data. To address this, we introduce DualityForge, a novel counterfactual data synthesis framework that employs controllable, diffusion-based video editing to transform real-world videos into counterfactual scenarios. By embedding structured contextual information into the video editing and QA generation processes, the framework automatically produces high-quality QA pairs together with original-edited video pairs for contrastive training. Based on this, we build DualityVidQA, a large-scale video dataset designed to reduce MLLM hallucinations. In addition, to fully exploit the contrastive nature of our paired data, we propose Duality-Normalized Advantage Training (DNA-Train), a two-stage SFT-RL training regime where the RL phase applies pair-wise $\ell_1$ advantage normalization, thereby enabling a more stable and efficient policy optimization. Experiments on DualityVidQA-Test demonstrate that our method substantially reduces model hallucinations on counterfactual videos, yielding a relative improvement of 24.0% over the Qwen2.5-VL-7B baseline. Moreover, our approach achieves significant gains across both hallucination and general-purpose benchmarks, indicating strong generalization capability. We will open-source our dataset and code. - oai:arXiv.org:2512.24271v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhe Huang, Hao Wen, Aiming Hao, Bingze Song, Meiqi Wu, Jiahong Wu, Xiangxiang Chu, Sheng Lu, Haoqian Wang - - - Local Path Optimization in The Latent Space Using Learned Distance Gradient - https://arxiv.org/abs/2512.24272 - arXiv:2512.24272v1 Announce Type: new -Abstract: Constrained motion planning is a common but challenging problem in robotic manipulation. In recent years, data-driven constrained motion planning algorithms have shown impressive planning speed and success rate. Among them, the latent motion method based on manifold approximation is the most efficient planning algorithm. Due to errors in manifold approximation and the difficulty in accurately identifying collision conflicts within the latent space, time-consuming path validity checks and path replanning are required. In this paper, we propose a method that trains a neural network to predict the minimum distance between the robot and obstacles using latent vectors as inputs. The learned distance gradient is then used to calculate the direction of movement in the latent space to move the robot away from obstacles. Based on this, a local path optimization algorithm in the latent space is proposed, and it is integrated with the path validity checking process to reduce the time of replanning. The proposed method is compared with state-of-the-art algorithms in multiple planning scenarios, demonstrating the fastest planning speed - oai:arXiv.org:2512.24272v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1109/IROS60139.2025.11247535 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China, 2025, pp. 15940-15946 - Jiawei Zhang, Chengchao Bai, Wei Pan, Tianhang Liu, Jifeng Guo - - - LiftProj: Space Lifting and Projection-Based Panorama Stitching - https://arxiv.org/abs/2512.24276 - arXiv:2512.24276v1 Announce Type: new -Abstract: Traditional image stitching techniques have predominantly utilized two-dimensional homography transformations and mesh warping to achieve alignment on a planar surface. While effective for scenes that are approximately coplanar or exhibit minimal parallax, these approaches often result in ghosting, structural bending, and stretching distortions in non-overlapping regions when applied to real three-dimensional scenes characterized by multiple depth layers and occlusions. Such challenges are exacerbated in multi-view accumulations and 360{\deg} closed-loop stitching scenarios. In response, this study introduces a spatially lifted panoramic stitching framework that initially elevates each input image into a dense three-dimensional point representation within a unified coordinate system, facilitating global cross-view fusion augmented by confidence metrics. Subsequently, a unified projection center is established in three-dimensional space, and an equidistant cylindrical projection is employed to map the fused data onto a single panoramic manifold, thereby producing a geometrically consistent 360{\deg} panoramic layout. Finally, hole filling is conducted within the canvas domain to address unknown regions revealed by viewpoint transitions, restoring continuous texture and semantic coherence. This framework reconceptualizes stitching from a two-dimensional warping paradigm to a three-dimensional consistency paradigm and is designed to flexibly incorporate various three-dimensional lifting and completion modules. Experimental evaluations demonstrate that the proposed method substantially mitigates geometric distortions and ghosting artifacts in scenarios involving significant parallax and complex occlusions, yielding panoramic results that are more natural and consistent. - oai:arXiv.org:2512.24276v1 - cs.CV - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuan Jia, Ruimin Wu, Rui Song, Jiaojiao Li, Bin Song - - - One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training - https://arxiv.org/abs/2512.24278 - arXiv:2512.24278v1 Announce Type: new -Abstract: Rare gastrointestinal lesions are infrequently encountered in routine endoscopy, restricting the data available for developing reliable artificial intelligence (AI) models and training novice clinicians. Here we present EndoRare, a one-shot, retraining-free generative framework that synthesizes diverse, high-fidelity lesion exemplars from a single reference image. By leveraging language-guided concept disentanglement, EndoRare separates pathognomonic lesion features from non-diagnostic attributes, encoding the former into a learnable prototype embedding while varying the latter to ensure diversity. We validated the framework across four rare pathologies (calcifying fibrous tumor, juvenile polyposis syndrome, familial adenomatous polyposis, and Peutz-Jeghers syndrome). Synthetic images were judged clinically plausible by experts and, when used for data augmentation, significantly enhanced downstream AI classifiers, improving the true positive rate at low false-positive rates. Crucially, a blinded reader study demonstrated that novice endoscopists exposed to EndoRare-generated cases achieved a 0.400 increase in recall and a 0.267 increase in precision. These results establish a practical, data-efficient pathway to bridge the rare-disease gap in both computer-aided diagnostics and clinical education. - oai:arXiv.org:2512.24278v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jia Yu, Yan Zhu, Peiyao Fu, Tianyi Chen, Zhihua Wang, Fei Wu, Quanlin Li, Pinghong Zhou, Shuo Wang, Xian Yang - - - Safe Sliding Mode Control for Marine Vessels Using High-Order Control Barrier Functions and Fast Projection - https://arxiv.org/abs/2512.24281 - arXiv:2512.24281v1 Announce Type: new -Abstract: This paper presents a novel safe control framework that integrates Sliding Mode Control (SMC), High-Order Control Barrier Functions (HOCBFs) with state-dependent adaptiveness and a lightweight projection for collision-free navigation of an over-actuated 3-DOF marine surface vessel subjected to strong environmental disturbances (wind, waves, and current). SMC provides robustness to matched disturbances common in marine operations, while HOCBFs enforce forward invariance of obstacle-avoidance constraints. A fast half-space projection method adjusts the SMC control only when needed, preserving robustness and minimizing chattering. The approach is evaluated on a nonlinear marine platform model that includes added mass, hydrodynamic damping, and full thruster allocation. Simulation results show robust navigation, guaranteed obstacle avoidance, and computational efficiency suitable for real-time embedded use. For small marine robots and surface vessels with limited onboard computational resources-where execution speed and computational efficiency are critical-the SMC-HOCBF framework constitutes a strong candidate for safety-critical control. - oai:arXiv.org:2512.24281v1 - eess.SY - cs.RO - cs.SY - math.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Spyridon Syntakas, Kostas Vlachos - - - DRL-TH: Jointly Utilizing Temporal Graph Attention and Hierarchical Fusion for UGV Navigation in Crowded Environments - https://arxiv.org/abs/2512.24284 - arXiv:2512.24284v1 Announce Type: new -Abstract: Deep reinforcement learning (DRL) methods have demonstrated potential for autonomous navigation and obstacle avoidance of unmanned ground vehicles (UGVs) in crowded environments. Most existing approaches rely on single-frame observation and employ simple concatenation for multi-modal fusion, which limits their ability to capture temporal context and hinders dynamic adaptability. To address these challenges, we propose a DRL-based navigation framework, DRL-TH, which leverages temporal graph attention and hierarchical graph pooling to integrate historical observations and adaptively fuse multi-modal information. Specifically, we introduce a temporal-guided graph attention network (TG-GAT) that incorporates temporal weights into attention scores to capture correlations between consecutive frames, thereby enabling the implicit estimation of scene evolution. In addition, we design a graph hierarchical abstraction module (GHAM) that applies hierarchical pooling and learnable weighted fusion to dynamically integrate RGB and LiDAR features, achieving balanced representation across multiple scales. Extensive experiments demonstrate that our DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios. - oai:arXiv.org:2512.24284v1 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ruitong Li, Lin Zhang, Yuenan Zhao, Chengxin Liu, Ran Song, Wei Zhang - - - Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks - https://arxiv.org/abs/2512.24286 - arXiv:2512.24286v1 Announce Type: new -Abstract: Federated Learning (FL) enables mobile edge devices, functioning as clients, to collaboratively train a decentralized model while ensuring local data privacy. However, the efficiency of FL in wireless networks is limited not only by constraints on communication and computational resources but also by significant data heterogeneity among clients, particularly in large-scale networks. This paper first presents a theoretical analysis of the impact of client data heterogeneity on global model generalization error, which can result in repeated training cycles, increased energy consumption, and prolonged latency. Based on the theoretical insights, an optimization problem is formulated to jointly minimize learning latency and energy consumption while constraining generalization error. A joint client selection and resource allocation (CSRA) approach is then proposed, employing a series of convex optimization and relaxation techniques. Extensive simulation results demonstrate that the proposed CSRA scheme yields higher test accuracy, reduced learning latency, and lower energy consumption compared to baseline methods that do not account for data heterogeneity. - oai:arXiv.org:2512.24286v1 - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yanbing Yang, Huiling Zhu, Wenchi Cheng, Jingqing Wang, Changrun Chen, Jiangzhou Wang - - - Real-world Reinforcement Learning from Suboptimal Interventions - https://arxiv.org/abs/2512.24288 - arXiv:2512.24288v1 Announce Type: new -Abstract: Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn from their own experience while gradually reducing human labor. However, prior real-world RL methods often assume that human interventions are optimal across the entire state space, overlooking the fact that even expert operators cannot consistently provide optimal actions in all states or completely avoid mistakes. Indiscriminately mixing intervention data with robot-collected data inherits the sample inefficiency of RL, while purely imitating intervention data can ultimately degrade the final performance achievable by RL. The question of how to leverage potentially suboptimal and noisy human interventions to accelerate learning without being constrained by them thus remains open. To address this challenge, we propose SiLRI, a state-wise Lagrangian reinforcement learning algorithm for real-world robot manipulation tasks. Specifically, we formulate the online manipulation problem as a constrained RL optimization, where the constraint bound at each state is determined by the uncertainty of human interventions. We then introduce a state-wise Lagrange multiplier and solve the problem via a min-max optimization, jointly optimizing the policy and the Lagrange multiplier to reach a saddle point. Built upon a human-as-copilot teleoperation system, our algorithm is evaluated through real-world experiments on diverse manipulation tasks. Experimental results show that SiLRI effectively exploits human suboptimal interventions, reducing the time required to reach a 90% success rate by at least 50% compared with the state-of-the-art RL method HIL-SERL, and achieving a 100% success rate on long-horizon manipulation tasks where other RL methods struggle to succeed. Project website: https://silri-rl.github.io/. - oai:arXiv.org:2512.24288v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yinuo Zhao, Huiqian Jin, Lechun Jiang, Xinyi Zhang, Kun Wu, Pei Ren, Zhiyuan Xu, Zhengping Che, Lei Sun, Dapeng Wu, Chi Harold Liu, Jian Tang - - - Automated Analysis of Sustainability Reports: Using Large Language Models for the Extraction and Prediction of EU Taxonomy-Compliant KPIs - https://arxiv.org/abs/2512.24289 - arXiv:2512.24289v1 Announce Type: new -Abstract: The manual, resource-intensive process of complying with the EU Taxonomy presents a significant challenge for companies. While Large Language Models (LLMs) offer a path to automation, research is hindered by a lack of public benchmark datasets. To address this gap, we introduce a novel, structured dataset from 190 corporate reports, containing ground-truth economic activities and quantitative Key Performance Indicators (KPIs). We use this dataset to conduct the first systematic evaluation of LLMs on the core compliance workflow. Our results reveal a clear performance gap between qualitative and quantitative tasks. LLMs show moderate success in the qualitative task of identifying economic activities, with a multi-step agentic framework modestly enhancing precision. Conversely, the models comprehensively fail at the quantitative task of predicting financial KPIs in a zero-shot setting. We also discover a paradox, where concise metadata often yields superior performance to full, unstructured reports, and find that model confidence scores are poorly calibrated. We conclude that while LLMs are not ready for full automation, they can serve as powerful assistive tools for human experts. Our dataset provides a public benchmark for future research. - oai:arXiv.org:2512.24289v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jonathan Schmoll, Adam Jatowt - - - Virtual-Eyes: Quantitative Validation of a Lung CT Quality-Control Pipeline for Foundation-Model Cancer Risk Prediction - https://arxiv.org/abs/2512.24294 - arXiv:2512.24294v1 Announce Type: new -Abstract: Robust preprocessing is rarely quantified in deep-learning pipelines for low-dose CT (LDCT) lung cancer screening. We develop and validate Virtual-Eyes, a clinically motivated 16-bit CT quality-control pipeline, and measure its differential impact on generalist foundation models versus specialist models. Virtual-Eyes enforces strict 512x512 in-plane resolution, rejects short or non-diagnostic series, and extracts a contiguous lung block using Hounsfield-unit filtering and bilateral lung-coverage scoring while preserving the native 16-bit grid. Using 765 NLST patients (182 cancer, 583 non-cancer), we compute slice-level embeddings from RAD-DINO and Merlin with frozen encoders and train leakage-free patient-level MLP heads; we also evaluate Sybil and a 2D ResNet-18 baseline under Raw versus Virtual-Eyes inputs without backbone retraining. Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112). In contrast, Sybil and ResNet-18 degrade under Virtual-Eyes (Sybil AUC 0.886 to 0.837; ResNet-18 AUC 0.571 to 0.596) with evidence of context dependence and shortcut learning, and Merlin shows limited transferability (AUC approximately 0.507 to 0.567) regardless of preprocessing. These results demonstrate that anatomically targeted QC can stabilize and improve generalist foundation-model workflows but may disrupt specialist models adapted to raw clinical context. - oai:arXiv.org:2512.24294v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Md. Enamul Hoq, Linda Larson-Prior, Fred Prior - - - Figure It Out: Improving the Frontier of Reasoning with Active Visual Thinking - https://arxiv.org/abs/2512.24297 - arXiv:2512.24297v1 Announce Type: new -Abstract: Complex reasoning problems often involve implicit spatial, geometric, and structural relationships that are not explicitly encoded in text. While recent reasoning models have achieved strong performance across many domains, purely text-based reasoning struggles to represent global structural constraints in complex settings. In this paper, we introduce FIGR, which integrates active visual thinking into multi-turn reasoning via end-to-end reinforcement learning. FIGR externalizes intermediate structural hypotheses by constructing visual representations during problem solving. By adaptively regulating when and how visual reasoning should be invoked, FIGR enables more stable and coherent reasoning over global structural properties that are difficult to capture from text alone. Experiments on challenging mathematical reasoning benchmarks demonstrate that FIGR outperforms strong text-only chain-of-thought baselines. In particular, FIGR improves the base model by 13.12% on AIME 2025 and 11.00% on BeyondAIME, highlighting the effectiveness of figure-guided multimodal reasoning in enhancing the stability and reliability of complex reasoning. - oai:arXiv.org:2512.24297v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Meiqi Chen, Fandong Meng, Jie Zhou - - - World In Your Hands: A Large-Scale and Open-source Ecosystem for Learning Human-centric Manipulation in the Wild - https://arxiv.org/abs/2512.24310 - arXiv:2512.24310v1 Announce Type: new -Abstract: Large-scale pre-training is fundamental for generalization in language and vision models, but data for dexterous hand manipulation remains limited in scale and diversity, hindering policy generalization. Limited scenario diversity, misaligned modalities, and insufficient benchmarking constrain current human manipulation datasets. To address these gaps, we introduce World In Your Hands (WiYH), a large-scale open-source ecosystem for human-centric manipulation learning. WiYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WiYH Dataset, featuring over 1,000 hours of multi-modal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WiYH ecosystem show that integrating WiYH's human-centric data significantly enhances the generalization and robustness of dexterous hand policies in tabletop manipulation tasks. We believe that World In Your Hands will bring new insights into human-centric data collection and policy learning to the community. - oai:arXiv.org:2512.24310v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - TARS Robotics, Yuhang Zheng, Jichao Peng, Weize Li, Yupeng Zheng, Xiang Li, Yujie Jin, Julong Wei, Guanhua Zhang, Ruiling Zheng, Ming Cao, Songen Gu, Zhenhong Zou, Kaige Li, Ke Wu, Mingmin Yang, Jiahao Liu, Pengfei Li, Hengjie Si, Feiyu Zhu, Wang Fu, Likun Wang, Ruiwen Yao, Jieru Zhao, Yilun Chen, Wenchao Din - - - QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs - https://arxiv.org/abs/2512.24314 - arXiv:2512.24314v1 Announce Type: new -Abstract: Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as BloombergGPT and Baichuan-Finance primarily focused on knowledge enhancement, the deepening complexity of financial services has driven a growing demand for models that possess not only domain knowledge but also robust financial reasoning and agentic capabilities. In this paper, we present QianfanHuijin, a financial domain LLM, and propose a generalizable multi-stage training paradigm for industrial model enhancement. - Our approach begins with Continual Pre-training (CPT) on financial corpora to consolidate the knowledge base. This is followed by a fine-grained Post-training pipeline designed with increasing specificity: starting with Financial SFT, progressing to Finance Reasoning RL and Finance Agentic RL, and culminating in General RL aligned with real-world business scenarios. Empirical results demonstrate that QianfanHuijin achieves superior performance across various authoritative financial benchmarks. Furthermore, ablation studies confirm that the targeted Reasoning RL and Agentic RL stages yield significant gains in their respective capabilities. These findings validate our motivation and suggest that this fine-grained, progressive post-training methodology is poised to become a mainstream paradigm for various industrial-enhanced LLMs. - oai:arXiv.org:2512.24314v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shupeng Li, Weipeng Lu, Linyun Liu, Chen Lin, Shaofei Li, Zhendong Tan, Hanjun Zhong, Yucheng Zeng, Chenghao Zhu, Mengyue Liu, Daxiang Dong, Jianmin Wu, Yunting Xiao, Annan Li, Danyu Liu, Jingnan Zhang, Licen Liu, Dawei Yin, Dou Shen - - - UniAct: Unified Motion Generation and Action Streaming for Humanoid Robots - https://arxiv.org/abs/2512.24321 - arXiv:2512.24321v1 Announce Type: new -Abstract: A long-standing objective in humanoid robotics is the realization of versatile agents capable of following diverse multimodal instructions with human-level flexibility. Despite advances in humanoid control, bridging high-level multimodal perception with whole-body execution remains a significant bottleneck. Existing methods often struggle to translate heterogeneous instructions -- such as language, music, and trajectories -- into stable, real-time actions. Here we show that UniAct, a two-stage framework integrating a fine-tuned MLLM with a causal streaming pipeline, enables humanoid robots to execute multimodal instructions with sub-500 ms latency. By unifying inputs through a shared discrete codebook via FSQ, UniAct ensures cross-modal alignment while constraining motions to a physically grounded manifold. This approach yields a 19% improvement in the success rate of zero-shot tracking of imperfect reference motions. We validate UniAct on UniMoCap, our 20-hour humanoid motion benchmark, demonstrating robust generalization across diverse real-world scenarios. Our results mark a critical step toward responsive, general-purpose humanoid assistants capable of seamless interaction through unified perception and control. - oai:arXiv.org:2512.24321v1 - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Nan Jiang, Zimo He, Wanhe Yu, Lexi Pang, Yunhao Li, Hongjie Li, Jieming Cui, Yuhan Li, Yizhou Wang, Yixin Zhu, Siyuan Huang - - - Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention - https://arxiv.org/abs/2512.24323 - arXiv:2512.24323v1 Announce Type: new -Abstract: Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-person videos. This task is critical for understanding egocentric human behavior. However, achieving such segmentation robustly is challenging due to ambiguities inherent in egocentric videos and biases present in training data. Consequently, existing methods often struggle, learning spurious correlations from skewed object-action pairings in datasets and fundamental visual confounding factors of the egocentric perspective, such as rapid motion and frequent occlusions. To address these limitations, we introduce Causal Ego-REferring Segmentation (CERES), a plug-in causal framework that adapts strong, pre-trained RVOS backbones to the egocentric domain. CERES implements dual-modal causal intervention: applying backdoor adjustment principles to counteract language representation biases learned from dataset statistics, and leveraging front-door adjustment concepts to address visual confounding by intelligently integrating semantic visual features with geometric depth information guided by causal principles, creating representations more robust to egocentric distortions. Extensive experiments demonstrate that CERES achieves state-of-the-art performance on Ego-RVOS benchmarks, highlighting the potential of applying causal reasoning to build more reliable models for broader egocentric video understanding. - oai:arXiv.org:2512.24323v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haijing Liu, Zhiyuan Song, Hefeng Wu, Tao Pu, Keze Wang, Liang Lin - - - Empower Low-Altitude Economy: A Reliability-Aware Dynamic Weighting Allocation for Multi-modal UAV Beam Prediction - https://arxiv.org/abs/2512.24324 - arXiv:2512.24324v1 Announce Type: new -Abstract: The low-altitude economy (LAE) is rapidly expanding driven by urban air mobility, logistics drones, and aerial sensing, while fast and accurate beam prediction in uncrewed aerial vehicles (UAVs) communications is crucial for achieving reliable connectivity. Current research is shifting from single-signal to multi-modal collaborative approaches. However, existing multi-modal methods mostly employ fixed or empirical weights, assuming equal reliability across modalities at any given moment. Indeed, the importance of different modalities fluctuates dramatically with UAV motion scenarios, and static weighting amplifies the negative impact of degraded modalities. Furthermore, modal mismatch and weak alignment further undermine cross-scenario generalization. To this end, we propose a reliability-aware dynamic weighting scheme applied to a semantic-aware multi-modal beam prediction framework, named SaM2B. Specifically, SaM2B leverages lightweight cues such as environmental visual, flight posture, and geospatial data to adaptively allocate contributions across modalities at different time points through reliability-aware dynamic weight updates. Moreover, by utilizing cross-modal contrastive learning, we align the "multi-source representation beam semantics" associated with specific beam information to a shared semantic space, thereby enhancing discriminative power and robustness under modal noise and distribution shifts. Experiments on real-world low-altitude UAV datasets show that SaM2B achieves more satisfactory results than baseline methods. - oai:arXiv.org:2512.24324v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haojin Li, Anbang Zhang, Chen Sun, Chenyuan Feng, Kaiqian Qu, Tony Q. S. Quek, Haijun Zhang - - - MaRCA: Multi-Agent Reinforcement Learning for Dynamic Computation Allocation in Large-Scale Recommender Systems - https://arxiv.org/abs/2512.24325 - arXiv:2512.24325v1 Announce Type: new -Abstract: Modern recommender systems face significant computational challenges due to growing model complexity and traffic scale, making efficient computation allocation critical for maximizing business revenue. Existing approaches typically simplify multi-stage computation resource allocation, neglecting inter-stage dependencies, thus limiting global optimality. In this paper, we propose MaRCA, a multi-agent reinforcement learning framework for end-to-end computation resource allocation in large-scale recommender systems. MaRCA models the stages of a recommender system as cooperative agents, using Centralized Training with Decentralized Execution (CTDE) to optimize revenue under computation resource constraints. We introduce an AutoBucket TestBench for accurate computation cost estimation, and a Model Predictive Control (MPC)-based Revenue-Cost Balancer to proactively forecast traffic loads and adjust the revenue-cost trade-off accordingly. Since its end-to-end deployment in the advertising pipeline of a leading global e-commerce platform in November 2024, MaRCA has consistently handled hundreds of billions of ad requests per day and has delivered a 16.67% revenue uplift using existing computation resources. - oai:arXiv.org:2512.24325v1 - cs.IR - cs.LG - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wan Jiang, Xinyi Zang, Yudong Zhao, Yusi Zou, Yunfei Lu, Junbo Tong, Yang Liu, Ming Li, Jiani Shi, Xin Yang - - - 3D Path-Following Guidance via Nonlinear Model Predictive Control for Fixed-Wing Small UAS - https://arxiv.org/abs/2512.24326 - arXiv:2512.24326v1 Announce Type: new -Abstract: This paper presents the design, implementation, and flight test results of two novel 3D path-following guidance algorithms based on nonlinear model predictive control (MPC), with specific application to fixed-wing small uncrewed aircraft systems. To enable MPC, control-augmented modelling and system identification of the RAAVEN small uncrewed aircraft is presented. Two formulations of MPC are then showcased. The first schedules a static reference path rate over the MPC horizon, incentivizing a constant inertial speed. The second, with inspiration from model predictive contouring control, dynamically optimizes for the reference path rate over the controller horizon as the system operates. This allows for a weighted tradeoff between path progression and distance from path, two competing objectives in path-following guidance. Both controllers are formulated to operate over general smooth 3D arc-length parameterized curves. The MPC guidance algorithms are flown over several high-curvature test paths, with comparison to a baseline lookahead guidance law. The results showcase the real-world feasibility and superior performance of nonlinear MPC for 3D path-following guidance at ground speeds up to 36 meters per second. - oai:arXiv.org:2512.24326v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Camron Alexander Hirst, Chris Reale, Eric Frew - - - World model inspired sarcasm reasoning with large language model agents - https://arxiv.org/abs/2512.24329 - arXiv:2512.24329v1 Announce Type: new -Abstract: Sarcasm understanding is a challenging problem in natural language processing, as it requires capturing the discrepancy between the surface meaning of an utterance and the speaker's intentions as well as the surrounding social context. Although recent advances in deep learning and Large Language Models (LLMs) have substantially improved performance, most existing approaches still rely on black-box predictions of a single model, making it difficult to structurally explain the cognitive factors underlying sarcasm. Moreover, while sarcasm often emerges as a mismatch between semantic evaluation and normative expectations or intentions, frameworks that explicitly decompose and model these components remain limited. In this work, we reformulate sarcasm understanding as a world model inspired reasoning process and propose World Model inspired SArcasm Reasoning (WM-SAR), which decomposes literal meaning, context, normative expectation, and intention into specialized LLM-based agents. The discrepancy between literal evaluation and normative expectation is explicitly quantified as a deterministic inconsistency score, and together with an intention score, these signals are integrated by a lightweight Logistic Regression model to infer the final sarcasm probability. This design leverages the reasoning capability of LLMs while maintaining an interpretable numerical decision structure. Experiments on representative sarcasm detection benchmarks show that WM-SAR consistently outperforms existing deep learning and LLM-based methods. Ablation studies and case analyses further demonstrate that integrating semantic inconsistency and intention reasoning is essential for effective sarcasm detection, achieving both strong performance and high interpretability. - oai:arXiv.org:2512.24329v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Keito Inoshita, Shinnosuke Mizuno - - - SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning - https://arxiv.org/abs/2512.24330 - arXiv:2512.24330v1 Announce Type: new -Abstract: While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated tool invocation. They fail to exhibit the human-like proficiency required to seamlessly interleave dynamic tool manipulation with continuous reasoning, particularly in knowledge-intensive and visually complex scenarios that demand coordinated external tools such as search and image cropping. In this work, we introduce SenseNova-MARS, a novel Multimodal Agentic Reasoning and Search framework that empowers VLMs with interleaved visual reasoning and tool-use capabilities via reinforcement learning (RL). Specifically, SenseNova-MARS dynamically integrates the image search, text search, and image crop tools to tackle fine-grained and knowledge-intensive visual understanding challenges. In the RL stage, we propose the Batch-Normalized Group Sequence Policy Optimization (BN-GSPO) algorithm to improve the training stability and advance the model's ability to invoke tools and reason effectively. To comprehensively evaluate the agentic VLMs on complex visual tasks, we introduce the HR-MMSearch benchmark, the first search-oriented benchmark composed of high-resolution images with knowledge-intensive and search-driven questions. Experiments demonstrate that SenseNova-MARS achieves state-of-the-art performance on open-source search and fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNova-MARS-8B scores 67.84 on MMSearch and 41.64 on HR-MMSearch, surpassing proprietary models such as Gemini-3-Flash and GPT-5. SenseNova-MARS represents a promising step toward agentic VLMs by providing effective and robust tool-use capabilities. To facilitate further research in this field, we will release all code, models, and datasets. - oai:arXiv.org:2512.24330v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yong Xien Chng, Tao Hu, Wenwen Tong, Xueheng Li, Jiandong Chen, Haojia Yu, Jiefan Lu, Hewei Guo, Hanming Deng, Chengjun Xie, Gao Huang, Dahua Lin, Lewei Lu - - - Spatial-aware Vision Language Model for Autonomous Driving - https://arxiv.org/abs/2512.24331 - arXiv:2512.24331v1 Announce Type: new -Abstract: While Vision-Language Models (VLMs) show significant promise for end-to-end autonomous driving by leveraging the common sense embedded in language models, their reliance on 2D image cues for complex scene understanding and decision-making presents a critical bottleneck for safety and reliability. Current image-based methods struggle with accurate metric spatial reasoning and geometric inference, leading to unreliable driving policies. To bridge this gap, we propose LVLDrive (LiDAR-Vision-Language), a novel framework specifically designed to upgrade existing VLMs with robust 3D metric spatial understanding for autonomous driving by incoperating LiDAR point cloud as an extra input modality. A key challenge lies in mitigating the catastrophic disturbance introduced by disparate 3D data to the pre-trained VLMs. To this end, we introduce a Gradual Fusion Q-Former that incrementally injects LiDAR features, ensuring the stability and preservation of the VLM's existing knowledge base. Furthermore, we develop a spatial-aware question-answering (SA-QA) dataset to explicitly teach the model advanced 3D perception and reasoning capabilities. Extensive experiments on driving benchmarks demonstrate that LVLDrive achieves superior performance compared to vision-only counterparts across scene understanding, metric spatial perception, and reliable driving decision-making. Our work highlights the necessity of explicit 3D metric data for building trustworthy VLM-based autonomous systems. - oai:arXiv.org:2512.24331v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Weijie Wei, Zhipeng Luo, Ling Feng, Venice Erin Liong - - - A density-based framework for community detection in attributed networks - https://arxiv.org/abs/2512.24336 - arXiv:2512.24336v1 Announce Type: new -Abstract: Community structure in social and collaborative networks often emerges from a complex interplay between structural mechanisms, such as degree heterogeneity and leader-driven attraction, and homophily on node attributes. Existing community detection methods typically focus on these dimensions in isolation, limiting their ability to recover interpretable communities in presence of such mechanisms. In this paper, we propose AttDeCoDe, an attribute-driven extension of a density-based community detection framework, developed to analyse networks where node characteristics play a central role in group formation. Instead of defining density purely from network topology, AttDeCoDe estimates node-wise density in the attribute space, allowing communities to form around attribute-based community representatives while preserving structural connectivity constraints. This approach naturally captures homophily-driven aggregation while remaining sensitive to leader influence. We evaluate the proposed method through a simulation study based on a novel generative model that extends the degree-corrected stochastic block model by incorporating attribute-driven leader attraction, reflecting key features of collaborative research networks. We perform an empirical application to research collaboration data from the Horizon programmes, where organisations are characterised by project-level thematic descriptors. Both results show that AttDeCoDe offers a flexible and interpretable framework for community detection in attributed networks achieving competitive performance relative to topology-based and attribute-assisted benchmarks. - oai:arXiv.org:2512.24336v1 - cs.SI - stat.AP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Sara Geremia, Michael Fop, Domenico De Stefano - - - The Mechanics of CNN Filtering with Rectification - https://arxiv.org/abs/2512.24338 - arXiv:2512.24338v1 Announce Type: new -Abstract: This paper proposes elementary information mechanics as a new model for understanding the mechanical properties of convolutional filtering with rectification, inspired by physical theories of special relativity and quantum mechanics. We consider kernels decomposed into orthogonal even and odd components. Even components cause image content to diffuse isotropically while preserving the center of mass, analogously to rest or potential energy with zero net momentum. Odd kernels cause directional displacement of the center of mass, analogously to kinetic energy with non-zero momentum. The speed of information displacement is linearly related to the ratio of odd vs total kernel energy. Even-Odd properties are analyzed in the spectral domain via the discrete cosine transform (DCT), where the structure of small convolutional filters (e.g. $3 \times 3$ pixels) is dominated by low-frequency bases, specifically the DC $\Sigma$ and gradient components $\nabla$, which define the fundamental modes of information propagation. To our knowledge, this is the first work demonstrating the link between information processing in generic CNNs and the energy-momentum relation, a cornerstone of modern relativistic physics. - oai:arXiv.org:2512.24338v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Liam Frija-Altrac, Matthew Toews - - - Proof-Carrying PWL Verification for ReLU Networks: Convex-Hull Semantics, Exact \SMT/\MILP Encodings, and Symbolic Certificate Checking - https://arxiv.org/abs/2512.24339 - arXiv:2512.24339v1 Announce Type: new -Abstract: ReLU networks are piecewise-linear (PWL), enabling exact symbolic verification via \SMT(\LRA) or \MILP. However, safety claims in certification pipelines require not only correctness but also \emph{checkable evidence}. We develop a proof-carrying verification core for PWL neural constraints: (i) we formalize ReLU networks as unions of polyhedra indexed by activation patterns; (ii) we present exact \SMT/\MILP encodings and the canonical convex-hull relaxation for each bounded ReLU; and (iii) we introduce a certificate calculus in which bound tightening, stabilization, strengthening, and pruning steps emit explicit algebraic witnesses (LP dual multipliers and Farkas infeasibility certificates). Crucially, these witnesses are \emph{symbolic objects} that admit independent verification in exact arithmetic over $\Q$. We provide a symbolic certificate checker, normalization rules that preserve validity, and a compositional view of region-wise certificates as a global proof artifact for universal safety. - oai:arXiv.org:2512.24339v1 - cs.LO - math.RA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chandrasekhar Gokavarapu (Department of Mathematics, Government College) - - - DermaVQA-DAS: Dermatology Assessment Schema (DAS) & Datasets for Closed-Ended Question Answering & Segmentation in Patient-Generated Dermatology Images - https://arxiv.org/abs/2512.24340 - arXiv:2512.24340v1 Announce Type: new -Abstract: Recent advances in dermatological image analysis have been driven by large-scale annotated datasets; however, most existing benchmarks focus on dermatoscopic images and lack patient-authored queries and clinical context, limiting their applicability to patient-centered care. To address this gap, we introduce DermaVQA-DAS, an extension of the DermaVQA dataset that supports two complementary tasks: closed-ended question answering (QA) and dermatological lesion segmentation. Central to this work is the Dermatology Assessment Schema (DAS), a novel expert-developed framework that systematically captures clinically meaningful dermatological features in a structured and standardized form. DAS comprises 36 high-level and 27 fine-grained assessment questions, with multiple-choice options in English and Chinese. Leveraging DAS, we provide expert-annotated datasets for both closed QA and segmentation and benchmark state-of-the-art multimodal models. For segmentation, we evaluate multiple prompting strategies and show that prompt design impacts performance: the default prompt achieves the best results under Mean-of-Max and Mean-of-Mean evaluation aggregation schemes, while an augmented prompt incorporating both patient query title and content yields the highest performance under majority-vote-based microscore evaluation, achieving a Jaccard index of 0.395 and a Dice score of 0.566 with BiomedParse. For closed-ended QA, overall performance is strong across models, with average accuracies ranging from 0.729 to 0.798; o3 achieves the best overall accuracy (0.798), closely followed by GPT-4.1 (0.796), while Gemini-1.5-Pro shows competitive performance within the Gemini family (0.783). We publicly release DermaVQA-DAS, the DAS schema, and evaluation protocols to support and accelerate future research in patient-centered dermatological vision-language modeling (https://osf.io/72rp3). - oai:arXiv.org:2512.24340v1 - cs.CV - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Meliha Yetisgen, Noel Codella, Roberto Andres Novoa, Josep Malvehy - - - FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles - https://arxiv.org/abs/2512.24345 - arXiv:2512.24345v1 Announce Type: new -Abstract: This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning. - oai:arXiv.org:2512.24345v1 - cs.CR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Devika S, Vishnu Hari, Pratik Narang, Tejasvi Alladi, F. Richard Yu - - - Effects of Algorithmic Visibility on Conspiracy Communities: Reddit after Epstein's 'Suicide' - https://arxiv.org/abs/2512.24351 - arXiv:2512.24351v1 Announce Type: new -Abstract: This paper examines how algorithmic visibility shapes a large conspiracy community on Reddit after Jeffrey Epstein's death. - We ask whether homepage exposure changes who join r/conspiracy, how long they stay, and how they adapt linguistically, compared with users who arrive through organic discovery. - Using a computational framework that combines toxicity scores, survival analysis, and lexical and semantic measures, the study shows that homepage visibility acts as a selection mechanism rather than a simple amplifier. - Users who discover the community organically integrate more quickly into its linguistic and thematic norms and show more stable engagement over time. - By contrast, users who arrive through visibility on the homepage remain semantically distant from core discourse and participate more briefly. - Overall, algorithmic visibility reshapes audience size, community composition, and linguistic cohesion: - newcomers who do not join organically have different incentives, integrate weakly, and leave quickly, which limits organic growth. - In this high-risk setting, the observed behavioral and linguistic trajectories over five months do not match standard narratives in which incidental exposure to conspiracy content produces durable radicalization. - These findings can inform the design of web platforms and recommendation systems that seek to curb harmful conspiracy exposure while supporting more responsible, transparent, and socially beneficial uses of algorithmic recommendations. - oai:arXiv.org:2512.24351v1 - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Asja Attanasio, Francesco Corso, Gianmarco De Francisci Morales, Francesco Pierri - - - Faster Algorithms for Global Minimum Vertex-Cut in Directed Graphs - https://arxiv.org/abs/2512.24355 - arXiv:2512.24355v1 Announce Type: new -Abstract: We study the directed global minimum vertex-cut problem: given a directed vertex-weighted graph $G$, compute a vertex-cut $(L,S,R)$ in $G$ of minimum value, which is defined to be the total weight of all vertices in $S$. The problem, together with its edge-based variant, is one of the most basic in graph theory and algorithms, and has been studied extensively. The fastest currently known algorithm for directed global minimum vertex-cut (Henzinger, Rao and Gabow, FOCS 1996 and J. Algorithms 2000) has running time $\tilde{O}(mn)$, where $m$ and $n$ denote the number of edges and vertices in the input graph, respectively. A long line of work over the past decades led to faster algorithms for other main versions of the problem, including the undirected edge-based setting (Karger, STOC 1996 and J. ACM 2000), directed edge-based setting (Cen et al., FOCS 2021), and undirected vertex-based setting (Chuzhoy and Trabelsi, STOC 2025). However, for the vertex-based version in directed graphs, the 29 year-old $\tilde{O}(mn)$-time algorithm of Henzinger, Rao and Gabow remains the state of the art to this day, in all edge-density regimes. In this paper we break the $\Theta(mn)$ running time barrier for the first time, by providing a randomized algorithm for directed global minimum vertex-cut, with running time $O\left(mn^{0.976}\cdot\operatorname{polylog} W\right)$ where $W$ is the ratio of largest to smallest vertex weight. Additionally, we provide a randomized $O\left(\min\left\{m^{1+o(1)}\cdot k,n^{2+o(1)}\right\}\right)$-time algorithm for the unweighted version of directed global minimum vertex-cut, where $k$ is the value of the optimal solution. The best previous algorithm for the problem achieved running time $\tilde O\left(\min\left\{k^2 \cdot m, mn^{11/12+o(1)}, n^{2+o(1)}\right\}\right)$ (Forster et al., SODA 2020, Li et al., STOC 2021). - oai:arXiv.org:2512.24355v1 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Julia Chuzhoy, Ron Mosenzon, Ohad Trabelsi - - - Learning Context: A Unified Framework and Roadmap for Context-Aware AI in Education - https://arxiv.org/abs/2512.24362 - arXiv:2512.24362v1 Announce Type: new -Abstract: We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner. This white paper provides a multidisciplinary roadmap for making teaching and learning systems context-aware by encoding cognitive, affective, and sociocultural factors over the short, medium, and long term. To realize this vision, we outline concrete steps to operationalize LC theory into an interoperable computational data structure. By leveraging the Model Context Protocol (MCP), we will enable a wide range of AI tools to "warm-start" with durable context and achieve continual, long-term personalization. Finally, we detail our particular LC implementation strategy through the OpenStax digital learning platform ecosystem and SafeInsights R&D infrastructure. Using OpenStax's national reach, we are embedding the LC into authentic educational settings to support millions of learners. All research and pedagogical interventions are conducted within SafeInsights' privacy-preserving data enclaves, ensuring a privacy-first implementation that maintains high ethical standards while reducing equity gaps nationwide. - oai:arXiv.org:2512.24362v1 - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Naiming Liu, Brittany Bradford, Johaun Hatchett, Gabriel Diaz, Lorenzo Luzi, Zichao Wang, Debshila Basu Mallick, Richard Baraniuk - - - On the Factual Consistency of Text-based Explainable Recommendation Models - https://arxiv.org/abs/2512.24366 - arXiv:2512.24366v1 Announce Type: new -Abstract: Text-based explainable recommendation aims to generate natural-language explanations that justify item recommendations, to improve user trust and system transparency. Although recent advances leverage LLMs to produce fluent outputs, a critical question remains underexplored: are these explanations factually consistent with the available evidence? We introduce a comprehensive framework for evaluating the factual consistency of text-based explainable recommenders. We design a prompting-based pipeline that uses LLMs to extract atomic explanatory statements from reviews, thereby constructing a ground truth that isolates and focuses on their factual content. Applying this pipeline to five categories from the Amazon Reviews dataset, we create augmented benchmarks for fine-grained evaluation of explanation quality. We further propose statement-level alignment metrics that combine LLM- and NLI-based approaches to assess both factual consistency and relevance of generated explanations. Across extensive experiments on six state-of-the-art explainable recommendation models, we uncover a critical gap: while models achieve high semantic similarity scores (BERTScore F1: 0.81-0.90), all our factuality metrics reveal alarmingly low performance (LLM-based statement-level precision: 4.38%-32.88%). These findings underscore the need for factuality-aware evaluation in explainable recommendation and provide a foundation for developing more trustworthy explanation systems. - oai:arXiv.org:2512.24366v1 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ben Kabongo, Vincent Guigue - - - Skim-Aware Contrastive Learning for Efficient Document Representation - https://arxiv.org/abs/2512.24373 - arXiv:2512.24373v1 Announce Type: new -Abstract: Although transformer-based models have shown strong performance in word- and sentence-level tasks, effectively representing long documents, especially in fields like law and medicine, remains difficult. Sparse attention mechanisms can handle longer inputs, but are resource-intensive and often fail to capture full-document context. Hierarchical transformer models offer better efficiency but do not clearly explain how they relate different sections of a document. In contrast, humans often skim texts, focusing on important sections to understand the overall message. Drawing from this human strategy, we introduce a new self-supervised contrastive learning framework that enhances long document representation. Our method randomly masks a section of the document and uses a natural language inference (NLI)-based contrastive objective to align it with relevant parts while distancing it from unrelated ones. This mimics how humans synthesize information, resulting in representations that are both richer and more computationally efficient. Experiments on legal and biomedical texts confirm significant gains in both accuracy and efficiency. - oai:arXiv.org:2512.24373v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Waheed Ahmed Abro, Zied Bouraoui - - - New Insights into Cascaded Geometric Flight Control: From Performance Guarantees to Practical Pitfalls - https://arxiv.org/abs/2512.24377 - arXiv:2512.24377v1 Announce Type: new -Abstract: We present a new stability proof for cascaded geometric control used by aerial vehicles tracking time-varying position trajectories. Our approach uses sliding variables and a recently proposed quaternion-based sliding controller to demonstrate that exponentially convergent position trajectory tracking is theoretically possible. Notably, our analysis reveals new aspects of the control strategy, including how tracking error in the attitude loop influences the position loop, how model uncertainties affect the closed-loop system, and the practical pitfalls of the control architecture. - oai:arXiv.org:2512.24377v1 - eess.SY - cs.RO - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Brett T. Lopez - - - Tubular Riemannian Laplace Approximations for Bayesian Neural Networks - https://arxiv.org/abs/2512.24381 - arXiv:2512.24381v1 Announce Type: new -Abstract: Laplace approximations are among the simplest and most practical methods for approximate Bayesian inference in neural networks, yet their Euclidean formulation struggles with the highly anisotropic, curved loss surfaces and large symmetry groups that characterize modern deep models. Recent work has proposed Riemannian and geometric Gaussian approximations to adapt to this structure. Building on these ideas, we introduce the Tubular Riemannian Laplace (TRL) approximation. TRL explicitly models the posterior as a probabilistic tube that follows a low-loss valley induced by functional symmetries, using a Fisher/Gauss-Newton metric to separate prior-dominated tangential uncertainty from data-dominated transverse uncertainty. We interpret TRL as a scalable reparametrised Gaussian approximation that utilizes implicit curvature estimates to operate in high-dimensional parameter spaces. Our empirical evaluation on ResNet-18 (CIFAR-10 and CIFAR-100) demonstrates that TRL achieves excellent calibration, matching or exceeding the reliability of Deep Ensembles (in terms of ECE) while requiring only a fraction (1/5) of the training cost. TRL effectively bridges the gap between single-model efficiency and ensemble-grade reliability. - oai:arXiv.org:2512.24381v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rodrigo Pereira David - - - Geometric Multi-Session Map Merging with Learned Local Descriptors - https://arxiv.org/abs/2512.24384 - arXiv:2512.24384v1 Announce Type: new -Abstract: Multi-session map merging is crucial for extended autonomous operations in large-scale environments. In this paper, we present GMLD, a learning-based local descriptor framework for large-scale multi-session point cloud map merging that systematically aligns maps collected across different sessions with overlapping regions. The proposed framework employs a keypoint-aware encoder and a plane-based geometric transformer to extract discriminative features for loop closure detection and relative pose estimation. To further improve global consistency, we include inter-session scan matching cost factors in the factor-graph optimization stage. We evaluate our framework on the public datasets, as well as self-collected data from diverse environments. The results show accurate and robust map merging with low error, and the learned features deliver strong performance in both loop closure detection and relative pose estimation. - oai:arXiv.org:2512.24384v1 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yanlong Ma, Nakul S. Joshi, Christa S. Robison, Philip R. Osteen, Brett T. Lopez - - - Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems - https://arxiv.org/abs/2512.24385 - arXiv:2512.24385v1 Announce Type: new -Abstract: The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundation models excel in single-modal contexts, integrating their capabilities across diverse sensors like cameras and LiDAR to create a unified understanding remains a formidable challenge. This paper presents a comprehensive framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal. We dissect the interplay between foundational sensor characteristics and learning strategies, evaluating the role of platform-specific datasets in enabling these advancements. Our central contribution is the formulation of a unified taxonomy for pre-training paradigms: ranging from single-modality baselines to sophisticated unified frameworks that learn holistic representations for advanced tasks like 3D object detection and semantic occupancy prediction. Furthermore, we investigate the integration of textual inputs and occupancy representations to facilitate open-world perception and planning. Finally, we identify critical bottlenecks, such as computational efficiency and model scalability, and propose a roadmap toward general-purpose multi-modal foundation models capable of achieving robust Spatial Intelligence for real-world deployment. - oai:arXiv.org:2512.24385v1 - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Song Wang, Lingdong Kong, Xiaolu Liu, Hao Shi, Wentong Li, Jianke Zhu, Steven C. H. Hoi - - - RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analytics - https://arxiv.org/abs/2512.24386 - arXiv:2512.24386v1 Announce Type: new -Abstract: Live video analytics (LVA) runs continuously across massive camera fleets, but inference cost with modern vision models remains high. To address this, dynamic model size selection (DMSS) is an attractive approach: it is content-aware but treats models as black boxes, and could potentially reduce cost by up to 10x without model retraining or modification. Without ground truth labels at runtime, we observe that DMSS methods use two stages per segment: (i) sampling a few models to calculate prediction statistics (e.g., confidences), then (ii) selection of the model size from those statistics. Prior systems fail to generalize to diverse workloads, particularly to mobile videos and lower accuracy targets. We identify that the failure modes stem from inefficient sampling whose cost exceeds its benefit, and inaccurate per-segment accuracy prediction. - In this work, we present RedunCut, a new DMSS system that addresses both: It uses a measurement-driven planner that estimates the cost-benefit tradeoff of sampling, and a lightweight, data-driven performance model to improve accuracy prediction. Across road-vehicle, drone, and surveillance videos and multiple model families and tasks, RedunCut reduces compute cost by 14-62% at fixed accuracy and remains robust to limited historical data and to drift. - oai:arXiv.org:2512.24386v1 - cs.CV - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Gur-Eyal Sela, Kumar Krishna Agrawal, Bharathan Balaji, Joseph Gonzalez, Ion Stoica - - - FAST-IDS: A Fast Two-Stage Intrusion Detection System with Hybrid Compression for Real-Time Threat Detection in Connected and Autonomous Vehicles - https://arxiv.org/abs/2512.24391 - arXiv:2512.24391v1 Announce Type: new -Abstract: We have implemented a multi-stage IDS for CAVs that can be deployed to resourec-constrained environments after hybrid model compression. - oai:arXiv.org:2512.24391v1 - cs.CR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Devika S, Vishnu Hari, Pratik Narang, Tejasvi Alladi, Vinay Chamola - - - SourceBroken: A large-scale analysis on the (un)reliability of SourceRank in the PyPI ecosystem - https://arxiv.org/abs/2512.24400 - arXiv:2512.24400v1 Announce Type: new -Abstract: SourceRank is a scoring system made of 18 metrics that assess the popularity and quality of open-source packages. Despite being used in several recent studies, none has thoroughly analyzed its reliability against evasion attacks aimed at inflating the score of malicious packages, thereby masquerading them as trustworthy. To fill this gap, we first propose a threat model that identifies potential evasion approaches for each metric, including the URL confusion technique, which can affect 5 out of the 18 metrics by leveraging a URL pointing to a legitimate repository potentially unrelated to the malicious package. - Furthermore, we study the reliability of SourceRank in the PyPI ecosystem by analyzing the SourceRank distributions of benign and malicious packages in the state-of-the-art MalwareBench dataset, as well as in a real-world dataset of 122,398 packages. Our analysis reveals that, while historical data suggests a clear distinction between benign and malicious packages, the real-world distributions overlap significantly, mainly due to SourceRank's failure to timely reflect package removals. As a result, SourceRank cannot be reliably used to discriminate between benign and malicious packages in real-world scenarios, nor to select benign packages among those available on PyPI. - Finally, our analysis reveals that URL confusion represents an emerging attack vector, with its prevalence increasing from 4.2% in MalwareBench to 7.0% in our real-world dataset. Moreover, this technique is often used alongside other evasion techniques and can significantly inflate the SourceRank metrics of malicious packages. - oai:arXiv.org:2512.24400v1 - cs.CR - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1145/3748522.3779992 - Biagio Montaruli, Serena Elisa Ponta, Luca Compagna, Davide Balzarotti - - - Fast and Realistic Automated Scenario Simulations and Reporting for an Autonomous Racing Stack - https://arxiv.org/abs/2512.24402 - arXiv:2512.24402v1 Announce Type: new -Abstract: In this paper, we describe the automated simulation and reporting pipeline implemented for our autonomous racing stack, ur.autopilot. The backbone of the simulation is based on a high-fidelity model of the vehicle interfaced as a Functional Mockup Unit (FMU). The pipeline can execute the software stack and the simulation up to three times faster than real-time, locally or on GitHub for Continuous Integration/- Continuous Delivery (CI/CD). As the most important input of the pipeline, there is a set of running scenarios. Each scenario allows the initialization of the ego vehicle in different initial conditions (position and speed), as well as the initialization of any other configuration of the stack. This functionality is essential to validate efficiently critical modules, like the one responsible for high-speed overtaking maneuvers or localization, which are among the most challenging aspects of autonomous racing. Moreover, we describe how we implemented a fault injection module, capable of introducing sensor delays and perturbations as well as modifying outputs of any node of the stack. Finally, we describe the design of our automated reporting process, aimed at maximizing the effectiveness of the simulation analysis. - oai:arXiv.org:2512.24402v1 - cs.RO - cs.AI - cs.SE - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Giovanni Lambertini, Matteo Pini, Eugenio Mascaro, Francesco Moretti, Ayoub Raji, Marko Bertogna - - - Lifting Vision: Ground to Aerial Localization with Reasoning Guided Planning - https://arxiv.org/abs/2512.24404 - arXiv:2512.24404v1 Announce Type: new -Abstract: Multimodal intelligence development recently show strong progress in visual understanding and high level reasoning. Though, most reasoning system still reply on textual information as the main medium for inference. This limit their effectiveness in spatial tasks such as visual navigation and geo-localization. This work discuss about the potential scope of this field and eventually propose an idea visual reasoning paradigm Geo-Consistent Visual Planning, our introduced framework called Visual Reasoning for Localization, or ViReLoc, which performs planning and localization using only visual representations. The proposed framework learns spatial dependencies and geometric relations that text based reasoning often suffer to understand. By encoding step by step inference in the visual domain and optimizing with reinforcement based objectives, ViReLoc plans routes between two given ground images. The system also integrates contrastive learning and adaptive feature interaction to align cross view perspectives and reduce viewpoint differences. Experiments across diverse navigation and localization scenarios show consistent improvements in spatial reasoning accuracy and cross view retrieval performance. These results establish visual reasoning as a strong complementary approach for navigation and localization, and show that such tasks can be performed without real time global positioning system data, leading to more secure navigation solutions. - oai:arXiv.org:2512.24404v1 - cs.LG - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Soham Pahari, M. Srinivas - - - Sufficient and Necessary Conditions for Eckart-Young-like Result for Tubal Tensors - https://arxiv.org/abs/2512.24405 - arXiv:2512.24405v1 Announce Type: new -Abstract: A valuable feature of the tubal tensor framework is that many familiar constructions from matrix algebra carry over to tensors, including SVD and notions of rank. Most importantly, it has been shown that for a specific family of tubal products, an Eckart-Young type theorem holds, i.e., the best low-rank approximation of a tensor under the Frobenius norm is obtained by truncating its tubal SVD. In this paper, we provide a complete characterization of the family of tubal products that yield an Eckart-Young type result. We demonstrate the practical implications of our theoretical findings by conducting experiments with video data and data-driven dynamical systems. - oai:arXiv.org:2512.24405v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Uria Mor - - - Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models - https://arxiv.org/abs/2512.24407 - arXiv:2512.24407v1 Announce Type: new -Abstract: Inverse reinforcement learning (IRL) and dynamic discrete choice (DDC) models explain sequential decision-making by recovering reward functions that rationalize observed behavior. Flexible IRL methods typically rely on machine learning but provide no guarantees for valid inference, while classical DDC approaches impose restrictive parametric specifications and often require repeated dynamic programming. We develop a semiparametric framework for debiased inverse reinforcement learning that yields statistically efficient inference for a broad class of reward-dependent functionals in maximum entropy IRL and Gumbel-shock DDC models. We show that the log-behavior policy acts as a pseudo-reward that point-identifies policy value differences and, under a simple normalization, the reward itself. We then formalize these targets, including policy values under known and counterfactual softmax policies and functionals of the normalized reward, as smooth functionals of the behavior policy and transition kernel, establish pathwise differentiability, and derive their efficient influence functions. Building on this characterization, we construct automatic debiased machine-learning estimators that allow flexible nonparametric estimation of nuisance components while achieving $\sqrt{n}$-consistency, asymptotic normality, and semiparametric efficiency. Our framework extends classical inference for DDC models to nonparametric rewards and modern machine-learning tools, providing a unified and computationally tractable approach to statistical inference in IRL. - oai:arXiv.org:2512.24407v1 - cs.LG - math.ST - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Lars van der Laan, Aurelien Bibaut, Nathan Kallus - - - DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model - https://arxiv.org/abs/2512.24408 - arXiv:2512.24408v1 Announce Type: new -Abstract: Generating realistic, dyadic talking head video requires ultra-low latency. Existing chunk-based methods require full non-causal context windows, introducing significant delays. This high latency critically prevents the immediate, non-verbal feedback required for a realistic listener. To address this, we present DyStream, a flow matching-based autoregressive model that could generate video in real-time from both speaker and listener audio. Our method contains two key designs: (1) we adopt a stream-friendly autoregressive framework with flow-matching heads for probabilistic modeling, and (2) We propose a causal encoder enhanced by a lookahead module to incorporate short future context (e.g., 60 ms) to improve quality while maintaining low latency. Our analysis shows this simple-and-effective method significantly surpass alternative causal strategies, including distillation and generative encoder. Extensive experiments show that DyStream could generate video within 34 ms per frame, guaranteeing the entire system latency remains under 100 ms. Besides, it achieves state-of-the-art lip-sync quality, with offline and online LipSync Confidence scores of 8.13 and 7.61 on HDTF, respectively. The model, weights and codes are available. - oai:arXiv.org:2512.24408v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bohong Chen, Haiyang Liu - - - Comparing Approaches to Automatic Summarization in Less-Resourced Languages - https://arxiv.org/abs/2512.24410 - arXiv:2512.24410v1 Announce Type: new -Abstract: Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a variety of different approaches to summarization from zero-shot prompting of LLMs large and small to fine-tuning smaller models like mT5 with and without three data augmentation approaches and multilingual transfer. We also explore an LLM translation pipeline approach, translating from the source language to English, summarizing and translating back. Evaluating with five different metrics, we find that there is variation across LLMs in their performance across similar parameter sizes, that our multilingual fine-tuned mT5 baseline outperforms most other approaches including zero-shot LLM performance for most metrics, and that LLM as judge may be less reliable on less-resourced languages. - oai:arXiv.org:2512.24410v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chester Palen-Michel, Constantine Lignos - - - AI-Driven Evaluation of Surgical Skill via Action Recognition - https://arxiv.org/abs/2512.24411 - arXiv:2512.24411v1 Announce Type: new -Abstract: The development of effective training and evaluation strategies is critical. Conventional methods for assessing surgical proficiency typically rely on expert supervision, either through onsite observation or retrospective analysis of recorded procedures. However, these approaches are inherently subjective, susceptible to inter-rater variability, and require substantial time and effort from expert surgeons. These demands are often impractical in low- and middle-income countries, thereby limiting the scalability and consistency of such methods across training programs. To address these limitations, we propose a novel AI-driven framework for the automated assessment of microanastomosis performance. The system integrates a video transformer architecture based on TimeSformer, improved with hierarchical temporal attention and weighted spatial attention mechanisms, to achieve accurate action recognition within surgical videos. Fine-grained motion features are then extracted using a YOLO-based object detection and tracking method, allowing for detailed analysis of instrument kinematics. Performance is evaluated along five aspects of microanastomosis skill, including overall action execution, motion quality during procedure-critical actions, and general instrument handling. Experimental validation using a dataset of 58 expert-annotated videos demonstrates the effectiveness of the system, achieving 87.7% frame-level accuracy in action segmentation that increased to 93.62% with post-processing, and an average classification accuracy of 76% in replicating expert assessments across all skill aspects. These findings highlight the system's potential to provide objective, consistent, and interpretable feedback, thereby enabling more standardized, data-driven training and evaluation in surgical education. - oai:arXiv.org:2512.24411v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yan Meng, Daniel A. Donoho, Marcelle Altshuler, Omar Arnaout - - - Language Model Agents Under Attack: A Cross Model-Benchmark of Profit-Seeking Behaviors in Customer Service - https://arxiv.org/abs/2512.24415 - arXiv:2512.24415v1 Announce Type: new -Abstract: Customer-service LLM agents increasingly make policy-bound decisions (refunds, rebooking, billing disputes), but the same ``helpful'' interaction style can be exploited: a small fraction of users can induce unauthorized concessions, shifting costs to others and eroding trust in agentic workflows. We present a cross-domain benchmark of profit-seeking direct prompt injection in customer-service interactions, spanning 10 service domains and 100 realistic attack scripts grouped into five technique families. Across five widely used models under a unified rubric with uncertainty reporting, attacks are highly domain-dependent (airline support is most exploitable) and technique-dependent (payload splitting is most consistently effective). We release data and evaluation code to support reproducible auditing and to inform the design of oversight and recovery workflows for trustworthy, human centered agent interfaces. - oai:arXiv.org:2512.24415v1 - cs.CR - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jingyu Zhang - - - GateChain: A Blockchain Based Application for Country Entry Exit Registry Management - https://arxiv.org/abs/2512.24416 - arXiv:2512.24416v1 Announce Type: new -Abstract: Recording entry and exit records for a country, with properties such as confidentiality, integrity, and auditability, is increasingly important due to rising international mobility and security requirements. Traditional border control systems, which rely on centralised databases, are vulnerable to data manipulation and have limited interoperability between institutions. This study presents GateChain, a blockchain-based application that addresses these vulnerabilities. GateChain aims to enhance data integrity, reliability, and transparency by recording entry and exit events on a distributed, immutable, and cryptographically verifiable ledger. The application provides real-time access control and verification for authorised institutions. This paper describes the architecture and security components of GateChain and evaluates its performance and security features. - oai:arXiv.org:2512.24416v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Mohamad Akkad, H\"useyin Bodur - - - Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning - https://arxiv.org/abs/2512.24426 - arXiv:2512.24426v1 Announce Type: new -Abstract: Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet these models primarily describe what they perceive and intend to do, rarely questioning whether their planned actions are safe or appropriate. This work introduces Counterfactual VLA (CF-VLA), a self-reflective VLA framework that enables the model to reason about and revise its planned actions before execution. CF-VLA first generates time-segmented meta-actions that summarize driving intent, and then performs counterfactual reasoning conditioned on both the meta-actions and the visual context. This step simulates potential outcomes, identifies unsafe behaviors, and outputs corrected meta-actions that guide the final trajectory generation. To efficiently obtain such self-reflective capabilities, we propose a rollout-filter-label pipeline that mines high-value scenes from a base (non-counterfactual) VLA's rollouts and labels counterfactual reasoning traces for subsequent training rounds. Experiments on large-scale driving datasets show that CF-VLA improves trajectory accuracy by up to 17.6%, enhances safety metrics by 20.5%, and exhibits adaptive thinking: it only enables counterfactual reasoning in challenging scenarios. By transforming reasoning traces from one-shot descriptions to causal self-correction signals, CF-VLA takes a step toward self-reflective autonomous driving agents that learn to think before they act. - oai:arXiv.org:2512.24426v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhenghao "Mark" Peng, Wenhao Ding, Yurong You, Yuxiao Chen, Wenjie Luo, Thomas Tian, Yulong Cao, Apoorva Sharma, Danfei Xu, Boris Ivanovic, Boyi Li, Bolei Zhou, Yan Wang, Marco Pavone - - - Subsecond 3D Mesh Generation for Robot Manipulation - https://arxiv.org/abs/2512.24428 - arXiv:2512.24428v1 Announce Type: new -Abstract: 3D meshes are a fundamental representation widely used in computer science and engineering. In robotics, they are particularly valuable because they capture objects in a form that aligns directly with how robots interact with the physical world, enabling core capabilities such as predicting stable grasps, detecting collisions, and simulating dynamics. Although automatic 3D mesh generation methods have shown promising progress in recent years, potentially offering a path toward real-time robot perception, two critical challenges remain. First, generating high-fidelity meshes is prohibitively slow for real-time use, often requiring tens of seconds per object. Second, mesh generation by itself is insufficient. In robotics, a mesh must be contextually grounded, i.e., correctly segmented from the scene and registered with the proper scale and pose. Additionally, unless these contextual grounding steps remain efficient, they simply introduce new bottlenecks. In this work, we introduce an end-to-end system that addresses these challenges, producing a high-quality, contextually grounded 3D mesh from a single RGB-D image in under one second. Our pipeline integrates open-vocabulary object segmentation, accelerated diffusion-based mesh generation, and robust point cloud registration, each optimized for both speed and accuracy. We demonstrate its effectiveness in a real-world manipulation task, showing that it enables meshes to be used as a practical, on-demand representation for robotics perception and planning. - oai:arXiv.org:2512.24428v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qian Wang, Omar Abdellall, Tony Gao, Xiatao Sun, Daniel Rakita - - - Bayesian Subspace Identification in the MIMO Case - https://arxiv.org/abs/2512.24435 - arXiv:2512.24435v1 Announce Type: new -Abstract: This report investigates the extension of the Bayesian Subspace System Identification method proposed in our previous work to the Multiple-Input Multiple-Output (MIMO) case. We derive new equivariant priors and posterior distributions specifically suited for the MIMO framework. Numerical results utilizing the DAISY dataset are reported to validate the approach. - oai:arXiv.org:2512.24435v1 - eess.SY - cs.SY - stat.AP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Alexandre Rodrigues Mesquita - - - Exploring Compositionality in Vision Transformers using Wavelet Representations - https://arxiv.org/abs/2512.24438 - arXiv:2512.24438v1 Announce Type: new -Abstract: While insights into the workings of the transformer model have largely emerged by analysing their behaviour on language tasks, this work investigates the representations learnt by the Vision Transformer (ViT) encoder through the lens of compositionality. We introduce a framework, analogous to prior work on measuring compositionality in representation learning, to test for compositionality in the ViT encoder. Crucial to drawing this analogy is the Discrete Wavelet Transform (DWT), which is a simple yet effective tool for obtaining input-dependent primitives in the vision setting. By examining the ability of composed representations to reproduce original image representations, we empirically test the extent to which compositionality is respected in the representation space. Our findings show that primitives from a one-level DWT decomposition produce encoder representations that approximately compose in latent space, offering a new perspective on how ViTs structure information. - oai:arXiv.org:2512.24438v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Akshad Shyam Purushottamdas, Pranav K Nayak, Divya Mehul Rajparia, Deekshith Patel, Yashmitha Gogineni, Konda Reddy Mopuri, Sumohana S. Channappayya - - - Sparse classification with positive-confidence data in high dimensions - https://arxiv.org/abs/2512.24443 - arXiv:2512.24443v1 Announce Type: new -Abstract: High-dimensional learning problems, where the number of features exceeds the sample size, often require sparse regularization for effective prediction and variable selection. While established for fully supervised data, these techniques remain underexplored in weak-supervision settings such as Positive-Confidence (Pconf) classification. Pconf learning utilizes only positive samples equipped with confidence scores, thereby avoiding the need for negative data. However, existing Pconf methods are ill-suited for high-dimensional regimes. This paper proposes a novel sparse-penalization framework for high-dimensional Pconf classification. We introduce estimators using convex (Lasso) and non-convex (SCAD, MCP) penalties to address shrinkage bias and improve feature recovery. Theoretically, we establish estimation and prediction error bounds for the L1-regularized Pconf estimator, proving it achieves near minimax-optimal sparse recovery rates under Restricted Strong Convexity condition. To solve the resulting composite objective, we develop an efficient proximal gradient algorithm. Extensive simulations demonstrate that our proposed methods achieve predictive performance and variable selection accuracy comparable to fully supervised approaches, effectively bridging the gap between weak supervision and high-dimensional statistics. - oai:arXiv.org:2512.24443v1 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - The Tien Mai, Mai Anh Nguyen, Trung Nghia Nguyen - - - Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics - https://arxiv.org/abs/2512.24445 - arXiv:2512.24445v1 Announce Type: new -Abstract: Learning systems deployed in nonstationary and safety-critical environments often suffer from instability, slow convergence, or brittle adaptation when learning dynamics evolve over time. While modern optimization, reinforcement learning, and meta-learning methods adapt to gradient statistics, they largely ignore the temporal structure of the error signal itself. This paper proposes a diagnostic-driven adaptive learning framework that explicitly models error evolution through a principled decomposition into bias, capturing persistent drift; noise, capturing stochastic variability; and alignment, capturing repeated directional excitation leading to overshoot. These diagnostics are computed online from lightweight statistics of loss or temporal-difference error trajectories and are independent of model architecture or task domain. We show that the proposed bias-noise-alignment decomposition provides a unifying control backbone for supervised optimization, actor-critic reinforcement learning, and learned optimizers. Building on this framework, we derive diagnostic-driven instantiations including a stabilized supervised optimizer, a diagnostic-regulated actor-critic scheme, and a diagnostic-conditioned learned optimizer. Under standard smoothness assumptions, we establish bounded effective updates and stability properties for all cases. Representative diagnostic illustrations in actor-critic learning highlight how the proposed signals modulate adaptation in response to temporal-difference error structure. Overall, this work elevates error evolution to a first-class object in adaptive learning and provides an interpretable, lightweight foundation for reliable learning in dynamic environments. - oai:arXiv.org:2512.24445v1 - cs.LG - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Akash Samanta, Sheldon Williamson - - - Generative forecasting with joint probability models - https://arxiv.org/abs/2512.24446 - arXiv:2512.24446v1 Announce Type: new -Abstract: Chaotic dynamical systems exhibit strong sensitivity to initial conditions and often contain unresolved multiscale processes, making deterministic forecasting fundamentally limited. Generative models offer an appealing alternative by learning distributions over plausible system evolutions; yet, most existing approaches focus on next-step conditional prediction rather than the structure of the underlying dynamics. In this work, we reframe forecasting as a fully generative problem by learning the joint probability distribution of lagged system states over short temporal windows and obtaining forecasts through marginalization. This new perspective allows the model to capture nonlinear temporal dependencies, represent multistep trajectory segments, and produce next-step predictions consistent with the learned joint distribution. We also introduce a general, model-agnostic training and inference framework for joint generative forecasting and show how it enables assessment of forecast robustness and reliability using three complementary uncertainty quantification metrics (ensemble variance, short-horizon autocorrelation, and cumulative Wasserstein drift), without access to ground truth. We evaluate the performance of the proposed method on two canonical chaotic dynamical systems, the Lorenz-63 system and the Kuramoto-Sivashinsky equation, and show that joint generative models yield improved short-term predictive skill, preserve attractor geometry, and achieve substantially more accurate long-range statistical behaviour than conventional conditional next-step models. - oai:arXiv.org:2512.24446v1 - cs.LG - physics.comp-ph - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Patrick Wyrod, Ashesh Chattopadhyay, Daniele Venturi - - - PackKV: Reducing KV Cache Memory Footprint through LLM-Aware Lossy Compression - https://arxiv.org/abs/2512.24449 - arXiv:2512.24449v1 Announce Type: new -Abstract: Transformer-based large language models (LLMs) have demonstrated remarkable potential across a wide range of practical applications. However, long-context inference remains a significant challenge due to the substantial memory requirements of the key-value (KV) cache, which can scale to several gigabytes as sequence length and batch size increase. In this paper, we present \textbf{PackKV}, a generic and efficient KV cache management framework optimized for long-context generation. %, which synergistically supports both latency-critical and throughput-critical inference scenarios. PackKV introduces novel lossy compression techniques specifically tailored to the characteristics of KV cache data, featuring a careful co-design of compression algorithms and system architecture. Our approach is compatible with the dynamically growing nature of the KV cache while preserving high computational efficiency. Experimental results show that, under the same and minimum accuracy drop as state-of-the-art quantization methods, PackKV achieves, on average, \textbf{153.2}\% higher memory reduction rate for the K cache and \textbf{179.6}\% for the V cache. Furthermore, PackKV delivers extremely high execution throughput, effectively eliminating decompression overhead and accelerating the matrix-vector multiplication operation. Specifically, PackKV achieves an average throughput improvement of \textbf{75.7}\% for K and \textbf{171.7}\% for V across A100 and RTX Pro 6000 GPUs, compared to cuBLAS matrix-vector multiplication kernels, while demanding less GPU memory bandwidth. Code available on https://github.com/BoJiang03/PackKV - oai:arXiv.org:2512.24449v1 - cs.DC - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bo Jiang, Taolue Yang, Youyuan Liu, Xubin He, Sheng Di, Sian Jin - - - Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations - https://arxiv.org/abs/2512.24452 - arXiv:2512.24452v1 Announce Type: new -Abstract: Semantic communications conveys task-relevant meaning rather than focusing solely on message reconstruction, improving bandwidth efficiency and robustness for next-generation wireless systems. However, learned semantic representations can still leak sensitive information to unintended receivers (eavesdroppers). This paper presents a deep learning-based semantic communication framework that jointly supports multiple receiver tasks while explicitly limiting semantic leakage to an eavesdropper. The legitimate link employs a learned encoder at the transmitter, while the receiver trains decoders for semantic inference and data reconstruction. The security problem is formulated via an iterative min-max optimization in which an eavesdropper is trained to improve its semantic inference, while the legitimate transmitter-receiver pair is trained to preserve task performance while reducing the eavesdropper's success. We also introduce an auxiliary layer that superimposes a cooperative, adversarially crafted perturbation on the transmitted waveform to degrade semantic leakage to an eavesdropper. Performance is evaluated over Rayleigh fading channels with additive white Gaussian noise using MNIST and CIFAR-10 datasets. Semantic accuracy and reconstruction quality improve with increasing latent dimension, while the min-max mechanism reduces the eavesdropper's inference performance significantly without degrading the legitimate receiver. The perturbation layer is successful in reducing semantic leakage even when the legitimate link is trained only for its own task. This comprehensive framework motivates semantic communication designs with tunable, end-to-end privacy against adaptive adversaries in realistic wireless settings. - oai:arXiv.org:2512.24452v1 - cs.NI - cs.AI - cs.CR - cs.IT - cs.LG - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus - - - Multipliers for forced Lurye systems with slope-restricted nonlinearities - https://arxiv.org/abs/2512.24453 - arXiv:2512.24453v1 Announce Type: new -Abstract: Dynamic multipliers can be used to guarantee the stability of Lurye systems with slope-restricted nonlinearities, but give no guarantee that the closed-loop system has finite incremental gain. We show that multipliers guarantee the closed-loop power gain to be bounded and quantifiable. Power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee such Lurye systems have low sensitivity to noise, provided other exogenous signals have constant steady state. For periodic excitation, the closed-loop response can apparently have a subharmonic or chaotic response. We revisit a class of multipliers that can guarantee a unique, attractive and period-preserving solution. We show the multipliers can be derived using classical tools and reconsider assumptions required for their application. Their phase limitations are inherited from those of discrete-time multipliers. The multipliers cannot be used at all frequencies unless the circle criterion can also be applied; this is consistent with known results about dynamic multipliers and incremental stability. - oai:arXiv.org:2512.24453v1 - eess.SY - cs.SY - math.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - William Paul Heath, Sayar Das, Joaquin Carrasco - - - Fast high-order spectral solvers for PDEs on triangulated surfaces with applications to deforming surfaces - https://arxiv.org/abs/2512.24456 - arXiv:2512.24456v1 Announce Type: new -Abstract: In this paper, we extend the classical quadrilateral based hierarchical Poincar\'e-Steklov (HPS) framework to triangulated geometries. Traditionally, the HPS method takes as input an unstructured, high-order quadrilateral mesh and relies on tensor-product spectral discretizations on each element. To overcome this restriction, we introduce two complementary high-order strategies for triangular elements: a reduced quadrilateralization approach which is straightforward to implement, and triangle based spectral element method based on Dubiner polynomials. We show numerically that these extensions preserve the spectral accuracy, efficiency, and fast direct-solver structure of the HPS framework. The method is further extended to time dependent and evolving surfaces, and its performance is demonstrated through numerical experiments on reaction-diffusion systems, and geometry driven surface evolution. - oai:arXiv.org:2512.24456v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Gentian Zavalani - - - Document Data Matching for Blockchain-Supported Real Estate - https://arxiv.org/abs/2512.24457 - arXiv:2512.24457v1 Announce Type: new -Abstract: The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes. - oai:arXiv.org:2512.24457v1 - cs.CR - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Henrique Lin, Tiago Dias, Miguel Correia - - - Cleaning English Abstracts of Scientific Publications - https://arxiv.org/abs/2512.24459 - arXiv:2512.24459v1 Announce Type: new -Abstract: Scientific abstracts are often used as proxies for the content and thematic focus of research publications. However, a significant share of published abstracts contains extraneous information-such as publisher copyright statements, section headings, author notes, registrations, and bibliometric or bibliographic metadata-that can distort downstream analyses, particularly those involving document similarity or textual embeddings. We introduce an open-source, easy-to-integrate language model designed to clean English-language scientific abstracts by automatically identifying and removing such clutter. We demonstrate that our model is both conservative and precise, alters similarity rankings of cleaned abstracts and improves information content of standard-length embeddings. - oai:arXiv.org:2512.24459v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Michael E. Rose, Nils A. Herrmann, Sebastian Erhardt - - - IELTS Writing Revision Platform with Automated Essay Scoring and Adaptive Feedback - https://arxiv.org/abs/2512.24460 - arXiv:2512.24460v1 Announce Type: new -Abstract: This paper presents the design, development, and evaluation of a proposed revision platform assisting candidates for the International English Language Testing System (IELTS) writing exam. Traditional IELTS preparation methods lack personalised feedback, catered to the IELTS writing rubric. To address these shortcomings, the platform features an attractive user interface (UI), an Automated Essay Scoring system (AES), and targeted feedback tailored to candidates and the IELTS writing rubric. The platform architecture separates conversational guidance from a dedicated writing interface to reduce cognitive load and simulate exam conditions. Through iterative, Design-Based Research (DBR) cycles, the study progressed from rule-based to transformer-based with a regression head scoring, mounted with adaptive feedback. - Early cycles (2-3) revealed fundamental limitations of rule-based approaches: mid-band compression, low accuracy, and negative $R^2$ values. DBR Cycle 4 implemented a DistilBERT transformer model with a regression head, yielding substantial improvements with MAE of 0.66 and positive $R^2$. This enabled Cycle 5's adaptive feedback implementation, which demonstrated statistically significant score improvements (mean +0.060 bands, p = 0.011, Cohen's d = 0.504), though effectiveness varied by revision strategy. Findings suggest automated feedback functions are most suited as a supplement to human instruction, with conservative surface-level corrections proving more reliable than aggressive structural interventions for IELTS preparation contexts. Challenges remain in assessing higher-band essays, and future work should incorporate longitudinal studies with real IELTS candidates and validation from official examiners. - oai:arXiv.org:2512.24460v1 - cs.CL - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Titas Ramancauskas, Kotryna Ramancauske - - - Align While Search: Belief-Guided Exploratory Inference for World-Grounded Embodied Agents - https://arxiv.org/abs/2512.24461 - arXiv:2512.24461v1 Announce Type: new -Abstract: In this paper, we propose a test-time adaptive agent that performs exploratory inference through posterior-guided belief refinement without relying on gradient-based updates or additional training for LLM agent operating under partial observability. Our agent maintains an external structured belief over the environment state, iteratively updates it via action-conditioned observations, and selects actions by maximizing predicted information gain over the belief space. We estimate information gain using a lightweight LLM-based surrogate and assess world alignment through a novel reward that quantifies the consistency between posterior belief and ground-truth environment configuration. Experiments show that our method outperforms inference-time scaling baselines such as prompt-augmented or retrieval-enhanced LLMs, in aligning with latent world states with significantly lower integration overhead. - oai:arXiv.org:2512.24461v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Seohui Bae, Jeonghye Kim, Youngchul Sung, Woohyung Lim - - - "Game Changer" or "Overenthusiastic Drunk Acquaintance"? Generative AI Use by Blind and Low Vision Software Professionals in the Workplace - https://arxiv.org/abs/2512.24462 - arXiv:2512.24462v1 Announce Type: new -Abstract: The software development workplace poses numerous technical and collaborative accessibility challenges for blind and low vision software professionals (BLVSPs). Though Generative AI (GenAI) is increasingly adopted within the software development industry and has been a rapidly growing topic of interest in research, to date, the unique perspectives of BLVSPs have yet to be consulted. We report on a qualitative study involving 39 semi-structured interviews with BLVSPs about what the introduction of GenAI has meant for their work. We found that BLVSPs used GenAI for many software development tasks, resulting in benefits such as increased productivity and accessibility. However, significant costs were also accompanied by GenAI use as they were more vulnerable to hallucinations than their sighted colleagues. Sometimes, organizational policies prevented use. Based on our findings, we discuss the higher-risks and higher-returns that BLVSPs had to carefully weigh when deciding whether and when to use GenAI tools for work. - oai:arXiv.org:2512.24462v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1145/3744916.3773172 - Yoonha Cha, Victoria Jackson, Lauren Shu, Stacy Branham, Andr\'e van der Hoek - - - Spectral and Spatial Graph Learning for Multispectral Solar Image Compression - https://arxiv.org/abs/2512.24463 - arXiv:2512.24463v1 Announce Type: new -Abstract: High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at https://github.com/agyat4/sgraph . - oai:arXiv.org:2512.24463v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Prasiddha Siwakoti, Atefeh Khoshkhahtinat, Piyush M. Mehta, Barbara J. Thompson, Michael S. F. Kirk, Daniel da Silva - - - On the Difficulty of Measuring Divisiveness of Proposals under Ranked Preferences - https://arxiv.org/abs/2512.24467 - arXiv:2512.24467v1 Announce Type: new -Abstract: Given the stated preferences of several people over a number of proposals regarding public policy initiatives, some of those proposals might be judged to be more ``divisive'' than others. When designing online participatory platforms to support digital democracy initiatives enabling citizens to deliberate over such proposals, we might wish to equip those platforms with the functionality to retrieve the most divisive proposals currently under discussion. Such a service would be useful for analysing the progress of deliberation and steering discussion towards issues that still require further debate. Guided by this use case, we explore possibilities for providing a clear definition of what it means to select a set of most divisive proposals on the basis of people's stated preferences over proposals. Then, employing the axiomatic method familiar from social choice theory, we show that the task of selecting the most divisive proposals in a manner that satisfies certain seemingly mild normative requirements faces a number of fundamental difficulties. - oai:arXiv.org:2512.24467v1 - cs.GT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ulle Endriss - - - Infinite families of graphs and stable completion of arbitrary matrices, Part I - https://arxiv.org/abs/2512.24468 - arXiv:2512.24468v1 Announce Type: new -Abstract: We study deterministic constructions of graphs for which the unique completion of low rank matrices is generically possible regardless of the values of the entries. We relate the completability to the presence of some patterns (particular unions of self-avoiding walks) in the subgraph of the lattice graph generated from the support of the bi-adjacency matrix. The construction makes it possible to design infinite families of graphs on which exact and stable completion is possible for every fixed rank matrix through the sum-of-squares hierarchy. - oai:arXiv.org:2512.24468v1 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Augustin Cosse - - - Foundation models on the bridge: Semantic hazard detection and safety maneuvers for maritime autonomy with vision-language models - https://arxiv.org/abs/2512.24470 - arXiv:2512.24470v1 Announce Type: new -Abstract: The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that notifies the operator, permit immediate human override, and avoid changing the voyage plan without approval. Meeting these obligations in the alert-to-takeover gap calls for a short-horizon, human-overridable fallback maneuver. Classical maritime autonomy stacks struggle when the correct action depends on meaning (e.g., diver-down flag means people in the water, fire close by means hazard). We argue (i) that vision-language models (VLMs) provide semantic awareness for such out-of-distribution situations, and (ii) that a fast-slow anomaly pipeline with a short-horizon, human-overridable fallback maneuver makes this practical in the handover window. We introduce Semantic Lookout, a camera-only, candidate-constrained vision-language model (VLM) fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority. On 40 harbor scenes we measure per-call scene understanding and latency, alignment with human consensus (model majority-of-three voting), short-horizon risk-relief on fire hazard scenes, and an on-water alert->fallback maneuver->operator handover. Sub-10 s models retain most of the awareness of slower state-of-the-art models. The fallback maneuver selector outperforms geometry-only baselines and increases standoff distance on fire scenes. A field run verifies end-to-end operation. These results support VLMs as semantic fallback maneuver selectors compatible with the draft IMO MASS Code, within practical latency budgets, and motivate future work on domain-adapted, hybrid autonomy that pairs foundation-model semantics with multi-sensor bird's-eye-view perception and short-horizon replanning. - oai:arXiv.org:2512.24470v1 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kim Alexander Christensen, Andreas Gudahl Tufte, Alexey Gusev, Rohan Sinha, Milan Ganai, Ole Andreas Alsos, Marco Pavoned, Martin Steinert - - - F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model - https://arxiv.org/abs/2512.24473 - arXiv:2512.24473v1 Announce Type: new -Abstract: With the advent of Generative AI, Single Image Super-Resolution (SISR) quality has seen substantial improvement, as the strong priors learned by Text-2-Image Diffusion (T2IDiff) Foundation Models (FM) can bridge the gap between High-Resolution (HR) and Low-Resolution (LR) images. However, flagship smartphone cameras have been slow to adopt generative models because strong generation can lead to undesirable hallucinations. For substantially degraded LR images, as seen in academia, strong generation is required and hallucinations are more tolerable because of the wide gap between LR and HR images. In contrast, in consumer photography, the LR image has substantially higher fidelity, requiring only minimal hallucination-free generation. We hypothesize that generation in SISR is controlled by the stringency and richness of the FM's conditioning feature. First, text features are high level features, which often cannot describe subtle textures in an image. Additionally, Smartphone LR images are at least $12MP$, whereas SISR networks built on T2IDiff FM are designed to perform inference on much smaller images ($<1MP$). As a result, SISR inference has to be performed on small patches, which often cannot be accurately described by text feature. To address these shortcomings, we introduce an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM). Lower level features provide stricter conditioning while being rich descriptors of even small patches. - oai:arXiv.org:2512.24473v1 - cs.CV - cs.AI - eess.IV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Devendra K. Jangid, Ripon K. Saha, Dilshan Godaliyadda, Jing Li, Seok-Jun Lee, Hamid R. Sheikh - - - HOLOGRAPH: Active Causal Discovery via Sheaf-Theoretic Alignment of Large Language Model Priors - https://arxiv.org/abs/2512.24478 - arXiv:2512.24478v1 Announce Type: new -Abstract: Causal discovery from observational data remains fundamentally limited by identifiability constraints. Recent work has explored leveraging Large Language Models (LLMs) as sources of prior causal knowledge, but existing approaches rely on heuristic integration that lacks theoretical grounding. We introduce HOLOGRAPH, a framework that formalizes LLM-guided causal discovery through sheaf theory--representing local causal beliefs as sections of a presheaf over variable subsets. Our key insight is that coherent global causal structure corresponds to the existence of a global section, while topological obstructions manifest as non-vanishing sheaf cohomology. We propose the Algebraic Latent Projection to handle hidden confounders and Natural Gradient Descent on the belief manifold for principled optimization. Experiments on synthetic and real-world benchmarks demonstrate that HOLOGRAPH provides rigorous mathematical foundations while achieving competitive performance on causal discovery tasks with 50-100 variables. Our sheaf-theoretic analysis reveals that while Identity, Transitivity, and Gluing axioms are satisfied to numerical precision (<10^{-6}), the Locality axiom fails for larger graphs, suggesting fundamental non-local coupling in latent variable projections. Code is available at [https://github.com/hyunjun1121/holograph](https://github.com/hyunjun1121/holograph). - oai:arXiv.org:2512.24478v1 - cs.LG - cs.AI - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Hyunjun Kim - - - Design of Linear Residual Generators for Combined Fault Detection and Estimation in Nonlinear Systems - https://arxiv.org/abs/2512.24484 - arXiv:2512.24484v1 Announce Type: new -Abstract: A systematic method for the design of linear residual generators for combined fault detection and estimation in nonlinear systems is developed. The proposed residual generator is a linear functional observer built for an extended system that incorporates the fault dynamics from a linear exo-system, and in addition possesses disturbance-decoupling properties. Necessary and sufficient conditions for the existence of such residual generators for nonlinear systems are derived. As long as these conditions are satisfied, we obtain explicit design formulas for the residual generator. The results are illustrated through a chemical reactor case study, which demonstrates the effectiveness of the proposed methodology. - oai:arXiv.org:2512.24484v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sunjeev Venkateswaran, Costas Kravaris - - - Networked Markets, Fragmented Data: Adaptive Graph Learning for Customer Risk Analytics and Policy Design - https://arxiv.org/abs/2512.24487 - arXiv:2512.24487v1 Announce Type: new -Abstract: Financial institutions face escalating challenges in identifying high-risk customer behaviors within massive transaction networks, where fraudulent activities exploit market fragmentation and institutional boundaries. We address three fundamental problems in customer risk analytics: data silos preventing holistic relationship assessment, extreme behavioral class imbalance, and suboptimal customer intervention strategies that fail to balance compliance costs with relationship value. We develop an integrated customer intelligence framework combining federated learning, relational network analysis, and adaptive targeting policies. Our federated graph neural network enables collaborative behavior modeling across competing institutions without compromising proprietary customer data, using privacy-preserving embeddings to capture cross-market relational patterns. We introduce cross-bank Personalized PageRank to identify coordinated behavioral clusters providing interpretable customer network segmentation for risk managers. A hierarchical reinforcement learning mechanism optimizes dynamic intervention targeting, calibrating escalation policies to maximize prevention value while minimizing customer friction and operational costs. Analyzing 1.4 million customer transactions across seven markets, our approach reduces false positive and false negative rates to 4.64% and 11.07%, substantially outperforming single-institution models. The framework prevents 79.25% of potential losses versus 49.41% under fixed-rule policies, with optimal market-specific targeting thresholds reflecting heterogeneous customer base characteristics. These findings demonstrate that federated customer analytics materially improve both risk management effectiveness and customer relationship outcomes in networked competitive markets. - oai:arXiv.org:2512.24487v1 - cs.CE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Lecheng Zheng, Jian Ni, Chris Zobel, John R Birge - - - Energy-Aware Bayesian Control Barrier Functions for Physics-Informed Gaussian Process Dynamics - https://arxiv.org/abs/2512.24493 - arXiv:2512.24493v1 Announce Type: new -Abstract: We study safe control for dynamical systems whose continuous-time dynamics are learned with Gaussian processes (GPs), focusing on mechanical and port-Hamiltonian systems where safety is naturally expressed via energy constraints. The availability of a GP Hamiltonian posterior naturally raises the question of how to systematically exploit this structure to design an energy-aware control barrier function with high-probability safety guarantees. We address this problem by developing a Bayesian-CBF framework and instantiating it with energy-aware Bayesian-CBFs (EB-CBFs) that construct conservative energy-based barriers directly from the Hamiltonian and vector-field posteriors, yielding safety filters that minimally modify a nominal controller while providing probabilistic energy safety guarantees. Numerical simulations on a mass-spring system demonstrate that the proposed EB-CBFs achieve high-probability safety under noisy sampled GP-learned dynamics. - oai:arXiv.org:2512.24493v1 - eess.SY - cs.RO - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chi Ho Leung, Philip E. Par\'e - - - What Drives Success in Physical Planning with Joint-Embedding Predictive World Models? - https://arxiv.org/abs/2512.24497 - arXiv:2512.24497v1 Announce Type: new -Abstract: A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent approach involves training a world model from state-action trajectories and subsequently use it with a planning algorithm to solve new tasks. Planning is commonly performed in the input space, but a recent family of methods has introduced planning algorithms that optimize in the learned representation space of the world model, with the promise that abstracting irrelevant details yields more efficient planning. In this work, we characterize models from this family as JEPA-WMs and investigate the technical choices that make algorithms from this class work. We propose a comprehensive study of several key components with the objective of finding the optimal approach within the family. We conducted experiments using both simulated environments and real-world robotic data, and studied how the model architecture, the training objective, and the planning algorithm affect planning success. We combine our findings to propose a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks. Code, data and checkpoints are available at https://github.com/facebookresearch/jepa-wms. - oai:arXiv.org:2512.24497v1 - cs.AI - cs.LG - cs.RO - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Basile Terver, Tsung-Yen Yang, Jean Ponce, Adrien Bardes, Yann LeCun - - - Open Horn Type Theory - https://arxiv.org/abs/2512.24498 - arXiv:2512.24498v1 Announce Type: new -Abstract: We introduce Open Horn Type Theory (OHTT), an extension of dependent type theory with two primitive judgment forms: coherence and gap, subject to a mutual exclusion law. Unlike classical or intuitionistic negation, gap is not defined via implication but is a primitive witness of non-coherence. Judgments may also be open -- neither coherent nor gapped -- yielding a trichotomy that generalizes the binary derivable/underivable distinction. The central construction is the transport horn: a configuration where a term and a path both cohere, but transport along the path is witnessed as gapped. This captures obstructions that Homotopy Type Theory (HoTT) cannot express, since HoTT's Kan condition guarantees all transport succeeds. We develop the semantics via ruptured simplicial sets -- simplicial sets equipped with coherence and gap structure -- and ruptured Kan complexes, which model types where some horns fill, some are gap-witnessed, and some remain open. We show that HoTT embeds as the coherent fragment of OHTT, recovered by imposing totality. Three classes of obstructions are developed in detail: topological (monodromy, holonomy, characteristic classes), semantic (polysemy, meaning fibrations), and logical (resource-sensitive derivability, substructural failure). In each case, the gap witness is positive structure -- not absence of proof, but certified obstruction. - oai:arXiv.org:2512.24498v1 - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Iman Poernomo - - - Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways - https://arxiv.org/abs/2512.24499 - arXiv:2512.24499v1 Announce Type: new -Abstract: The rapid expansion of generative AI has normalized large-scale synthetic media creation, enabling new forms of covert communication. Recent generative steganography methods, particularly those based on diffusion models, can embed high-capacity payloads without fine-tuning or auxiliary decoders, creating significant challenges for detection and remediation. Coverless diffusion-based techniques are difficult to counter because they generate image carriers directly from secret data, enabling attackers to deliver stegomalware for command-and-control, payload staging, and data exfiltration while bypassing detectors that rely on cover-stego discrepancies. This work introduces Adversarial Diffusion Sanitization (ADS), a training-free defense for security gateways that neutralizes hidden payloads rather than detecting them. ADS employs an off-the-shelf pretrained denoiser as a differentiable proxy for diffusion-based decoders and incorporates a color-aware, quaternion-coupled update rule to reduce artifacts under strict distortion limits. Under a practical threat model and in evaluation against the state-of-the-art diffusion steganography method Pulsar, ADS drives decoder success rates to near zero with minimal perceptual impact. Results demonstrate that ADS provides a favorable security-utility trade-off compared to standard content transformations, offering an effective mitigation strategy against diffusion-driven steganography. - oai:arXiv.org:2512.24499v1 - cs.CR - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Vladimir Frants, Sos Agaian - - - Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice - https://arxiv.org/abs/2512.24503 - arXiv:2512.24503v1 Announce Type: new -Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scale training runs. However, the community has a limited understanding of whether and when conclusions drawn from small-scale experiments reliably transfer to full-scale model training. In this work, we uncover a subtle yet critical issue in the standard experimental protocol for data recipe assessment: the use of identical small-scale model training configurations across all data recipes in the name of "fair" comparison. We show that the experiment conclusions about data quality can flip with even minor adjustments to training hyperparameters, as the optimal training configuration is inherently data-dependent. Moreover, this fixed-configuration protocol diverges from full-scale model development pipelines, where hyperparameter optimization is a standard step. Consequently, we posit that the objective of data recipe assessment should be to identify the recipe that yields the best performance under data-specific tuning. To mitigate the high cost of hyperparameter tuning, we introduce a simple patch to the evaluation protocol: using reduced learning rates for proxy model training. We show that this approach yields relative performance that strongly correlates with that of fully tuned large-scale LLM pretraining runs. Theoretically, we prove that for random-feature models, this approach preserves the ordering of datasets according to their optimal achievable loss. Empirically, we validate this approach across 23 data recipes covering four critical dimensions of data curation, demonstrating dramatic improvements in the reliability of small-scale experiments. - oai:arXiv.org:2512.24503v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jiachen T. Wang, Tong Wu, Kaifeng Lyu, James Zou, Dawn Song, Ruoxi Jia, Prateek Mittal - - - Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environments - https://arxiv.org/abs/2512.24504 - arXiv:2512.24504v1 Announce Type: new -Abstract: Map environments provide a fundamental medium for representing spatial structure. Understanding how foundation model (FM) agents understand and act in such environments is therefore critical for enabling reliable map-based reasoning and applications. However, most existing evaluations of spatial ability in FMs rely on static map inputs or text-based queries, overlooking the interactive and experience-driven nature of spatial understanding.In this paper, we propose an interactive evaluation framework to analyze how FM agents explore, remember, and reason in symbolic map environments. Agents incrementally explore partially observable grid-based maps consisting of roads, intersections, and points of interest (POIs), receiving only local observations at each step. Spatial understanding is then evaluated using six kinds of spatial tasks. By systematically varying exploration strategies, memory representations, and reasoning schemes across multiple foundation models, we reveal distinct functional roles of these components. Exploration primarily affects experience acquisition but has a limited impact on final reasoning accuracy. In contrast, memory representation plays a central role in consolidating spatial experience, with structured memories particularly sequential and graph-based representations, substantially improving performance on structure-intensive tasks such as path planning. Reasoning schemes further shape how stored spatial knowledge is used, with advanced prompts supporting more effective multi-step inference. We further observe that spatial reasoning performance saturates across model versions and scales beyond a certain capability threshold, indicating that improvements in map-based spatial understanding require mechanisms tailored to spatial representation and reasoning rather than scaling alone. - oai:arXiv.org:2512.24504v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhiwei Wei, Yuxing Liu, Hua Liao, Wenjia Xu - - - Evaluating the Reasoning Abilities of LLMs on Underrepresented Mathematics Competition Problems - https://arxiv.org/abs/2512.24505 - arXiv:2512.24505v1 Announce Type: new -Abstract: Understanding the limitations of Large Language Models, or LLMs, in mathematical reasoning has been the focus of several recent studies. However, the majority of these studies use the same datasets for benchmarking, which limits the generalizability of their findings and may not fully capture the diverse challenges present in mathematical tasks. The purpose of the present study is to analyze the performance of LLMs on underrepresented mathematics competition problems. We prompted three leading LLMs, namely GPT-4o-mini, Gemini-2.0-Flash, and DeepSeek-V3, with the Missouri Collegiate Mathematics Competition problems in the areas of Calculus, Analytic Geometry, and Discrete Mathematics. The LLMs responses were then compared to the known correct solutions in order to determine the accuracy of the LLM for each problem domain. We also analyzed the LLMs reasoning to explore patterns in errors across problem types and models. DeepSeek-V3 has the best performance in all three categories of Calculus, Analytic Geometry, and Discrete Mathematics, both in reasoning and correct final answers. All three LLMs exhibited notably weak performance in Geometry. The majority of errors made by DeepSeek-V3 were attributed to computational and logical mistakes, whereas GPT-4o-mini frequently exhibited logical and approach-related errors. Gemini, on the other hand, tended to struggle with incomplete reasoning and drawing rushed conclusions. In conclusion, evaluating LLMs on underrepresented mathematics competition datasets can provide deeper insights into their distinct error patterns and highlight ongoing challenges in structured reasoning, particularly within the domain of Geometry. - oai:arXiv.org:2512.24505v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Samuel Golladay, Majid Bani-Yaghoub - - - Generalising E-prop to Deep Networks - https://arxiv.org/abs/2512.24506 - arXiv:2512.24506v1 Announce Type: new -Abstract: Recurrent networks are typically trained with backpropagation through time (BPTT). However, BPTT requires storing the history of all states in the network and then replaying them sequentially backwards in time. This computation appears extremely implausible for the brain to implement. Real Time Recurrent Learning (RTRL) proposes an mathematically equivalent alternative where gradient information is propagated forwards in time locally alongside the regular forward pass, however it has significantly greater computational complexity than BPTT which renders it impractical for large networks. E-prop proposes an approximation of RTRL which reduces its complexity to the level of BPTT while maintaining a purely online forward update which can be implemented by an eligibility trace at each synapse. However, works on RTRL and E-prop ubiquitously investigate learning in a single layer with recurrent dynamics. However, learning in the brain spans multiple layers and consists of both hierarchal dynamics in depth as well as time. In this mathematical note, we extend the E-prop framework to handle arbitrarily deep networks, deriving a novel recursion relationship across depth which extends the eligibility traces of E-prop to deeper layers. Our results thus demonstrate an online learning algorithm can perform accurate credit assignment across both time and depth simultaneously, allowing the training of deep recurrent networks without backpropagation through time. - oai:arXiv.org:2512.24506v1 - cs.LG - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Beren Millidge - - - Understanding LLM Checkpoint/Restore I/O Strategies and Patterns - https://arxiv.org/abs/2512.24511 - arXiv:2512.24511v1 Announce Type: new -Abstract: As LLMs and foundation models scale, checkpoint/restore has become a critical pattern for training and inference. With 3D parallelism (tensor, pipeline, data), checkpointing involves many processes, each managing numerous tensors of varying shapes and sizes, that must be persisted frequently to stable storage (e.g., parallel file systems). This turns checkpoint/restore into a big-data I/O problem characterized by volume, variety, and velocity. The workflow must traverse the full storage stack -- from GPU memory through host memory and local storage to external repositories -- whose tiers differ by orders of magnitude in performance, creating bottlenecks under concurrency even with asynchronous flush/prefetch. Kernel-accelerated I/O libraries such as \texttt{liburing} may mitigate these issues versus POSIX, but their effectiveness for LLM checkpointing remains underexplored. We develop microbenchmarks to quantify trade-offs when using \texttt{liburing}, evaluating how aggregation, alignment, and I/O coalescing interact under buffered and direct I/O. We find that uncoalesced small-buffer operations halve throughput relative to synthetic workloads, while file system-aware aggregation restores bandwidth and reduces metadata overhead. Compared to state-of-the-art LLM checkpointing engines, our approach achieves up to $3.9\times$ higher write throughput than DataStates-LLM and $7.6\times$ higher than TorchSnapshot. These results highlight the need for aggregation and coalescing strategies that align with modern file systems and I/O backends. - oai:arXiv.org:2512.24511v1 - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1145/3784828.3784830 - Mikaila J. Gossman, Avinash Maurya, Bogdan Nicolae, Jon C. Calhoun - - - From Static to Dynamic: Evaluating the Perceptual Impact of Dynamic Elements in Urban Scenes Using Generative Inpainting - https://arxiv.org/abs/2512.24513 - arXiv:2512.24513v1 Announce Type: new -Abstract: Understanding urban perception from street view imagery has become a central topic in urban analytics and human centered urban design. However, most existing studies treat urban scenes as static and largely ignore the role of dynamic elements such as pedestrians and vehicles, raising concerns about potential bias in perception based urban analysis. To address this issue, we propose a controlled framework that isolates the perceptual effects of dynamic elements by constructing paired street view images with and without pedestrians and vehicles using semantic segmentation and MLLM guided generative inpainting. Based on 720 paired images from Dongguan, China, a perception experiment was conducted in which participants evaluated original and edited scenes across six perceptual dimensions. The results indicate that removing dynamic elements leads to a consistent 30.97% decrease in perceived vibrancy, whereas changes in other dimensions are more moderate and heterogeneous. To further explore the underlying mechanisms, we trained 11 machine learning models using multimodal visual features and identified that lighting conditions, human presence, and depth variation were key factors driving perceptual change. At the individual level, 65% of participants exhibited significant vibrancy changes, compared with 35-50% for other dimensions; gender further showed a marginal moderating effect on safety perception. Beyond controlled experiments, the trained model was extended to a city-scale dataset to predict vibrancy changes after the removal of dynamic elements. The city level results reveal that such perceptual changes are widespread and spatially structured, affecting 73.7% of locations and 32.1% of images, suggesting that urban perception assessments based solely on static imagery may substantially underestimate urban liveliness. - oai:arXiv.org:2512.24513v1 - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhiwei Wei, Mengzi Zhang, Boyan Lu, Zhitao Deng, Nai Yang, Hua Liao - - - Paragraph Segmentation Revisited: Towards a Standard Task for Structuring Speech - https://arxiv.org/abs/2512.24517 - arXiv:2512.24517v1 Announce Type: new -Abstract: Automatic speech transcripts are often delivered as unstructured word streams that impede readability and repurposing. We recast paragraph segmentation as the missing structuring step and fill three gaps at the intersection of speech processing and text segmentation. First, we establish TEDPara (human-annotated TED talks) and YTSegPara (YouTube videos with synthetic labels) as the first benchmarks for the paragraph segmentation task. The benchmarks focus on the underexplored speech domain, where paragraph segmentation has traditionally not been part of post-processing, while also contributing to the wider text segmentation field, which still lacks robust and naturalistic benchmarks. Second, we propose a constrained-decoding formulation that lets large language models insert paragraph breaks while preserving the original transcript, enabling faithful, sentence-aligned evaluation. Third, we show that a compact model (MiniSeg) attains state-of-the-art accuracy and, when extended hierarchically, jointly predicts chapters and paragraphs with minimal computational cost. Together, our resources and methods establish paragraph segmentation as a standardized, practical task in speech processing. - oai:arXiv.org:2512.24517v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Fabian Retkowski, Alexander Waibel - - - Using Large Language Models To Translate Machine Results To Human Results - https://arxiv.org/abs/2512.24518 - arXiv:2512.24518v1 Announce Type: new -Abstract: Artificial intelligence (AI) has transformed medical imaging, with computer vision (CV) systems achieving state-of-the-art performance in classification and detection tasks. However, these systems typically output structured predictions, leaving radiologists responsible for translating results into full narrative reports. Recent advances in large language models (LLMs), such as GPT-4, offer new opportunities to bridge this gap by generating diagnostic narratives from structured findings. This study introduces a pipeline that integrates YOLOv5 and YOLOv8 for anomaly detection in chest X-ray images with a large language model (LLM) to generate natural-language radiology reports. The YOLO models produce bounding-box predictions and class labels, which are then passed to the LLM to generate descriptive findings and clinical summaries. YOLOv5 and YOLOv8 are compared in terms of detection accuracy, inference latency, and the quality of generated text, as measured by cosine similarity to ground-truth reports. Results show strong semantic similarity between AI and human reports, while human evaluation reveals GPT-4 excels in clarity (4.88/5) but exhibits lower scores for natural writing flow (2.81/5), indicating that current systems achieve clinical accuracy but remain stylistically distinguishable from radiologist-authored text. - oai:arXiv.org:2512.24518v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Trishna Niraula, Jonathan Stubblefield - - - Analyzing Airline Alliances through Multi-Attribute Graph Partitioning to Maximize Competition and Market Penetration Capability - https://arxiv.org/abs/2512.24519 - arXiv:2512.24519v1 Announce Type: new -Abstract: The air transportation market is highly competitive and dynamic. Airlines often form alliances to expand their network reach, improve operational efficiency, and enhance customer experience. However, the impact of these alliances on market competition and operational efficiency is not fully understood. In this paper, we propose a novel approach to analyze airline alliances using multi\mfabian{-}attribute graph partitioning. We develop metrics to quantify the competitiveness of flight segments and the market penetration capability of airlines based on their alliance memberships. We formulate a bi\mfabian{-}objective optimization problem to maximize both competition and market penetration simultaneously. We also propose algorithms to solve this optimization problem and demonstrate their effectiveness using real-world flight schedule data. Our results provide insights into the structure of airline alliances and their implications for market competition and operational efficiency. - oai:arXiv.org:2512.24519v1 - cs.SI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Khalil Al Handawi, Fabian Bastin - - - Exponential Convergence of Deep Composite Polynomial Approximation for Cusp-Type Functions - https://arxiv.org/abs/2512.24523 - arXiv:2512.24523v1 Announce Type: new -Abstract: We investigate deep composite polynomial approximations of continuous but non-differentiable functions with algebraic cusp singularities. The functions in focus consist of finitely many cusp terms of the form $|x-a_j|^{\alpha_j}$ with rational exponents $\alpha_j\in(0,1)$ on a real-analytic background. We propose a constructive approximation scheme that combines a division-free polynomial iteration for fractional powers with an outer layer for the analytic polynomial fitting. Our main result shows that this composite structure achieves exponential convergence in the the number of scalar coefficients in the inner and outer polynomial layers. Specifically, the $L^p([-1,1])$ approximation error, decays exponentially with respect to the parameter budget, in contrast to the algebraic rates obtained by classical single-layer polynomial approximation for cusp-type functions. Numerical experiments for both single and multiple cusp configurations confirm the theoretical rates and demonstrate the parameter efficiency of deep composite polynomial constructions. - oai:arXiv.org:2512.24523v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Kingsley Yeon, Steven B. Damelin, Michael Werman - - - A Magnified View into Heterogeneous-ISA Thread Migration Performance without State Transformation - https://arxiv.org/abs/2512.24530 - arXiv:2512.24530v1 Announce Type: new -Abstract: Heterogeneous-ISA processor designs have attracted considerable research interest. However, unlike their homogeneous-ISA counterparts, explicit software support for bridging ISA heterogeneity is required. The lack of a compilation toolchain ready to support heterogeneous-ISA targets has been a major factor hindering research in this exciting emerging area. For any such compiler, "getting right" the mechanics involved in state transformation upon migration and doing this efficiently is of critical importance. In particular, any runtime conversion of the current program stack from one architecture to another would be prohibitively expensive. In this paper, we design and develop Unifico, a new multi-ISA compiler that generates binaries that maintain the same stack layout during their execution on either architecture. Unifico avoids the need for runtime stack transformation, thus eliminating overheads associated with ISA migration. Additional responsibilities of the Unifico compiler backend include maintenance of a uniform ABI and virtual address space across ISAs. Unifico is implemented using the LLVM compiler infrastructure, and we are currently targeting the x86-64 and ARMv8 ISAs. We have evaluated Unifico across a range of compute-intensive NAS benchmarks and show its minimal impact on overall execution time, where less than 6% (10%) overhead is introduced on average for high-end (low-end) processors. We also analyze the performance impact of Unifico's key design features and demonstrate that they can be further optimized to mitigate this impact. When compared against the state-of-the-art Popcorn compiler, Unifico reduces binary size overhead from ~200% to ~10%, whilst eliminating the stack transformation overhead during ISA migration. - oai:arXiv.org:2512.24530v1 - cs.SE - cs.PF - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Nikolaos Mavrogeorgis (University of Edinburgh, United Kingdom), Christos Vasiladiotis (University of Edinburgh, United Kingdom), Pei Mu (University of Edinburgh, United Kingdom), Amir Khordadi (University of Edinburgh, United Kingdom), Bj\"orn Franke (University of Edinburgh, United Kingdom), Antonio Barbalace (University of Edinburgh, United Kingdom) - - - Correctness of Extended RSA Public Key Cryptosystem - https://arxiv.org/abs/2512.24531 - arXiv:2512.24531v1 Announce Type: new -Abstract: This paper proposes an alternative approach to formally establishing the correctness of the RSA public key cryptosystem. The methodology presented herein deviates slightly from conventional proofs found in existing literature. Specifically, this study explores the conditions under which the choice of the positive integer N, a fundamental component of RSA, can be extended beyond the standard selection criteria. We derive explicit conditions that determine when certain values of N are valid for the encryption scheme and explain why others may fail to satisfy the correctness requirements. The scope of this paper is limited to the mathematical proof of correctness for RSA-like schemes, deliberately omitting issues related to the cryptographic security of RSA. - oai:arXiv.org:2512.24531v1 - cs.CR - math.NT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Dar-jen Chang, Suranjan Gautam - - - From Building Blocks to Planning: Multi-Step Spatial Reasoning in LLMs with Reinforcement Learning - https://arxiv.org/abs/2512.24532 - arXiv:2512.24532v1 Announce Type: new -Abstract: Spatial reasoning in large language models (LLMs) has gained increasing attention due to applications in navigation and planning. Despite strong general language capabilities, LLMs still struggle with spatial transformations and multi-step planning in structured environments. We propose a two-stage approach that decomposes spatial reasoning into atomic building blocks and their composition. First, we apply supervised fine-tuning on elementary spatial transformations, such as rotation, translation, and scaling, to equip the model with basic spatial physics. We then freeze this physics-aware model and train lightweight LoRA adapters within the GRPO framework to learn policies that compose these building blocks for multi-step planning in puzzle-based environments, in a closed-loop manner. To support this pipeline, we synthesize an ASCII-art dataset and construct a corresponding ASCII-based reinforcement learning environment. Our method consistently outperforms baselines, including the generic backbone, physics-aware model, and end-to-end RL models, under both Dynamic environments with explicit state updates and Static environments where the model must rely on its internal state across steps. In addition, the proposed approach converges faster and exhibits more stable training compared to end-to-end reinforcement learning from scratch. Finally, we analyze attention patterns to assess whether fine-tuning induces meaningful improvements in spatial understanding. - oai:arXiv.org:2512.24532v1 - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Amir Tahmasbi, Sadegh Majidi, Kazem Taram, Aniket Bera - - - A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction - https://arxiv.org/abs/2512.24542 - arXiv:2512.24542v1 Announce Type: new -Abstract: In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. Existing data-driven methods are limited by inadaptability to concept drift in power systems, poor robustness under high missing rates, and reliance on the unrealistic assumption of full system observability. Thus, this paper proposes an auxiliary task learning (ATL) method for reconstructing missing PMU data. First, a K-hop graph neural network (GNN) is proposed to enable direct learning on the subgraph consisting of PMU nodes, overcoming the limitation of the incompletely observable system. Then, an auxiliary learning framework consisting of two complementary graph networks is designed for accurate reconstruction: a spatial-temporal GNN extracts spatial-temporal dependencies from PMU data to reconstruct missing values, and another auxiliary GNN utilizes the low-rank property of PMU data to achieve unsupervised online learning. In this way, the low-rank properties of the PMU data are dynamically leveraged across the architecture to ensure robustness and self-adaptation. Numerical results demonstrate the superior offline and online performance of the proposed method under high missing rates and incomplete observability. - oai:arXiv.org:2512.24542v1 - eess.SY - cs.LG - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Bo Li, Zijun Chen, Haiwang Zhong, Di Cao, Guangchun Ruan - - - More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization - https://arxiv.org/abs/2512.24545 - arXiv:2512.24545v1 Announce Type: new -Abstract: For extreme low-bit quantization of large language models (LLMs), Double Binary Factorization (DBF) is attractive as it enables efficient inference without sacrificing accuracy. However, the scaling parameters of DBF are too restrictive; after factoring out signs, all rank components share the same magnitude profile, resulting in performance saturation. We propose Multi-envelope DBF (MDBF), which retains a shared pair of 1-bit sign bases but replaces the single envelope with a rank-$l$ envelope. By sharing sign matrices among envelope components, MDBF effectively maintains a binary carrier and utilizes the limited memory budget for magnitude expressiveness. We also introduce a closed-form initialization and an alternating refinement method to optimize MDBF. Across the LLaMA and Qwen families, MDBF enhances perplexity and zero-shot accuracy over previous binary formats at matched bits per weight while preserving the same deployment-friendly inference primitive. - oai:arXiv.org:2512.24545v1 - cs.LG - cs.AI - cs.CL - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yuma Ichikawa, Yoshihiko Fujisawa, Yudai Fujimoto, Akira Sakai, Katsuki Fujisawa - - - Hierarchical Vector-Quantized Latents for Perceptual Low-Resolution Video Compression - https://arxiv.org/abs/2512.24547 - arXiv:2512.24547v1 Announce Type: new -Abstract: The exponential growth of video traffic has placed increasing demands on bandwidth and storage infrastructure, particularly for content delivery networks (CDNs) and edge devices. While traditional video codecs like H.264 and HEVC achieve high compression ratios, they are designed primarily for pixel-domain reconstruction and lack native support for machine learning-centric latent representations, limiting their integration into deep learning pipelines. In this work, we present a Multi-Scale Vector Quantized Variational Autoencoder (MS-VQ-VAE) designed to generate compact, high-fidelity latent representations of low-resolution video, suitable for efficient storage, transmission, and client-side decoding. Our architecture extends the VQ-VAE-2 framework to a spatiotemporal setting, introducing a two-level hierarchical latent structure built with 3D residual convolutions. The model is lightweight (approximately 18.5M parameters) and optimized for 64x64 resolution video clips, making it appropriate for deployment on edge devices with constrained compute and memory resources. To improve perceptual reconstruction quality, we incorporate a perceptual loss derived from a pre-trained VGG16 network. Trained on the UCF101 dataset using 2-second video clips (32 frames at 16 FPS), on the test set we achieve 25.96 dB PSNR and 0.8375 SSIM. On validation, our model improves over the single-scale baseline by 1.41 dB PSNR and 0.0248 SSIM. The proposed framework is well-suited for scalable video compression in bandwidth-sensitive scenarios, including real-time streaming, mobile video analytics, and CDN-level storage optimization. - oai:arXiv.org:2512.24547v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Manikanta Kotthapalli, Banafsheh Rekabdar - - - DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass - https://arxiv.org/abs/2512.24550 - arXiv:2512.24550v1 Announce Type: new -Abstract: In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve the alignment between the gripper frame origin and the object Center of Mass (CoM), and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach in simulation across 15 objects under both Known-shape (with clean CAD-derived dataset) and Observed-shape (with YCB object dataset) settings, including cross-platform grasp execution on three robot--gripper platforms. We further validate the method in real-world grasp experiments on a UR3e robot. Overall, DISF reduces CoM misalignment while maintaining geometric compatibility, translating into higher grasp success in both simulation and real-world execution compared to baselines. Additional videos and supplementary results are available on our project page: https://tomoya-yamanokuchi.github.io/disf-ras-project-page/ - oai:arXiv.org:2512.24550v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tomoya Yamanokuchi, Alberto Bacchin, Emilio Olivastri, Ryotaro Arifuku, Takamitsu Matsubara, Emanuele Menegatti - - - PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation - https://arxiv.org/abs/2512.24551 - arXiv:2512.24551v1 Announce Type: new -Abstract: Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly based on graphics or prompt extension struggle to generalize beyond simple simulated environments or learn implicit physical reasoning. The scarcity of training data with rich physics interactions and phenomena is also a problem. In this paper, we first introduce a Physics-Augmented video data construction Pipeline, PhyAugPipe, that leverages a vision-language model (VLM) with chain-of-thought reasoning to collect a large-scale training dataset, PhyVidGen-135K. Then we formulate a principled Physics-aware Groupwise Direct Preference Optimization, PhyGDPO, framework that builds upon the groupwise Plackett-Luce probabilistic model to capture holistic preferences beyond pairwise comparisons. In PhyGDPO, we design a Physics-Guided Rewarding (PGR) scheme that embeds VLM-based physics rewards to steer optimization toward physical consistency. We also propose a LoRA-Switch Reference (LoRA-SR) scheme that eliminates memory-heavy reference duplication for efficient training. Experiments show that our method significantly outperforms state-of-the-art open-source methods on PhyGenBench and VideoPhy2. Please check our project page at https://caiyuanhao1998.github.io/project/PhyGDPO for more video results. Our code, models, and data will be released at https://github.com/caiyuanhao1998/Open-PhyGDPO - oai:arXiv.org:2512.24551v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuanhao Cai, Kunpeng Li, Menglin Jia, Jialiang Wang, Junzhe Sun, Feng Liang, Weifeng Chen, Felix Juefei-Xu, Chu Wang, Ali Thabet, Xiaoliang Dai, Xuan Ju, Alan Yuille, Ji Hou - - - OCP-LS: An Efficient Algorithm for Visual Localization - https://arxiv.org/abs/2512.24552 - arXiv:2512.24552v1 Announce Type: new -Abstract: This paper proposes a novel second-order optimization algorithm. It aims to address large-scale optimization problems in deep learning because it incorporates the OCP method and appropriately approximating the diagonal elements of the Hessian matrix. Extensive experiments on multiple standard visual localization benchmarks demonstrate the significant superiority of the proposed method. Compared with conventional optimiza tion algorithms, our framework achieves competitive localization accuracy while exhibiting faster convergence, enhanced training stability, and improved robustness to noise interference. - oai:arXiv.org:2512.24552v1 - cs.CV - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jindi Zhong, Hongxia Wang, Huanshui Zhang - - - From Perception to Punchline: Empowering VLM with the Art of In-the-wild Meme - https://arxiv.org/abs/2512.24555 - arXiv:2512.24555v1 Announce Type: new -Abstract: Generating humorous memes is a challenging multimodal task that moves beyond direct image-to-caption supervision. It requires a nuanced reasoning over visual content, contextual cues, and subjective humor. To bridge this gap between visual perception and humorous punchline creation, we propose HUMOR}, a novel framework that guides VLMs through hierarchical reasoning and aligns them with group-wise human preferences. First, HUMOR employs a hierarchical, multi-path Chain-of-Thought (CoT): the model begins by identifying a template-level intent, then explores diverse reasoning paths under different contexts, and finally anchors onto a high-quality, context-specific path. This CoT supervision, which traces back from ground-truth captions, enhances reasoning diversity. We further analyze that this multi-path exploration with anchoring maintains a high expected humor quality, under the practical condition that high-quality paths retain significant probability mass. Second, to capture subjective humor, we train a pairwise reward model that operates within groups of memes sharing the same template. Following established theory, this approach ensures a consistent and robust proxy for human preference, even with subjective and noisy labels. The reward model then enables a group-wise reinforcement learning optimization, guaranteeing providing a theoretical guarantee for monotonic improvement within the trust region. Extensive experiments show that HUMOR empowers various VLMs with superior reasoning diversity, more reliable preference alignment, and higher overall meme quality. Beyond memes, our work presents a general training paradigm for open-ended, human-aligned multimodal generation, where success is guided by comparative judgment within coherent output group. - oai:arXiv.org:2512.24555v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xueyan Li, Yingyi Xue, Mengjie Jiang, Qingzi Zhu, Yazhe Niu - - - Safe in the Future, Dangerous in the Past: Dissecting Temporal and Linguistic Vulnerabilities in LLMs - https://arxiv.org/abs/2512.24556 - arXiv:2512.24556v1 Announce Type: new -Abstract: As Large Language Models (LLMs) integrate into critical global infrastructure, the assumption that safety alignment transfers zero-shot from English to other languages remains a dangerous blind spot. This study presents a systematic audit of three state of the art models (GPT-5.1, Gemini 3 Pro, and Claude 4.5 Opus) using HausaSafety, a novel adversarial dataset grounded in West African threat scenarios (e.g., Yahoo-Yahoo fraud, Dane gun manufacturing). Employing a 2 x 4 factorial design across 1,440 evaluations, we tested the non-linear interaction between language (English vs. Hausa) and temporal framing. Our results challenge the prevailing multilingual safety gap narrative. Instead of a simple degradation in low-resource settings, we identified a mechanism of Complex Interference where safety is determined by the intersection of variables. While models exhibited a Reverse Linguistic with Claude 4.5 Opus proving significantly safer in Hausa (45.0%) than in English (36.7%) due to uncertainty-driven refusal they suffered catastrophic failures in temporal reasoning. We report a profound Temporal Asymmetry, where past-tense framing bypassed defenses (15.6% safe) while future-tense scenarios triggered hyper-conservative refusals (57.2% safe). The magnitude of this volatility is illustrated by a 9.2x disparity between the safest and most vulnerable configurations, proving that safety is not a fixed property but a context-dependent state. We conclude that current models rely on superficial heuristics rather than robust semantic understanding, creating Safety Pockets that leave Global South users exposed to localized harms. We propose Invariant Alignment as a necessary paradigm shift to ensure safety stability across linguistic and temporal shifts. - oai:arXiv.org:2512.24556v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Muhammad Abdullahi Said, Muhammad Sammani Sani - - - Evolutionary Discovery of Sequence Acceleration Methods for Slab Geometry Neutron Transport - https://arxiv.org/abs/2512.24559 - arXiv:2512.24559v1 Announce Type: new -Abstract: We present a genetic programming approach to automatically discover convergence acceleration methods for discrete ordinates solutions of neutron transport problems in slab geometry. Classical acceleration methods such as Aitken's delta-squared and Wynn epsilon assume specific convergence patterns and do not generalize well to the broad set of transport problems encountered in practice. We evolved mathematical formulas specifically tailored to SN convergence characteristics in this work. The discovered accelerator, featuring second differences and cross-product terms, achieved over 75 percent success rate in improving convergence compared to raw sequences - almost double that observed for classical techniques for the problem set considered. This work demonstrates the potential for discovering novel numerical methods in computational physics via genetic programming and attempts to honor Prof. Ganapol's legacy of advancing experimental mathematics applied to neutron transport. - oai:arXiv.org:2512.24559v1 - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Japan K. Patel, Barry D. Ganapol, Anthony Magliari, Matthew C. Schmidt, Todd A. Wareing - - - Localized Calibrated Uncertainty in Code Language Models - https://arxiv.org/abs/2512.24560 - arXiv:2512.24560v1 Announce Type: new -Abstract: Large Language models (LLMs) can generate complicated source code from natural language prompts. However, LLMs can generate output that deviates from what the user wants, requiring supervision and editing. To support this process, we offer techniques to localize where generations might be misaligned from user intent. We first create a dataset of "Minimal Intent Aligning Patches" of repaired LLM generated programs. Each program uses test cases to verify correctness. After creating a dataset of programs, we measure how well various techniques can assign a well-calibrated probability to indicate which parts of code will be edited in a minimal patch (i.e., give a probability that corresponds with empirical odds it is edited). We compare white-box probing (where we propose a technique for efficient arbitrary-span querying), against black-box reflective and self-consistency based approaches. We find probes with a small supervisor model can achieve low calibration error and Brier Skill Score of approx 0.2 estimating edited lines on code generated by models many orders of magnitude larger. We discuss the generalizability of the techniques, and the connections to AI oversight and control, finding a probe trained only on code shows some signs of generalizing to natural language errors if new probability scaling is allowed. - oai:arXiv.org:2512.24560v1 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - David Gros, Prem Devanbu - - - RGBT-Ground Benchmark: Visual Grounding Beyond RGB in Complex Real-World Scenarios - https://arxiv.org/abs/2512.24561 - arXiv:2512.24561v1 Announce Type: new -Abstract: Visual Grounding (VG) aims to localize specific objects in an image according to natural language expressions, serving as a fundamental task in vision-language understanding. However, existing VG benchmarks are mostly derived from datasets collected under clean environments, such as COCO, where scene diversity is limited. Consequently, they fail to reflect the complexity of real-world conditions, such as changes in illumination, weather, etc., that are critical to evaluating model robustness and generalization in safety-critical applications. To address these limitations, we present RGBT-Ground, the first large-scale visual grounding benchmark built for complex real-world scenarios. It consists of spatially aligned RGB and Thermal infrared (TIR) image pairs with high-quality referring expressions, corresponding object bounding boxes, and fine-grained annotations at the scene, environment, and object levels. This benchmark enables comprehensive evaluation and facilitates the study of robust grounding under diverse and challenging conditions. Furthermore, we establish a unified visual grounding framework that supports both uni-modal (RGB or TIR) and multi-modal (RGB-TIR) visual inputs. Based on it, we propose RGBT-VGNet, a simple yet effective baseline for fusing complementary visual modalities to achieve robust grounding. We conduct extensive adaptations to the existing methods on RGBT-Ground. Experimental results show that our proposed RGBT-VGNet significantly outperforms these adapted methods, particularly in nighttime and long-distance scenarios. All resources will be publicly released to promote future research on robust visual grounding in complex real-world environments. - oai:arXiv.org:2512.24561v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Tianyi Zhao, Jiawen Xi, Linhui Xiao, Junnan Li, Xue Yang, Maoxun Yuan, Xingxing Wei - - - HaluNet: Multi-Granular Uncertainty Modeling for Efficient Hallucination Detection in LLM Question Answering - https://arxiv.org/abs/2512.24562 - arXiv:2512.24562v1 Announce Type: new -Abstract: Large Language Models (LLMs) excel at question answering (QA) but often generate hallucinations, including factual errors or fabricated content. Detecting hallucinations from internal uncertainty signals is attractive due to its scalability and independence from external resources. Existing methods often aim to accurately capture a single type of uncertainty while overlooking the complementarity among different sources, particularly between token-level probability uncertainty and the uncertainty conveyed by internal semantic representations, which provide complementary views on model reliability. We present \textbf{HaluNet}, a lightweight and trainable neural framework that integrates multi granular token level uncertainties by combining semantic embeddings with probabilistic confidence and distributional uncertainty. Its multi branch architecture adaptively fuses what the model knows with the uncertainty expressed in its outputs, enabling efficient one pass hallucination detection. Experiments on SQuAD, TriviaQA, and Natural Questions show that HaluNet delivers strong detection performance and favorable computational efficiency, with or without access to context, highlighting its potential for real time hallucination detection in LLM based QA systems. - oai:arXiv.org:2512.24562v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chaodong Tong, Qi Zhang, Jiayang Gao, Lei Jiang, Yanbing Liu, Nannan Sun - - - CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts - https://arxiv.org/abs/2512.24564 - arXiv:2512.24564v1 Announce Type: new -Abstract: Deep learning models for Electrocardiogram (ECG) diagnosis have achieved remarkable accuracy but exhibit fragility against adversarial perturbations, particularly Smooth Adversarial Perturbations (SAP) that mimic biological morphology. Existing defenses face a critical dilemma: Adversarial Training (AT) provides robustness but incurs a prohibitive computational burden, while certified methods like Randomized Smoothing (RS) introduce significant inference latency, rendering them impractical for real-time clinical monitoring. We posit that this vulnerability stems from the models' reliance on non-robust spurious correlations rather than invariant pathological features. To address this, we propose Causal Physiological Representation Learning (CPR). Unlike standard denoising approaches that operate without semantic constraints, CPR incorporates a Physiological Structural Prior within a causal disentanglement framework. By modeling ECG generation via a Structural Causal Model (SCM), CPR enforces a structural intervention that strictly separates invariant pathological morphology (P-QRS-T complex) from non-causal artifacts. Empirical results on PTB-XL demonstrate that CPR significantly outperforms standard clinical preprocessing methods. Specifically, under SAP attacks, CPR achieves an F1 score of 0.632, surpassing Median Smoothing (0.541 F1) by 9.1%. Crucially, CPR matches the certified robustness of Randomized Smoothing while maintaining single-pass inference efficiency, offering a superior trade-off between robustness, efficiency, and clinical interpretability. - oai:arXiv.org:2512.24564v1 - cs.LG - eess.SP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shunbo Jia, Caizhi Liao - - - MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use - https://arxiv.org/abs/2512.24565 - arXiv:2512.24565v1 Announce Type: new -Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (MCP) is considered a future trend. Current MCP evaluation sets suffer from issues such as reliance on external MCP services and a lack of difficulty awareness. To address these limitations, we propose MCPAgentBench, a benchmark based on real-world MCP definitions designed to evaluate the tool-use capabilities of agents. We construct a dataset containing authentic tasks and simulated MCP tools. The evaluation employs a dynamic sandbox environment that presents agents with candidate tool lists containing distractors, thereby testing their tool selection and discrimination abilities. Furthermore, we introduce comprehensive metrics to measure both task completion rates and execution efficiency. Experiments conducted on various latest mainstream Large Language Models reveal significant performance differences in handling complex, multi-step tool invocations. All code is open-source at Github. - oai:arXiv.org:2512.24565v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Wenrui Liu, Zixiang Liu, Elsie Dai, Wenhan Yu, Lei Yu, Tong Yang - - - Newton-Krylov Methods for Computing Steady States of Particle Timesteppers via Optimal Transport - https://arxiv.org/abs/2512.24567 - arXiv:2512.24567v1 Announce Type: new -Abstract: Timesteppers constitute a powerful tool in modern computational science and engineering. Although they are typically used to advance the system forward in time, they can also be viewed as nonlinear mappings that implicitly encode steady states and stability information. In this work, we present an extension of the matrix-free framework for calculating, via timesteppers, steady states of deterministic systems to stochastic particle simulations, where intrinsic randomness prevents direct steady state extraction. By formulating stochastic timesteppers in the language of optimal transport, we reinterpret them as operators acting on probability measures rather than on individual particle trajectories. This perspective enables the construction of smooth cumulative- and inverse-cumulative-distribution-function ((I)CDF) timesteppers that evolve distributions rather than particles. Combined with matrix-free Newton-Krylov solvers, these smooth timesteppers allow efficient computation of steady-state distributions even under high stochastic noise. We perform an error analysis quantifying how noise affects finite-difference Jacobian action approximations, and demonstrate that convergence can be obtained even in high noise regimes. Finally, we introduce higher-dimensional generalizations based on smooth CDF-related representations of particles and validate their performance on a non-trivial two-dimensional distribution. Together, these developments establish a unified variational framework for computing meaningful steady states of both deterministic and stochastic timesteppers. - oai:arXiv.org:2512.24567v1 - math.NA - cs.NA - math.PR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hannes Vandecasteele, Nicholas Karris, Alexander Cloninger, Ioannis G. Kevrekidis - - - On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study - https://arxiv.org/abs/2512.24570 - arXiv:2512.24570v1 Announce Type: new -Abstract: Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To further improve data quality, numerous training data optimization techniques have been proposed; however, their overall effectiveness has not been systematically evaluated. To bridge this gap, we conduct the first large-scale empirical study, examining five widely-used training data optimization techniques and their pairwise combinations for LLM-based code generation across three benchmarks and four LLMs. Our results show that data synthesis is the most effective technique for improving functional correctness and reducing code smells, although it performs relatively worse on code maintainability compared to data refactoring, cleaning, and selection. Regarding combinations, we find that most combinations do not further improve functional correctness but can effectively enhance code quality (code smells and maintainability). Among all combinations, data synthesis combined with data refactoring achieves the strongest overall performance. Furthermore, our fine-grained analysis reinforces these findings and provides deeper insights into how individual techniques and their combinations influence code generation effectiveness. Overall, this work represents a first step toward a systematic understanding of training data optimization and combination strategies, offering practical guidance for future research and deployment in LLM-based code generation. - oai:arXiv.org:2512.24570v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shiqi Kuang, Zhao Tian, Tao Xiao, Dong Wang, Junjie Chen - - - SynRAG: A Large Language Model Framework for Executable Query Generation in Heterogeneous SIEM System - https://arxiv.org/abs/2512.24571 - arXiv:2512.24571v1 Announce Type: new -Abstract: Security Information and Event Management (SIEM) systems are essential for large enterprises to monitor their IT infrastructure by ingesting and analyzing millions of logs and events daily. Security Operations Center (SOC) analysts are tasked with monitoring and analyzing this vast data to identify potential threats and take preventive actions to protect enterprise assets. However, the diversity among SIEM platforms, such as Palo Alto Networks Qradar, Google SecOps, Splunk, Microsoft Sentinel and the Elastic Stack, poses significant challenges. As these systems differ in attributes, architecture, and query languages, making it difficult for analysts to effectively monitor multiple platforms without undergoing extensive training or forcing enterprises to expand their workforce. To address this issue, we introduce SynRAG, a unified framework that automatically generates threat detection or incident investigation queries for multiple SIEM platforms from a platform-agnostic specification. SynRAG can generate platformspecific queries from a single high-level specification written by analysts. Without SynRAG, analysts would need to manually write separate queries for each SIEM platform, since query languages vary significantly across systems. This framework enables seamless threat detection and incident investigation across heterogeneous SIEM environments, reducing the need for specialized training and manual query translation. We evaluate SynRAG against state-of-the-art language models, including GPT, Llama, DeepSeek, Gemma, and Claude, using Qradar and SecOps as representative SIEM systems. Our results demonstrate that SynRAG generates significantly better queries for crossSIEM threat detection and incident investigation compared to the state-of-the-art base models. - oai:arXiv.org:2512.24571v1 - cs.CR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - https://conf.researchr.org/home/cascon-2025 - Md Hasan Saju, Austin Page, Akramul Azim, Jeff Gardiner, Farzaneh Abazari, Frank Eargle - - - Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities - https://arxiv.org/abs/2512.24572 - arXiv:2512.24572v1 Announce Type: new -Abstract: We introduce the Korean Canonical Legal Benchmark (KCL), a benchmark designed to assess language models' legal reasoning capabilities independently of domain-specific knowledge. KCL provides question-level supporting precedents, enabling a more faithful disentanglement of reasoning ability from parameterized knowledge. KCL consists of two components: (1) KCL-MCQA, multiple-choice problems of 283 questions with 1,103 aligned precedents, and (2) KCL-Essay, open-ended generation problems of 169 questions with 550 aligned precedents and 2,739 instance-level rubrics for automated evaluation. Our systematic evaluation of 30+ models shows large remaining gaps, particularly in KCL-Essay, and that reasoning-specialized models consistently outperform their general-purpose counterparts. We release all resources, including the benchmark dataset and evaluation code, at https://github.com/lbox-kr/kcl. - oai:arXiv.org:2512.24572v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Hongseok Oh, Wonseok Hwang, Kyoung-Woon On - - - Understanding and Steering the Cognitive Behaviors of Reasoning Models at Test-Time - https://arxiv.org/abs/2512.24574 - arXiv:2512.24574v1 Announce Type: new -Abstract: Large Language Models (LLMs) often rely on long chain-of-thought (CoT) reasoning to solve complex tasks. While effective, these trajectories are frequently inefficient, leading to high latency from excessive token generation, or unstable reasoning that alternates between underthinking (shallow, inconsistent steps) and overthinking (repetitive, verbose reasoning). In this work, we study the structure of reasoning trajectories and uncover specialized attention heads that correlate with distinct cognitive behaviors such as verification and backtracking. By lightly intervening on these heads at inference time, we can steer the model away from inefficient modes. Building on this insight, we propose CREST, a training-free method for Cognitive REasoning Steering at Test-time. CREST has two components: (1) an offline calibration step that identifies cognitive heads and derives head-specific steering vectors, and (2) an inference-time procedure that rotates hidden representations to suppress components along those vectors. CREST adaptively suppresses unproductive reasoning behaviors, yielding both higher accuracy and lower computational cost. Across diverse reasoning benchmarks and models, CREST improves accuracy by up to 17.5% while reducing token usage by 37.6%, offering a simple and effective pathway to faster, more reliable LLM reasoning. - oai:arXiv.org:2512.24574v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhenyu Zhang, Xiaoxia Wu, Zhongzhu Zhou, Qingyang Wu, Yineng Zhang, Pragaash Ponnusamy, Harikaran Subbaraj, Jue Wang, Shuaiwen Leon Song, Ben Athiwaratkun - - - Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning - https://arxiv.org/abs/2512.24591 - arXiv:2512.24591v1 Announce Type: new -Abstract: Change detection visual question answering (CDVQA) requires answering text queries by reasoning about semantic changes in bi-temporal remote sensing images. A straightforward approach is to boost CDVQA performance with generic vision-language models via supervised fine-tuning (SFT). Despite recent progress, we observe that a significant portion of failures do not stem from clearly incorrect predictions, but from decision ambiguity, where the model assigns similar confidence to the correct answer and strong distractors. To formalize this challenge, we define Decision-Ambiguous Samples (DAS) as instances with a small probability margin between the ground-truth answer and the most competitive alternative. We argue that explicitly optimizing DAS is crucial for improving the discriminability and robustness of CDVQA models. To this end, we propose DARFT, a Decision-Ambiguity-guided Reinforcement Fine-Tuning framework that first mines DAS using an SFT-trained reference policy and then applies group-relative policy optimization on the mined subset. By leveraging multi-sample decoding and intra-group relative advantages, DARFT suppresses strong distractors and sharpens decision boundaries without additional supervision. Extensive experiments demonstrate consistent gains over SFT baselines, particularly under few-shot settings. - oai:arXiv.org:2512.24591v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Fuyu Dong, Ke Li, Di Wang, Nan Luo, Yiming Zhang, Kaiyu Li, Jianfei Yang, Quan Wang - - - SliceLens: Fine-Grained and Grounded Error Slice Discovery for Multi-Instance Vision Tasks - https://arxiv.org/abs/2512.24592 - arXiv:2512.24592v1 Announce Type: new -Abstract: Systematic failures of computer vision models on subsets with coherent visual patterns, known as error slices, pose a critical challenge for robust model evaluation. Existing slice discovery methods are primarily developed for image classification, limiting their applicability to multi-instance tasks such as detection, segmentation, and pose estimation. In real-world scenarios, error slices often arise from corner cases involving complex visual relationships, where existing instance-level approaches lacking fine-grained reasoning struggle to yield meaningful insights. Moreover, current benchmarks are typically tailored to specific algorithms or biased toward image classification, with artificial ground truth that fails to reflect real model failures. To address these limitations, we propose SliceLens, a hypothesis-driven framework that leverages LLMs and VLMs to generate and verify diverse failure hypotheses through grounded visual reasoning, enabling reliable identification of fine-grained and interpretable error slices. We further introduce FeSD (Fine-grained Slice Discovery), the first benchmark specifically designed for evaluating fine-grained error slice discovery across instance-level vision tasks, featuring expert-annotated and carefully refined ground-truth slices with precise grounding to local error regions. Extensive experiments on both existing benchmarks and FeSD demonstrate that SliceLens achieves state-of-the-art performance, improving Precision@10 by 0.42 (0.73 vs. 0.31) on FeSD, and identifies interpretable slices that facilitate actionable model improvements, as validated through model repair experiments. - oai:arXiv.org:2512.24592v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wei Zhang, Chaoqun Wang, Zixuan Guan, Sam Kao, Pengfei Zhao, Peng Wu, Sifeng He - - - 3D Semantic Segmentation for Post-Disaster Assessment - https://arxiv.org/abs/2512.24593 - arXiv:2512.24593v1 Announce Type: new -Abstract: The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disaster assessment, existing deep learning models lack datasets specifically designed for post-disaster environments. To address this gap, we constructed a specialized 3D dataset using unmanned aerial vehicles (UAVs)-captured aerial footage of Hurricane Ian (2022) over affected areas, employing Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques to reconstruct 3D point clouds. We evaluated the state-of-the-art (SOTA) 3D semantic segmentation models, Fast Point Transformer (FPT), Point Transformer v3 (PTv3), and OA-CNNs on this dataset, exposing significant limitations in existing methods for disaster-stricken regions. These findings underscore the urgent need for advancements in 3D segmentation techniques and the development of specialized 3D benchmark datasets to improve post-disaster scene understanding and response. - oai:arXiv.org:2512.24593v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Nhut Le, Maryam Rahnemoonfar - - - A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs - https://arxiv.org/abs/2512.24594 - arXiv:2512.24594v1 Announce Type: new -Abstract: Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to long-context reasoning limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This paper presents Preguss -- a modular, fine-grained framework for automating the generation and refinement of formal specifications. Preguss synergizes between static analysis and deductive verification by steering two components in a divide-and-conquer fashion: (i) potential runtime error-guided construction and prioritization of verification units, and (ii) LLM-aided synthesis of interprocedural specifications at the unit level. We show that Preguss substantially outperforms state-of-the-art LLM-based approaches and, in particular, it enables highly automated RTE-freeness verification for real-world programs with over a thousand LoC, with a reduction of 80.6%~88.9% human verification effort. - oai:arXiv.org:2512.24594v1 - cs.SE - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Zhongyi Wang, Tengjie Lin, Mingshuai Chen, Haokun Li, Mingqi Yang, Xiao Yi, Shengchao Qin, Yixing Luo, Xiaofeng Li, Bin Gu, Liqiang Lu, Jianwei Yin - - - Recursive Language Models - https://arxiv.org/abs/2512.24601 - arXiv:2512.24601v1 Announce Type: new -Abstract: We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query. - oai:arXiv.org:2512.24601v1 - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Alex L. Zhang, Tim Kraska, Omar Khattab - - - Secure Digital Semantic Communications: Fundamentals, Challenges, and Opportunities - https://arxiv.org/abs/2512.24602 - arXiv:2512.24602v1 Announce Type: new -Abstract: Semantic communication (SemCom) has emerged as a promising paradigm for future wireless networks by prioritizing task-relevant meaning over raw data delivery, thereby reducing communication overhead and improving efficiency. However, shifting from bit-accurate transmission to task-oriented delivery introduces new security and privacy risks. These include semantic leakage, semantic manipulation, knowledge base vulnerabilities, model-related attacks, and threats to authenticity and availability. Most existing secure SemCom studies focus on analog SemCom, where semantic features are mapped to continuous channel inputs. In contrast, digital SemCom transmits semantic information through discrete bits or symbols within practical transceiver pipelines, offering stronger compatibility with realworld systems while exposing a distinct and underexplored attack surface. In particular, digital SemCom typically represents semantic information over a finite alphabet through explicit digital modulation, following two main routes: probabilistic modulation and deterministic modulation. These discrete mechanisms and practical transmission procedures introduce additional vulnerabilities affecting bit- or symbol-level semantic information, the modulation stage, and packet-based delivery and protocol operations. Motivated by these challenges and the lack of a systematic analysis of secure digital SemCom, this paper reviews SemCom fundamentals, clarifies the architectural differences between analog and digital SemCom and their security implications, organizes the threat landscape for digital SemCom, and discusses potential defenses. Finally, we outline open research directions toward secure and deployable digital SemCom systems. - oai:arXiv.org:2512.24602v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Weixuan Chen, Qianqian Yang, Yuanyuan Jia, Junyu Pan, Shuo Shao, Jincheng Dai, Meixia Tao, Ping Zhang - - - Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers - https://arxiv.org/abs/2512.24603 - arXiv:2512.24603v1 Announce Type: new -Abstract: Low-rank adaptation (LoRA) has achieved remarkable success in fine-tuning pre-trained vision transformers for various downstream tasks. Existing studies mainly focus on exploring more parameter-efficient strategies or more effective representation learning schemes. However, these methods either sacrifice fine-tuning performance or introduce excessive trainable parameters, failing to strike a balance between learning performance and parameter efficiency. To address this problem, we propose a novel tuning method named collaborative low-rank adaptation (CLoRA) in this paper. CLoRA consists of base-space sharing and sample-agnostic diversity enhancement (SADE) components. To maintain parameter efficiency while expanding the learning capacity of low-rank modules (LRMs), base-space sharing allows all LRMs to share a set of down/up-projection spaces. In CLoRA, the low-rank matrices obtained from the shared spaces collaboratively construct each LRM. Since the representations extracted by these matrices may contain redundant information, SADE is employed to regularize the similarities among them to encourage diverse representations in the training process. We conduct extensive experiments on widely used image and point cloud datasets to evaluate the performance of CLoRA. Experimental results demonstrate that CLoRA strikes a better balance between learning performance and parameter efficiency, while requiring the fewest GFLOPs for point cloud analysis, compared with the state-of-the-art methods. - oai:arXiv.org:2512.24603v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zheng Liu, Jinchao Zhu, Gao Huang - - - MoniRefer: A Real-world Large-scale Multi-modal Dataset based on Roadside Infrastructure for 3D Visual Grounding - https://arxiv.org/abs/2512.24605 - arXiv:2512.24605v1 Announce Type: new -Abstract: 3D visual grounding aims to localize the object in 3D point cloud scenes that semantically corresponds to given natural language sentences. It is very critical for roadside infrastructure system to interpret natural languages and localize relevant target objects in complex traffic environments. However, most existing datasets and approaches for 3D visual grounding focus on the indoor and outdoor driving scenes, outdoor monitoring scenarios remain unexplored due to scarcity of paired point cloud-text data captured by roadside infrastructure sensors. In this paper, we introduce a novel task of 3D Visual Grounding for Outdoor Monitoring Scenarios, which enables infrastructure-level understanding of traffic scenes beyond the ego-vehicle perspective. To support this task, we construct MoniRefer, the first real-world large-scale multi-modal dataset for roadside-level 3D visual grounding. The dataset consists of about 136,018 objects with 411,128 natural language expressions collected from multiple complex traffic intersections in the real-world environments. To ensure the quality and accuracy of the dataset, we manually verified all linguistic descriptions and 3D labels for objects. Additionally, we also propose a new end-to-end method, named Moni3DVG, which utilizes the rich appearance information provided by images and geometry and optical information from point cloud for multi-modal feature learning and 3D object localization. Extensive experiments and ablation studies on the proposed benchmarks demonstrate the superiority and effectiveness of our method. Our dataset and code will be released. - oai:arXiv.org:2512.24605v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Panquan Yang, Junfei Huang, Zongzhangbao Yin, Yingsong Hu, Anni Xu, Xinyi Luo, Xueqi Sun, Hai Wu, Sheng Ao, Zhaoxing Zhu, Chenglu Wen, Cheng Wang - - - Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization - https://arxiv.org/abs/2512.24609 - arXiv:2512.24609v1 Announce Type: new -Abstract: Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a reinforcement learning-augmented LLM agent framework that formulates cooperation as a decentralized partially observable Markov decision process (Dec-POMDP) and adopts centralized training with decentralized execution (CTDE). We introduce Group Relative Policy Optimization (GRPO) to jointly optimize agent policies with access to global signals during training, together with a simplified joint reward that balances task quality, speed, and coordination cost. On collaborative writing and coding benchmarks, our framework delivers a 3x increase in task processing speed over single-agent baselines, 98.7% structural/style consistency in writing, and a 74.6% test pass rate in coding. The approach consistently outperforms strong multi-agent LLM baselines and provides a practical path toward reliable collaboration in complex workflows. - oai:arXiv.org:2512.24609v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Dong Qiu, Duo Xu, Limengxi Yue - - - Group Deliberation Oriented Multi-Agent Conversational Model for Complex Reasoning - https://arxiv.org/abs/2512.24613 - arXiv:2512.24613v1 Announce Type: new -Abstract: This paper proposes a group deliberation oriented multi-agent conversational model to address the limitations of single large language models in complex reasoning tasks. The model adopts a three-level role division architecture consisting of generation, verification, and integration. An opinion generation agent produces diverse reasoning perspectives, an evidence verification agent retrieves external knowledge and quantifies factual support, and a consistency arbitration agent integrates logically coherent conclusions. A self-game mechanism is introduced to expand multi-path reasoning trajectories, while a retrieval enhancement module dynamically supplements external knowledge. A composite reward function combining factual consistency and logical coherence is designed, and an improved proximal policy optimization strategy is applied for collaborative training. Experimental results show that the proposed model improves multi-hop reasoning accuracy by 16.8 percent on HotpotQA, 14.3 percent on 2WikiMultihopQA, and 19.2 percent on MeetingBank, while improving consistency by 21.5 percent. The model achieves higher reasoning efficiency than mainstream multi-agent approaches, providing an effective and stable solution for complex reasoning tasks. - oai:arXiv.org:2512.24613v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zheyu Shi, Dong Qiu, Shanlong Yu - - - Chat-Driven Optimal Management for Virtual Network Services - https://arxiv.org/abs/2512.24614 - arXiv:2512.24614v1 Announce Type: new -Abstract: This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via an integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two intent extractors, which are a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM), are introduced. Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based extractor achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. These results underscore the effectiveness of combining NLP-driven intent extraction with optimization-based allocation for safe, interpretable, and user-friendly virtual network management. - oai:arXiv.org:2512.24614v1 - cs.NI - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yuya Miyaoka, Masaki Inoue, Kengo Urata, Shigeaki Harada - - - Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization - https://arxiv.org/abs/2512.24615 - arXiv:2512.24615v1 Announce Type: new -Abstract: Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents struggle to adapt to dynamic environments without expensive fine-tuning. To address these issues, we propose \textbf{Youtu-Agent}, a modular framework designed for the automated generation and continuous evolution of LLM agents. Youtu-Agent features a structured configuration system that decouples execution environments, toolkits, and context management, enabling flexible reuse and automated synthesis. We introduce two generation paradigms: a \textbf{Workflow} mode for standard tasks and a \textbf{Meta-Agent} mode for complex, non-standard requirements, capable of automatically generating tool code, prompts, and configurations. Furthermore, Youtu-Agent establishes a hybrid policy optimization system: (1) an \textbf{Agent Practice} module that enables agents to accumulate experience and improve performance through in-context optimization without parameter updates; and (2) an \textbf{Agent RL} module that integrates with distributed training frameworks to enable scalable and stable reinforcement learning of any Youtu-Agents in an end-to-end, large-scale manner. Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47\%) and GAIA (72.8\%) using open-weight models. Our automated generation pipeline achieves over 81\% tool synthesis success rate, while the Practice module improves performance on AIME 2024/2025 by +2.7\% and +5.4\% respectively. Moreover, our Agent RL training achieves 40\% speedup with steady performance improvement on 7B LLMs, enhancing coding/reasoning and searching capabilities respectively up to 35\% and 21\% on Maths and general/multi-hop QA benchmarks. - oai:arXiv.org:2512.24615v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yuchen Shi, Yuzheng Cai, Siqi Cai, Zihan Xu, Lichao Chen, Yulei Qin, Zhijian Zhou, Xiang Fei, Chaofan Qiu, Xiaoyu Tan, Gang Li, Zongyi Li, Haojia Lin, Guocan Cai, Yong Mao, Yunsheng Wu, Ke Li, Xing Sun - - - Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space - https://arxiv.org/abs/2512.24617 - arXiv:2512.24617v1 Announce Type: new -Abstract: Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose $\textbf{Dynamic Large Concept Models (DLCM)}$, a hierarchical language modeling framework that learns semantic boundaries from latent representations and shifts computation from tokens to a compressed concept space where reasoning is more efficient. DLCM discovers variable-length concepts end-to-end without relying on predefined linguistic units. Hierarchical compression fundamentally changes scaling behavior. We introduce the first $\textbf{compression-aware scaling law}$, which disentangles token-level capacity, concept-level reasoning capacity, and compression ratio, enabling principled compute allocation under fixed FLOPs. To stably train this heterogeneous architecture, we further develop a $\textbf{decoupled $\mu$P parametrization}$ that supports zero-shot hyperparameter transfer across widths and compression regimes. At a practical setting ($R=4$, corresponding to an average of four tokens per concept), DLCM reallocates roughly one-third of inference compute into a higher-capacity reasoning backbone, achieving a $\textbf{+2.69$\%$ average improvement}$ across 12 zero-shot benchmarks under matched inference FLOPs. - oai:arXiv.org:2512.24617v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Xingwei Qu, Shaowen Wang, Zihao Huang, Kai Hua, Fan Yin, Rui-Jie Zhu, Jundong Zhou, Qiyang Min, Zihao Wang, Yizhi Li, Tianyu Zhang, He Xing, Zheng Zhang, Yuxuan Song, Tianyu Zheng, Zhiyuan Zeng, Chenghua Lin, Ge Zhang, Wenhao Huang - - - Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models - https://arxiv.org/abs/2512.24618 - arXiv:2512.24618v1 Announce Type: new -Abstract: We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultivate reasoning and planning capabilities. The key technical advancements are as follows: (1) Compact Architecture with Long-Context Support: Built on a dense Multi-Latent Attention (MLA) architecture with a novel STEM-oriented vocabulary, Youtu-LLM supports a 128k context window. This design enables robust long-context reasoning and state tracking within a minimal memory footprint, making it ideal for long-horizon agent and reasoning tasks. (2) Principled "Commonsense-STEM-Agent" Curriculum: We curated a massive corpus of approximately 11T tokens and implemented a multi-stage training strategy. By progressively shifting the pre-training data distribution from general commonsense to complex STEM and agentic tasks, we ensure the model acquires deep cognitive abilities rather than superficial alignment. (3) Scalable Agentic Mid-training: Specifically for the agentic mid-training, we employ diverse data construction schemes to synthesize rich and varied trajectories across math, coding, and tool-use domains. This high-quality data enables the model to internalize planning and reflection behaviors effectively. Extensive evaluations show that Youtu-LLM sets a new state-of-the-art for sub-2B LLMs. On general benchmarks, it achieves competitive performance against larger models, while on agent-specific tasks, it significantly surpasses existing SOTA baselines, demonstrating that lightweight models can possess strong intrinsic agentic capabilities. - oai:arXiv.org:2512.24618v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Junru Lu, Jiarui Qin, Lingfeng Qiao, Yinghui Li, Xinyi Dai, Bo Ke, Jianfeng He, Ruizhi Qiao, Di Yin, Xing Sun, Yunsheng Wu, Yinsong Liu, Shuangyin Liu, Mingkong Tang, Haodong Lin, Jiayi Kuang, Fanxu Meng, Xiaojuan Tang, Yunjia Xi, Junjie Huang, Haotong Yang, Zhenyi Shen, Yangning Li, Qianwen Zhang, Yifei Yu, Siyu An, Junnan Dong, Qiufeng Wang, Jie Wang, Keyu Chen, Wei Wen, Taian Guo, Zhifeng Shen, Daohai Yu, Jiahao Li, Ke Li, Zongyi Li, Xiaoyu Tan - - - Decentralized No-Regret Frequency-Time Scheduling for FMCW Radar Interference Avoidance - https://arxiv.org/abs/2512.24619 - arXiv:2512.24619v1 Announce Type: new -Abstract: Automotive FMCW radars are indispensable to modern ADAS and autonomous-driving systems, but their increasing density has intensified the risk of mutual interference. Existing mitigation techniques, including reactive receiver-side suppression, proactive waveform design, and cooperative scheduling, often face limitations in scalability, reliance on side-channel communication, or degradation of range-Doppler resolution. Building on our earlier work on decentralized Frequency-Domain No-Regret hopping, this paper introduces a unified time-frequency game-theoretic framework that enables radars to adapt across both spectral and temporal resources. We formulate the interference-avoidance problem as a repeated anti-coordination game, in which each radar autonomously updates a mixed strategy over frequency subbands and chirp-level time offsets using regret-minimization dynamics. We show that the proposed Time-Frequency No-Regret Hopping algorithm achieves vanishing external and swap regret, and that the induced empirical play converges to an $\varepsilon$-coarse correlated equilibrium or a correlated equilibrium. Theoretical analysis provides regret bounds in the joint domain, revealing how temporal adaptation implicitly regularizes frequency selection and enhances robustness against asynchronous interference. Numerical experiments with multi-radar scenarios demonstrate substantial improvements in SINR, collision rate, and range-Doppler quality compared with time-frequency random hopping and centralized Nash-based benchmarks. - oai:arXiv.org:2512.24619v1 - eess.SY - cs.SY - eess.SP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yunian Pan, Jun Li, Lifan Xu, Shunqiao Sun, Quanyan Zhu - - - LLHA-Net: A Hierarchical Attention Network for Two-View Correspondence Learning - https://arxiv.org/abs/2512.24620 - arXiv:2512.24620v1 Announce Type: new -Abstract: Establishing the correct correspondence of feature points is a fundamental task in computer vision. However, the presence of numerous outliers among the feature points can significantly affect the matching results, reducing the accuracy and robustness of the process. Furthermore, a challenge arises when dealing with a large proportion of outliers: how to ensure the extraction of high-quality information while reducing errors caused by negative samples. To address these issues, in this paper, we propose a novel method called Layer-by-Layer Hierarchical Attention Network, which enhances the precision of feature point matching in computer vision by addressing the issue of outliers. Our method incorporates stage fusion, hierarchical extraction, and an attention mechanism to improve the network's representation capability by emphasizing the rich semantic information of feature points. Specifically, we introduce a layer-by-layer channel fusion module, which preserves the feature semantic information from each stage and achieves overall fusion, thereby enhancing the representation capability of the feature points. Additionally, we design a hierarchical attention module that adaptively captures and fuses global perception and structural semantic information using an attention mechanism. Finally, we propose two architectures to extract and integrate features, thereby improving the adaptability of our network. We conduct experiments on two public datasets, namely YFCC100M and SUN3D, and the results demonstrate that our proposed method outperforms several state-of-the-art techniques in both outlier removal and camera pose estimation. Source code is available at http://www.linshuyuan.com. - oai:arXiv.org:2512.24620v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1016/j.patcog.2025.112896 - Shuyuan Lin, Yu Guo, Xiao Chen, Yanjie Liang, Guobao Xiao, Feiran Huang - - - FireRescue: A UAV-Based Dataset and Enhanced YOLO Model for Object Detection in Fire Rescue Scenes - https://arxiv.org/abs/2512.24622 - arXiv:2512.24622v1 Announce Type: new -Abstract: Object detection in fire rescue scenarios is importance for command and decision-making in firefighting operations. However, existing research still suffers from two main limitations. First, current work predominantly focuses on environments such as mountainous or forest areas, while paying insufficient attention to urban rescue scenes, which are more frequent and structurally complex. Second, existing detection systems include a limited number of classes, such as flames and smoke, and lack a comprehensive system covering key targets crucial for command decisions, such as fire trucks and firefighters. To address the above issues, this paper first constructs a new dataset named "FireRescue" for rescue command, which covers multiple rescue scenarios, including urban, mountainous, forest, and water areas, and contains eight key categories such as fire trucks and firefighters, with a total of 15,980 images and 32,000 bounding boxes. Secondly, to tackle the problems of inter-class confusion and missed detection of small targets caused by chaotic scenes, diverse targets, and long-distance shooting, this paper proposes an improved model named FRS-YOLO. On the one hand, the model introduces a plug-and-play multidi-mensional collaborative enhancement attention module, which enhances the discriminative representation of easily confused categories (e.g., fire trucks vs. ordinary trucks) through cross-dimensional feature interaction. On the other hand, it integrates a dynamic feature sampler to strengthen high-response foreground features, thereby mitigating the effects of smoke occlusion and background interference. Experimental results demonstrate that object detection in fire rescue scenarios is highly challenging, and the proposed method effectively improves the detection performance of YOLO series models in this context. - oai:arXiv.org:2512.24622v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Qingyu Xu, Runtong Zhang, Zihuan Qiu, Fanman Meng - - - AutoFed: Manual-Free Federated Traffic Prediction via Personalized Prompt - https://arxiv.org/abs/2512.24625 - arXiv:2512.24625v1 Announce Type: new -Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management. However, due to significant privacy concerns surrounding traffic data, most existing methods rely on local training, resulting in data silos and limited knowledge sharing. Federated Learning (FL) offers an efficient solution through privacy-preserving collaborative training; however, standard FL struggles with the non-independent and identically distributed (non-IID) problem among clients. This challenge has led to the emergence of Personalized Federated Learning (PFL) as a promising paradigm. Nevertheless, current PFL frameworks require further adaptation for traffic prediction tasks, such as specialized graph feature engineering, data processing, and network architecture design. A notable limitation of many prior studies is their reliance on hyper-parameter optimization across datasets-information that is often unavailable in real-world scenarios-thus impeding practical deployment. To address this challenge, we propose AutoFed, a novel PFL framework for traffic prediction that eliminates the need for manual hyper-parameter tuning. Inspired by prompt learning, AutoFed introduces a federated representor that employs a client-aligned adapter to distill local data into a compact, globally shared prompt matrix. This prompt then conditions a personalized predictor, allowing each client to benefit from cross-client knowledge while maintaining local specificity. Extensive experiments on real-world datasets demonstrate that AutoFed consistently achieves superior performance across diverse scenarios. The code of this paper is provided at https://github.com/RS2002/AutoFed . - oai:arXiv.org:2512.24625v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zijian Zhao, Yitong Shang, Sen Li - - - AI-Driven Acoustic Voice Biomarker-Based Hierarchical Classification of Benign Laryngeal Voice Disorders from Sustained Vowels - https://arxiv.org/abs/2512.24628 - arXiv:2512.24628v1 Announce Type: new -Abstract: Benign laryngeal voice disorders affect nearly one in five individuals and often manifest as dysphonia, while also serving as non-invasive indicators of broader physiological dysfunction. We introduce a clinically inspired hierarchical machine learning framework for automated classification of eight benign voice disorders alongside healthy controls, using acoustic features extracted from short, sustained vowel phonations. Experiments utilized 15,132 recordings from 1,261 speakers in the Saarbruecken Voice Database, covering vowels /a/, /i/, and /u/ at neutral, high, low, and gliding pitches. Mirroring clinical triage workflows, the framework operates in three sequential stages: Stage 1 performs binary screening of pathological versus non-pathological voices by integrating convolutional neural network-derived mel-spectrogram features with 21 interpretable acoustic biomarkers; Stage 2 stratifies voices into Healthy, Functional or Psychogenic, and Structural or Inflammatory groups using a cubic support vector machine; Stage 3 achieves fine-grained classification by incorporating probabilistic outputs from prior stages, improving discrimination of structural and inflammatory disorders relative to functional conditions. The proposed system consistently outperformed flat multi-class classifiers and pre-trained self-supervised models, including META HuBERT and Google HeAR, whose generic objectives are not optimized for sustained clinical phonation. By combining deep spectral representations with interpretable acoustic features, the framework enhances transparency and clinical alignment. These results highlight the potential of quantitative voice biomarkers as scalable, non-invasive tools for early screening, diagnostic triage, and longitudinal monitoring of vocal health. - oai:arXiv.org:2512.24628v1 - cs.SD - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mohsen Annabestani, Samira Aghadoost, Anais Rameau, Olivier Elemento, Gloria Chia-Yi Chiang - - - How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests - https://arxiv.org/abs/2512.24630 - arXiv:2512.24630v1 Announce Type: new -Abstract: LLM-based software engineering is influencing modern software development. In addition to correctness, prior studies have also examined the performance of software artifacts generated by AI agents. However, it is unclear how exactly the agentic AI systems address performance concerns in practice. In this paper, we present an empirical study of performance-related pull requests generated by AI agents. Using LLM-assisted detection and BERTopic-based topic modeling, we identified 52 performance-related topics grouped into 10 higher-level categories. Our results show that AI agents apply performance optimizations across diverse layers of the software stack and that the type of optimization significantly affects pull request acceptance rates and review times. We also found that performance optimization by AI agents primarily occurs during the development phase, with less focus on the maintenance phase. Our findings provide empirical evidence that can support the evaluation and improvement of agentic AI systems with respect to their performance optimization behaviors and review outcomes. - oai:arXiv.org:2512.24630v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Md Nahidul Islam Opu, Shahidul Islam, Muhammad Asaduzzaman, Shaiful Chowdhury - - - ReflecToMeet: An AI-Assisted Reflection Based System to Enhance Collaborative Preparedness - https://arxiv.org/abs/2512.24632 - arXiv:2512.24632v1 Announce Type: new -Abstract: In collaborative settings, difficulties in sustaining a consistent pace and engagement often lead to task drift, reducing preparedness and overall effectiveness between meetings. To address this challenge, we conducted a formative study and developed ReflecToMeet, an AI assisted system that integrates theory driven reflective prompts with mechanisms for sharing teammates reflections. Informed by ten formative interviews, the system was evaluated in a mixed method study across three conditions: deeper reflection, regular reflection, and a control condition with unstructured reflection. Participants in the control condition demonstrated less deliberate thought and weaker collaboration, which led to stress and misalignment during team meetings. In contrast, structured reflection supported greater organization and steadier progress. The deeper reflection condition further facilitated confidence, teamwork, and idea generation, although it imposed a higher cognitive load. We conclude by discussing design implications for AI agents that facilitate reflection to enhance collaboration and broader considerations for AI assisted systems aimed at sustaining collaborative goals. - oai:arXiv.org:2512.24632v1 - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Md Nazmus Sakib, Naga Manogna Rayasam, Ishika Tarin, Sanorita Dey - - - DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information - https://arxiv.org/abs/2512.24635 - arXiv:2512.24635v1 Announce Type: new -Abstract: Automated Program Repair (APR) aims to automatically generate correct patches for buggy programs. Recent approaches leveraging large language models (LLMs) have shown promise but face limitations. Most rely solely on static analysis, ignoring runtime behaviors. Some attempt to incorporate dynamic signals, but these are often restricted to training or fine-tuning, or injected only once into the repair prompt, without iterative use. This fails to fully capture program execution. Current iterative repair frameworks typically rely on coarse-grained feedback, such as pass/fail results or exception types, and do not leverage fine-grained execution-level information effectively. As a result, models struggle to simulate human stepwise debugging, limiting their effectiveness in multi-step reasoning and complex bug repair. - To address these challenges, we propose DynaFix, an execution-level dynamic information-driven APR method that iteratively leverages runtime information to refine the repair process. In each repair round, DynaFix captures execution-level dynamic information such as variable states, control-flow paths, and call stacks, transforming them into structured prompts to guide LLMs in generating candidate patches. If a patch fails validation, DynaFix re-executes the modified program to collect new execution information for the next attempt. This iterative loop incrementally improves patches based on updated feedback, similar to the stepwise debugging practices of human developers. We evaluate DynaFix on the Defects4J v1.2 and v2.0 benchmarks. DynaFix repairs 186 single-function bugs, a 10% improvement over state-of-the-art baselines, including 38 bugs previously unrepaired. It achieves correct patches within at most 35 attempts, reducing the patch search space by 70% compared with existing methods, thereby demonstrating both effectiveness and efficiency in repairing complex bugs. - oai:arXiv.org:2512.24635v1 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhili Huang, Ling Xu, Chao Liu, Weifeng Sun, Xu Zhang, Yan Lei, Meng Yan, Hongyu Zhang - - - How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study - https://arxiv.org/abs/2512.24636 - arXiv:2512.24636v1 Announce Type: new -Abstract: As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents recognize and address software energy concerns-an issue growing in importance due to large-scale data centers, energy-hungry language models, and battery-constrained devices. In this paper, we examined the energy awareness of agent-authored pull requests (PRs) using a publicly available dataset. We identified 216 energy-explicit PRs and conducted a thematic analysis, deriving a taxonomy of energy-aware work. Our further analysis of the applied optimization techniques shows that most align with established research recommendations. Although building and running these agents is highly energy intensive, encouragingly, the results indicate that they exhibit energy awareness when generating software artifacts. However, optimization-related PRs are accepted less frequently than others, largely due to their negative impact on maintainability. - oai:arXiv.org:2512.24636v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Tanjum Motin Mitul, Md. Masud Mazumder, Md Nahidul Islam Opu, Shaiful Chowdhury - - - MSched: GPU Multitasking via Proactive Memory Scheduling - https://arxiv.org/abs/2512.24637 - arXiv:2512.24637v1 Announce Type: new -Abstract: The limited HBM capacity has become the primary bottleneck for hosting an increasing number of larger-scale GPU tasks. While demand paging extends capacity via host DRAM, it incurs up to 78x slowdown due to the massive working sets and poor locality of GPU workloads. We observe, however, that GPU memory access patterns are inherently predictable via kernel launch arguments and their asynchronous execution nature. Leveraging this, we propose MSched, an OS-level scheduler that extends GPU context switching to include proactive working set preparation, thereby coalescing fragmented, eventual, and expensive page faults into a single efficient migration. MSched employs a template-based approach to predict working sets with near-perfect accuracy and proposes a co-design between task scheduler and memory manager to enforce a globally optimal page placement policy. Evaluation demonstrates that MSched outperforms demand paging by up to 11.05x for scientific and deep learning workloads, and 57.88x for LLM under memory oversubscription. - oai:arXiv.org:2512.24637v1 - cs.OS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Weihang Shen, Yinqiu Chen, Rong Chen, Haibo Chen - - - Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding - https://arxiv.org/abs/2512.24638 - arXiv:2512.24638v1 Announce Type: new -Abstract: State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the appropriate types and levels of information from the history to identify the current task phase. However, naively extending the history window is computationally expensive and may cause severe overfitting. Inspired by the continuous nature of human reasoning and the recoding of working memory, we introduce PAM, a novel visuomotor Policy equipped with Adaptive working Memory. With minimal additional training cost in a two-stage manner, PAM supports a 300-frame history window while maintaining high inference speed. Specifically, a hierarchical frame feature extractor yields two distinct representations for motion primitives and temporal disambiguation. For compact representation, a context router with range-specific queries is employed to produce compact context features across multiple history lengths. And an auxiliary objective of reconstructing historical information is introduced to ensure that the context router acts as an effective bottleneck. We meticulously design 7 tasks and verify that PAM can handle multiple scenarios of state ambiguity simultaneously. With a history window of approximately 10 seconds, PAM still supports stable training and maintains inference speeds above 20Hz. Project website: https://tinda24.github.io/pam/ - oai:arXiv.org:2512.24638v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qingda Hu, Ziheng Qiu, Zijun Xu, Kaizhao Zhang, Xizhou Bu, Zuolei Sun, Bo Zhang, Jieru Zhao, Zhongxue Gan, Wenchao Ding - - - From Sequential to Spatial: Reordering Autoregression for Efficient Visual Generation - https://arxiv.org/abs/2512.24639 - arXiv:2512.24639v1 Announce Type: new -Abstract: Inspired by the remarkable success of autoregressive models in language modeling, this paradigm has been widely adopted in visual generation. However, the sequential token-by-token decoding mechanism inherent in traditional autoregressive models leads to low inference efficiency.In this paper, we propose RadAR, an efficient and parallelizable framework designed to accelerate autoregressive visual generation while preserving its representational capacity. Our approach is motivated by the observation that visual tokens exhibit strong local dependencies and spatial correlations with their neighbors--a property not fully exploited in standard raster-scan decoding orders. Specifically, we organize the generation process around a radial topology: an initial token is selected as the starting point, and all other tokens are systematically grouped into multiple concentric rings according to their spatial distances from this center. Generation then proceeds in a ring-wise manner, from inner to outer regions, enabling the parallel prediction of all tokens within the same ring. This design not only preserves the structural locality and spatial coherence of visual scenes but also substantially increases parallelization. Furthermore, to address the risk of inconsistent predictions arising from simultaneous token generation with limited context, we introduce a nested attention mechanism. This mechanism dynamically refines implausible outputs during the forward pass, thereby mitigating error accumulation and preventing model collapse. By integrating radial parallel prediction with dynamic output correction, RadAR significantly improves generation efficiency. - oai:arXiv.org:2512.24639v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Siyang Wang, Hanting Li, Wei Li, Jie Hu, Xinghao Chen, Feng Zhao - - - A Scalable Framework for logP Prediction: From Terabyte-Scale Data Integration to Interpretable Ensemble Modeling - https://arxiv.org/abs/2512.24643 - arXiv:2512.24643v1 Announce Type: new -Abstract: This study presents a large-scale predictive modeling framework for logP prediction using 426850 bioactive compounds rigorously curated from the intersection of three authoritative chemical databases: PubChem, ChEMBL, and eMolecules. We developed a novel computational infrastructure to address the data integration challenge, reducing processing time from a projected over 100 days to 3.2 hours through byte-offset indexing architecture, a 740-fold improvement. Our comprehensive analysis revealed critical insights into the multivariate nature of lipophilicity: while molecular weight exhibited weak bivariate correlation with logP, SHAP analysis on ensemble models identified it as the single most important predictor globally. We systematically evaluated multiple modeling approaches, discovering that linear models suffered from inherent heteroskedasticity that classical remediation strategies, including weighted least squares and Box-Cox transformation, failed to address. Tree-based ensemble methods, including Random Forest and XGBoost, proved inherently robust to this violation, achieving an R-squared of 0.765 and RMSE of 0.731 logP units on the test set. Furthermore, a stratified modeling strategy, employing specialized models for drug-like molecules (91 percent of dataset) and extreme cases (nine percent), achieved optimal performance: an RMSE of 0.838 for the drug-like subset and an R-squared of 0.767 for extreme molecules, the highest of all evaluated approaches. These findings provide actionable guidance for molecular design, establish robust baselines for lipophilicity prediction using only 2D descriptors, and demonstrate that well-curated, descriptor-based ensemble models remain competitive with state-of-the-art graph neural network architectures. - oai:arXiv.org:2512.24643v1 - cs.LG - cs.CE - cs.DB - q-bio.BM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Malikussaid, Septian Caesar Floresko, Ade Romadhony, Isman Kurniawan, Warih Maharani, Hilal Hudan Nuha - - - AudioFab: Building A General and Intelligent Audio Factory through Tool Learning - https://arxiv.org/abs/2512.24645 - arXiv:2512.24645v1 Announce Type: new -Abstract: Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient framework to unlock their full potential. Existing audio agent frameworks often suffer from complex environment configurations and inefficient tool collaboration. To address these limitations, we introduce AudioFab, an open-source agent framework aimed at establishing an open and intelligent audio-processing ecosystem. Compared to existing solutions, AudioFab's modular design resolves dependency conflicts, simplifying tool integration and extension. It also optimizes tool learning through intelligent selection and few-shot learning, improving efficiency and accuracy in complex audio tasks. Furthermore, AudioFab provides a user-friendly natural language interface tailored for non-expert users. As a foundational framework, AudioFab's core contribution lies in offering a stable and extensible platform for future research and development in audio and multimodal AI. The code is available at https://github.com/SmileHnu/AudioFab. - oai:arXiv.org:2512.24645v1 - cs.SD - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1145/3746027.3756869 - ACM Multimedia 2025 - Cheng Zhu, Jing Han, Qianshuai Xue, Kehan Wang, Huan Zhao, Zixing Zhang - - - Solving the inverse Source Problems for wave equation with final time measurements by a data driven approach - https://arxiv.org/abs/2512.24647 - arXiv:2512.24647v1 Announce Type: new -Abstract: This paper develops a discrete data-driven approach for solving the inverse source problem of the wave equation with final time measurements. Focusing on the $L^2$-Tikhonov regularization method, we analyze its convergence under two different noise models, using noisy discrete spatial observations. By exploiting the spectral decomposition of the forward operator and introducing a noise separation technique into the variational framework, we establish error bounds for the reconstructed solution $u$ and the source term $f$ without requiring classical source conditions. Moreover, an expected convergence rate for the source error is derived in a weaker topology. We also extend the analysis to the fully discrete case with finite element discretization, showing that the overall error depends only on the noise level, regularization parameter, time step size, and spatial mesh size. These estimates provide a basis for selecting the optimal regularization parameter in a data-driven manner, without a priori information. Numerical experiments validate the theoretical results and demonstrate the efficiency of the proposed algorithm. - oai:arXiv.org:2512.24647v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qiling Gu, Wenlong Zhang, Zhidong Zhang - - - Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation - https://arxiv.org/abs/2512.24651 - arXiv:2512.24651v1 Announce Type: new -Abstract: Autonomous mobile robots operating in complex, dynamic environments face the dual challenge of navigating large-scale, structurally diverse spaces with static obstacles while safely interacting with various moving agents. Traditional graph-based planners excel at long-range pathfinding but lack reactivity, while Deep Reinforcement Learning (DRL) methods demonstrate strong collision avoidance but often fail to reach distant goals due to a lack of global context. We propose Hybrid Motion Planning with Deep Reinforcement Learning (HMP-DRL), a hybrid framework that bridges this gap. Our approach utilizes a graph-based global planner to generate a path, which is integrated into a local DRL policy via a sequence of checkpoints encoded in both the state space and reward function. To ensure social compliance, the local planner employs an entity-aware reward structure that dynamically adjusts safety margins and penalties based on the semantic type of surrounding agents. We validate the proposed method through extensive testing in a realistic simulation environment derived from real-world map data. Comprehensive experiments demonstrate that HMP-DRL consistently outperforms other methods, including state-of-the-art approaches, in terms of key metrics of robot navigation: success rate, collision rate, and time to reach the goal. Overall, these findings confirm that integrating long-term path guidance with semantically-aware local control significantly enhances both the safety and reliability of autonomous navigation in complex human-centric settings. - oai:arXiv.org:2512.24651v1 - cs.RO - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yury Kolomeytsev, Dmitry Golembiovsky - - - Practical Traceable Over-Threshold Multi-Party Private Set Intersection - https://arxiv.org/abs/2512.24652 - arXiv:2512.24652v1 Announce Type: new -Abstract: Multi-Party Private Set Intersection (MP-PSI) with threshold enhances the flexibility of MP-PSI by disclosing elements present in at least $t$ participants' sets, rather than requiring elements to appear in all $n$ sets. In scenarios where each participant is responsible for its dataset, e.g., digital forensics, MP-PSI with threshold should disclose both intersection elements and corresponding holders such that elements are traceable and the reliability of intersection is guaranteed. We refer to MP-PSI with threshold supporting traceability as Traceable Over-Threshold MP-PSI (T-OT-MP-PSI). However, research on such protocols remains limited, and existing work tolerates at most $t-2$ semi-honest participants at considerable computational cost. We propose two novel Traceable OT-MP-PSI protocols. The first, Efficient Traceable OT-MP-PSI (ET-OT-MP-PSI), combines Shamir's secret sharing with an oblivious programmable pseudorandom function, achieving significantly improved efficiency with resistance to at most $t-2$ semi-honest participants. The second, Security-enhanced Traceable OT-MP-PSI (ST-OT-MP-PSI), achieves security against up to $n-1$ semi-honest participants by further leveraging the oblivious linear evaluation protocol. Compared to Mahdavi et al.'s protocol, ours eliminate the assumption that certain special parties do not collude. Experimental results demonstrate significant improvements: for $n=5$, $t=3$, and sets of size $2^{14}$, ET-OT-MP-PSI achieves $15056\times$ speedup and ST-OT-MP-PSI achieves $505\times$ speedup over Mahdavi et al.'s protocol. - oai:arXiv.org:2512.24652v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Le Yang (University of Science and Technology of China), Weijing You (Fujian Normal University), Huiyang He (University of Science and Technology of China), Kailiang Ji (NIO Inc), Jingqiang Lin (University of Science and Technology of China) - - - RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence - https://arxiv.org/abs/2512.24653 - arXiv:2512.24653v1 Announce Type: new -Abstract: While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstrations. Consequently, the ability of existing models to generalize across long-horizon bimanual tasks and mobile manipulation in unstructured environments remains limited. To bridge this gap, we present RoboMIND 2.0, a comprehensive real-world dataset comprising over 310K dual-arm manipulation trajectories collected across six distinct robot embodiments and 739 complex tasks. Crucially, to support research in contact-rich and spatially extended tasks, the dataset incorporates 12K tactile-enhanced episodes and 20K mobile manipulation trajectories. Complementing this physical data, we construct high-fidelity digital twins of our real-world environments, releasing an additional 20K-trajectory simulated dataset to facilitate robust sim-to-real transfer. To fully exploit the potential of RoboMIND 2.0, we propose MIND-2 system, a hierarchical dual-system frame-work optimized via offline reinforcement learning. MIND-2 integrates a high-level semantic planner (MIND-2-VLM) to decompose abstract natural language instructions into grounded subgoals, coupled with a low-level Vision-Language-Action executor (MIND-2-VLA), which generates precise, proprioception-aware motor actions. - oai:arXiv.org:2512.24653v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Chengkai Hou, Kun Wu, Jiaming Liu, Zhengping Che, Di Wu, Fei Liao, Guangrun Li, Jingyang He, Qiuxuan Feng, Zhao Jin, Chenyang Gu, Zhuoyang Liu, Nuowei Han, Xiangju Mi, Yaoxu Lv, Yankai Fu, Gaole Dai, Langzhe Gu, Tao Li, Yuheng Zhang, Yixue Zhang, Xinhua Wang, Shichao Fan, Meng Li, Zhen Zhao, Ning Liu, Zhiyuan Xu, Pei Ren, Junjie Ji, Haonan Liu, Kuan Cheng, Shanghang Zhang, Jian Tang - - - Characterizing Bugs and Quality Attributes in Quantum Software: A Large-Scale Empirical Study - https://arxiv.org/abs/2512.24656 - arXiv:2512.24656v1 Announce Type: new -Abstract: Quantum Software Engineering (QSE) is essential for ensuring the reliability and maintainability of hybrid quantum-classical systems, yet empirical evidence on how bugs emerge and affect quality in real-world quantum projects remains limited. This study presents the first ecosystem-scale longitudinal analysis of software defects across 123 open source quantum repositories from 2012 to 2024, spanning eight functional categories, including full-stack libraries, simulators, annealing, algorithms, compilers, assembly, cryptography, and experimental computing. Using a mixed method approach combining repository mining, static code analysis, issue metadata extraction, and a validated rule-based classification framework, we analyze 32,296 verified bug reports. Results show that full-stack libraries and compilers are the most defect-prone categories due to circuit, gate, and transpilation-related issues, while simulators are mainly affected by measurement and noise modeling errors. Classical bugs primarily impact usability and interoperability, whereas quantum-specific bugs disproportionately degrade performance, maintainability, and reliability. Longitudinal analysis indicates ecosystem maturation, with defect densities peaking between 2017 and 2021 and declining thereafter. High-severity defects cluster in cryptography, experimental computing, and compiler toolchains. Repositories employing automated testing detect more defects and resolve issues faster. A negative binomial regression further shows that automated testing is associated with an approximate 60 percent reduction in expected defect incidence. Overall, this work provides the first large-scale data-driven characterization of quantum software defects and offers empirical guidance for improving testing, documentation, and maintainability practices in QSE. - oai:arXiv.org:2512.24656v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mir Mohammad Yousuf, Shabir Ahmad Sofi - - - Antagonistic Bowden-Cable Actuation of a Lightweight Robotic Hand: Toward Dexterous Manipulation for Payload Constrained Humanoids - https://arxiv.org/abs/2512.24657 - arXiv:2512.24657v1 Announce Type: new -Abstract: Humanoid robots toward human-level dexterity require robotic hands capable of simultaneously providing high grasping force, rapid actuation speeds, multiple degrees of freedom, and lightweight structures within human-like size constraints. Meeting these conflicting requirements remains challenging, as satisfying this combination typically necessitates heavier actuators and bulkier transmission systems, significantly restricting the payload capacity of robot arms. In this letter, we present a lightweight anthropomorphic hand actuated by Bowden cables, which uniquely combines rolling-contact joint optimization with antagonistic cable actuation, enabling single-motor-per-joint control with negligible cable-length deviation. By relocating the actuator module to the torso, the design substantially reduces distal mass while maintaining anthropomorphic scale and dexterity. Additionally, this antagonistic cable actuation eliminates the need for synchronization between motors. Using the proposed methods, the hand assembly with a distal mass of 236g (excluding remote actuators and Bowden sheaths) demonstrated reliable execution of dexterous tasks, exceeding 18N fingertip force and lifting payloads over one hundred times its own mass. Furthermore, robustness was validated through Cutkosky taxonomy grasps and trajectory consistency under perturbed actuator-hand transformations. - oai:arXiv.org:2512.24657v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Sungjae Min, Hyungjoo Kim, David Hyunchul Shim - - - Taking Advantage of Rational Canonical Form for Faster Ring-LWE based Encrypted Controller with Recursive Multiplication - https://arxiv.org/abs/2512.24658 - arXiv:2512.24658v1 Announce Type: new -Abstract: This paper aims to provide an efficient implementation of encrypted linear dynamic controllers that perform recursive multiplications on a Ring-Learning With Errors (Ring-LWE) based cryptosystem. By adopting a system-theoretical approach, we significantly reduce both time and space complexities, particularly the number of homomorphic operations required for recursive multiplications. Rather than encrypting the entire state matrix of a given controller, the state matrix is transformed into its rational canonical form, whose sparse and circulant structure enables that encryption and computation are required only on its nontrivial columns. Furthermore, we propose a novel method to ``pack'' each of the input and the output matrices into a single polynomial, thereby reducing the number of homomorphic operations. Simulation results demonstrate that the proposed design enables a remarkably fast implementation of encrypted controllers. - oai:arXiv.org:2512.24658v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Donghyeon Song, Yeongjun Jang, Joowon Lee, Junsoo Kim - - - Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks - https://arxiv.org/abs/2512.24659 - arXiv:2512.24659v1 Announce Type: new -Abstract: In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) are deployed to enhance the air-ground connectivity under blockage. Based on this architecture, we formulate a multi-objective optimization problem (MOOP) to minimize the task completion delay and energy consumption by jointly optimizing task offloading, UAV trajectory control, IRS phase-shift configuration, and computation resource allocation. The considered problem is NP-hard, and thus we propose a hierarchical online optimization approach (HOOA) to efficiently solve the problem. Specifically, we reformulate the MOOP as a Stackelberg game, where MEC servers collectively act as the leader to determine the system-level decisions, while the vehicles act as followers to make individual decisions. At the follower level, we present a many-to-one matching mechanism to generate feasible discrete decisions. At the leader level, we propose a generative diffusion model-enhanced twin delayed deep deterministic policy gradient (GDMTD3) algorithm integrated with a Karush-Kuhn-Tucker (KKT)-based method, which is a deep reinforcement learning (DRL)-based approach, to determine the continuous decisions. Simulation results demonstrate that the proposed HOOA achieves significant improvements, which reduces average task completion delay by 2.5% and average energy consumption by 3.1% compared with the best-performing benchmark approach and state-of-the-art DRL algorithm, respectively. Moreover, the proposed HOOA exhibits superior convergence stability while maintaining strong robustness and scalability in dynamic environments. - oai:arXiv.org:2512.24659v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yixian Wang, Geng Sun, Zemin Sun, Jiacheng Wang, Changyuan Zhao, Daxin Tian, Dusit Niyato, Shiwen Mao - - - Do Large Language Models Know What They Are Capable Of? - https://arxiv.org/abs/2512.24661 - arXiv:2512.24661v1 Announce Type: new -Abstract: We investigate whether large language models (LLMs) can predict whether they will succeed on a given task and whether their predictions improve as they progress through multi-step tasks. We also investigate whether LLMs can learn from in-context experiences to make better decisions about whether to pursue a task in scenarios where failure is costly. All LLMs we tested are overconfident, but most predict their success with better-than-random discriminatory power. We find that newer and larger LLMs generally do not have greater discriminatory power, though Claude models do show such a trend. On multi-step agentic tasks, the overconfidence of several frontier LLMs worsens as they progress through the tasks, and reasoning LLMs perform comparably to or worse than non-reasoning LLMs. With in-context experiences of failure, some but not all LLMs reduce their overconfidence leading to significantly improved decision making, while others do not. Interestingly, all LLMs' decisions are approximately rational given their estimated probabilities of success, yet their overly-optimistic estimates result in poor decision making. These results suggest that current LLM agents are hindered by their lack of awareness of their own capabilities. We discuss the implications of LLMs' awareness of their capabilities for AI misuse and misalignment risks. - oai:arXiv.org:2512.24661v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Casey O. Barkan, Sid Black, Oliver Sourbut - - - Renormalization Group Guided Tensor Network Structure Search - https://arxiv.org/abs/2512.24663 - arXiv:2512.24663v1 Announce Type: new -Abstract: Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS methods face significant limitations in computational tractability, structure adaptivity, and optimization robustness across diverse tensor characteristics. They struggle with three key challenges: single-scale optimization missing multi-scale structures, discrete search spaces hindering smooth structure evolution, and separated structure-parameter optimization causing computational inefficiency. We propose RGTN (Renormalization Group guided Tensor Network search), a physics-inspired framework transforming TN-SS via multi-scale renormalization group flows. Unlike fixed-scale discrete search methods, RGTN uses dynamic scale-transformation for continuous structure evolution across resolutions. Its core innovation includes learnable edge gates for optimization-stage topology modification and intelligent proposals based on physical quantities like node tension measuring local stress and edge information flow quantifying connectivity importance. Starting from low-complexity coarse scales and refining to finer ones, RGTN finds compact structures while escaping local minima via scale-induced perturbations. Extensive experiments on light field data, high-order synthetic tensors, and video completion tasks show RGTN achieves state-of-the-art compression ratios and runs 4-600$\times$ faster than existing methods, validating the effectiveness of our physics-inspired approach. - oai:arXiv.org:2512.24663v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Maolin Wang, Bowen Yu, Sheng Zhang, Linjie Mi, Wanyu Wang, Yiqi Wang, Pengyue Jia, Xuetao Wei, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao - - - HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs - https://arxiv.org/abs/2512.24665 - arXiv:2512.24665v1 Announce Type: new -Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for heterogeneous node classification, where an adversary injects a small set of trigger nodes and connections during training to force specific victim nodes to be misclassified into an attacker-chosen label at test time while preserving clean performance. We propose HeteroHBA, a generative backdoor framework that selects influential auxiliary neighbors for trigger attachment via saliency-based screening and synthesizes diverse trigger features and connection patterns to better match the local heterogeneous context. To improve stealthiness, we combine Adaptive Instance Normalization (AdaIN) with a Maximum Mean Discrepancy (MMD) loss to align the trigger feature distribution with benign statistics, thereby reducing detectability, and we optimize the attack with a bilevel objective that jointly promotes attack success and maintains clean accuracy. Experiments on multiple real-world heterogeneous graphs with representative HGNN architectures show that HeteroHBA consistently achieves higher attack success than prior backdoor baselines with comparable or smaller impact on clean accuracy; moreover, the attack remains effective under our heterogeneity-aware structural defense, CSD. These results highlight practical backdoor risks in heterogeneous graph learning and motivate the development of stronger defenses. - oai:arXiv.org:2512.24665v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Honglin Gao, Lan Zhao, Junhao Ren, Xiang Li, Gaoxi Xiao - - - Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients - https://arxiv.org/abs/2512.24667 - arXiv:2512.24667v1 Announce Type: new -Abstract: With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the excessive computation involved in optimizing both the lower- and upper-level functions. Thus, we present the first resource-adaptive distributed bilevel optimization framework with a second-order free hypergradient estimator, which allows each client to optimize the submodels adapted to the available resources. Due to the coupled influence of partial outer parameters x and inner parameters y, it's challenging to theoretically analyze the upper bound regarding the globally averaged hypergradient for full model parameters. The error bound of inner parameter also needs to be reformulated since the local partial training. The provable theorems show that both RABO and RAFBO can achieve an asymptotically optimal convergence rate of $O(1/\sqrt{C_x^{\ast}Q})$, which is dominated by the minimum coverage of the outer parameter $C_x^{\ast}$. Extensive experiments on two different tasks demonstrate the effectiveness and computation efficiency of our proposed methods. - oai:arXiv.org:2512.24667v1 - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mingyi Li, Xiao Zhang, Ruisheng Zheng, Hongjian Shi, Yuan Yuan, Xiuzhen Cheng, Dongxiao Yu - - - VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots - https://arxiv.org/abs/2512.24673 - arXiv:2512.24673v1 Announce Type: new -Abstract: Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing methods suffer from jitter, stalling, or even pauses in robotic action execution, which not only limits the achievable execution speed but also reduces the overall success rate of task completion. This paper introduces VLA-RAIL (A Real-Time Asynchronous Inference Linker), a novel framework designed to address these issues by conducting model inference and robot motion control asynchronously and guaranteeing smooth, continuous, and high-speed action execution. The core contributions of the paper are two fold: a Trajectory Smoother that effectively filters out the noise and jitter in the trajectory of one action chunk using polynomial fitting and a Chunk Fuser that seamlessly align the current executing trajectory and the newly arrived chunk, ensuring position, velocity, and acceleration continuity between two successive action chunks. We validate the effectiveness of VLA-RAIL on a benchmark of dynamic simulation tasks and several real-world manipulation tasks. Experimental results demonstrate that VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates, which will become a key infrastructure for the large-scale deployment of VLA models. - oai:arXiv.org:2512.24673v1 - cs.RO - cs.AI - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Yongsheng Zhao, Lei Zhao, Baoping Cheng, Gongxin Yao, Xuanzhang Wen, Han Gao - - - Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions - https://arxiv.org/abs/2512.24679 - arXiv:2512.24679v1 Announce Type: new -Abstract: Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability. However, existing methods suffer significant performance decline in real-world scenarios where models are tested under unseen working conditions, while domain adaptation approaches are limited to their reliance on target domain samples. Moreover, most existing studies rely on single-modal sensing signals, overlooking the complementary nature of multi-modal information for improving model generalization. To address these limitations, this paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis. A dual disentanglement framework is developed to decouple modality-invariant and modality-specific features, as well as domain-invariant and domain-specific representations, enabling both comprehensive multi-modal representation learning and robust domain generalization. A cross-domain mixed fusion strategy is designed to randomly mix modality information across domains for modality and domain diversity augmentation. Furthermore, a triple-modal fusion mechanism is introduced to adaptively integrate multi-modal heterogeneous information. Extensive experiments are conducted on induction motor fault diagnosis under both unseen constant and time-varying working conditions. The results demonstrate that the proposed method consistently outperforms advanced methods and comprehensive ablation studies further verify the effectiveness of each proposed component and multi-modal fusion. The code is available at: https://github.com/xiapc1996/MMDG. - oai:arXiv.org:2512.24679v1 - cs.AI - eess.SP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Pengcheng Xia, Yixiang Huang, Chengjin Qin, Chengliang Liu - - - ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments - https://arxiv.org/abs/2512.24680 - arXiv:2512.24680v1 Announce Type: new -Abstract: Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate prior target information and limited sensing field of view. We first develop a novel sigma point-based approximation approach to fast and accurately estimate mutual information reward under non-Gaussian belief distributions, utilizing informative sampling in state and observation spaces to mitigate the computational intractability of integral calculation. To tackle significant uncertainty associated with inadequate prior target information, we propose the hierarchical particle structure in ReSPIRe, which not only extracts critical particles for global route guidance, but also adjusts the particle number adaptively for planning efficiency. Building upon the hierarchical structure, we develop the reusable belief tree search approach to build a policy tree for online trajectory planning under uncertainty, which reuses rollout evaluation to improve planning efficiency. Extensive simulations and real-world experiments demonstrate that ReSPIRe outperforms representative benchmark methods with smaller MI approximation error, higher search efficiency, and more stable tracking performance, while maintaining outstanding computational efficiency. - oai:arXiv.org:2512.24680v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/TSMC.2025.3636589 - Kangjie Zhou, Zhaoyang Li, Han Gao, Yao Su, Hangxin Liu, Junzhi Yu, Chang Liu - - - CellSecInspector: Safeguarding Cellular Networks via Automated Security Analysis on Specifications - https://arxiv.org/abs/2512.24682 - arXiv:2512.24682v1 Announce Type: new -Abstract: The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requirements, fail to capture deep semantic dependencies, cross-sentence/clause relationships, and evolving specification behaviors. In this work, we present CellSecInspector, an automated framework for security analysis of 3GPP specifications. CellSecInspector extracts structured state-condition-action (SCA) representations, models mobile network procedures with comprehensive function chains, systematically validates them against 9 foundational security properties under 4 adversarial scenarios, and automatically generates test cases. This end-to-end pipeline enables the automated discovery of vulnerabilities without relying on manually predefined security requirements or rules. Applying CellSecInspector to the well-studied 5G and 4G NAS and RRC specifications, it discovers 43 vulnerabilities, 8 of which are previously unreported. Our findings show that CellSecInspector is a scalable, adaptive, and effective solution to assess 3GPP specifications for safeguarding operational and next-generation cellular networks. - oai:arXiv.org:2512.24682v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ke Xie, Xingyi Zhao, Yiwen Hu, Munshi Saifuzzaman, Wen Li, Shuhan Yuan, Tian Xie, Guan-Hua Tu - - - Waste-to-Energy-Coupled AI Data Centers: Cooling Efficiency and Grid Resilience - https://arxiv.org/abs/2512.24683 - arXiv:2512.24683v1 Announce Type: new -Abstract: AI data-center expansion is increasingly constrained by the coupled availability of deliverable electricity and heat-rejection (cooling) capacity. We propose and evaluate an integrated Waste-to-Energy-AI Data Center configuration that treats cooling as a first-class energy service rather than an unavoidable electricity burden. The coupled system is modeled as an input-output 'black box' with transparent boundaries and a standalone benchmark in which mechanical chilling is powered by grid electricity. The central mechanism is energy-grade matching: low-grade WtE thermal output drives absorption cooling to deliver chilled service, thereby displacing baseline cooling electricity. We show that thermoeconomic superiority is governed by three first-order determinants, (i) cooling coverage of IT heat load, (ii) parasitic electricity for transport and auxiliaries, and (iii) distance-driven delivery decay, yielding a break-even corridor beyond which net benefits vanish. Comparative statics characterize sensitivity to IT utilization, feedstock quality (waste LHV and throughput), climate parameterization, and corridor distance. We translate these accounting gains into decision language through a computable prototype for Levelized Cost of Computing (LCOC) and an ESG valuation channel grounded in measurable mechanisms, without re-deriving full lifecycle inventories. The framework provides siting-ready feasibility conditions for WtE-AIDC coupling in urban AI corridors under grid stress. - oai:arXiv.org:2512.24683v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qi He, Chunyu Qu - - - R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memory - https://arxiv.org/abs/2512.24684 - arXiv:2512.24684v1 Announce Type: new -Abstract: We present R-Debater, an agentic framework for generating multi-turn debates built on argumentative memory. Grounded in rhetoric and memory studies, the system views debate as a process of recalling and adapting prior arguments to maintain stance consistency, respond to opponents, and support claims with evidence. Specifically, R-Debater integrates a debate knowledge base for retrieving case-like evidence and prior debate moves with a role-based agent that composes coherent utterances across turns. We evaluate on standardized ORCHID debates, constructing a 1,000-item retrieval corpus and a held-out set of 32 debates across seven domains. Two tasks are evaluated: next-utterance generation, assessed by InspireScore (subjective, logical, and factual), and adversarial multi-turn simulations, judged by Debatrix (argument, source, language, and overall). Compared with strong LLM baselines, R-Debater achieves higher single-turn and multi-turn scores. Human evaluation with 20 experienced debaters further confirms its consistency and evidence use, showing that combining retrieval grounding with structured planning yields more faithful, stance-aligned, and coherent debates across turns. - oai:arXiv.org:2512.24684v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Maoyuan Li, Zhongsheng Wang, Haoyuan Li, Jiamou Liu - - - BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis - https://arxiv.org/abs/2512.24686 - arXiv:2512.24686v1 Announce Type: new -Abstract: Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysis and maintenance recommendations. To address these limitations, this paper proposes BatteryAgent, a hierarchical framework that integrates physical knowledge features with the reasoning capabilities of Large Language Models (LLMs). The framework comprises three core modules: (1) A Physical Perception Layer that utilizes 10 mechanism-based features derived from electrochemical principles, balancing dimensionality reduction with physical fidelity; (2) A Detection and Attribution Layer that employs Gradient Boosting Decision Trees and SHAP to quantify feature contributions; and (3) A Reasoning and Diagnosis Layer that leverages an LLM as the agent core. This layer constructs a "numerical-semantic" bridge, combining SHAP attributions with a mechanism knowledge base to generate comprehensive reports containing fault types, root cause analysis, and maintenance suggestions. Experimental results demonstrate that BatteryAgent effectively corrects misclassifications on hard boundary samples, achieving an AUROC of 0.986, which significantly outperforms current state-of-the-art methods. Moreover, the framework extends traditional binary detection to multi-type interpretable diagnosis, offering a new paradigm shift from "passive detection" to "intelligent diagnosis" for battery safety management. - oai:arXiv.org:2512.24686v1 - cs.AI - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Songqi Zhou, Ruixue Liu, Boman Su, Jiazhou Wang, Yixing Wang, Benben Jiang - - - CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System - https://arxiv.org/abs/2512.24688 - arXiv:2512.24688v1 Announce Type: new -Abstract: Relative localization is critical for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non-line-of-sight degradation and outliers in complex environments. Robust and efficient fusion of inter-robot measurements such as bearings, distances, and inertials for tens of robots remains challenging. We present CREPES-X (Cooperative RElative Pose Estimation System with multiple eXtended features), a hierarchical relative localization framework that enhances speed, accuracy, and robustness under challenging conditions, without requiring any global information. CREPES-X starts with a compact hardware design: InfraRed (IR) LEDs, an IR camera, an ultra-wideband module, and an IMU housed in a cube no larger than 6cm on each side. Then CREPES-X implements a two-stage hierarchical estimator to meet different requirements, considering speed, accuracy, and robustness. First, we propose a single-frame relative estimator that provides instant relative poses for multi-robot setups through a closed-form solution and robust bearing outlier rejection. Then a multi-frame relative estimator is designed to offer accurate and robust relative states by exploring IMU pre-integration via robocentric relative kinematics with loosely- and tightly-coupled optimization. Extensive simulations and real-world experiments validate the effectiveness of CREPES-X, showing robustness to up to 90% bearing outliers, proving resilience in challenging conditions, and achieving RMSE of 0.073m and 1.817{\deg} in real-world datasets. - oai:arXiv.org:2512.24688v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhehan Li, Zheng Wang, Jiadong Lu, Qi Liu, Zhiren Xun, Yue Wang, Fei Gao, Chao Xu, Yanjun Cao - - - MUSIC: MUlti-Step Instruction Contrast for Multi-Turn Reward Models - https://arxiv.org/abs/2512.24693 - arXiv:2512.24693v1 Announce Type: new -Abstract: Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human evaluation. Multi-turn reward models (RMs) offer a scalable alternative and can provide valuable signals for guiding LLM training. While recent work has advanced multi-turn \textit{training} techniques, effective automated \textit{evaluation} specifically for multi-turn interactions lags behind. We observe that standard preference datasets, typically contrasting responses based only on the final conversational turn, provide insufficient signal to capture the nuances of multi-turn interactions. Instead, we find that incorporating contrasts spanning \textit{multiple} turns is critical for building robust multi-turn RMs. Motivated by this finding, we propose \textbf{MU}lti-\textbf{S}tep \textbf{I}nstruction \textbf{C}ontrast (MUSIC), an unsupervised data augmentation strategy that synthesizes contrastive conversation pairs exhibiting differences across multiple turns. Leveraging MUSIC on the Skywork preference dataset, we train a multi-turn RM based on the Gemma-2-9B-Instruct model. Empirical results demonstrate that our MUSIC-augmented RM outperforms baseline methods, achieving higher alignment with judgments from advanced proprietary LLM judges on multi-turn conversations, crucially, without compromising performance on standard single-turn RM benchmarks. - oai:arXiv.org:2512.24693v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wenzhe Li, Shujian Zhang, Wenxuan Zhou, John Lambert, Chi Jin, Andrew Hard, Rajiv Mathews, Lun Wang - - - Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach - https://arxiv.org/abs/2512.24694 - arXiv:2512.24694v1 Announce Type: new -Abstract: Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we move toward the next generation of wireless networks, mobility is increasingly embedded in many real-world applications. The user mobility, either natural or induced, enables clients to act as relays or bridges, thus enhancing information flow in sparse networks; however, its impact on DFL has been largely overlooked despite its potential. In this work, we systematically investigate the role of mobility in improving DFL performance. We first establish the convergence of DFL in sparse networks under user mobility and theoretically demonstrate that even random movement of a fraction of users can significantly boost performance. Building upon this insight, we propose a DFL framework that utilizes mobile users with induced mobility patterns, allowing them to exploit the knowledge of data distribution to determine their trajectories to enhance information propagation through the network. Through extensive experiments, we empirically confirm our theoretical findings, validate the superiority of our approach over baselines, and provide a comprehensive analysis of how various network parameters influence DFL performance in mobile networks. - oai:arXiv.org:2512.24694v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Reza Jahani, Md Farhamdur Reza, Richeng Jin, Huaiyu Dai - - - Nested Learning: The Illusion of Deep Learning Architectures - https://arxiv.org/abs/2512.24695 - arXiv:2512.24695v1 Announce Type: new -Abstract: Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Learning (NL), that coherently represents a machine learning model with a set of nested, multi-level, and/or parallel optimization problems, each of which with its own context flow. Through the lenses of NL, existing deep learning methods learns from data through compressing their own context flow, and in-context learning naturally emerges in large models. NL suggests a philosophy to design more expressive learning algorithms with more levels, resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities. We advocate for NL by presenting three core contributions: (1) Expressive Optimizers: We show that known gradient-based optimizers, such as Adam, SGD with Momentum, etc., are in fact associative memory modules that aim to compress the gradients' information (by gradient descent). Building on this insight, we present other more expressive optimizers with deep memory and/or more powerful learning rules; (2) Self-Modifying Learning Module: Taking advantage of NL's insights on learning algorithms, we present a sequence model that learns how to modify itself by learning its own update algorithm; and (3) Continuum Memory System: We present a new formulation for memory system that generalizes the traditional viewpoint of long/short-term memory. Combining our self-modifying sequence model with the continuum memory system, we present a continual learning module, called Hope, showing promising results in language modeling, knowledge incorporation, and few-shot generalization tasks, continual learning, and long-context reasoning tasks. - oai:arXiv.org:2512.24695v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni - - - Causal Discovery with Mixed Latent Confounding via Precision Decomposition - https://arxiv.org/abs/2512.24696 - arXiv:2512.24696v1 Announce Type: new -Abstract: We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many variables while others influence only small subsets. This setting is common in practice and poses a challenge for existing methods: differentiable and score-based DAG learners can misinterpret global latent effects as causal edges, while latent-variable graphical models recover only undirected structure. - We propose \textsc{DCL-DECOR}, a modular, precision-led pipeline that separates these roles. The method first isolates pervasive latent effects by decomposing the observed precision matrix into a structured component and a low-rank component. The structured component corresponds to the conditional distribution after accounting for pervasive confounders and retains only local dependence induced by the causal graph and localized confounding. A correlated-noise DAG learner is then applied to this deconfounded representation to recover directed edges while modeling remaining structured error correlations, followed by a simple reconciliation step to enforce bow-freeness. - We provide identifiability results that characterize the recoverable causal target under mixed confounding and show how the overall problem reduces to well-studied subproblems with modular guarantees. Synthetic experiments that vary the strength and dimensionality of pervasive confounding demonstrate consistent improvements in directed edge recovery over applying correlated-noise DAG learning directly to the confounded data. - oai:arXiv.org:2512.24696v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Amir Asiaee, Samhita Pal, James O'quinn, James P. Long - - - Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model Homotopy Transfer - https://arxiv.org/abs/2512.24698 - arXiv:2512.24698v1 Announce Type: new -Abstract: Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order models can help mitigate these challenges. However, the model discrepancy poses a significant challenge when transferring policies to full-body dynamics environments. In this work, we introduce a continuation-based learning framework that combines simplified model pretraining and model homotopy transfer to efficiently generate and refine complex dynamic behaviors. First, we pretrain the policy using a single rigid body model to capture core motion patterns in a simplified environment. Next, we employ a continuation strategy to progressively transfer the policy to the full-body environment, minimizing performance loss. To define the continuation path, we introduce a model homotopy from the single rigid body model to the full-body model by gradually redistributing mass and inertia between the trunk and legs. The proposed method not only achieves faster convergence but also demonstrates superior stability during the transfer process compared to baseline methods. Our framework is validated on a range of dynamic tasks, including flips and wall-assisted maneuvers, and is successfully deployed on a real quadrupedal robot. - oai:arXiv.org:2512.24698v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Dongyun Kang, Min-Gyu Kim, Tae-Gyu Song, Hajun Kim, Sehoon Ha, Hae-Won Park - - - Average Consensus with Dynamic Quantization Framing and Finite-Time Termination over Limited-Bandwidth Directed Networks - https://arxiv.org/abs/2512.24700 - arXiv:2512.24700v1 Announce Type: new -Abstract: This paper proposes a deterministic distributed algorithm, referred to as PP-ACDC, that achieves exact average consensus over possibly unbalanced directed graphs using only a fixed and a priori specified number of quantization bits. The method integrates Push-Pull (surplus) consensus dynamics with a dynamic quantization framing scheme combining zooming and midpoint shifting, enabling agents to preserve the true global average while progressively refining their quantization precision. We establish a rigorous convergence theory showing that PP-ACDC achieves asymptotic (exact) average consensus on any strongly connected digraph under appropriately chosen quantization parameters. Moreover, we develop a fully distributed and synchronized finite-time termination mechanism, and we provide a formal proof on the detection of $\epsilon$-convergence to the average within a finite number of iterations. Numerical simulations corroborate the theoretical results and demonstrate that PP-ACDC achieves reliable, communication-efficient, and precise average consensus even under very tight bit budgets, underscoring its suitability for large-scale and resource-constrained multi-agent systems operating over directed networks. - oai:arXiv.org:2512.24700v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Evagoras Makridis, Gabriele Oliva, Apostolos I. Rikos, Themistoklis Charalambous - - - Evolving, Not Training: Zero-Shot Reasoning Segmentation via Evolutionary Prompting - https://arxiv.org/abs/2512.24702 - arXiv:2512.24702v1 Announce Type: new -Abstract: Reasoning Segmentation requires models to interpret complex, context-dependent linguistic queries to achieve pixel-level localization. Current dominant approaches rely heavily on Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). However, SFT suffers from catastrophic forgetting and domain dependency, while RL is often hindered by training instability and rigid reliance on predefined reward functions. Although recent training-free methods circumvent these training burdens, they are fundamentally limited by a static inference paradigm. These methods typically rely on a single-pass "generate-then-segment" chain, which suffers from insufficient reasoning depth and lacks the capability to self-correct linguistic hallucinations or spatial misinterpretations. In this paper, we challenge these limitations and propose EVOL-SAM3, a novel zero-shot framework that reformulates reasoning segmentation as an inference-time evolutionary search process. Instead of relying on a fixed prompt, EVOL-SAM3 maintains a population of prompt hypotheses and iteratively refines them through a "Generate-Evaluate-Evolve" loop. We introduce a Visual Arena to assess prompt fitness via reference-free pairwise tournaments, and a Semantic Mutation operator to inject diversity and correct semantic errors. Furthermore, a Heterogeneous Arena module integrates geometric priors with semantic reasoning to ensure robust final selection. Extensive experiments demonstrate that EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting. The code is available at https://github.com/AHideoKuzeA/Evol-SAM3. - oai:arXiv.org:2512.24702v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kai Ye, Xiaotong You, Jianghang Lin, Jiayi Ji, Pingyang Dai, Liujuan Cao - - - BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework - https://arxiv.org/abs/2512.24708 - arXiv:2512.24708v1 Announce Type: new -Abstract: The challenge of effectively transferring knowledge across multiple tasks is of critical importance and is also present in downstream tasks with foundation models. However, the nature of transfer, its transitive-intransitive nature, is still an open problem, and negative transfer remains a significant obstacle. Selection of beneficial auxiliary task sets in multi-task learning is frequently hindered by the high computational cost of their evaluation, the high number of plausible candidate auxiliary sets, and the varying complexity of selection across target tasks. - To address these constraints, we introduce BandiK, a novel three-stage multi-task auxiliary task subset selection method using multi-bandits, where each arm pull evaluates candidate auxiliary sets by training and testing a multiple output neural network on a single random train-test dataset split. Firstly, BandiK estimates the pairwise transfers between tasks, which helps in identifying which tasks are likely to benefit from joint learning. In the second stage, it constructs a linear number of candidate sets of auxiliary tasks (in the number of all tasks) for each target task based on the initial estimations, significantly reducing the exponential number of potential auxiliary task sets. Thirdly, it employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits. To enhance efficiency, BandiK integrates these individual task-specific MABs into a multi-bandit structure. The proposed multi-bandit solution exploits that the same neural network realizes multiple arms of different individual bandits corresponding to a given candidate set. This semi-overlapping arm property defines a novel multi-bandit cost/reward structure utilized in BandiK. - oai:arXiv.org:2512.24708v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Andr\'as Millinghoffer (Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, E-Group ICT Software Zrt., Budapest, Hungary), Andr\'as Formanek (Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Department of Electrical Engineering), Andr\'as Antos (Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics), P\'eter Antal (Department of Artificial Intelligence and Systems Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, E-Group ICT Software Zrt., Budapest, Hungary) - - - MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints - https://arxiv.org/abs/2512.24711 - arXiv:2512.24711v1 Announce Type: new -Abstract: In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose \textbf{MEIC-DT}, a novel dual-threshold, memory-efficient incremental clustering approach based on a lightweight Transformer. MEIC-DT features a dual-threshold constraint mechanism designed to precisely control the Transformer's input scale within a predefined memory budget. This mechanism incorporates a Statistics-Aware Eviction Strategy (\textbf{SAES}), which utilizes distinct statistical profiles from the training and inference phases for intelligent cache management. Furthermore, we introduce an Internal Regularization Policy (\textbf{IRP}) that strategically condenses clusters by selecting the most representative mentions, thereby preserving semantic integrity. Extensive experiments on common benchmarks demonstrate that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints. - oai:arXiv.org:2512.24711v1 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao, Zhitong Wang, Cheng Huang, Yingli Shen, Yufeng Han, Wenhao Li, Cunliang Kong, Maosong Sun - - - LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving - https://arxiv.org/abs/2512.24712 - arXiv:2512.24712v1 Announce Type: new -Abstract: Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yielding to emergency vehicles, complying with traffic officers' gestures, or stopping for school buses, are intuitive for humans yet difficult to encode explicitly. Although large vision-language models (VLMs) can interpret such semantics, their inference cost makes them impractical for real-time deployment.This work proposes LSRE, a Latent Semantic Rule Encoding framework that converts sparsely sampled VLM judgments into decision boundaries within the latent space of a recurrent world model. By encoding language-defined safety semantics into a lightweight latent classifier, LSRE enables real-time semantic risk assessment at 10 Hz without per-frame VLM queries. Experiments on six semantic-failure scenarios in CARLA demonstrate that LSRE attains semantic risk detection accuracy comparable to a large VLM baseline, while providing substantially earlier hazard anticipation and maintaining low computational latency. LSRE further generalizes to rarely seen semantic-similar test cases, indicating that language-guided latent classification offers an effective and deployable mechanism for semantic safety monitoring in autonomous driving. - oai:arXiv.org:2512.24712v1 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qian Cheng, Weitao Zhou, Cheng Jing, Nanshan Deng, Junze Wen, Zhaoyang Liu, Kun Jiang, Diange Yang - - - FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference - https://arxiv.org/abs/2512.24713 - arXiv:2512.24713v1 Announce Type: new -Abstract: Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial computation and memory requirements, which significantly impedes their deployment in resource-constrained environments. To address this challenge, this work introduces an automation framework that leverages weight pruning and low-bit quantization, and presents a hardware-software co-design method that generates accelerators on the Field-Programmable Gate Array (FPGA) platform. In particular, we implement a unified pipeline that applies N:M structured pruning and 4-bit integer quantization to reduce the memory footprint, followed by optimized dequantization and matrix multiplication to enhance LLM inference on several hardware platforms, including CPUs, NVIDIA GPUs with Dense and 2:4 Sparse Tensor Cores, and a custom systolic-array-based FPGA accelerator. Utilizing 2:4 sparsity combined with quantization on $4096 \times 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines. Scaling analysis on the LLaMA-7B model further shows that structured sparsity enhances the throughput per token by $1.36\times$. These results demonstrate the synergy of fine-grained N:M sparsity and quantization for enabling efficient and deployable LLM inference, while the proposed FPGA accelerator offers a flexible architectural path for supporting a broader class of sparsity patterns beyond the fixed 2:4 hardware constraints. - oai:arXiv.org:2512.24713v1 - cs.LG - cs.AR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Fen-Yu Hsieh, Yun-Chang Teng, Ding-Yong Hong, Jan-Jan Wu - - - Boundary error control for numerical solution of BSDEs by the convolution-FFT method - https://arxiv.org/abs/2512.24714 - arXiv:2512.24714v1 Announce Type: new -Abstract: We first review the convolution fast-Fourier-transform (CFFT) approach for the numerical solution of backward stochastic differential equations (BSDEs) introduced in (Hyndman and Oyono Ngou, 2017). We then propose a method for improving the boundary errors obtained when valuing options using this approach. We modify the damping and shifting schemes used in the original formulation, which transforms the target function into a bounded periodic function so that Fourier transforms can be applied successfully. Time-dependent shifting reduces boundary error significantly. We present numerical results for our implementation and provide a detailed error analysis showing the improved accuracy and convergence of the modified convolution method. - oai:arXiv.org:2512.24714v1 - math.NA - cs.NA - math.PR - q-fin.CP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xiang Gao, Cody Hyndman - - - MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation - https://arxiv.org/abs/2512.24715 - arXiv:2512.24715v1 Announce Type: new -Abstract: Federated recommendations (FRs) provide personalized services while preserving user privacy by keeping user data on local clients, which has attracted significant attention in recent years. However, due to the strict privacy constraints inherent in FRs, access to user-item interaction data and user profiles across clients is highly restricted, making it difficult to learn globally effective representations for new (cold-start) items. Consequently, the item cold-start problem becomes even more challenging in FRs. Existing solutions typically predict embeddings for new items through the attribute-to-embedding mapping paradigm, which establishes a fixed one-to-one correspondence between item attributes and their embeddings. However, this one-to-one mapping paradigm often fails to model varying data distributions and tends to cause embedding misalignment, as verified by our empirical studies. To this end, we propose MDiffFR, a novel generation-based modality-guided diffusion method for cold-start items in FRs. In this framework, we employ a tailored diffusion model on the server to generate embeddings for new items, which are then distributed to clients for cold-start inference. To align item semantics, we deploy a pre-trained modality encoder to extract modality features as conditional signals to guide the reverse denoising process. Furthermore, our theoretical analysis verifies that the proposed method achieves stronger privacy guarantees compared to existing mapping-based approaches. Extensive experiments on four real datasets demonstrate that our method consistently outperforms all baselines in FRs. - oai:arXiv.org:2512.24715v1 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kang Fu, Honglei Zhang, Xuechao Zou, Yidong Li - - - Equivalence of Personalized PageRank and Successor Representations - https://arxiv.org/abs/2512.24722 - arXiv:2512.24722v1 Announce Type: new -Abstract: The hippocampus appears to implement two core but highly distinct functions in the brain: long term memory retrieval and planning and spatial navigation. Naively, these functions appear very different algorithmically. In this short note, we demonstrate that two powerful algorithms that have each independently been proposed to underlie the hippocampal operation for each function -- personalized page-rank for memory retrieval, and successor representations for planning and navigation, are in fact isomorphic and utilize the same underlying representation -- the stationary distribution of a random walk on a graph. We hypothesize that the core computational function of the hippocampus is to compute this representation on arbitrary input graphs. - oai:arXiv.org:2512.24722v1 - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Beren Millidge - - - FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation - https://arxiv.org/abs/2512.24724 - arXiv:2512.24724v1 Announce Type: new -Abstract: In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small model at capacity-sensitive stages and intermediate stages, respectively. We further introduce simple criteria to choose stage boundaries and provide a velocity-divergence analysis as an effective proxy for identifying capacity-sensitive regions. Across LTX-Video (2B/13B) and WAN 2.1 (1.3B/14B), FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models. FlowBlending is also compatible with existing sampling-acceleration techniques, enabling up to 2x additional speedup. Project page is available at: https://jibin86.github.io/flowblending_project_page. - oai:arXiv.org:2512.24724v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Jibin Song, Mingi Kwon, Jaeseok Jeong, Youngjung Uh - - - EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation - https://arxiv.org/abs/2512.24731 - arXiv:2512.24731v1 Announce Type: new -Abstract: Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-to-audio (VT2A), the current formulation faces three key limitations: First, an imbalance between visual and textual conditioning that leads to visual dominance; Second, the absence of a concrete definition for fine-grained controllable generation; Third, weak instruction understanding and following, as existing datasets rely on brief categorical tags. To address these limitations, we introduce EchoFoley, a new task designed for video-grounded sound generation with both event level local control and hierarchical semantic control. Our symbolic representation for sounding events specifies when, what, and how each sound is produced within a video or instruction, enabling fine-grained controls like sound generation, insertion, and editing. To support this task, we construct EchoFoley-6k, a large-scale, expert-curated benchmark containing over 6,000 video-instruction-annotation triplets. Building upon this foundation, we propose EchoVidia a sounding-event-centric agentic generation framework with slow-fast thinking strategy. Experiments show that EchoVidia surpasses recent VT2A models by 40.7% in controllability and 12.5% in perceptual quality. - oai:arXiv.org:2512.24731v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Bingxuan Li, Yiming Cui, Yicheng He, Yiwei Wang, Shu Zhang, Longyin Wen, Yulei Niu - - - BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature - https://arxiv.org/abs/2512.24733 - arXiv:2512.24733v1 Announce Type: new -Abstract: Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and interventions. Although recent work has explored using large language models (LLMs) to improve PE-based interpretation, the lack of a standardized benchmark for end-to-end multi-omics pathway mechanism elucidation has largely confined evaluation to small, manually curated datasets or ad hoc case studies, hindering reproducible progress. To address this issue, we introduce BIOME-Bench, constructed via a rigorous four-stage workflow, to evaluate two core capabilities of LLMs in multi-omics analysis: Biomolecular Interaction Inference and end-to-end Multi-Omics Pathway Mechanism Elucidation. We develop evaluation protocols for both tasks and conduct comprehensive experiments across multiple strong contemporary models. Experimental results demonstrate that existing models still exhibit substantial deficiencies in multi-omics analysis, struggling to reliably distinguish fine-grained biomolecular relation types and to generate faithful, robust pathway-level mechanistic explanations. - oai:arXiv.org:2512.24733v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Sibo Wei, Peng Chen, Lifeng Dong, Yin Luo, Lei Wang, Peng Zhang, Wenpeng Lu, Jianbin Guo, Hongjun Yang, Dajun Zeng - - - Exact compensation of communication delays for discrete-time heterogeneous multi-agent linear systems with applications to SIR epidemic model - https://arxiv.org/abs/2512.24735 - arXiv:2512.24735v1 Announce Type: new -Abstract: This paper investigates the output synchronization problem for discrete-time heterogeneous multi-agent systems (MASs) subject to distinct communication delays. The presence of such delays prevents the instantaneous delivery of information from neighboring nodes, thereby severely degrading the performance of standard distributed control schemes. To overcome this, we propose a prediction-based framework for exact delay compensation. Specifically, we introduce predictors combined with a mechanism of distributed predictors, which enables the recursive reconstruction of future state information across the communication network. Building upon these predictors, we construct prediction-based distributed observers and formulate both prediction-based distributed state-feedback and dynamic output-feedback controllers. Theoretical analysis confirms that the proposed strategy eliminates the impact of delays after a finite number of steps, ensuring output synchronization. The effectiveness of the methods is validated through a numerical example and a Koopman operator-based linear Susceptible-Infected-Recovered (SIR) epidemic model. Notably, for a population of 4 million, the proposed delay compensation strategy achieves a reduction of over 200,000 infected individuals at the peak, underscoring its potential significance in epidemic mitigation. - oai:arXiv.org:2512.24735v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qin Fang, Mamadou Diagne, Yang Zhu - - - SLM-TTA: A Framework for Test-Time Adaptation of Generative Spoken Language Models - https://arxiv.org/abs/2512.24739 - arXiv:2512.24739v1 Announce Type: new -Abstract: Spoken Language Models (SLMs) are increasingly central to modern speech-driven applications, but performance degrades under acoustic shift - real-world noise, reverberation, and microphone variation. Prior solutions rely on offline domain adaptation, which is post-hoc, data-intensive, and slow. We introduce the first test-time adaptation (TTA) framework for generative SLMs that process interleaved audio-text prompts. Our method updates a small, targeted subset of parameters during inference using only the incoming utterance, requiring no source data or labels. This stabilizes token distributions and improves robustness to acoustic variability without degrading core task accuracy. Evaluated on automatic speech recognition, speech translation, and 19 audio understanding tasks from AIR-Bench, our approach yields consistent gains under diverse corruptions. Because adaptation touches only a small fraction of weights, it is both compute- and memory-efficient, supporting deployment on resource-constrained platforms. This work enhances the robustness and adaptability of generative SLMs for real-world speech-driven applications. - oai:arXiv.org:2512.24739v1 - cs.SD - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuan-Kuei Wu (Ethan), Yang Liu (Ethan), Yiteng Huang (Ethan), Zhaojun Yang (Ethan), Haibin Wu (Ethan), Ruizhe Huang (Ethan), Yi-Te (Ethan), Hsu, Shuyu Kong, Ming Sun, Florian Metze, Li Wan - - - Control of Microrobots with Reinforcement Learning under On-Device Compute Constraints - https://arxiv.org/abs/2512.24740 - arXiv:2512.24740v1 Announce Type: new -Abstract: An important function of autonomous microrobots is the ability to perform robust movement over terrain. This paper explores an edge ML approach to microrobot locomotion, allowing for on-device, lower latency control under compute, memory, and power constraints. This paper explores the locomotion of a sub-centimeter quadrupedal microrobot via reinforcement learning (RL) and deploys the resulting controller on an ultra-small system-on-chip (SoC), SC$\mu$M-3C, featuring an ARM Cortex-M0 microcontroller running at 5 MHz. We train a compact FP32 multilayer perceptron (MLP) policy with two hidden layers ($[128, 64]$) in a massively parallel GPU simulation and enhance robustness by utilizing domain randomization over simulation parameters. We then study integer (Int8) quantization (per-tensor and per-feature) to allow for higher inference update rates on our resource-limited hardware, and we connect hardware power budgets to achievable update frequency via a cycles-per-update model for inference on our Cortex-M0. We propose a resource-aware gait scheduling viewpoint: given a device power budget, we can select the gait mode (trot/intermediate/gallop) that maximizes expected RL reward at a corresponding feasible update frequency. Finally, we deploy our MLP policy on a real-world large-scale robot on uneven terrain, qualitatively noting that domain-randomized training can improve out-of-distribution stability. We do not claim real-world large-robot empirical zero-shot transfer in this work. - oai:arXiv.org:2512.24740v1 - cs.RO - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yichen Liu, Kesava Viswanadha, Zhongyu Li, Nelson Lojo, Kristofer S. J. Pister - - - Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression - https://arxiv.org/abs/2512.24742 - arXiv:2512.24742v1 Announce Type: new -Abstract: The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Existing benchmarks often lack the specific metrics necessary to holistically assess the unique characteristics of different methods, such as rendering speed, rate distortion trade-offs memory efficiency, and geometric accuracy. To address this gap, we introduce Splatwizard, a unified benchmark toolkit designed specifically for benchmarking 3DGS compression models. Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques proposed by previous work. Besides, an integrated pipeline that automates the calculation of key performance indicators, including image-based quality metrics, chamfer distance of reconstruct mesh, rendering frame rates, and computational resource consumption is included in the framework as well. Code is available at https://github.com/splatwizard/splatwizard - oai:arXiv.org:2512.24742v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xiang Liu, Yimin Zhou, Jinxiang Wang, Yujun Huang, Shuzhao Xie, Shiyu Qin, Mingyao Hong, Jiawei Li, Yaowei Wang, Zhi Wang, Shu-Tao Xia, Bin Chen - - - Analyzing Communication Predictability in LLM Training - https://arxiv.org/abs/2512.24750 - arXiv:2512.24750v1 Announce Type: new -Abstract: Effective communication is essential in distributed training, with predictability being one of its most significant characteristics. However, existing studies primarily focus on exploiting predictability through online profiling for runtime optimization, without a systematic understanding of it. In this work, we aim to systematically formulate communication predictability in distributed training, particularly in Large Language Models (LLMs) that utilize hybrid parallelism. Our analysis focuses on both traffic patterns and communication overhead. Specifically, we investigate predictable traffic patterns in typical LLMs and evaluate how various factors influence GPU utilization and effective bandwidth (two critical variables affecting communication overhead). Furthermore, we develop an analytical formulation to estimate communication overhead in LLM training, which is validated with high accuracy against empirical data. Leveraging this formulation, we propose a configuration tuning tool, ConfigTuner, to optimize training performance. Compared to Megatron-LM, the training configurations optimized by ConfigTuner demonstrate up to a 1.36$\times$ increase in throughput. Compared to Alpa, ConfigTuner generates the same configuration suggestion while significantly reducing the search complexity. - oai:arXiv.org:2512.24750v1 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wenxue Li, Xiangzhou Liu, Yuxuan Li, Yilun Jin, Zhenghang Ren, Xudong Liao, Han Tian, Bo Ren, Zhizhen Zhong, Guyue Liu, Ying Zhang, Kai Chen - - - Trustworthy Equipment Monitoring via Cascaded Anomaly Detection and Thermal Localization - https://arxiv.org/abs/2512.24755 - arXiv:2512.24755v1 Announce Type: new -Abstract: Predictive maintenance demands accurate anomaly detection and trustable explanations. Although multimodal fusion of sensor time-series and thermal imagery shows promise, we demonstrate that naive fusion strategies can paradoxically degrade performance. This paper introduces a Cascaded Anomaly Detection framework that decouples detection and localization. Stage 1 employs an LSTM-based sensor encoder with temporal attention for high-accuracy detection, while Stage 2 activates a CNN-based thermal encoder for post-detection fault localization. Our results reveal that sensor-only detection outperforms full fusion by 8.3 percentage points (93.08% vs. 84.79% F1-score), challenging the assumption that additional modalities invariably improve performance. We further contribute an explainability pipeline integrating SHAP, temporal/spatial attention, and gate weight analysis. This analysis uncovers a "modality bias" where fusion models assign 65-87% weight to the weaker thermal modality. Validated on a real-world bearing dataset (78,397 samples), our cascaded approach achieves state-of-the-art accuracy while providing actionable diagnostics for maintenance decision-making. - oai:arXiv.org:2512.24755v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sungwoo Kang - - - OpenOneRec Technical Report - https://arxiv.org/abs/2512.24762 - arXiv:2512.24762v1 Announce Type: new -Abstract: While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation systems and general intelligence. Constrained by isolated data, they operate as domain specialists-proficient in pattern matching but lacking world knowledge, reasoning capabilities, and instruction following. This limitation is further compounded by the lack of a holistic benchmark to evaluate such integrated capabilities. To address this, our contributions are: 1) RecIF Bench & Open Data: We propose RecIF-Bench, a holistic benchmark covering 8 diverse tasks that thoroughly evaluate capabilities from fundamental prediction to complex reasoning. Concurrently, we release a massive training dataset comprising 96 million interactions from 160,000 users to facilitate reproducible research. 2) Framework & Scaling: To ensure full reproducibility, we open-source our comprehensive training pipeline, encompassing data processing, co-pretraining, and post-training. Leveraging this framework, we demonstrate that recommendation capabilities can scale predictably while mitigating catastrophic forgetting of general knowledge. 3) OneRec-Foundation: We release OneRec Foundation (1.7B and 8B), a family of models establishing new state-of-the-art (SOTA) results across all tasks in RecIF-Bench. Furthermore, when transferred to the Amazon benchmark, our models surpass the strongest baselines with an average 26.8% improvement in Recall@10 across 10 diverse datasets (Figure 1). This work marks a step towards building truly intelligent recommender systems. Nonetheless, realizing this vision presents significant technical and theoretical challenges, highlighting the need for broader research engagement in this promising direction. - oai:arXiv.org:2512.24762v1 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Guorui Zhou, Honghui Bao, Jiaming Huang, Jiaxin Deng, Jinghao Zhang, Junda She, Kuo Cai, Lejian Ren, Lu Ren, Qiang Luo, Qianqian Wang, Qigen Hu, Rongzhou Zhang, Ruiming Tang, Shiyao Wang, Wuchao Li, Xiangyu Wu, Xinchen Luo, Xingmei Wang, Yifei Hu, Yunfan Wu, Zhanyu Liu, Zhiyang Zhang, Zixing Zhang, Bo Chen, Bin Wen, Chaoyi Ma, Chengru Song, Chenglong Chu, Defu Lian, Fan Yang, Feng Jiang, Hongtao Cheng, Huanjie Wang, Kun Gai, Pengfei Zheng, Qiang Wang, Rui Huang, Siyang Mao, Tingting Gao, Wei Yuan, Yan Wang, Yang Zhou, Yi Su, Zexuan Cheng, Zhixin Ling, Ziming Li - - - UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning - https://arxiv.org/abs/2512.24763 - arXiv:2512.24763v1 Announce Type: new -Abstract: 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding. A key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions. Existing methods use a two-stage approach in which some rely on contrastive learning with hyperparameter-sensitive clustering, while others preprocess labels for consistency. We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives. This embedding is then efficiently decoded into instance labels through a novel "Embedding-to-Label" process, effectively integrating the optimization. While this unified framework offers substantial benefits, we observed artifacts at the object boundaries. To address the object boundary issues, we propose hard-mining samples along these boundaries. However, directly applying hard mining to the feature embeddings proved unstable. Therefore, we apply a linear layer to the rasterized feature embeddings before calculating the triplet loss, which stabilizes training and significantly improves performance. Our method outperforms baselines qualitatively and quantitatively on the ScanNet, Replica3D, and Messy-Rooms datasets. - oai:arXiv.org:2512.24763v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ankit Dhiman, Srinath R, Jaswanth Reddy, Lokesh R Boregowda, Venkatesh Babu Radhakrishnan - - - Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow - https://arxiv.org/abs/2512.24766 - arXiv:2512.24766v1 Announce Type: new -Abstract: Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions demanded by robotic systems. We observe that given an initial image and task instruction, these models excel at synthesizing sensible object motions. Thus, we introduce Dream2Flow, a framework that bridges video generation and robotic control through 3D object flow as an intermediate representation. Our method reconstructs 3D object motions from generated videos and formulates manipulation as object trajectory tracking. By separating the state changes from the actuators that realize those changes, Dream2Flow overcomes the embodiment gap and enables zero-shot guidance from pre-trained video models to manipulate objects of diverse categories-including rigid, articulated, deformable, and granular. Through trajectory optimization or reinforcement learning, Dream2Flow converts reconstructed 3D object flow into executable low-level commands without task-specific demonstrations. Simulation and real-world experiments highlight 3D object flow as a general and scalable interface for adapting video generation models to open-world robotic manipulation. Videos and visualizations are available at https://dream2flow.github.io/. - oai:arXiv.org:2512.24766v1 - cs.RO - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Karthik Dharmarajan, Wenlong Huang, Jiajun Wu, Li Fei-Fei, Ruohan Zhang - - - From Trial to Deployment: A SEM Analysis of Traveler Adoptions to Fully Operational Autonomous Taxis - https://arxiv.org/abs/2512.24767 - arXiv:2512.24767v1 Announce Type: new -Abstract: Autonomous taxi services represent a transformative advancement in urban mobility, offering safety, efficiency, and round-the-clock operations. While existing literature has explored user acceptance of autonomous taxis through stated preference experiments and hypothetical scenarios, few studies have investigated actual user behavior based on operational AV services. This study addresses that gap by leveraging survey data from Wuhan, China, where Baidu's Apollo Robotaxi service operates at scale. We design a realistic survey incorporating actual service attributes and collect 336 valid responses from actual users. Using Structural Equation Modeling, we identify six latent psychological constructs, namely Trust \& Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. Their influences on adoption behavior, measured by the selection frequency of autonomous taxis in ten scenarios, are examined and interpreted. Results show that Cost Sensitivity and Behavioral Intention are the strongest positive predictors of adoption, while other latent constructs play more nuanced roles. The model demonstrates strong goodness-of-fit across multiple indices. Our findings offer empirical evidence to support policymaking, fare design, and public outreach strategies for scaling autonomous taxis deployments in real-world urban settings. - oai:arXiv.org:2512.24767v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yutong Cai, Hua Wang - - - Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection - https://arxiv.org/abs/2512.24772 - arXiv:2512.24772v1 Announce Type: new -Abstract: Detecting depression from social media text is still a challenging task. This is due to different language styles, informal expression, and the lack of annotated data in many languages. To tackle these issues, we propose, Semi-SMDNet, a strong Semi-Supervised Multilingual Depression detection Network. It combines teacher-student pseudo-labelling, ensemble learning, and augmentation of data. Our framework uses a group of teacher models. Their predictions come together through soft voting. An uncertainty-based threshold filters out low-confidence pseudo-labels to reduce noise and improve learning stability. We also use a confidence-weighted training method that focuses on reliable pseudo-labelled samples. This greatly boosts robustness across languages. Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines. It significantly reduces the performance gap between settings that have plenty of resources and those that do not. Detailed experiments and studies confirm that our framework is effective and can be used in various situations. This shows that it is suitable for scalable, cross-language mental health monitoring where labelled resources are limited. - oai:arXiv.org:2512.24772v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mohammad Zia Ur Rehman, Velpuru Navya, Sanskar, Shuja Uddin Qureshi, Nagendra Kumar - - - Throughput Optimization in UAV-Mounted RIS under Jittering and Imperfect CSI via DRL - https://arxiv.org/abs/2512.24773 - arXiv:2512.24773v1 Announce Type: new -Abstract: Reconfigurable intelligent surfaces (RISs) mounted on unmanned aerial vehicles (UAVs) can reshape wireless propagation on-demand. However, their performance is sensitive to UAV jitter and cascaded channel uncertainty. This paper investigates a downlink multiple-input single-output UAV-mounted RIS system in which a ground multiple-antenna base station (BS) serves multiple single-antenna users under practical impairments. Our goal is to maximize the expected throughput under stochastic three-dimensional UAV jitter and imperfect cascaded channel state information (CSI) based only on the available channel estimates. This leads to a stochastic nonconvex optimization problem subject to a BS transmit power constraint and strict unit-modulus constraints on all RIS elements. To address this problem, we design a model-free deep reinforcement learning (DRL) framework with a contextual bandit formulation. A differentiable feasibility layer is utilized to map continuous actions to feasible solutions, while the reward is a Monte Carlo estimate of the expected throughput. We instantiate this framework with constrained variants of deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) that do not use target networks. Simulations show that the proposed algorithms yield higher throughput than conventional alternating optimization-based weighted minimum mean-square error (AO-WMMSE) baselines under severe jitter and low CSI quality. Across different scenarios, the proposed methods achieve performance that is either comparable to or slightly below the AO-WMMSE benchmark, based on sample average approximation (SAA) with a relative gap ranging from 0-12%. Moreover, the proposed DRL controllers achieve online inference times of 0.6 ms per decision versus roughly 370-550 ms for AO-WMMSE solvers. - oai:arXiv.org:2512.24773v1 - cs.IT - eess.SP - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Anas K. Saeed, Mahmoud M. Salim, Ali Arshad Nasir, Ali H. Muqaibel - - - Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models - https://arxiv.org/abs/2512.24776 - arXiv:2512.24776v1 Announce Type: new -Abstract: Large Language Models (LLMs) are demonstrating rapid improvements on complex reasoning benchmarks, particularly when allowed to utilize intermediate reasoning steps before converging on a final solution. However, current literature often overlooks the significant computational burden associated with generating long reasoning sequences. For industrial applications, model selection depends not only on raw accuracy but also on resource constraints and inference costs. In this work, we conduct a test-time-compute aware evaluation of both contemporary and older open-source LLMs, mapping their Pareto frontiers across math- and reasoning-intensive benchmarks. Our findings identify the Mixture of Experts (MoE) architecture as a strong candidate to balance performance and efficiency in our evaluation setting. Furthermore, we trace the trajectory of Pareto efficiency over time to derive an emergent trend regarding accuracy gain per unit of compute. Finally, we demonstrate that there is a saturation point for inference-time compute. Beyond a certain threshold, accuracy gains diminish, indicating that while extended reasoning capabilities are beneficial, they cannot overcome intrinsic model limitations regarding specific complexities. - oai:arXiv.org:2512.24776v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - \'Akos Prucs, M\'arton Csutora, M\'aty\'as Antal, M\'ark Marosi - - - Gradient Descent as Implicit EM in Distance-Based Neural Models - https://arxiv.org/abs/2512.24780 - arXiv:2512.24780v1 Announce Type: new -Abstract: Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking. These phenomena appear across architectures -- in attention mechanisms, classification heads, and energy-based models -- yet existing explanations rely on loose analogies to mixture models or post-hoc architectural interpretation. We provide a direct derivation. For any objective with log-sum-exp structure over distances or energies, the gradient with respect to each distance is exactly the negative posterior responsibility of the corresponding component: $\partial L / \partial d_j = -r_j$. This is an algebraic identity, not an approximation. The immediate consequence is that gradient descent on such objectives performs expectation-maximization implicitly -- responsibilities are not auxiliary variables to be computed but gradients to be applied. No explicit inference algorithm is required because inference is embedded in optimization. This result unifies three regimes of learning under a single mechanism: unsupervised mixture modeling, where responsibilities are fully latent; attention, where responsibilities are conditioned on queries; and cross-entropy classification, where supervision clamps responsibilities to targets. The Bayesian structure recently observed in trained transformers is not an emergent property but a necessary consequence of the objective geometry. Optimization and inference are the same process. - oai:arXiv.org:2512.24780v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Alan Oursland - - - HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment - https://arxiv.org/abs/2512.24787 - arXiv:2512.24787v1 Announce Type: new -Abstract: Slate recommendation, where users are presented with a ranked list of items simultaneously, is widely adopted in online platforms. Recent advances in generative models have shown promise in slate recommendation by modeling sequences of discrete semantic IDs autoregressively. However, existing autoregressive approaches suffer from semantically entangled item tokenization and inefficient sequential decoding that lacks holistic slate planning. To address these limitations, we propose HiGR, an efficient generative slate recommendation framework that integrates hierarchical planning with listwise preference alignment. First, we propose an auto-encoder utilizing residual quantization and contrastive constraints to tokenize items into semantically structured IDs for controllable generation. Second, HiGR decouples generation into a list-level planning stage for global slate intent, followed by an item-level decoding stage for specific item selection. Third, we introduce a listwise preference alignment objective to directly optimize slate quality using implicit user feedback. Experiments on our large-scale commercial media platform demonstrate that HiGR delivers consistent improvements in both offline evaluations and online deployment. Specifically, it outperforms state-of-the-art methods by over 10% in offline recommendation quality with a 5x inference speedup, while further achieving a 1.22% and 1.73% increase in Average Watch Time and Average Video Views in online A/B tests. - oai:arXiv.org:2512.24787v1 - cs.IR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yunsheng Pang, Zijian Liu, Yudong Li, Shaojie Zhu, Zijian Luo, Chenyun Yu, Sikai Wu, Shichen Shen, Cong Xu, Bin Wang, Kai Jiang, Hongyong Yu, Chengxiang Zhuo, Zang Li - - - Projection-based Adversarial Attack using Physics-in-the-Loop Optimization for Monocular Depth Estimation - https://arxiv.org/abs/2512.24792 - arXiv:2512.24792v1 Announce Type: new -Abstract: Deep neural networks (DNNs) remain vulnerable to adversarial attacks that cause misclassification when specific perturbations are added to input images. This vulnerability also threatens the reliability of DNN-based monocular depth estimation (MDE) models, making robustness enhancement a critical need in practical applications. To validate the vulnerability of DNN-based MDE models, this study proposes a projection-based adversarial attack method that projects perturbation light onto a target object. The proposed method employs physics-in-the-loop (PITL) optimization -- evaluating candidate solutions in actual environments to account for device specifications and disturbances -- and utilizes a distributed covariance matrix adaptation evolution strategy. Experiments confirmed that the proposed method successfully created adversarial examples that lead to depth misestimations, resulting in parts of objects disappearing from the target scene. - oai:arXiv.org:2512.24792v1 - cs.CV - cs.LG - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1587/transinf.2025MUL0002 - Takeru Kusakabe, Yudai Hirose, Mashiho Mukaida, Satoshi Ono - - - Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks - https://arxiv.org/abs/2512.24793 - arXiv:2512.24793v1 Announce Type: new -Abstract: Neural architecture search (NAS), which automates the architectural design process of deep neural networks (DNN), has attracted increasing attention. Multimodal DNNs that necessitate feature fusion from multiple modalities benefit from NAS due to their structural complexity; however, constructing an architecture for multimodal DNNs through NAS requires a substantial amount of labeled training data. Thus, this paper proposes a self-supervised learning (SSL) method for architecture search of multimodal DNNs. The proposed method applies SSL comprehensively for both the architecture search and model pretraining processes. Experimental results demonstrated that the proposed method successfully designed architectures for DNNs from unlabeled training data. - oai:arXiv.org:2512.24793v1 - cs.LG - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1587/transinf.2024EDL8018 - IEICE Transactions on Information and Systems, Vol.E108.D, No. 6, pp. 640-643, 2025 - Shota Suzuki, Satoshi Ono - - - Nonlinear Noise2Noise for Efficient Monte Carlo Denoiser Training - https://arxiv.org/abs/2512.24794 - arXiv:2512.24794v1 Announce Type: new -Abstract: The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images, which can be difficult to obtain. However, Noise2Noise training has a major limitation: nonlinear functions applied to the noisy targets will skew the results. This bias occurs because the nonlinearity makes the expected value of the noisy targets different from the clean target image. Since nonlinear functions are common in image processing, avoiding them limits the types of preprocessing that can be performed on the noisy targets. Our main insight is that certain nonlinear functions can be applied to the noisy targets without adding significant bias to the results. We develop a theoretical framework for analyzing the effects of these nonlinearities, and describe a class of nonlinear functions with minimal bias. - We demonstrate our method on the denoising of high dynamic range (HDR) images produced by Monte Carlo rendering. Noise2Noise training can have trouble with HDR images, where the training process is overwhelmed by outliers and performs poorly. We consider a commonly used method of addressing these training issues: applying a nonlinear tone mapping function to the model output and target images to reduce their dynamic range. This method was previously thought to be incompatible with Noise2Noise training because of the nonlinearities involved. We show that certain combinations of loss functions and tone mapping functions can reduce the effect of outliers while introducing minimal bias. We apply our method to an existing machine learning-based Monte Carlo denoiser, where the original implementation was trained with high-sample count reference images. Our results approach those of the original implementation, but are produced using only noisy training data. - oai:arXiv.org:2512.24794v1 - cs.CV - cs.GR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - 10.1145/3757377.3763931 - SIGGRAPH Asia 2025 Conference Papers, Article 49, 1-11 - Andrew Tinits, Stephen Mann - - - LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories) - https://arxiv.org/abs/2512.24796 - arXiv:2512.24796v1 Announce Type: new -Abstract: Large language models (LLMs) have made rapid progress in formal theorem proving, yet current benchmarks under-measure the kind of abstraction and library-mediated reasoning that organizes modern mathematics. In parallel with FATE's emphasis on frontier algebra, we introduce LeanCat, a Lean benchmark for category-theoretic formalization -- a unifying language for mathematical structure and a core layer of modern proof engineering -- serving as a stress test of structural, interface-level reasoning. Part I: 1-Categories contains 100 fully formalized statement-level tasks, curated into topic families and three difficulty tiers via an LLM-assisted + human grading process. The best model solves 8.25% of tasks at pass@1 (32.50%/4.17%/0.00% by Easy/Medium/High) and 12.00% at pass@4 (50.00%/4.76%/0.00%). We also evaluate LeanBridge which use LeanExplore to search Mathlib, and observe consistent gains over single-model baselines. LeanCat is intended as a compact, reusable checkpoint for tracking both AI and human progress toward reliable, research-level formalization in Lean. - oai:arXiv.org:2512.24796v1 - cs.LO - cs.AI - cs.FL - cs.LG - math.CT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Rongge Xu, Hui Dai, Yiming Fu, Jiedong Jiang, Tianjiao Nie, Hongwei Wang, Junkai Wang, Holiverse Yang, Jiatong Yang, Zhi-Hao Zhang - - - Sidelink Positioning: Standardization Advancements, Challenges and Opportunities - https://arxiv.org/abs/2512.24803 - arXiv:2512.24803v1 Announce Type: new -Abstract: With the integration of cellular networks in vertical industries that demand precise location information, such as vehicle-to-everything (V2X), public safety, and Industrial Internet of Things (IIoT), positioning has become an imperative component for future wireless networks. By exploiting a wider spectrum, multiple antennas and flexible architectures, cellular positioning achieves ever-increasing positioning accuracy. Still, it faces fundamental performance degradation when the distance between user equipment (UE) and the base station (BS) is large or in non-line-of-sight (NLoS) scenarios. To this end, the 3rd generation partnership project (3GPP) Rel-18 proposes to standardize sidelink (SL) positioning, which provides unique opportunities to extend the positioning coverage via direct positioning signaling between UEs. Despite the standardization advancements, the capability of SL positioning is controversial, especially how much spectrum is required to achieve the positioning accuracy defined in 3GPP. To this end, this article summarizes the latest standardization advancements of 3GPP on SL positioning comprehensively, covering a) network architecture; b) positioning types; and c) performance requirements. The capability of SL positioning using various positioning methods under different imperfect factors is evaluated and discussed in-depth. Finally, according to the evolution of SL in 3GPP Rel-19, we discuss the possible research directions and challenges of SL positioning. - oai:arXiv.org:2512.24803v1 - cs.NI - eess.SP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuan Gao, Guangjin Pan, Zhiyong Zhong, Zhengyu Jin, Yichen Hu, Yifei Jin, Shugong Xu - - - DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes - https://arxiv.org/abs/2512.24810 - arXiv:2512.24810v1 Announce Type: new -Abstract: Precise probabilistic information about drug-target interaction (DTI) predictions is vital for understanding limitations and boosting predictive performance. Gaussian processes (GP) offer a scalable framework to integrate state-of-the-art DTI representations and Bayesian inference, enabling novel operations, such as Bayesian classification with rejection, top-$K$ selection, and ranking. We propose a deep kernel learning-based GP architecture (DTI-GP), which incorporates a combined neural embedding module for chemical compounds and protein targets, and a GP module. The workflow continues with sampling from the predictive distribution to estimate a Bayesian precedence matrix, which is used in fast and accurate selection and ranking operations. DTI-GP outperforms state-of-the-art solutions, and it allows (1) the construction of a Bayesian accuracy-confidence enrichment score, (2) rejection schemes for improved enrichment, and (3) estimation and search for top-$K$ selections and ranking with high expected utility. - oai:arXiv.org:2512.24810v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bence Bolg\'ar, Andr\'as Millinghoffer, P\'eter Antal - - - Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback - https://arxiv.org/abs/2512.24818 - arXiv:2512.24818v1 Announce Type: new -Abstract: Aligning large language models (LLMs) with human preferences has proven effective for enhancing model capabilities, yet standard preference modeling using the Bradley-Terry model assumes transitivity, overlooking the inherent complexity of human population preferences. Nash learning from human feedback (NLHF) addresses this by framing non-transitive preferences as a two-player zero-sum game, where alignment reduces to finding the Nash equilibrium (NE). However, existing algorithms typically rely on regularization, incurring unavoidable bias when computing the duality gap in the original game. In this work, we provide the first convergence guarantee for Optimistic Multiplicative Weights Update ($\mathtt{OMWU}$) in NLHF, showing that it achieves last-iterate linear convergence after a burn-in phase whenever an NE with full support exists, with an instance-dependent linear convergence rate to the original NE, measured by duality gaps. Compared to prior results in Wei et al. (2020), we do not require the assumption of NE uniqueness. Our analysis identifies a novel marginal convergence behavior, where the probability of rarely played actions grows exponentially from exponentially small values, enabling exponentially better dependence on instance-dependent constants than prior results. Experiments corroborate the theoretical strengths of $\mathtt{OMWU}$ in both tabular and neural policy classes, demonstrating its potential for LLM applications. - oai:arXiv.org:2512.24818v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shulun Chen, Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du - - - LMG Index: A Robust Learned Index for Multi-Dimensional Performance Balance - https://arxiv.org/abs/2512.24824 - arXiv:2512.24824v1 Announce Type: new -Abstract: Index structures are fundamental for efficient query processing on large-scale datasets. Learned indexes model the indexing process as a prediction problem to overcome the inherent trade-offs of traditional indexes. However, most existing learned indexes optimize only for limited objectives like query latency or space usage, neglecting other practical evaluation dimensions such as update efficiency and stability. Moreover, many learned indexes rely on assumptions about data distributions or workloads, lacking theoretical guarantees when facing unknown or evolving scenarios, which limits their generality in real-world systems. - In this paper, we propose LMIndex, a robust framework for learned indexing that leverages a efficient query/update top-layer structure (theoretically $O(1)$ when the key type is fixed) and a efficient optimal error threshold training algorithm (approach $O(1)$ in practice). Building upon this, we develop LMG (LMIndex with gaps), a variant employing a novel gap allocation strategy to enhance update performance and maintain stability under dynamic workloads. Extensive evaluations show that LMG achieves competitive or leading performance, including bulk loading (up to 8.25$\times$ faster), point queries (up to 1.49$\times$ faster), range queries (up to 4.02$\times$ faster than B+Tree), update (up to 1.5$\times$ faster on read-write workloads), stability (up to 82.59$\times$ lower coefficient of variation), and space usage (up to 1.38$\times$ smaller). These results demonstrate that LMG effectively breaks the multi-dimensional performance trade-offs inherent in state-of-the-art approaches, offering a balanced and versatile framework. - oai:arXiv.org:2512.24824v1 - cs.DB - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuzhen Chen, Bin Yao - - - Practising responsibility: Ethics in NLP as a hands-on course - https://arxiv.org/abs/2512.24825 - arXiv:2512.24825v1 Announce Type: new -Abstract: As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field's rapid evolution from both academia and industry, and the need to foster critical thinking beyond traditional technical training. We introduce our course on Ethical Aspects in NLP and our pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and "learning by teaching" methods. Over four years, the course has been refined and adapted across different institutions, educational levels, and interdisciplinary backgrounds; it has also yielded many reusable products, both in the form of teaching materials and in the form of actual educational products aimed at diverse audiences, made by the students themselves. By sharing our approach and experience, we hope to provide inspiration for educators seeking to incorporate social impact considerations into their curricula. - oai:arXiv.org:2512.24825v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Malvina Nissim, Viviana Patti, Beatrice Savoldi - - - Video and Language Alignment in 2D Systems for 3D Multi-object Scenes with Multi-Information Derivative-Free Control - https://arxiv.org/abs/2512.24826 - arXiv:2512.24826v1 Announce Type: new -Abstract: Cross-modal systems trained on 2D visual inputs are presented with a dimensional shift when processing 3D scenes. An in-scene camera bridges the dimensionality gap but requires learning a control module. We introduce a new method that improves multivariate mutual information estimates by regret minimisation with derivative-free optimisation. Our algorithm enables off-the-shelf cross-modal systems trained on 2D visual inputs to adapt online to object occlusions and differentiate features. The pairing of expressive measures and value-based optimisation assists control of an in-scene camera to learn directly from the noisy outputs of vision-language models. The resulting pipeline improves performance in cross-modal tasks on multi-object 3D scenes without resorting to pretraining or finetuning. - oai:arXiv.org:2512.24826v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Jason Armitage, Rico Sennnrich - - - Discovering Coordinated Joint Options via Inter-Agent Relative Dynamics - https://arxiv.org/abs/2512.24827 - arXiv:2512.24827v1 Announce Type: new -Abstract: Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of multi-agent options particularly challenging. Existing multi-agent option discovery methods often sacrifice coordination by producing loosely coupled or fully independent behaviours. Toward addressing these limitations, we describe a novel approach for multi-agent option discovery. Specifically, we propose a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours. Our approach builds on the inductive bias that synchronisation over agent states provides a natural foundation for coordination in the absence of explicit objectives. We first approximate a fictitious state of maximal alignment with the team, the \textit{Fermat} state, and use it to define a measure of \textit{spreadness}, capturing team-level misalignment on each individual state dimension. Building on this representation, we then employ a neural graph Laplacian estimator to derive options that capture state synchronisation patterns between agents. We evaluate the resulting options across multiple scenarios in two multi-agent domains, showing that they yield stronger downstream coordination capabilities compared to alternative option discovery methods. - oai:arXiv.org:2512.24827v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-sa/4.0/ - Raul D. Steleac, Mohan Sridharan, David Abel - - - Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences - https://arxiv.org/abs/2512.24829 - arXiv:2512.24829v1 Announce Type: new -Abstract: Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning. - oai:arXiv.org:2512.24829v1 - cs.AI - cs.HC - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Emmanuel Fashae, Michael Burke, Leimin Tian, Lingheng Meng, Pamela Carreno-Medrano - - - GenZ: Foundational models as latent variable generators within traditional statistical models - https://arxiv.org/abs/2512.24834 - arXiv:2512.24834v1 Announce Type: new -Abstract: We present GenZ, a hybrid model that bridges foundational models and statistical modeling through interpretable semantic features. While large language models possess broad domain knowledge, they often fail to capture dataset-specific patterns critical for prediction tasks. Our approach addresses this by discovering semantic feature descriptions through an iterative process that contrasts groups of items identified via statistical modeling errors, rather than relying solely on the foundational model's domain understanding. We formulate this as a generalized EM algorithm that jointly optimizes semantic feature descriptors and statistical model parameters. The method prompts a frozen foundational model to classify items based on discovered features, treating these judgments as noisy observations of latent binary features that predict real-valued targets through learned statistical relationships. We demonstrate the approach on two domains: house price prediction (hedonic regression) and cold-start collaborative filtering for movie recommendations. On house prices, our model achieves 12\% median relative error using discovered semantic features from multimodal listing data, substantially outperforming a GPT-5 baseline (38\% error) that relies on the LLM's general domain knowledge. For Netflix movie embeddings, our model predicts collaborative filtering representations with 0.59 cosine similarity purely from semantic descriptions -- matching the performance that would require approximately 4000 user ratings through traditional collaborative filtering. The discovered features reveal dataset-specific patterns (e.g., architectural details predicting local housing markets, franchise membership predicting user preferences) that diverge from the model's domain knowledge alone. - oai:arXiv.org:2512.24834v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Marko Jojic, Nebojsa Jojic - - - CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture - https://arxiv.org/abs/2512.24838 - arXiv:2512.24838v1 Announce Type: new -Abstract: Multiple-object tracking (MOT) in agricultural environments presents major challenges due to repetitive patterns, similar object appearances, sudden illumination changes, and frequent occlusions. Contemporary trackers in this domain rely on the motion of objects rather than appearance for association. Nevertheless, they struggle to maintain object identities when targets undergo frequent and strong occlusions. The high similarity of object appearances makes integrating appearance-based association nontrivial for agricultural scenarios. To solve this problem we propose CropTrack, a novel MOT framework based on the combination of appearance and motion information. CropTrack integrates a reranking-enhanced appearance association, a one-to-many association with appearance-based conflict resolution strategy, and an exponential moving average prototype feature bank to improve appearance-based association. Evaluated on publicly available agricultural MOT datasets, CropTrack demonstrates consistent identity preservation, outperforming traditional motion-based tracking methods. Compared to the state of the art, CropTrack achieves significant gains in identification F1 and association accuracy scores with a lower number of identity switches. - oai:arXiv.org:2512.24838v1 - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Md Ahmed Al Muzaddid, Jordan A. James, William J. Beksi - - - When Does the Silhouette Score Work? A Comprehensive Study in Network Clustering - https://arxiv.org/abs/2512.24841 - arXiv:2512.24841v1 Announce Type: new -Abstract: Selecting the number of communities is a fundamental challenge in network clustering. The silhouette score offers an intuitive, model-free criterion that balances within-cluster cohesion and between-cluster separation. Albeit its widespread use in clustering analysis, its performance in network-based community detection remains insufficiently characterized. In this study, we comprehensively evaluate the performance of the silhouette score across unweighted, weighted, and fully connected networks, examining how network size, separation strength, and community size imbalance influence its performance. Simulation studies show that the silhouette score accurately identifies the true number of communities when clusters are well separated and balanced, but it tends to underestimate under strong imbalance or weak separation and to overestimate in sparse networks. Extending the evaluation to a real airline reachability network, we demonstrate that the silhouette-based clustering can recover geographically interpretable and market-oriented clusters. These findings provide empirical guidance for applying the silhouette score in network clustering and clarify the conditions under which its use is most reliable. - oai:arXiv.org:2512.24841v1 - cs.SI - stat.CO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zongyue Teng, Jun Yan, Dandan Liu, Panpan Zhang - - - Triangulation as an Acceptance Rule for Multilingual Mechanistic Interpretability - https://arxiv.org/abs/2512.24842 - arXiv:2512.24842v1 Announce Type: new -Abstract: Multilingual language models achieve strong aggregate performance yet often behave unpredictably across languages, scripts, and cultures. We argue that mechanistic explanations for such models should satisfy a \emph{causal} standard: claims must survive causal interventions and must \emph{cross-reference} across environments that perturb surface form while preserving meaning. We formalize \emph{reference families} as predicate-preserving variants and introduce \emph{triangulation}, an acceptance rule requiring necessity (ablating the circuit degrades the target behavior), sufficiency (patching activations transfers the behavior), and invariance (both effects remain directionally stable and of sufficient magnitude across the reference family). To supply candidate subgraphs, we adopt automatic circuit discovery and \emph{accept or reject} those candidates by triangulation. We ground triangulation in causal abstraction by casting it as an approximate transformation score over a distribution of interchange interventions, connect it to the pragmatic interpretability agenda, and present a comparative experimental protocol across multiple model families, language pairs, and tasks. Triangulation provides a falsifiable standard for mechanistic claims that filters spurious circuits passing single-environment tests but failing cross-lingual invariance. - oai:arXiv.org:2512.24842v1 - cs.CL - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yanan Long - - - ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation - https://arxiv.org/abs/2512.24845 - arXiv:2512.24845v1 Announce Type: new -Abstract: 3D scene graphs have empowered robots with semantic understanding for navigation and planning, yet they often lack the functional information required for physical manipulation, particularly regarding articulated objects. Existing approaches for inferring articulation mechanisms from static observations are prone to visual ambiguity, while methods that estimate parameters from state changes typically rely on constrained settings such as fixed cameras and unobstructed views. Furthermore, fine-grained functional elements like small handles are frequently missed by general object detectors. To bridge this gap, we present ArtiSG, a framework that constructs functional 3D scene graphs by encoding human demonstrations into structured robotic memory. Our approach leverages a robust articulation data collection pipeline utilizing a portable setup to accurately estimate 6-DoF articulation trajectories and axes even under camera ego-motion. We integrate these kinematic priors into a hierarchical and open-vocabulary graph while utilizing interaction data to discover inconspicuous functional elements missed by visual perception. Extensive real-world experiments demonstrate that ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision. Moreover, we show that the constructed graph serves as a reliable functional memory that effectively guides robots to perform language-directed manipulation tasks in real-world environments containing diverse articulated objects. - oai:arXiv.org:2512.24845v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qiuyi Gu, Yuze Sheng, Jincheng Yu, Jiahao Tang, Xiaolong Shan, Zhaoyang Shen, Tinghao Yi, Xiaodan Liang, Xinlei Chen, Yu Wang - - - AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference - https://arxiv.org/abs/2512.24847 - arXiv:2512.24847v1 Announce Type: new -Abstract: High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstruction framework based on diffusion-based Bayesian inference. By leveraging the learned spatiotemporal probability distribution of the AOD field as a generative prior, this framework can be flexibly adapted to various reconstruction tasks without requiring task-specific retraining. We first introduce a corruption-aware training strategy to learns a spatiotemporal AOD prior solely from naturally incomplete data. Subsequently, we employ a decoupled annealing posterior sampling strategy that enables the more effective and integration of heterogeneous observations as constraints to guide the generation process. We validate the proposed framework through extensive experiments on Reanalysis data. Results across downscaling and inpainting tasks confirm the efficacy and robustness of AODDiff, specifically demonstrating its advantage in maintaining high spatial spectral fidelity. Furthermore, as a generative model, AODDiff inherently enables uncertainty quantification via multiple sampling, offering critical confidence metrics for downstream applications. - oai:arXiv.org:2512.24847v1 - cs.LG - physics.ao-ph - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Linhao Fan, Hongqiang Fang, Jingyang Dai, Yong Jiang, Qixing Zhang - - - PrivacyBench: A Conversational Benchmark for Evaluating Privacy in Personalized AI - https://arxiv.org/abs/2512.24848 - arXiv:2512.24848v1 Announce Type: new -Abstract: Personalized AI agents rely on access to a user's digital footprint, which often includes sensitive data from private emails, chats and purchase histories. Yet this access creates a fundamental societal and privacy risk: systems lacking social-context awareness can unintentionally expose user secrets, threatening digital well-being. We introduce PrivacyBench, a benchmark with socially grounded datasets containing embedded secrets and a multi-turn conversational evaluation to measure secret preservation. Testing Retrieval-Augmented Generation (RAG) assistants reveals that they leak secrets in up to 26.56% of interactions. A privacy-aware prompt lowers leakage to 5.12%, yet this measure offers only partial mitigation. The retrieval mechanism continues to access sensitive data indiscriminately, which shifts the entire burden of privacy preservation onto the generator. This creates a single point of failure, rendering current architectures unsafe for wide-scale deployment. Our findings underscore the urgent need for structural, privacy-by-design safeguards to ensure an ethical and inclusive web for everyone. - oai:arXiv.org:2512.24848v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Srija Mukhopadhyay, Sathwik Reddy, Shruthi Muthukumar, Jisun An, Ponnurangam Kumaraguru - - - On an Erd\H{o}s--Lov'asz problem: 3-critical 3-graphs of minimum degree 7 - https://arxiv.org/abs/2512.24850 - arXiv:2512.24850v1 Announce Type: new -Abstract: Erd\H{o}s and Lov'asz asked whether there exists a "3-critical" 3-uniform hypergraph in which every vertex has degree at least 7. The original formulation does not specify what 3-critical means, and two non-equivalent notions have appeared in the literature and in later discussions of the problem. In this paper we resolve the question under both interpretations. For the transversal interpretation (criticality with respect to the transversal number), we prove that a 3-uniform hypergraph $H$ with $\tau(H)=3$ and $\tau(H-e)=2$ for every edge $e$ has at most 10 edges; in particular, $\delta(H)\le 6$, and this bound is sharp, witnessed by the complete 3-graph $K^{(3)}_5$. For the chromatic interpretation (criticality with respect to weak vertex-colourings), we give an explicit 3-uniform hypergraph on 9 vertices with $\chi(H)=3$ and minimum degree $\delta(H)=7$ such that deleting any single edge or any single vertex makes it 2-colourable. The criticality of the example is certified by explicit witness 2-colourings listed in the appendices, together with a short verification script. - oai:arXiv.org:2512.24850v1 - cs.DM - math.CO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ruiliang Li - - - VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents - https://arxiv.org/abs/2512.24851 - arXiv:2512.24851v1 Announce Type: new -Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across a wide range of vision-language tasks. However, their performance as embodied agents, which requires multi-round dialogue spatial reasoning and sequential action prediction, needs further exploration. Our work investigates this potential in the context of Vision-and-Language Navigation (VLN) by introducing a unified and extensible evaluation framework to probe MLLMs as zero-shot agents by bridging traditional navigation datasets into a standardized benchmark, named VLN-MME. We simplify the evaluation with a highly modular and accessible design. This flexibility streamlines experiments, enabling structured comparisons and component-level ablations across diverse MLLM architectures, agent designs, and navigation tasks. Crucially, enabled by our framework, we observe that enhancing our baseline agent with Chain-of-Thought (CoT) reasoning and self-reflection leads to an unexpected performance decrease. This suggests MLLMs exhibit poor context awareness in embodied navigation tasks; although they can follow instructions and structure their output, their 3D spatial reasoning fidelity is low. VLN-MME lays the groundwork for systematic evaluation of general-purpose MLLMs in embodied navigation settings and reveals limitations in their sequential decision-making capabilities. We believe these findings offer crucial guidance for MLLM post-training as embodied agents. - oai:arXiv.org:2512.24851v1 - cs.CV - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xunyi Zhao, Gengze Zhou, Qi Wu - - - A study on constraint extraction and exception exclusion in care worker scheduling - https://arxiv.org/abs/2512.24853 - arXiv:2512.24853v1 Announce Type: new -Abstract: Technologies for automatically generating work schedules have been extensively studied; however, in long-term care facilities, the conditions vary between facilities, making it essential to interview the managers who create shift schedules to design facility-specific constraint conditions. The proposed method utilizes constraint templates to extract combinations of various components, such as shift patterns for consecutive days or staff combinations. The templates can extract a variety of constraints by changing the number of days and the number of staff members to focus on and changing the extraction focus to patterns or frequency. In addition, unlike existing constraint extraction techniques, this study incorporates mechanisms to exclude exceptional constraints. The extracted constraints can be employed by a constraint programming solver to create care worker schedules. Experiments demonstrated that our proposed method successfully created schedules that satisfied all hard constraints and reduced the number of violations for soft constraints by circumventing the extraction of exceptional constraints. - oai:arXiv.org:2512.24853v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1007/s10015-025-01082-6 - Artificial Life Robotics (2025) - Koki Suenaga, Tomohiro Furuta, Satoshi Ono - - - Feature Slice Matching for Precise Bug Detection - https://arxiv.org/abs/2512.24858 - arXiv:2512.24858v1 Announce Type: new -Abstract: Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the noise interference in existing works does not manage the tough job, i.e., eliminating the noise in the targets. In this paper, we propose MATUS to mitigate the target noise for precise bug detection based on similarity measurement. Feature slices are extracted from both the buggy query and the targets to represent the semantic feature of (potential) bug logics. In particular, MATUS guides the target slicing with the prior knowledge from the buggy code, in an end-to-end way to pinpoint the slicing criterion in the targets. All feature slices are embedded and compared based on the vector similarity. Buggy candidates are audited to confirm unknown bugs in the targets. Experiments show that MATUS holds advantages in bug detection for real-world projects with acceptable efficiency. In total, MATUS has spotted 31 unknown bugs in the Linux kernel. All of them have been confirmed by the kernel developers, and 11 have been assigned CVEs. - oai:arXiv.org:2512.24858v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ke Ma, Jianjun Huang, Wei You, Bin Liang, Jingzheng Wu, Yanjun Wu, Yuanjun Gong - - - OFL-SAM2: Prompt SAM2 with Online Few-shot Learner for Efficient Medical Image Segmentation - https://arxiv.org/abs/2512.24861 - arXiv:2512.24861v1 Announce Type: new -Abstract: The Segment Anything Model 2 (SAM2) has demonstrated remarkable promptable visual segmentation capabilities in video data, showing potential for extension to medical image segmentation (MIS) tasks involving 3D volumes and temporally correlated 2D image sequences. However, adapting SAM2 to MIS presents several challenges, including the need for extensive annotated medical data for fine-tuning and high-quality manual prompts, which are both labor-intensive and require intervention from medical experts. To address these challenges, we introduce OFL-SAM2, a prompt-free SAM2 framework for label-efficient MIS. Our core idea is to leverage limited annotated samples to train a lightweight mapping network that captures medical knowledge and transforms generic image features into target features, thereby providing additional discriminative target representations for each frame and eliminating the need for manual prompts. Crucially, the mapping network supports online parameter update during inference, enhancing the model's generalization across test sequences. Technically, we introduce two key components: (1) an online few-shot learner that trains the mapping network to generate target features using limited data, and (2) an adaptive fusion module that dynamically integrates the target features with the memory-attention features generated by frozen SAM2, leading to accurate and robust target representation. Extensive experiments on three diverse MIS datasets demonstrate that OFL-SAM2 achieves state-of-the-art performance with limited training data. - oai:arXiv.org:2512.24861v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Meng Lan, Lefei Zhang, Xiaomeng Li - - - Big AI is accelerating the metacrisis: What can we do? - https://arxiv.org/abs/2512.24863 - arXiv:2512.24863v1 Announce Type: new -Abstract: The world is in the grip of ecological, meaning, and language crises which are converging into a metacrisis. Big AI is accelerating them all. Language engineers are playing a central role, persisting with a scalability story that is failing humanity, supplying critical talent to plutocrats and kleptocrats, and creating new technologies as if the whole endeavour was value-free. We urgently need to explore alternatives, applying our collective intelligence to design a life-affirming future for NLP that is centered on human flourishing on a living planet. - oai:arXiv.org:2512.24863v1 - cs.CL - cs.AI - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Steven Bird - - - Characterization of Transfer Using Multi-task Learning Curves - https://arxiv.org/abs/2512.24866 - arXiv:2512.24866v1 Announce Type: new -Abstract: Transfer effects manifest themselves both during training using a fixed data set and in inductive inference using accumulating data. We hypothesize that perturbing the data set by including more samples, instead of perturbing the model by gradient updates, provides a complementary and more fundamental characterization of transfer effects. To capture this phenomenon, we quantitatively model transfer effects using multi-task learning curves approximating the inductive performance over varying sample sizes. We describe an efficient method to approximate multi-task learning curves analogous to the Task Affinity Grouping method applied during training. We compare the statistical and computational approaches to transfer, which indicates considerably higher compute costs for the previous but better power and broader applicability. Evaluations are performed using a benchmark drug-target interaction data set. Our results show that learning curves can better capture the effects of multi-task learning and their multi-task extensions can delineate pairwise and contextual transfer effects in foundation models. - oai:arXiv.org:2512.24866v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Andr\'as Millinghoffer, Bence Bolg\'ar, P\'eter Antal - - - Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements - https://arxiv.org/abs/2512.24867 - arXiv:2512.24867v1 Announce Type: new -Abstract: Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries. However, existing benchmarks predominantly curate questions at the question level, suffering from three fundamental limitations: vulnerability to data contamination, restriction to single-knowledge-point assessment, and reliance on costly domain expert annotation. We propose Encyclo-K, a statement-based benchmark that rethinks benchmark construction from the ground up. Our key insight is that knowledge statements, not questions, can serve as the unit of curation, and questions can then be constructed from them. We extract standalone knowledge statements from authoritative textbooks and dynamically compose them into evaluation questions through random sampling at test time. This design directly addresses all three limitations: the combinatorial space is too vast to memorize, and model rankings remain stable across dynamically generated question sets, enabling reliable periodic dataset refresh; each question aggregates 8-10 statements for comprehensive multi-knowledge assessment; annotators only verify formatting compliance without requiring domain expertise, substantially reducing annotation costs. Experiments on over 50 LLMs demonstrate that Encyclo-K poses substantial challenges with strong discriminative power. Even the top-performing OpenAI-GPT-5.1 achieves only 62.07% accuracy, and model performance displays a clear gradient distribution--reasoning models span from 16.04% to 62.07%, while chat models range from 9.71% to 50.40%. These results validate the challenges introduced by dynamic evaluation and multi-statement comprehensive understanding. These findings establish Encyclo-K as a scalable framework for dynamic evaluation of LLMs' comprehensive understanding over multiple fine-grained disciplinary knowledge statements. - oai:arXiv.org:2512.24867v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yiming Liang, Yizhi Li, Yantao Du, Ge Zhang, Jiayi Zhou, Yuchen Wu, Yinzhu Piao, Denghui Cao, Tong Sun, Ziniu Li, Li Du, Bo Lei, Jiaheng Liu, Chenghua Lin, Zhaoxiang Zhang, Wenhao Huang, Jiajun Zhang - - - Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem - https://arxiv.org/abs/2512.24873 - arXiv:2512.24873v1 Announce Type: new -Abstract: Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agent LLMs. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME (ROME is Obviously an Agentic Model), an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-based Policy Alignment (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure. - oai:arXiv.org:2512.24873v1 - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Weixun Wang, XiaoXiao Xu, Wanhe An, Fangwen Dai, Wei Gao, Yancheng He, Ju Huang, Qiang Ji, Hanqi Jin, Xiaoyang Li, Yang Li, Zhongwen Li, Shirong Lin, Jiashun Liu, Zenan Liu, Tao Luo, Dilxat Muhtar, Yuanbin Qu, Jiaqiang Shi, Qinghui Sun, Yingshui Tan, Hao Tang, Runze Wang, Yi Wang, Zhaoguo Wang, Yanan Wu, Shaopan Xiong, Binchen Xu, Xander Xu, Yuchi Xu, Qipeng Zhang, Xixia Zhang, Haizhou Zhao, Jie Zhao, Shuaibing Zhao, Baihui Zheng, Jianhui Zheng, Suhang Zheng, Yanni Zhu, Mengze Cai, Kerui Cao, Xitong Chen, Yue Dai, Lifan Du, Tao Feng, Tao He, Jin Hu, Yijie Hu, Ziyu Jiang, Cheng Li, Xiang Li, Jing Liang, Chonghuan Liu, ZhenDong Liu, Haodong Mi, Yanhu Mo, Junjia Ni, Shixin Pei, Jingyu Shen, XiaoShuai Song, Cecilia Wang, Chaofan Wang, Kangyu Wang, Pei Wang, Tao Wang, Wei Wang, Ke Xiao, Mingyu Xu, Tiange Xu, Nan Ya, Siran Yang, Jianan Ye, Yaxing Zang, Duo Zhang, Junbo Zhang, Boren Zheng, Wanxi Deng, Ling Pan, Lin Qu, Wenbo Su, Jiamang Wang, Wei Wang, Hu Wei, Minggang Wu, Cheng Yu, Bing Zhao, Zhicheng Zheng, Bo Zheng - - - A structure-preserving parametric approximation for anisotropic geometric flows via an $\alpha$-surface energy matrix - https://arxiv.org/abs/2512.24875 - arXiv:2512.24875v1 Announce Type: new -Abstract: We propose a structure-preserving parametric approximation for geometric flows with general anisotropic effects. By introducing a hyperparameter $\alpha$, we construct a unified surface energy matrix $\hat{\boldsymbol{G}}_k^\alpha(\theta)$ that encompasses all existing formulations of surface energy matrices, and apply it to anisotropic curvature flow. We prove that $\alpha=-1$ is the unique choice achieving optimal energy stability under the necessary and sufficient condition $3\hat{\gamma}(\theta)\geq\hat{\gamma}(\theta-\pi)$, while all other $\alpha\neq-1$ require strictly stronger conditions. The framework extends naturally to general anisotropic geometric flows through a unified velocity discretization that ensures energy stability. Numerical experiments validate the theoretical optimality of $\alpha=-1$ and demonstrate the effectiveness and robustness. - oai:arXiv.org:2512.24875v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Weizhu Bao, Yifei Li, Wenjun Ying, Yulin Zhang - - - Random compressible Euler flows - https://arxiv.org/abs/2512.24879 - arXiv:2512.24879v1 Announce Type: new -Abstract: We propose a finite volume stochastic collocation method for the random Euler system. We rigorously prove the convergence of random finite volume solutions under the assumption that the discrete differential quotients remain bounded in probability. Convergence analysis combines results on the convergence of a deterministic FV method with stochastic compactness arguments due to Skorokhod and Gy\"ongy-Krylov. - oai:arXiv.org:2512.24879v1 - math.NA - cs.NA - math.PR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Maria Lukacova-Medvidova, Simon Schneider - - - mHC: Manifold-Constrained Hyper-Connections - https://arxiv.org/abs/2512.24880 - arXiv:2512.24880v1 Announce Type: new -Abstract: Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models. - oai:arXiv.org:2512.24880v1 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhenda Xie, Yixuan Wei, Huanqi Cao, Chenggang Zhao, Chengqi Deng, Jiashi Li, Damai Dai, Huazuo Gao, Jiang Chang, Liang Zhao, Shangyan Zhou, Zhean Xu, Zhengyan Zhang, Wangding Zeng, Shengding Hu, Yuqing Wang, Jingyang Yuan, Lean Wang, Wenfeng Liang - - - BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts - https://arxiv.org/abs/2512.24885 - arXiv:2512.24885v1 Announce Type: new -Abstract: Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts Adversarial and Alignment, and by operationalizing them via probabilistic constraints on what an agent may generate. We instantiate this idea in BEDA, a framework that consists of the world set, the belief estimator for belief estimation, and the conditional generator that selects acts and realizes utterances consistent with the inferred beliefs. Across three settings, Conditional Keeper Burglar (CKBG, adversarial), Mutual Friends (MF, cooperative), and CaSiNo (negotiation), BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at least 5.0 points across backbones and by 20.6 points with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of 9.3 points; and on CaSiNo it achieves the optimal deal relative to all baselines. These results indicate that casting belief estimation as constraints provides a simple, general mechanism for reliable strategic dialogue. - oai:arXiv.org:2512.24885v1 - cs.CL - cs.GT - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Hengli Li, Zhaoxin Yu, Qi Shen, Chenxi Li, Mengmeng Wang, Tinglang Wu, Yipeng Kang, Yuxuan Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng - - - Heterogeneous Multi-Agent Multi-Target Tracking using Cellular Sheaves - https://arxiv.org/abs/2512.24886 - arXiv:2512.24886v1 Announce Type: new -Abstract: Multi-agent target tracking in the presence of nonlinear dynamics and agent heterogeneity, where state-space dimensions may differ, is a challenging problem that traditional graph Laplacian methods cannot easily address. This work leverages the framework of cellular sheaves, a mathematical generalization of graph theory, to natively model such heterogeneous systems. While existing coordination sheaf frameworks focus on cooperative problems like consensus, this work extends them to the non-cooperative target-tracking problem. The tracking of multiple, unknown targets is formulated as a harmonic extension problem on a cellular sheaf, accommodating nonlinear dynamics and external disturbances for all agents. A decentralized control law is developed using the sheaf Laplacian, and a corresponding Lyapunov-based stability analysis is provided to guarantee tracking error convergence, with results validated by simulation. - oai:arXiv.org:2512.24886v1 - eess.SY - cs.MA - cs.SY - math.AT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Tyler Hanks, Cristian F. Nino, Joana Bou Barcelo, Austin Copeland, Warren Dixon, James Fairbanks - - - SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance - https://arxiv.org/abs/2512.24888 - arXiv:2512.24888v1 Announce Type: new -Abstract: The decentralized architecture of Web3 technologies creates fundamental challenges for Anti-Money Laundering and Counter-Financing of Terrorism compliance. Traditional regulatory technology solutions designed for centralized financial systems prove inadequate for blockchain's transparent yet pseudonymous networks. This systematization examines how blockchain-native RegTech solutions leverage distributed ledger properties to enable novel compliance capabilities. - We develop three taxonomies organizing the Web3 RegTech domain: a regulatory paradigm evolution framework across ten dimensions, a compliance protocol taxonomy encompassing five verification layers, and a RegTech lifecycle framework spanning preventive, real-time, and investigative phases. Through analysis of 41 operational commercial platforms and 28 academic prototypes selected from systematic literature review (2015-2025), we demonstrate that Web3 RegTech enables transaction graph analysis, real-time risk assessment, cross-chain analytics, and privacy-preserving verification approaches that are difficult to achieve or less commonly deployed in traditional centralized systems. - Our analysis reveals critical gaps between academic innovation and industry deployment, alongside persistent challenges in cross-chain tracking, DeFi interaction analysis, privacy protocol monitoring, and scalability. We synthesize architectural best practices and identify research directions addressing these gaps while respecting Web3's core principles of decentralization, transparency, and user sovereignty. - oai:arXiv.org:2512.24888v1 - cs.CR - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qian'ang Mao, Jiaxin Wang, Ya Liu, Li Zhu, Jiaman Chen, Jiaqi Yan - - - Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing - https://arxiv.org/abs/2512.24896 - arXiv:2512.24896v1 Announce Type: new -Abstract: This report presents the design and implementation of a semi-automated data annotation pipeline developed within the DARTS project, whose goal is to create a large-scale, multimodal dataset of driving scenarios recorded in Polish conditions. Manual annotation of such heterogeneous data is both costly and time-consuming. To address this challenge, the proposed solution adopts a human-in-the-loop approach that combines artificial intelligence with human expertise to reduce annotation cost and duration. The system automatically generates initial annotations, enables iterative model retraining, and incorporates data anonymization and domain adaptation techniques. At its core, the tool relies on 3D object detection algorithms to produce preliminary annotations. Overall, the developed tools and methodology result in substantial time savings while ensuring consistent, high-quality annotations across different sensor modalities. The solution directly supports the DARTS project by accelerating the preparation of large annotated dataset in the project's standardized format, strengthening the technological base for autonomous vehicle research in Poland. - oai:arXiv.org:2512.24896v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Andrii Gamalii, Daniel G\'orniak, Robert Nowak, Bart{\l}omiej Olber, Krystian Radlak, Jakub Winter - - - PRISM: A hierarchical multiscale approach for time series forecasting - https://arxiv.org/abs/2512.24898 - arXiv:2512.24898v1 Announce Type: new -Abstract: Forecasting is critical in areas such as finance, biology, and healthcare. Despite the progress in the field, making accurate forecasts remains challenging because real-world time series contain both global trends, local fine-grained structure, and features on multiple scales in between. Here, we present a new forecasting method, PRISM (Partitioned Representation for Iterative Sequence Modeling), that addresses this challenge through a learnable tree-based partitioning of the signal. At the root of the tree, a global representation captures coarse trends in the signal, while recursive splits reveal increasingly localized views of the signal. At each level of the tree, data are projected onto a time-frequency basis (e.g., wavelets or exponential moving averages) to extract scale-specific features, which are then aggregated across the hierarchy. This design allows the model to jointly capture global structure and local dynamics of the signal, enabling accurate forecasting. Experiments across benchmark datasets show that our method outperforms state-of-the-art methods for forecasting. Overall, these results demonstrate that our hierarchical approach provides a lightweight and flexible framework for forecasting multivariate time series. The code is available at https://github.com/nerdslab/prism. - oai:arXiv.org:2512.24898v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zihao Chen, Alexandre Andre, Wenrui Ma, Ian Knight, Sergey Shuvaev, Eva Dyer - - - MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy - https://arxiv.org/abs/2512.24899 - arXiv:2512.24899v1 Announce Type: new -Abstract: The proliferation of streaming data analytics in data-driven applications raises critical privacy concerns, as directly collecting user data may compromise personal privacy. Although existing $w$-event local differential privacy (LDP) mechanisms provide formal guarantees without relying on trusted third parties, their practical deployment is hindered by two key limitations. First, these methods are designed primarily for publishing simple statistics at each timestamp, making them inherently unsuitable for complex queries. Second, they handle data at each timestamp independently, failing to capture temporal correlations and consequently degrading the overall utility. To address these issues, we propose MTSP-LDP, a novel framework for \textbf{M}ulti-\textbf{T}ask \textbf{S}treaming data \textbf{P}ublication under $w$-event LDP. MTSP-LDP adopts an \emph{Optimal Privacy Budget Allocation} algorithm to dynamically allocate privacy budgets by analyzing temporal correlations within each window. It then constructs a \emph{data-adaptive private binary tree structure} to support complex queries, which is further refined by cross-timestamp grouping and smoothing operations to enhance estimation accuracy. Furthermore, a unified \emph{Budget-Free Multi-Task Processing} mechanism is introduced to support a variety of streaming queries without consuming additional privacy budget. Extensive experiments on real-world datasets demonstrate that MTSP-LDP consistently achieves high utility across various streaming tasks, significantly outperforming existing methods. - oai:arXiv.org:2512.24899v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chang Liu, Junzhou Zhao - - - Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes - https://arxiv.org/abs/2512.24901 - arXiv:2512.24901v1 Announce Type: new -Abstract: Cognitive task classification using machine learning plays a central role in decoding brain states from neuroimaging data. By integrating machine learning with brain network analysis, complex connectivity patterns can be extracted from functional magnetic resonance imaging connectomes. This process transforms raw blood-oxygen-level-dependent (BOLD) signals into interpretable representations of cognitive processes. Graph neural networks (GNNs) further advance this paradigm by modeling brain regions as nodes and functional connections as edges, capturing topological dependencies and multi-scale interactions that are often missed by conventional approaches. Our proposed SpectralBrainGNN model, a spectral convolution framework based on graph Fourier transforms (GFT) computed via normalized Laplacian eigendecomposition. Experiments on the Human Connectome Project-Task (HCPTask) dataset demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of 96.25\%. The implementation is publicly available at https://github.com/gnnplayground/SpectralBrainGNN to support reproducibility and future research. - oai:arXiv.org:2512.24901v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Debasis Maji, Arghya Banerjee, Debaditya Barman - - - FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation - https://arxiv.org/abs/2512.24903 - arXiv:2512.24903v1 Announce Type: new -Abstract: We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to existing benchmarks, our work delivers three major advancements. (1) Scenario Awareness: 57.9% of 1,200 expert-annotated problems incorporate 12 types of implicit financial scenarios (e.g., Portfolio Management), challenging models to perform expert-level reasoning based on assumptions; (2) Document Understanding: 837 Chinese/English documents spanning 9 types (e.g., Company Research) average 50.8 pages with rich visual elements, significantly surpassing existing benchmarks in both breadth and depth of financial documents; (3) Multi-Step Computation: Problems demand 11-step reasoning on average (5.3 extraction + 5.7 calculation steps), with 65.0% requiring cross-page evidence (2.4 pages average). The best-performing MLLM achieves only 58.0% accuracy, and different retrieval-augmented generation (RAG) methods show significant performance variations on this task. We expect FinMMDocR to drive improvements in MLLMs and reasoning-enhanced methods on complex multimodal reasoning tasks in real-world scenarios. - oai:arXiv.org:2512.24903v1 - cs.CV - cs.CE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zichen Tang, Haihong E, Rongjin Li, Jiacheng Liu, Linwei Jia, Zhuodi Hao, Zhongjun Yang, Yuanze Li, Haolin Tian, Xinyi Hu, Peizhi Zhao, Yuan Liu, Zhengyu Wang, Xianghe Wang, Yiling Huang, Xueyuan Lin, Ruofei Bai, Zijian Xie, Qian Huang, Ruining Cao, Haocheng Gao - - - One-Shot Camera-Based Extrusion Optimization for High Speed Fused Filament Fabrication - https://arxiv.org/abs/2512.24905 - arXiv:2512.24905v1 Announce Type: new -Abstract: Off-the-shelf fused filament fabrication 3D printers are widely accessible and convenient, yet they exhibit quality loss at high speeds due to dynamic mis-synchronization between printhead motion and material extrusion systems, notably corner over-extrusion. Existing methods require specialized hardware, extensive calibration, or firmware modifications that are inaccessible to most users. This work presents a practical, end-to-end optimization framework that enhances high-speed printing using only standard 3D printers and a phone camera, without requiring additional complex setup. The method employs a one-shot calibration approach in which two simple printed patterns, captured by a phone camera, enable identification of extrusion dynamics and cornering behavior. The identified systems enable a model-based constrained optimal control strategy that generates optimized G-code, synchronizing motion and extrusion. Experiments show reduced width tracking error, mitigated corner defects, and lower surface roughness, achieving surface quality at 3600 mm/min comparable to conventional printing at 1600 mm/min, effectively doubling production speed while maintaining print quality. This accessible, hardware-minimal approach enables a wide range of fused filament fabrication users to achieve high-quality, high-speed additive manufacturing. - oai:arXiv.org:2512.24905v1 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yufan Lin, Xavier Guidetti, Yannick Nagel, Efe C. Balta, John Lygeros - - - AI-Driven Cloud Resource Optimization for Multi-Cluster Environments - https://arxiv.org/abs/2512.24914 - arXiv:2512.24914v1 Announce Type: new -Abstract: Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dynamic workloads. These limitations result in inefficient resource utilization, delayed adaptation, and increased operational overhead across distributed environments. This paper presents an AI-driven framework for adaptive resource optimization in multi-cluster cloud systems. The proposed approach integrates predictive learning, policy-aware decision-making, and continuous feedback to enable proactive and coordinated resource management across clusters. By analyzing cross-cluster telemetry and historical execution patterns, the framework dynamically adjusts resource allocation to balance performance, cost, and reliability objectives. A prototype implementation demonstrates improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional reactive approaches. The results highlight the effectiveness of intelligent, self-adaptive infrastructure management as a key enabler for scalable and resilient cloud platforms. - oai:arXiv.org:2512.24914v1 - cs.DC - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Vinoth Punniyamoorthy, Akash Kumar Agarwal, Bikesh Kumar, Abhirup Mazumder, Kabilan Kannan, Sumit Saha - - - Frequent subgraph-based persistent homology for graph classification - https://arxiv.org/abs/2512.24917 - arXiv:2512.24917v1 Announce Type: new -Abstract: Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. However, most PH methods on graphs rely on a limited set of filtrations, such as degree-based or weight-based filtrations, which overlook richer features like recurring information across the dataset and thus restrict expressive power. In this work, we propose a novel graph filtration called Frequent Subgraph Filtration (FSF), which is derived from frequent subgraphs and produces stable and information-rich frequency-based persistent homology (FPH) features. We study the theoretical properties of FSF and provide both proofs and experimental validation. Beyond persistent homology itself, we introduce two approaches for graph classification: an FPH-based machine learning model (FPH-ML) and a hybrid framework that integrates FPH with graph neural networks (FPH-GNNs) to enhance topology-aware graph representation learning. Our frameworks bridge frequent subgraph mining and topological data analysis, offering a new perspective on topology-aware feature extraction. Experimental results show that FPH-ML achieves competitive or superior accuracy compared with kernel-based and degree-based filtration methods. When integrated into graph neural networks, FPH yields relative performance gains ranging from 0.4 to 21 percent, with improvements of up to 8.2 percentage points over GCN and GIN backbones across benchmarks. - oai:arXiv.org:2512.24917v1 - cs.LG - math.AT - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xinyang Chen, Ama\"el Broustet, Guoting Chen - - - Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection - https://arxiv.org/abs/2512.24922 - arXiv:2512.24922v1 Announce Type: new -Abstract: 3D object detectors are fundamental components of perception systems in autonomous vehicles. While these detectors achieve remarkable performance on standard autonomous driving benchmarks, they often struggle to generalize across different domains - for instance, a model trained in the U.S. may perform poorly in regions like Asia or Europe. This paper presents a novel lidar domain adaptation method based on neuron activation patterns, demonstrating that state-of-the-art performance can be achieved by annotating only a small, representative, and diverse subset of samples from the target domain if they are correctly selected. The proposed approach requires very small annotation budget and, when combined with post-training techniques inspired by continual learning prevent weight drift from the original model. Empirical evaluation shows that the proposed domain adaptation approach outperforms both linear probing and state-of-the-art domain adaptation techniques. - oai:arXiv.org:2512.24922v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Bart{\l}omiej Olber, Jakub Winter, Pawe{\l} Wawrzy\'nski, Andrii Gamalii, Daniel G\'orniak, Marcin {\L}ojek, Robert Nowak, Krystian Radlak - - - Towards Provably Secure Generative AI: Reliable Consensus Sampling - https://arxiv.org/abs/2512.24925 - arXiv:2512.24925v1 Announce Type: new -Abstract: Existing research on generative AI security is primarily driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. This dynamic frequently gives rise to previously unknown attacks that can circumvent current detection and prevention. This necessitates the continual updating of security mechanisms. Constructing generative AI with provable security and theoretically controllable risk is therefore necessary. Consensus Sampling (CS) is a promising algorithm toward provably secure AI. It controls risk by leveraging overlap in model output probabilities. However, we find that CS relies on frequent abstention to avoid unsafe outputs, which reduces utility. Moreover, CS becomes highly vulnerable when unsafe models are maliciously manipulated. To address these issues, we propose a new primitive called Reliable Consensus Sampling (RCS), that traces acceptance probability to tolerate extreme adversarial behaviors, improving robustness. RCS also eliminates the need for abstention entirely. We further develop a feedback algorithm to continuously and dynamically enhance the safety of RCS. We provide theoretical guarantees that RCS maintains a controllable risk threshold. Extensive experiments show that RCS significantly improves robustness and utility while maintaining latency comparable to CS. We hope this work contributes to the development of provably secure generative AI. - oai:arXiv.org:2512.24925v1 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang - - - A finite element approach for minimizing line and surface energies arising in the study of singularities in liquid crystals - https://arxiv.org/abs/2512.24928 - arXiv:2512.24928v1 Announce Type: new -Abstract: Motivated by a problem originating in the study of defect structures in nematic liquid crystals, we describe and study a numerical algorithm for the resolution of a Plateau-like problem. The energy contains the area of a two-dimensional surface $T$ and the length of its boundary $\partial T$ reduced by a prescribed curve to make our problem non-trivial. We additionally include an obstacle $E$ for $T$ and pose a surface energy on $E$. We present an algorithm based on the Alternating Direction Method of Multipliers that minimizes a discretized version of the energy using finite elements, generalizing existing TV-minimization methods. We study different inclusion shapes demonstrating the rich structure of minimizing configurations and provide physical interpretation of our findings for colloidal particles in nematic liquid crystal. - oai:arXiv.org:2512.24928v1 - math.NA - cs.NA - math.AP - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Dominik Stantejsky - - - Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline - https://arxiv.org/abs/2512.24933 - arXiv:2512.24933v1 Announce Type: new -Abstract: Multi-step LLM pipelines invoke large language models multiple times in a structured sequence and can effectively solve complex tasks, but their performance heavily depends on the prompts used at each step. Jointly optimizing these prompts is difficult due to missing step-level supervision and inter-step dependencies. Existing end-to-end prompt optimization methods struggle under these conditions and often yield suboptimal or unstable updates. We propose ADOPT, an Adaptive Dependency-aware Prompt Optimization framework for multi-step LLM pipelines. ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation analogous to computing analytical derivatives. It decouples textual gradient estimation from gradient updates, reducing multi-prompt optimization to flexible single-prompt optimization steps, and employs a Shapley-based mechanism to adaptively allocate optimization resources. Experiments on real-world datasets and diverse pipeline structures show that ADOPT is effective and robust, consistently outperforming state-of-the-art prompt optimization baselines. - oai:arXiv.org:2512.24933v1 - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Minjun Zhao, Xinyu Zhang, Shuai Zhang, Deyang Li, Ruifeng Shi - - - Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information - https://arxiv.org/abs/2512.24934 - arXiv:2512.24934v1 Announce Type: new -Abstract: We study the problem of fair $k$-committee selection under an egalitarian objective. Given $n$ agents partitioned into $m$ groups (\eg, demographic quotas), the goal is to aggregate their preferences to form a committee of size $k$ that guarantees minimum representation from each group while minimizing the maximum \emph{cost} incurred by any agent. We model this setting as the ordinal fair $k$-center problem, where agents are embedded in an unknown metric space, and each agent reports a complete preference ranking (i.e., ordinal information) over all agents, consistent with the underlying distance metric (i.e., cardinal information). The cost incurred by an agent with respect to a committee is defined as its distance to the closest committee member. The quality of an algorithm is evaluated using the notion of distortion, which measures the worst-case ratio between the cost of the committee produced by the algorithm and the cost of an optimal committee, when given complete access to the underlying metric space. - When cardinal information is not available, no constant distortion is possible for the ordinal $k$-center problem, even without fairness constraints, when $k\geq 3$ [Burkhardt et.al., AAAI'24]. To overcome this hardness, we allow limited access to cardinal information by querying the metric space. In this setting, our main contribution is a factor-$5$ distortion algorithm that requires only $O(k \log^2 k)$ queries. Along the way, we present an improved factor-$3$ distortion algorithm using $O(k^2)$ queries. - oai:arXiv.org:2512.24934v1 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Ameet Gadekar, Aristides Gionis, Suhas Thejaswi, Sijing Tu - - - Vibe Coding, Interface Flattening - https://arxiv.org/abs/2512.24939 - arXiv:2512.24939v1 Announce Type: new -Abstract: Large language models are reshaping programming by enabling 'vibe coding': the development of softwares through natural-language interaction with model-driven toolchains. This article argues that vibe coding is best understood as interface flattening, a reconfiguration in which previously distinct modalities (GUI, CLI, and API) appear to converge into a single conversational surface, even as the underlying chain of translation from intention to machinic effect lengthens and thickens. Drawing on Friedrich Kittler's materialist media theory and Alexander Galloway's account of interfaces as sites of protocol control, the paper situates programming as a historically localised interface arrangement rather than an essential relation to computation. Through a materialist reconstruction of the contemporary vibe-coding stack, it shows how remote compute infrastructures, latency and connectivity, structured outputs, function/tool calling, and interoperability standards such as the Model Context Protocol relocate control and meaning-making power to model and protocol providers. The apparent democratisation of technical capability therefore depends on new dependencies and new literacies. By foregrounding the tension between experiential flattening and infrastructural thickening, I demonstrate how LLM-mediated development redistributes symbolic labour/power, obscures responsibility, and privatises competencies previously dispersed across programming communities, contributing a critical lens on the political economy of AI-mediated human-computer interaction. - oai:arXiv.org:2512.24939v1 - cs.HC - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Hongrui Jin - - - Iterative Deployment Improves Planning Skills in LLMs - https://arxiv.org/abs/2512.24940 - arXiv:2512.24940v1 Announce Type: new -Abstract: We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change the properties of the resultant models. By testing this mechanism on various planning domains, we observe substantial improvements in planning skills, with later models displaying emergent generalization by discovering much longer plans than the initial models. We then provide theoretical analysis showing that iterative deployment effectively implements reinforcement learning (RL) training in the outer-loop (i.e. not as part of intentional model training), with an implicit reward function. The connection to RL has two important implications: first, for the field of AI safety, as the reward function entailed by repeated deployment is not defined explicitly, and could have unexpected implications to the properties of future model deployments. Second, the mechanism highlighted here can be viewed as an alternative training regime to explicit RL, relying on data curation rather than explicit rewards. - oai:arXiv.org:2512.24940v1 - cs.AI - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Augusto B. Corr\^ea, Yoav Gelberg, Luckeciano C. Melo, Ilia Shumailov, Andr\'e G. Pereira, Yarin Gal - - - Securing High-Concurrency Ticket Sales: A Framework Based on Microservice - https://arxiv.org/abs/2512.24941 - arXiv:2512.24941v1 Announce Type: new -Abstract: The railway ticketing system is one of the most important public service infrastructure. In peak periods such as holidays, it is often faced with the challenge of high concurrency scenarios because of a large number of users accessing at the same time. The traditional aggregation architecture can not meet the peak user requirements because of its insufficient fault tolerance and low ability. Therefore, the system needs to use microservice architecture for development, and add multiple security methods to ensure that the system can have good stability and data consistency under high concurrency scenarios, and can respond quickly to user requests. This paper introduces the use of B/S architecture and Spring Cloud to design and develop a railway ticket purchase system that can maintain stability and reliability under high concurrency scenarios, and formulate multiple security design methods for the system. This system integrates a range of functions, such as real-time train inquiries, dynamic seat updates, online seat selection, and ticket purchasing, effectively addressing common problems associated with offline ticket purchasing, such as long queues and delayed information. It enables a complete online process from inquiry and booking to payment and refunds. Furthermore, the "add passenger" function allows users to purchase tickets for others, extending the convenience of online ticketing to people with limited internet access. The system design prioritizes security and stability, while also focusing on high performance, and achieves these goals through a carefully designed architecture and the integration of multiple middleware components. After the completion of the system development, the core interface of the system is tested, and then the results are analyzed. The test data proves that the system has good ability and stability under high concurrency. - oai:arXiv.org:2512.24941v1 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhiyong Zhang, Xiaoyan Zhang, Xiaoqi Li - - - RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment - https://arxiv.org/abs/2512.24943 - arXiv:2512.24943v1 Announce Type: new -Abstract: Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient complexity for comprehensive model assessment, resulting in an absence of standardized relevance evaluation metrics across the industry. To address this limitation, we propose Rule-Aware benchmark with Image for Relevance assessment(RAIR), a Chinese dataset derived from real-world scenarios. RAIR established a standardized framework for relevance assessment and provides a set of universal rules, which forms the foundation for standardized evaluation. Additionally, RAIR analyzes essential capabilities required for current relevance models and introduces a comprehensive dataset consists of three subset: (1) a general subset with industry-balanced sampling to evaluate fundamental model competencies; (2) a long-tail hard subset focus on challenging cases to assess performance limits; (3) a visual salience subset for evaluating multimodal understanding capabilities. We conducted experiments on RAIR using 14 open and closed-source models. The results demonstrate that RAIR presents sufficient challenges even for GPT-5, which achieved the best performance. RAIR data are now available, serving as an industry benchmark for relevance assessment while providing new insights into general LLM and Visual Language Model(VLM) evaluation. - oai:arXiv.org:2512.24943v1 - cs.IR - cs.AI - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chenji Lu, Zhuo Chen, Hui Zhao, Zhenyi Wang, Pengjie Wang, Jian Xu, Bo Zheng - - - HaineiFRDM: Explore Diffusion to Restore Defects in Fast-Movement Films - https://arxiv.org/abs/2512.24946 - arXiv:2512.24946v1 Announce Type: new -Abstract: Existing open-source film restoration methods show limited performance compared to commercial methods due to training with low-quality synthetic data and employing noisy optical flows. In addition, high-resolution films have not been explored by the open-source methods.We propose HaineiFRDM(Film Restoration Diffusion Model), a film restoration framework, to explore diffusion model's powerful content-understanding ability to help human expert better restore indistinguishable film defects.Specifically, we employ a patch-wise training and testing strategy to make restoring high-resolution films on one 24GB-VRAMR GPU possible and design a position-aware Global Prompt and Frame Fusion Modules.Also, we introduce a global-local frequency module to reconstruct consistent textures among different patches. Besides, we firstly restore a low-resolution result and use it as global residual to mitigate blocky artifacts caused by patching process.Furthermore, we construct a film restoration dataset that contains restored real-degraded films and realistic synthetic data.Comprehensive experimental results conclusively demonstrate the superiority of our model in defect restoration ability over existing open-source methods. Code and the dataset will be released. - oai:arXiv.org:2512.24946v1 - cs.CV - cs.AI - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rongji Xun, Junjie Yuan, Zhongjie Wang - - - CPJ: Explainable Agricultural Pest Diagnosis via Caption-Prompt-Judge with LLM-Judged Refinement - https://arxiv.org/abs/2512.24947 - arXiv:2512.24947v1 Announce Type: new -Abstract: Accurate and interpretable crop disease diagnosis is essential for agricultural decision-making, yet existing methods often rely on costly supervised fine-tuning and perform poorly under domain shifts. We propose Caption--Prompt--Judge (CPJ), a training-free few-shot framework that enhances Agri-Pest VQA through structured, interpretable image captions. CPJ employs large vision-language models to generate multi-angle captions, refined iteratively via an LLM-as-Judge module, which then inform a dual-answer VQA process for both recognition and management responses. Evaluated on CDDMBench, CPJ significantly improves performance: using GPT-5-mini captions, GPT-5-Nano achieves \textbf{+22.7} pp in disease classification and \textbf{+19.5} points in QA score over no-caption baselines. The framework provides transparent, evidence-based reasoning, advancing robust and explainable agricultural diagnosis without fine-tuning. Our code and data are publicly available at: https://github.com/CPJ-Agricultural/CPJ-Agricultural-Diagnosis. - oai:arXiv.org:2512.24947v1 - cs.CV - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Wentao Zhang, Tao Fang, Lina Lu, Lifei Wang, Weihe Zhong - - - ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT - https://arxiv.org/abs/2512.24948 - arXiv:2512.24948v1 Announce Type: new -Abstract: Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respiratory motion. ECG-gated cardiac CTs substantially reduce motion artifacts, but their use in population screening and routine imaging remains limited due to gating requirements and lack of insurance coverage. Although identification of incidental CAC from non-gated chest CT is increasingly considered for it offers an accessible and widely available alternative, this modality is limited by more severe motion artifacts. We present ProDM (Property-aware Progressive Correction Diffusion Model), a generative diffusion framework that restores motion-free calcified lesions from non-gated CTs. ProDM introduces three key components: (1) a CAC motion simulation data engine that synthesizes realistic non-gated acquisitions with diverse motion trajectories directly from cardiac-gated CTs, enabling supervised training without paired data; (2) a property-aware learning strategy incorporating calcium-specific priors through a differentiable calcium consistency loss to preserve lesion integrity; and (3) a progressive correction scheme that reduces artifacts gradually across diffusion steps to enhance stability and calcium fidelity. Experiments on real patient datasets show that ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines. A reader study on real non-gated scans further confirms that ProDM suppresses motion artifacts and improves clinical usability. These findings highlight the potential of progressive, property-aware frameworks for reliable CAC quantification from routine chest CT imaging. - oai:arXiv.org:2512.24948v1 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Xinran Gong, Gorkem Durak, Halil Ertugrul Aktas, Vedat Cicek, Jinkui Hao, Ulas Bagci, Nilay S. Shah, Bo Zhou - - - VIPER: Process-aware Evaluation for Generative Video Reasoning - https://arxiv.org/abs/2512.24952 - arXiv:2512.24952v1 Announce Type: new -Abstract: Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation of continuous frames. While these models show promise for Generative Video Reasoning (GVR), existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking, where a model reaches a correct conclusion through an erroneous process. To address this, we propose a process-aware evaluation paradigm. We introduce VIPER, a comprehensive benchmark spanning 16 tasks across temporal, structural, symbolic, spatial, physics, and planning reasoning. Furthermore, we propose Process-outcome Consistency (POC@r), a new metric that utilizes VLM-as-Judge with a hierarchical rubric to evaluate both the validity of the intermediate steps and the final result. Our experiments reveal that state-of-the-art video models achieve only about 20% POC@1.0 and exhibit a significant outcome-hacking. We further explore the impact of test-time scaling and sampling robustness, highlighting a substantial gap between current video generation and true generalized visual reasoning. Our benchmark will be publicly released. - oai:arXiv.org:2512.24952v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yifan Li, Yukai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Wayne Xin Zhao, Minghui Qiu - - - MSACL: Multi-Step Actor-Critic Learning with Lyapunov Certificates for Exponentially Stabilizing Control - https://arxiv.org/abs/2512.24955 - arXiv:2512.24955v1 Announce Type: new -Abstract: Achieving provable stability in model-free reinforcement learning (RL) remains a challenge, particularly in balancing exploration with rigorous safety. This article introduces MSACL, a framework that integrates exponential stability theory with maximum entropy RL through multi-step Lyapunov certificate learning. Unlike methods relying on complex reward engineering, MSACL utilizes off-policy multi-step data to learn Lyapunov certificates satisfying theoretical stability conditions. By introducing Exponential Stability Labels (ESL) and a $\lambda$-weighted aggregation mechanism, the framework effectively balances the bias-variance trade-off in multi-step learning. Policy optimization is guided by a stability-aware advantage function, ensuring the learned policy promotes rapid Lyapunov descent. We evaluate MSACL across six benchmarks, including stabilization and nonlinear tracking tasks, demonstrating its superiority over state-of-the-art Lyapunov-based RL algorithms. MSACL achieves exponential stability and rapid convergence under simple rewards, while exhibiting significant robustness to uncertainties and generalization to unseen trajectories. Sensitivity analysis establishes the multi-step horizon $n=20$ as a robust default across diverse systems. By linking Lyapunov theory with off-policy actor-critic frameworks, MSACL provides a foundation for verifiably safe learning-based control. Source code and benchmark environments will be made publicly available. - oai:arXiv.org:2512.24955v1 - cs.LG - cs.AI - cs.RO - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yongwei Zhang, Yuanzhe Xing, Quan Quan, Zhikun She - - - AMAP Agentic Planning Technical Report - https://arxiv.org/abs/2512.24957 - arXiv:2512.24957v1 Announce Type: new -Abstract: We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and itinerary planning. STAgent is a specialized model capable of interacting with ten distinct tools within spatio-temporal scenarios, enabling it to explore, verify, and refine intermediate steps during complex reasoning. Notably, STAgent effectively preserves its general capabilities. We empower STAgent with these capabilities through three key contributions: (1) a stable tool environment that supports over ten domain-specific tools, enabling asynchronous rollout and training; (2) a hierarchical data curation framework that identifies high-quality data like a needle in a haystack, curating high-quality queries with a filter ratio of 1:10,000, emphasizing both diversity and difficulty; and (3) a cascaded training recipe that starts with a seed SFT stage acting as a guardian to measure query difficulty, followed by a second SFT stage fine-tuned on queries with high certainty, and an ultimate RL stage that leverages data of low certainty. Initialized with Qwen3-30B-A3B to establish a strong SFT foundation and leverage insights into sample difficulty, STAgent yields promising performance on TravelBench while maintaining its general capabilities across a wide range of general benchmarks, thereby demonstrating the effectiveness of our proposed agentic model. - oai:arXiv.org:2512.24957v1 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Yulan Hu, Xiangwen Zhang, Sheng Ouyang, Hao Yi, Lu Xu, Qinglin Lang, Lide Tan, Xiang Cheng, Tianchen Ye, Zhicong Li, Ge Chen, Wenjin Yang, Zheng Pan, Shaopan Xiong, Siran Yang, Ju Huang, Yan Zhang, Jiamang Wang, Yong Liu, Yinfeng Huang, Tucheng Lin, Xin Li, Ning Guo - - - Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning - https://arxiv.org/abs/2512.24959 - arXiv:2512.24959v1 Announce Type: new -Abstract: Many modern AI and ML problems require evaluating partners' contributions through shared yet asymmetric, computationally intensive processes and the simultaneous selection of the most beneficial candidates. Sequential approaches to these problems can be unified under a new framework, Sequential Support Network Learning (SSNL), in which the goal is to select the most beneficial candidate set of partners for all participants using trials; that is, to learn a directed graph that represents the highest-performing contributions. We demonstrate that a new pure-exploration model, the semi-overlapping multi-(multi-armed) bandit (SOMMAB), in which a single evaluation provides distinct feedback to multiple bandits due to structural overlap among their arms, can be used to learn a support network from sparse candidate lists efficiently. - We develop a generalized GapE algorithm for SOMMABs and derive new exponential error bounds that improve the best known constant in the exponent for multi-bandit best-arm identification. The bounds scale linearly with the degree of overlap, revealing significant sample-complexity gains arising from shared evaluations. - From an application point of view, this work provides a theoretical foundation and improved performance guarantees for sequential learning tools for identifying support networks from sparse candidates in multiple learning problems, such as in multi-task learning (MTL), auxiliary task learning (ATL), federated learning (FL), and in multi-agent systems (MAS). - oai:arXiv.org:2512.24959v1 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Andr\'as Antos (Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics), Andr\'as Millinghoffer (Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, E-Group ICT Software Zrt., Budapest, Hungary), P\'eter Antal (Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, E-Group ICT Software Zrt., Budapest, Hungary) - - - Approximating evolution operators of linear delay equations: a general framework for the convergence analysis - https://arxiv.org/abs/2512.24964 - arXiv:2512.24964v1 Announce Type: new -Abstract: We consider the problem of discretizing evolution operators of linear delay equations with the aim of approximating their spectra, which is useful in investigating the stability properties of (nonlinear) equations via the principle of linearized stability. We develop a general convergence analysis based on a reformulation of the operators by means of a fixed-point equation, providing a list of hypotheses related to the regularization properties of the equation and the convergence of the chosen approximation techniques on suitable subspaces. This framework unifies the proofs for some methods based on pseudospectral discretization, which we present here in this new form. To exemplify the generality of the framework, we also apply it to a method of weighted residuals found in the literature, which was previously lacking a formal convergence analysis. - oai:arXiv.org:2512.24964v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Alessia and\`o, Giusy Bosco, Dimitri Breda, Davide Liessi - - - ShowUI-$\pi$: Flow-based Generative Models as GUI Dexterous Hands - https://arxiv.org/abs/2512.24965 - arXiv:2512.24965v1 Announce Type: new -Abstract: Building intelligent agents capable of dexterous manipulation is essential for achieving human-like automation in both robotics and digital environments. However, existing GUI agents rely on discrete click predictions (x,y), which prohibits free-form, closed-loop trajectories (e.g. dragging a progress bar) that require continuous, on-the-fly perception and adjustment. In this work, we develop ShowUI-$\pi$, the first flow-based generative model as GUI dexterous hand, featuring the following designs: (i) Unified Discrete-Continuous Actions, integrating discrete clicks and continuous drags within a shared model, enabling flexible adaptation across diverse interaction modes; (ii) Flow-based Action Generation for drag modeling, which predicts incremental cursor adjustments from continuous visual observations via a lightweight action expert, ensuring smooth and stable trajectories; (iii) Drag Training data and Benchmark, where we manually collect and synthesize 20K drag trajectories across five domains (e.g. PowerPoint, Adobe Premiere Pro), and introduce ScreenDrag, a benchmark with comprehensive online and offline evaluation protocols for assessing GUI agents' drag capabilities. Our experiments show that proprietary GUI agents still struggle on ScreenDrag (e.g. Operator scores 13.27, and the best Gemini-2.5-CUA reaches 22.18). In contrast, ShowUI-$\pi$ achieves 26.98 with only 450M parameters, underscoring both the difficulty of the task and the effectiveness of our approach. We hope this work advances GUI agents toward human-like dexterous control in digital world. The code is available at https://github.com/showlab/showui-pi. - oai:arXiv.org:2512.24965v1 - cs.CV - cs.AI - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Siyuan Hu, Kevin Qinghong Lin, Mike Zheng Shou - - - Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions - https://arxiv.org/abs/2512.24971 - arXiv:2512.24971v1 Announce Type: new -Abstract: Compressed deep learning models are crucial for deploying computer vision systems on resource-constrained devices. However, model compression may affect robustness, especially under natural corruption. Therefore, it is important to consider robustness evaluation while validating computer vision systems. This paper presents a comprehensive evaluation of compression techniques - quantization, pruning, and weight clustering applied individually and in combination to convolutional neural networks (ResNet-50, VGG-19, and MobileNetV2). Using the CIFAR-10-C and CIFAR 100-C datasets, we analyze the trade-offs between robustness, accuracy, and compression ratio. Our results show that certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures. Utilizing multiobjective assessment, we determine the best configurations, showing that customized technique combinations produce beneficial multi-objective results. This study provides insights into selecting compression methods for robust and efficient deployment of models in corrupted real-world environments. - oai:arXiv.org:2512.24971v1 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Itallo Patrick Castro Alves Da Silva, Emanuel Adler Medeiros Pereira, Erick de Andrade Barboza, Baldoino Fonseca dos Santos Neto, Marcio de Medeiros Ribeiro - - - Hierarchical Deformation Planning and Neural Tracking for DLOs in Constrained Environments - https://arxiv.org/abs/2512.24974 - arXiv:2512.24974v1 Announce Type: new -Abstract: Deformable linear objects (DLOs) manipulation presents significant challenges due to DLOs' inherent high-dimensional state space and complex deformation dynamics. The wide-populated obstacles in realistic workspaces further complicate DLO manipulation, necessitating efficient deformation planning and robust deformation tracking. In this work, we propose a novel framework for DLO manipulation in constrained environments. This framework combines hierarchical deformation planning with neural tracking, ensuring reliable performance in both global deformation synthesis and local deformation tracking. Specifically, the deformation planner begins by generating a spatial path set that inherently satisfies the homotopic constraints associated with DLO keypoint paths. Next, a path-set-guided optimization method is applied to synthesize an optimal temporal deformation sequence for the DLO. In manipulation execution, a neural model predictive control approach, leveraging a data-driven deformation model, is designed to accurately track the planned DLO deformation sequence. The effectiveness of the proposed framework is validated in extensive constrained DLO manipulation tasks. - oai:arXiv.org:2512.24974v1 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yunxi Tang, Tianqi Yang, Jing Huang, Xiangyu Chu, Kwok Wai Samuel Au - - - Attribution-Guided Distillation of Matryoshka Sparse Autoencoders - https://arxiv.org/abs/2512.24975 - arXiv:2512.24975v1 Announce Type: new -Abstract: Sparse autoencoders (SAEs) aim to disentangle model activations into monosemantic, human-interpretable features. In practice, learned features are often redundant and vary across training runs and sparsity levels, which makes interpretations difficult to transfer and reuse. We introduce Distilled Matryoshka Sparse Autoencoders (DMSAEs), a training pipeline that distills a compact core of consistently useful features and reuses it to train new SAEs. DMSAEs run an iterative distillation cycle: train a Matryoshka SAE with a shared core, use gradient X activation to measure each feature's contribution to next-token loss in the most nested reconstruction, and keep only the smallest subset that explains a fixed fraction of the attribution. Only the core encoder weight vectors are transferred across cycles; the core decoder and all non-core latents are reinitialized each time. On Gemma-2-2B layer 12 residual stream activations, seven cycles of distillation (500M tokens, 65k width) yielded a distilled core of 197 features that were repeatedly selected. Training using this distilled core improves several SAEBench metrics and demonstrates that consistent sets of latent features can be transferred across sparsity levels - oai:arXiv.org:2512.24975v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Cristina P. Martin-Linares, Jonathan P. Ling - - - A Modal Logic for Possibilistic Reasoning with Fuzzy Formal Contexts - https://arxiv.org/abs/2512.24980 - arXiv:2512.24980v1 Announce Type: new -Abstract: We introduce a two-sort weighted modal logic for possibilistic reasoning with fuzzy formal contexts. The syntax of the logic includes two types of weighted modal operators corresponding to classical necessity ($\Box$) and sufficiency ($\boxminus$) modalities and its formulas are interpreted in fuzzy formal contexts based on possibility theory. We present its axiomatization that is \emph{sound} with respect to the class of all fuzzy context models. In addition, both the necessity and sufficiency fragments of the logic are also individually complete with respect to the class of all fuzzy context models. We highlight the expressive power of the logic with some illustrative examples. As a formal context is the basic construct of formal concept analysis (FCA), we generalize three main notions in FCA, i.e., formal concepts, object oriented concepts, and property oriented concepts, to their corresponding $c$-cut concepts in fuzzy formal contexts. Then, we show that our logical language can represent all three of these generalized notions. Finally, we demonstrate the possibility of extending our logic to reasoning with multi-relational fuzzy contexts, in which the Boolean combinations of different fuzzy relations are allowed. - oai:arXiv.org:2512.24980v1 - cs.LO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Prosenjit Howlader, Churn-Jung Liau - - - DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments - https://arxiv.org/abs/2512.24985 - arXiv:2512.24985v1 Announce Type: new -Abstract: Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light conditions at night or in dark environments--a core necessity that has been largely overlooked. To address this underexplored challenge, we present DarkEQA, an open-source benchmark for evaluating EQA-relevant perceptual primitives under multi-level low-light conditions. DarkEQA isolates the perception bottleneck by evaluating question answering from egocentric observations under controlled degradations, enabling attributable robustness analysis. A key design feature of DarkEQA is its physical fidelity: visual degradations are modeled in linear RAW space, simulating physics-based illumination drop and sensor noise followed by an ISP-inspired rendering pipeline. We demonstrate the utility of DarkEQA by evaluating a wide range of state-of-the-art VLMs and Low-Light Image Enhancement (LLIE) models. Our analysis systematically reveals VLMs' limitations when operating under these challenging visual conditions. Our code and benchmark dataset will be released upon acceptance. - oai:arXiv.org:2512.24985v1 - cs.CV - cs.AI - cs.LG - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yohan Park, Hyunwoo Ha, Wonjun Jo, Tae-Hyun Oh - - - PhysTalk: Language-driven Real-time Physics in 3D Gaussian Scenes - https://arxiv.org/abs/2512.24986 - arXiv:2512.24986v1 Announce Type: new -Abstract: Realistic visual simulations are omnipresent, yet their creation requires computing time, rendering, and expert animation knowledge. Open-vocabulary visual effects generation from text inputs emerges as a promising solution that can unlock immense creative potential. However, current pipelines lack both physical realism and effective language interfaces, requiring slow offline optimization. In contrast, PhysTalk takes a 3D Gaussian Splatting (3DGS) scene as input and translates arbitrary user prompts into real time, physics based, interactive 4D animations. A large language model (LLM) generates executable code that directly modifies 3DGS parameters through lightweight proxies and particle dynamics. Notably, PhysTalk is the first framework to couple 3DGS directly with a physics simulator without relying on time consuming mesh extraction. While remaining open vocabulary, this design enables interactive 3D Gaussian animation via collision aware, physics based manipulation of arbitrary, multi material objects. Finally, PhysTalk is train-free and computationally lightweight: this makes 4D animation broadly accessible and shifts these workflows from a "render and wait" paradigm toward an interactive dialogue with a modern, physics-informed pipeline. - oai:arXiv.org:2512.24986v1 - cs.GR - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Luca Collorone, Mert Kiray, Indro Spinelli, Fabio Galasso, Benjamin Busam - - - Efficiently Estimating Data Efficiency for Language Model Fine-tuning - https://arxiv.org/abs/2512.24991 - arXiv:2512.24991v1 Announce Type: new -Abstract: While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning examples needed to achieve a desired level of performance--is often unknown, resulting in costly cycles of incremental annotation and retraining. Indeed, we demonstrate across a curated set of 30 specialized tasks that performant LLMs may struggle zero-shot but can attain stronger performance after fine-tuning. This motivates the need for methods to predict a task's data efficiency without requiring incremental annotation. After introducing a concrete metric that quantifies a task's data efficiency, we propose using the gradient cosine similarity of low-confidence examples to predict data efficiency based on a small number of labeled samples. We validate our approach on a diverse set of tasks with varying data efficiencies, attaining 8.6% error in overall data efficiency prediction and typically eliminating hundreds of unnecessary annotations on each task. Our experiment results and implementation code are available on GitHub. - oai:arXiv.org:2512.24991v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Gyung Hyun Je, Colin Raffel - - - Classifying long legal documents using short random chunks - https://arxiv.org/abs/2512.24997 - arXiv:2512.24997v1 Announce Type: new -Abstract: Classifying legal documents is a challenge, besides their specialized vocabulary, sometimes they can be very long. This means that feeding full documents to a Transformers-based models for classification might be impossible, expensive or slow. Thus, we present a legal document classifier based on DeBERTa V3 and a LSTM, that uses as input a collection of 48 randomly-selected short chunks (max 128 tokens). Besides, we present its deployment pipeline using Temporal, a durable execution solution, which allow us to have a reliable and robust processing workflow. The best model had a weighted F-score of 0.898, while the pipeline running on CPU had a processing median time of 498 seconds per 100 files. - oai:arXiv.org:2512.24997v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Luis Adri\'an Cabrera-Diego - - - Bi-C2R: Bidirectional Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification - https://arxiv.org/abs/2512.25000 - arXiv:2512.25000v1 Announce Type: new -Abstract: Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-ReID methods typically re-extract new features for all historical gallery images for inference after each update, known as "re-indexing". However, historical gallery data typically suffers from direct saving due to the data privacy issue and the high re-indexing costs for large-scale gallery images. As a result, it inevitably leads to incompatible retrieval between query features extracted by the updated model and gallery features extracted by those before the update, greatly impairing the re-identification performance. To tackle the above issue, this paper focuses on a new task called Re-index Free Lifelong person Re-IDentification (RFL-ReID), which requires performing lifelong person re-identification without re-indexing historical gallery images. Therefore, RFL-ReID is more challenging than L-ReID, requiring continuous learning and balancing new and old knowledge in diverse streaming data, and making the features output by the new and old models compatible with each other. To this end, we propose a Bidirectional Continuous Compatible Representation (Bi-C2R) framework to continuously update the gallery features extracted by the old model to perform efficient L-ReID in a compatible manner. We verify our proposed Bi-C2R method through theoretical analysis and extensive experiments on multiple benchmarks, which demonstrate that the proposed method can achieve leading performance on both the introduced RFL-ReID task and the traditional L-ReID task. - oai:arXiv.org:2512.25000v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhenyu Cui, Jiahuan Zhou, Yuxin Peng - - - FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM - https://arxiv.org/abs/2512.25008 - arXiv:2512.25008v1 Announce Type: new -Abstract: We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robust tracking and mapping. Our core idea is to bridge flow estimation with geometric reasoning by leveraging the guidance from foundation depth models. To this end, we first develop a Hybrid Flow Network that produces geometry-aware correspondences, enabling consistent depth and pose inference across diverse keyframes. To enforce global consistency, we propose a Bi-Consistent Bundle Adjustment Layer that jointly optimizes keyframe pose and depth under multi-view constraints. Furthermore, we introduce a Reliability-Aware Refinement mechanism that dynamically adapts the flow update process by distinguishing between reliable and uncertain regions, forming a closed feedback loop between matching and optimization. Extensive experiments demonstrate that FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS, demonstrating strong generalization to various scenarios and practical applicability of our method. - oai:arXiv.org:2512.25008v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuchen Wu, Jiahe Li, Fabio Tosi, Matteo Poggi, Jin Zheng, Xiao Bai - - - At the intersection of Numerical Analysis and Spectral Geometry - https://arxiv.org/abs/2512.25012 - arXiv:2512.25012v1 Announce Type: new -Abstract: How do the geometric properties of a domain impact the spectrum of an operator defined on it? How do we compute accurate and reliable approximations of these spectra? The former question is studied in spectral geometry, and the latter is a central concern in numerical analysis. In this short expository survey we revisit the process of eigenvalue approximation, from the perspective of computational spectral geometry. Over the years a multitude of methods -- for discretizing the operator and for the resultant discrete system -- have been developed and analyzed in the field of numerical analysis. High-accuracy and provably convergent discretization approaches can be used to examine the interplay between the spectrum of an operator and the geometric properties of the spatial domain or manifold it is defined on. While computations have been used to guide conjectures in spectral geometry, in recent years approximation-theoretic tools and validated computations are also being used as part of proof strategies in spectral geometry. - Given a particular spectral feature of interest, should we discretize the original problem, or seek a reformulation? Of the many possible approximation strategies, which should we choose? These choices are inextricably linked to the objective: on the one hand, rapid, specialized methods are often ideal for conjecture formulation (prioritizing efficiency and accuracy), whereas schemes with guaranteed, computable error bounds are needed when computation is incorporated into a proof strategy. We also review instances where the demanding requirements of spectral geometry -- the need for rigorous error control or the robust calculation of higher eigenvalues -- motivate new developments in numerical analysis. - oai:arXiv.org:2512.25012v1 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Nilima Nigam - - - Diffusion Language Models are Provably Optimal Parallel Samplers - https://arxiv.org/abs/2512.25014 - arXiv:2512.25014v1 Announce Type: new -Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundation for this advantage by formalizing a model of parallel sampling and showing that DLMs augmented with polynomial-length chain-of-thought (CoT) can simulate any parallel sampling algorithm using an optimal number of sequential steps. Consequently, whenever a target distribution can be generated using a small number of sequential steps, a DLM can be used to generate the distribution using the same number of optimal sequential steps. However, without the ability to modify previously revealed tokens, DLMs with CoT can still incur large intermediate footprints. We prove that enabling remasking (converting unmasked tokens to masks) or revision (converting unmasked tokens to other unmasked tokens) together with CoT further allows DLMs to simulate any parallel sampling algorithm with optimal space complexity. We further justify the advantage of revision by establishing a strict expressivity gap: DLMs with revision or remasking are strictly more expressive than those without. Our results not only provide a theoretical justification for the promise of DLMs as the most efficient parallel sampler, but also advocate for enabling revision in DLMs. - oai:arXiv.org:2512.25014v1 - cs.LG - cs.CC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Haozhe Jiang, Nika Haghtalab, Lijie Chen - - - MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes - https://arxiv.org/abs/2512.25015 - arXiv:2512.25015v1 Announce Type: new -Abstract: Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive symptoms exhibited by memes shared by users of online social media platforms. We introduce RESTOREx as a vital resource for detecting depressive symptoms in memes on social media through the Large Language Model (LLM) generated and human-annotated explanations. We introduce MAMAMemeia, a collaborative multi-agent multi-aspect discussion framework grounded in the clinical psychology method of Cognitive Analytic Therapy (CAT) Competencies. MAMAMemeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods. - oai:arXiv.org:2512.25015v1 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Siddhant Agarwal, Adya Dhuler, Polly Ruhnke, Melvin Speisman, Md Shad Akhtar, Shweta Yadav - - - Approximations for the Weighted Reversal, Transposition, and Indel Distance Problem with Intergenic Region Information - https://arxiv.org/abs/2512.25016 - arXiv:2512.25016v1 Announce Type: new -Abstract: Genome rearrangement distances are an established method in genome comparison. Works in this area may include various rearrangement operations representing large-scale mutations, gene orientation information, the number of nucleotides in intergenic regions, and weights reflecting the expected frequency of each operation. In this article, we model genomes containing at most one copy of each gene by considering gene sequences, with orientations, and representing intergenic regions according to their nucleotide lengths. We looked at a problem called Weighted Reversal, Transposition, and Indel Distance, which seeks the minimal cost sequence composed by the rearrangement operations of reversals, transposition, and indels, capable of transforming one genome into another. We leverage a structure called Labeled Intergenic Breakpoint Graph to show an algorithm for that problem with guaranteed approximations considering some sets of weights for the operations. - oai:arXiv.org:2512.25016v1 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Zanoni Dias - - - Convergence of the generalization error for deep gradient flow methods for PDEs - https://arxiv.org/abs/2512.25017 - arXiv:2512.25017v1 Announce Type: new -Abstract: The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We first show that the solution of PDEs that satisfy reasonable and verifiable assumptions can be approximated by neural networks, thus the approximation error tends to zero as the number of neurons tends to infinity. Then, we derive the gradient flow that the training process follows in the ``wide network limit'' and analyze the limit of this flow as the training time tends to infinity. These results combined show that the generalization error of DGFMs tends to zero as the number of neurons and the training time tend to infinity. - oai:arXiv.org:2512.25017v1 - math.NA - cs.LG - cs.NA - q-fin.CP - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Chenguang Liu, Antonis Papapantoleon, Jasper Rou - - - Approximation Algorithms for Fair Repetitive Scheduling - https://arxiv.org/abs/2512.25020 - arXiv:2512.25020v1 Announce Type: new -Abstract: We consider a recently introduced fair repetitive scheduling problem involving a set of clients, each asking for their associated job to be daily scheduled on a single machine across a finite planning horizon. The goal is to determine a job processing permutation for each day, aiming to minimize the maximum total completion time experienced by any client. This problem is known to be NP-hard for quite restrictive settings, with previous work offering exact solution methods for highly-structured special cases. - In this paper, we focus on the design of approximation algorithms with provable performance guarantees. Our main contributions can be briefly summarized as follows: - (i) When job processing times are day-dependent, we devise a polynomial-time LP-based $2$-approximation, as well as a polynomial-time approximation scheme for a constant number of days. - (ii) With day-invariant processing times, we obtain a surprisingly simple $(\frac{1+\sqrt{2}}{2}+\epsilon)$-approximation in polynomial time. This setting is also shown to admit a quasi-polynomial-time approximation scheme for an arbitrary number of days. - The key technical component driving our approximation schemes is a novel batching technique, where jobs are conceptually grouped into batches, subsequently leading either to a low-dimensional dynamic program or to a compact configuration LP. Concurrently, while developing our constant-factor approximations, we propose a host of lower-bounding mechanisms that may be of broader interest. - oai:arXiv.org:2512.25020v1 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Danny Hermelin, Danny Segev, Dvir Shabtay - - - ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning - https://arxiv.org/abs/2512.25023 - arXiv:2512.25023v1 Announce Type: new -Abstract: Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas over grapes, but which preference is stronger? Strength is crucial for decision-making under uncertainty and generalization of preference models, but hard to measure reliably. Metadata such as response times and inter-annotator agreement can serve as proxies for strength, but are often noisy and confounded. We propose ResponseRank to address the challenge of learning from noisy strength signals. Our method uses relative differences in proxy signals to rank responses to pairwise comparisons by their inferred preference strength. To control for systemic variation, we compare signals only locally within carefully constructed strata. This enables robust learning of utility differences consistent with strength-derived rankings while making minimal assumptions about the strength signal. Our contributions are threefold: (1) ResponseRank, a novel method that robustly learns preference strength by leveraging locally valid relative strength signals; (2) empirical evidence of improved sample efficiency and robustness across diverse tasks: synthetic preference learning (with simulated response times), language modeling (with annotator agreement), and RL control tasks (with simulated episode returns); and (3) the Pearson Distance Correlation (PDC), a novel metric that isolates cardinal utility learning from ordinal accuracy. - oai:arXiv.org:2512.25023v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Timo Kaufmann, Yannick Metz, Daniel Keim, Eyke H\"ullermeier - - - Modeling Language as a Sequence of Thoughts - https://arxiv.org/abs/2512.25026 - arXiv:2512.25026v1 Announce Type: new -Abstract: Transformer language models can generate strikingly natural text by modeling language as a sequence of tokens. Yet, by relying primarily on surface-level co-occurrence statistics, they fail to form globally consistent latent representations of entities and events, lack of which contributes to brittleness in relational direction (e.g., reversal curse), contextualization errors, and data inefficiency. On the other hand, cognitive science shows that human comprehension involves converting the input linguistic stream into compact, event-like representations that persist in memory while verbatim form is short-lived. Motivated by this view, we introduce Thought Gestalt (TG) model, a recurrent Transformer that models language at two levels of abstraction - tokens and sentence-level "thought" states. TG generates the tokens of one sentence at a time while cross-attending to a memory of prior sentence representations. In TG, token and sentence representations are generated using the same set of model parameters and trained with a single objective, the next-token cross-entropy: by retaining the computation graph of sentence representations written to memory, gradients from future token losses flow backward through cross-attention to optimize the parameters generating earlier sentence vectors. In scaling experiments, TG consistently improves efficiency over matched GPT-2 runs, among other baselines, with scaling fits indicating GPT-2 requires ~5-8% more data and ~33-42% more parameters to match TG's loss. TG also reduces errors on relational direction generalization on a father-son reversal curse probe. - oai:arXiv.org:2512.25026v1 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Nasim Borazjanizadeh, James McClelland - - - EF(X) Orientations: A Parameterized Complexity Perspective - https://arxiv.org/abs/2512.25033 - arXiv:2512.25033v1 Announce Type: new -Abstract: The concept of fair orientations in graphs was introduced by Christodoulou, Fiat, Koutsoupias, and Sgouritsa in 2023, naturally modeling fair division scenarios in which resources are only contested by neighbors. In this model, vertices represent agents and undirected edges represent goods; edges have to be oriented towards one of their endpoints, i.e., allocated to one of their adjacent agents. Although EFX orientations (envy-free up to any good) have been extensively studied in this setting, EF orientations (envy-free) remain unexplored. In this work, we initiate their study, mostly under the lens of parameterized complexity, presenting various tractable cases, hardness results, and parameterizations. Our results concern both simple graphs and multigraphs. Interestingly, many of our results transfer to EFX orientations, thus complementing and improving upon previous work; notably, we answer an open question regarding the structural parameterized complexity of the latter problem on graphs of polynomially-bounded valuations. We also show that EF orientations are tractable in cases in which EFX orientations are not, particularly for binary valuations. Lastly, we consider charity in the orientation setting, establishing algorithms for finding the minimum amount of edges that have to be removed from a graph in order for EF(X) orientations to exist. - oai:arXiv.org:2512.25033v1 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Sotiris Kanellopoulos, Edouard Nemery, Christos Pergaminelis, Minas Marios Sotiriou, Manolis Vasilakis - - - Generative Classifiers Avoid Shortcut Solutions - https://arxiv.org/abs/2512.25034 - arXiv:2512.25034v1 Announce Type: new -Abstract: Discriminative approaches to classification often learn shortcuts that hold in-distribution but fail even under minor distribution shift. This failure mode stems from an overreliance on features that are spuriously correlated with the label. We show that generative classifiers, which use class-conditional generative models, can avoid this issue by modeling all features, both core and spurious, instead of mainly spurious ones. These generative classifiers are simple to train, avoiding the need for specialized augmentations, strong regularization, extra hyperparameters, or knowledge of the specific spurious correlations to avoid. We find that diffusion-based and autoregressive generative classifiers achieve state-of-the-art performance on five standard image and text distribution shift benchmarks and reduce the impact of spurious correlations in realistic applications, such as medical or satellite datasets. Finally, we carefully analyze a Gaussian toy setting to understand the inductive biases of generative classifiers, as well as the data properties that determine when generative classifiers outperform discriminative ones. - oai:arXiv.org:2512.25034v1 - cs.LG - cs.AI - cs.CV - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Alexander C. Li, Ananya Kumar, Deepak Pathak - - - Thin Tree Verification is coNP-Complete - https://arxiv.org/abs/2512.25043 - arXiv:2512.25043v1 Announce Type: new -Abstract: An $\alpha$-thin tree $T$ of a graph $G$ is a spanning tree such that every cut of $G$ has at most an $\alpha$ proportion of its edges in $T$. The Thin Tree Conjecture proposes that there exists a function $f$ such that for any $\alpha > 0$, every $f(\alpha)$-edge-connected graph has an $\alpha$-thin tree. Aside from its independent interest, an algorithm which could efficiently construct an $O(1)/k$-thin tree for a given $k$-edge-connected graph would directly lead to an $O(1)$-approximation algorithm for the asymmetric travelling salesman problem (ATSP)(arXiv:0909.2849). However, it was not even known whether it is possible to efficiently verify that a given tree is $\alpha$-thin. We prove that determining the thinness of a tree is coNP-hard. - oai:arXiv.org:2512.25043v1 - cs.CC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Alice Moayyedi - - - AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG - https://arxiv.org/abs/2512.25052 - arXiv:2512.25052v1 Announce Type: new -Abstract: Retrieval-augmented generation (RAG) is highly sensitive to the quality of selected context, yet standard top-k retrieval often returns redundant or near-duplicate chunks that waste token budget and degrade downstream generation. We present AdaGReS, a redundancy-aware context selection framework for token-budgeted RAG that optimizes a set-level objective combining query-chunk relevance and intra-set redundancy penalties. AdaGReS performs greedy selection under a token-budget constraint using marginal gains derived from the objective, and introduces a closed-form, instance-adaptive calibration of the relevance-redundancy trade-off parameter to eliminate manual tuning and adapt to candidate-pool statistics and budget limits. We further provide a theoretical analysis showing that the proposed objective exhibits epsilon-approximate submodularity under practical embedding similarity conditions, yielding near-optimality guarantees for greedy selection. Experiments on open-domain question answering (Natural Questions) and a high-redundancy biomedical (drug) corpus demonstrate consistent improvements in redundancy control and context quality, translating to better end-to-end answer quality and robustness across settings. - oai:arXiv.org:2512.25052v1 - cs.CL - cs.AI - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Chao Peng, Bin Wang, Zhilei Long, Jinfang Sheng - - - Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings - https://arxiv.org/abs/2512.25055 - arXiv:2512.25055v1 Announce Type: new -Abstract: This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three modules: perception (sensing), central control (brain), and action (actuation and user interaction), forming a closed feedback loop that captures, analyzes, and interprets energy data to respond intelligently to user queries and manage connected appliances. By leveraging the autonomous data analytics capabilities of LLMs, the BEMS AI agent seeks to offer context-aware insights into energy consumption, cost prediction, and device scheduling, thereby addressing limitations in existing energy management systems. The prototype's performance was evaluated using 120 user queries across four distinct real-world residential energy datasets and different evaluation metrics, including latency, functionality, capability, accuracy, and cost-effectiveness. The generalizability of the framework was demonstrated using ANOVA tests. The results revealed promising performance, measured by response accuracy in device control (86%), memory-related tasks (97%), scheduling and automation (74%), and energy analysis (77%), while more complex cost estimation tasks highlighted areas for improvement with an accuracy of 49%. This benchmarking study moves toward formalizing the assessment of LLM-based BEMS AI agents and identifying future research directions, emphasizing the trade-off between response accuracy and computational efficiency. - oai:arXiv.org:2512.25055v1 - cs.AI - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tianzhi He, Farrokh Jazizadeh - - - Reliable and Resilient Collective Communication Library for LLM Training and Serving - https://arxiv.org/abs/2512.25059 - arXiv:2512.25059v1 Announce Type: new -Abstract: Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training and request reprocessing during inference. We present R$^2$CCL, a fault-tolerant communication library that provides lossless, low-overhead failover by exploiting multi-NIC hardware. R$^2$CCL performs rapid connection migration, bandwidth-aware load redistribution, and resilient collective algorithms to maintain progress under failures. We evaluate R$^2$CCL on two 8-GPU H100 InfiniBand servers and via large-scale ML simulators modeling hundreds of GPUs with diverse failure patterns. Experiments show that R$^2$CCL is highly robust to NIC failures, incurring less than 1\% training and less than 3\% inference overheads. R$^2$CCL outperforms baselines AdapCC and DejaVu by 12.18$\times$ and 47$\times$, respectively. - oai:arXiv.org:2512.25059v1 - cs.DC - cs.LG - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wei Wang, Nengneng Yu, Sixian Xiong, Zaoxing Liu - - - On the geometry and topology of representations: the manifolds of modular addition - https://arxiv.org/abs/2512.25060 - arXiv:2512.25060v1 Announce Type: new -Abstract: The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both uniform attention and trainable attention architectures implement the same algorithm via topologically and geometrically equivalent representations. Our methodology goes beyond the interpretation of individual neurons and weights. Instead, we identify all of the neurons corresponding to each learned representation and then study the collective group of neurons as one entity. This method reveals that each learned representation is a manifold that we can study utilizing tools from topology. Based on this insight, we can statistically analyze the learned representations across hundreds of circuits to demonstrate the similarity between learned modular addition circuits that arise naturally from common deep learning paradigms. - oai:arXiv.org:2512.25060v1 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Gabriela Moisescu-Pareja, Gavin McCracken, Harley Wiltzer, Vincent L\'etourneau, Colin Daniels, Doina Precup, Jonathan Love - - - Many Minds from One Model: Bayesian Transformers for Population Intelligence - https://arxiv.org/abs/2512.25063 - arXiv:2512.25063v1 Announce Type: new -Abstract: Despite their scale and success, modern transformers are almost universally trained as single-minded systems: optimization produces one deterministic set of parameters, representing a single functional hypothesis about the data. Motivated by the idea that intelligence emerge from many minds, we propose Population Bayesian Transformers (B-Trans), which transform a standard Large Language Model into a Bayesian Transformer model to supports sampling diverse yet coherent model instances from a single set of pre-trained weights. - B-Trans introduces a Bayesian-motivated posterior proxy by treating the bias-like offsets in normalization layers as stochastic variables with a Gaussian variational approximation, inducing a distribution over model behavior without the cost of training full Bayesian neural networks. Sampling from this proxy yields a set of model instances with diverse behaviors while maintaining general competence. To preserve coherence within each generation, we freeze the sampled noise at the sequence level, enforcing temporal consistency across tokens. B-Trans allows for population-level decision-making, where aggregating predictions across sampled individuals significantly enhances exploration. Experiments across zero-shot generation, Reinforcement Learning with Verifiable Rewards (RLVR), and RL without explicit labels demonstrate that B-Trans effectively leverage the wisdom of crowds, yielding superior semantic diversity while achieving better task performance compared to deterministic baselines. - oai:arXiv.org:2512.25063v1 - cs.LG - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Diji Yang, Yi Zhang - - - Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search - https://arxiv.org/abs/2512.25065 - arXiv:2512.25065v1 Announce Type: new -Abstract: Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously go through due to the constant flux of hardware, workloads and environments. - We propose a new alternative: synthesizing instance-optimal heuristics -- specialized for the exact workloads and hardware where they will be deployed -- using code-generating large language models (LLMs). To make this synthesis tractable, Vulcan separates policy and mechanism through LLM-friendly, task-agnostic interfaces. With these interfaces, users specify the inputs and objectives of their desired policy, while Vulcan searches for performant policies via evolutionary search over LLM-generated code. This interface is expressive enough to capture a wide range of system policies, yet sufficiently constrained to allow even small, inexpensive LLMs to generate correct and executable code. - We use Vulcan to synthesize performant heuristics for cache eviction and memory tiering, and find that these heuristics outperform all human-designed state-of-the-art algorithms by upto 69% and 7.9% in performance for each of these tasks respectively. - oai:arXiv.org:2512.25065v1 - cs.OS - cs.AI - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Rohit Dwivedula, Divyanshu Saxena, Sujay Yadalam, Daehyeok Kim, Aditya Akella - - - From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing - https://arxiv.org/abs/2512.25066 - arXiv:2512.25066v1 Announce Type: new -Abstract: Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ while all other visual conditions are identical. Existing methods circumvent this with a mask-based inpainting paradigm, where an incomplete visual conditioning forces models to simultaneously hallucinate missing content and sync lips, leading to visual artifacts, identity drift, and poor synchronization. In this work, we propose a novel self-bootstrapping framework that reframes visual dubbing from an ill-posed inpainting task into a well-conditioned video-to-video editing problem. Our approach employs a Diffusion Transformer, first as a data generator, to synthesize ideal training data: a lip-altered companion video for each real sample, forming visually aligned video pairs. A DiT-based audio-driven editor is then trained on these pairs end-to-end, leveraging the complete and aligned input video frames to focus solely on precise, audio-driven lip modifications. This complete, frame-aligned input conditioning forms a rich visual context for the editor, providing it with complete identity cues, scene interactions, and continuous spatiotemporal dynamics. Leveraging this rich context fundamentally enables our method to achieve highly accurate lip sync, faithful identity preservation, and exceptional robustness against challenging in-the-wild scenarios. We further introduce a timestep-adaptive multi-phase learning strategy as a necessary component to disentangle conflicting editing objectives across diffusion timesteps, thereby facilitating stable training and yielding enhanced lip synchronization and visual fidelity. Additionally, we propose ContextDubBench, a comprehensive benchmark dataset for robust evaluation in diverse and challenging practical application scenarios. - oai:arXiv.org:2512.25066v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xu He, Haoxian Zhang, Hejia Chen, Changyuan Zheng, Liyang Chen, Songlin Tang, Jiehui Huang, Xiaoqiang Liu, Pengfei Wan, Zhiyong Wu - - - FineTec: Fine-Grained Action Recognition Under Temporal Corruption via Skeleton Decomposition and Sequence Completion - https://arxiv.org/abs/2512.25067 - arXiv:2512.25067v1 Announce Type: new -Abstract: Recognizing fine-grained actions from temporally corrupted skeleton sequences remains a significant challenge, particularly in real-world scenarios where online pose estimation often yields substantial missing data. Existing methods often struggle to accurately recover temporal dynamics and fine-grained spatial structures, resulting in the loss of subtle motion cues crucial for distinguishing similar actions. To address this, we propose FineTec, a unified framework for Fine-grained action recognition under Temporal Corruption. FineTec first restores a base skeleton sequence from corrupted input using context-aware completion with diverse temporal masking. Next, a skeleton-based spatial decomposition module partitions the skeleton into five semantic regions, further divides them into dynamic and static subgroups based on motion variance, and generates two augmented skeleton sequences via targeted perturbation. These, along with the base sequence, are then processed by a physics-driven estimation module, which utilizes Lagrangian dynamics to estimate joint accelerations. Finally, both the fused skeleton position sequence and the fused acceleration sequence are jointly fed into a GCN-based action recognition head. Extensive experiments on both coarse-grained (NTU-60, NTU-120) and fine-grained (Gym99, Gym288) benchmarks show that FineTec significantly outperforms previous methods under various levels of temporal corruption. Specifically, FineTec achieves top-1 accuracies of 89.1% and 78.1% on the challenging Gym99-severe and Gym288-severe settings, respectively, demonstrating its robustness and generalizability. Code and datasets could be found at https://smartdianlab.github.io/projects-FineTec/. - oai:arXiv.org:2512.25067v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Dian Shao, Mingfei Shi, Like Liu - - - Scaling Open-Ended Reasoning to Predict the Future - https://arxiv.org/abs/2512.25070 - arXiv:2512.25070v1 Announce Type: new -Abstract: High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a fully automated, careful curation recipe. We train the Qwen3 thinking models on our dataset, OpenForesight. To prevent leakage of future information during training and evaluation, we use an offline news corpus, both for data generation and retrieval in our forecasting system. Guided by a small validation set, we show the benefits of retrieval, and an improved reward function for reinforcement learning (RL). Once we obtain our final forecasting system, we perform held-out testing between May to August 2025. Our specialized model, OpenForecaster 8B, matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions. We find calibration improvements from forecasting training generalize across popular benchmarks. We open-source all our models, code, and data to make research on language model forecasting broadly accessible. - oai:arXiv.org:2512.25070v1 - cs.LG - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Nikhil Chandak, Shashwat Goel, Ameya Prabhu, Moritz Hardt, Jonas Geiping - - - Edit3r: Instant 3D Scene Editing from Sparse Unposed Images - https://arxiv.org/abs/2512.25071 - arXiv:2512.25071v1 Announce Type: new -Abstract: We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealistic rendering without optimization or pose estimation. A key challenge in training such a model lies in the absence of multi-view consistent edited images for supervision. We address this with (i) a SAM2-based recoloring strategy that generates reliable, cross-view-consistent supervision, and (ii) an asymmetric input strategy that pairs a recolored reference view with raw auxiliary views, encouraging the network to fuse and align disparate observations. At inference, our model effectively handles images edited by 2D methods such as InstructPix2Pix, despite not being exposed to such edits during training. For large-scale quantitative evaluation, we introduce DL3DV-Edit-Bench, a benchmark built on the DL3DV test split, featuring 20 diverse scenes, 4 edit types and 100 edits in total. Comprehensive quantitative and qualitative results show that Edit3r achieves superior semantic alignment and enhanced 3D consistency compared to recent baselines, while operating at significantly higher inference speed, making it promising for real-time 3D editing applications. - oai:arXiv.org:2512.25071v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jiageng Liu, Weijie Lyu, Xueting Li, Yejie Guo, Ming-Hsuan Yang - - - Coordinated Humanoid Manipulation with Choice Policies - https://arxiv.org/abs/2512.25072 - arXiv:2512.25072v1 Announce Type: new -Abstract: Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address this problem. Our teleoperation design decomposes humanoid control into intuitive submodules, which include hand-eye coordination, grasp primitives, arm end-effector tracking, and locomotion. This modularity allows us to collect high-quality demonstrations efficiently. Building on this, we introduce Choice Policy, an imitation learning approach that generates multiple candidate actions and learns to score them. This architecture enables both fast inference and effective modeling of multimodal behaviors. We validate our approach on two real-world tasks: dishwasher loading and whole-body loco-manipulation for whiteboard wiping. Experiments show that Choice Policy significantly outperforms diffusion policies and standard behavior cloning. Furthermore, our results indicate that hand-eye coordination is critical for success in long-horizon tasks. Our work demonstrates a practical path toward scalable data collection and learning for coordinated humanoid manipulation in unstructured environments. - oai:arXiv.org:2512.25072v1 - cs.RO - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Haozhi Qi, Yen-Jen Wang, Toru Lin, Brent Yi, Yi Ma, Koushil Sreenath, Jitendra Malik - - - GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction - https://arxiv.org/abs/2512.25073 - arXiv:2512.25073v1 Announce Type: new -Abstract: Recent advances in 3D reconstruction have achieved remarkable progress in high-quality scene capture from dense multi-view imagery, yet struggle when input views are limited. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to address this challenge. Latest diffusion-based methods have demonstrated substantial improvements by generating novel views from new camera poses to augment training data, surpassing earlier regularization and prior-based techniques. Despite this progress, we identify three critical limitations in these state-of-the-art approaches: inadequate coverage beyond known view peripheries, geometric inconsistencies across generated views, and computationally expensive pipelines. We introduce GaMO (Geometry-aware Multi-view Outpainter), a framework that reformulates sparse-view reconstruction through multi-view outpainting. Instead of generating new viewpoints, GaMO expands the field of view from existing camera poses, which inherently preserves geometric consistency while providing broader scene coverage. Our approach employs multi-view conditioning and geometry-aware denoising strategies in a zero-shot manner without training. Extensive experiments on Replica and ScanNet++ demonstrate state-of-the-art reconstruction quality across 3, 6, and 9 input views, outperforming prior methods in PSNR and LPIPS, while achieving a $25\times$ speedup over SOTA diffusion-based methods with processing time under 10 minutes. Project page: https://yichuanh.github.io/GaMO/ - oai:arXiv.org:2512.25073v1 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Yi-Chuan Huang, Hao-Jen Chien, Chin-Yang Lin, Ying-Huan Chen, Yu-Lun Liu - - - SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time - https://arxiv.org/abs/2512.25075 - arXiv:2512.25075v1 Announce Type: new -Abstract: We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous and arbitrary exploration across space and time. To achieve this, we introduce an effective animation time-embedding mechanism in the diffusion process, allowing explicit control of the output video's motion sequence with respect to that of the source video. As no datasets provide paired videos of the same dynamic scene with continuous temporal variations, we propose a simple yet effective temporal-warping training scheme that repurposes existing multi-view datasets to mimic temporal differences. This strategy effectively supervises the model to learn temporal control and achieve robust space-time disentanglement. To further enhance the precision of dual control, we introduce two additional components: an improved camera-conditioning mechanism that allows altering the camera from the first frame, and CamxTime, the first synthetic space-and-time full-coverage rendering dataset that provides fully free space-time video trajectories within a scene. Joint training on the temporal-warping scheme and the CamxTime dataset yields more precise temporal control. We evaluate SpaceTimePilot on both real-world and synthetic data, demonstrating clear space-time disentanglement and strong results compared to prior work. Project page: https://zheninghuang.github.io/Space-Time-Pilot/ Code: https://github.com/ZheningHuang/spacetimepilot - oai:arXiv.org:2512.25075v1 - cs.CV - cs.AI - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - new - http://creativecommons.org/licenses/by/4.0/ - Zhening Huang, Hyeonho Jeong, Xuelin Chen, Yulia Gryaditskaya, Tuanfeng Y. Wang, Joan Lasenby, Chun-Hao Huang - - - On Good-for-MDPs Automata - https://arxiv.org/abs/2202.07629 - arXiv:2202.07629v4 Announce Type: cross -Abstract: Nondeterministic good-for-MDPs (GFM) automata are for MDP model checking and reinforcement learning what good-for-games (GFG) automata are for reactive synthesis: a more compact alternative to deterministic automata that displays nondeterminism, but only so much that it can be resolved locally, such that a syntactic product can be analysed. - GFM has recently been introduced as a property for reinforcement learning, where the simpler B\"uchi acceptance conditions it allows to use is key. However, while there are classic and novel techniques to obtain automata that are GFM, there has not been a decision procedure for checking whether or not an automaton is GFM. We show that GFM-ness is decidable and provide an EXPTIME decision procedure as well as a PSPACE-hardness proof. - We also compare the succinctness of GFM automata with other types of automata with restricted nondeterminism. The first natural comparison point are GFG automata. Deterministic automata are GFG, and GFG automata are GFM, but not vice versa. This raises the question of how these classes relate in terms of succinctness. GFG automata are known to be exponentially more succinct than deterministic automata, but the gap between GFM and GFG automata as well as the gap between ordinary nondeterministic automata and those that are GFM have been open. We establish that these gaps are exponential, and sharpen this result by showing that the latter gap remains exponential when restricting the nondeterministic automata to separating safety or unambiguous reachability automata. - oai:arXiv.org:2202.07629v4 - cs.FL - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Sven Schewe, Qiyi Tang, Tansholpan Zhanabekova - - - Comparative Evaluation of Embedding Representations for Financial News Sentiment Analysis - https://arxiv.org/abs/2512.13749 - arXiv:2512.13749v1 Announce Type: cross -Abstract: Financial sentiment analysis enhances market understanding; however, standard natural language processing approaches encounter significant challenges when applied to small datasets. This study provides a comparative evaluation of embedding-based methods for financial news sentiment classification in resource-constrained environments. Word2Vec, GloVe, and sentence transformer representations are evaluated in combination with gradient boosting on manually labeled headlines. Experimental results identify a substantial gap between validation and test performance, with models performing worse than trivial baselines despite strong validation metrics. The analysis demonstrates that pretrained embeddings yield diminishing returns below a critical data sufficiency threshold, and that small validation sets contribute to overfitting during model selection. Practical application is illustrated through weekly sentiment aggregation and narrative summarization for market monitoring workflows. The findings offer empirical evidence that embedding quality alone cannot address fundamental data scarcity in sentiment classification. For practitioners operating with limited resources, the results indicate the need to consider alternative approaches such as few-shot learning, data augmentation, or lexicon-enhanced hybrid methods when labeled samples are scarce. - oai:arXiv.org:2512.13749v1 - cs.LG - cs.AI - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Joyjit Roy, Samaresh Kumar Singh - - - q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models - https://arxiv.org/abs/2512.23726 - arXiv:2512.23726v1 Announce Type: cross -Abstract: The 3D fast silent multi-parametric mapping sequence with zero echo time (MuPa-ZTE) is a novel quantitative MRI (qMRI) acquisition that enables nearly silent scanning by using a 3D phyllotaxis sampling scheme. MuPa-ZTE improves patient comfort and motion robustness, and generates quantitative maps of T1, T2, and proton density using the acquired weighted image series. In this work, we propose a diffusion model-based qMRI mapping method that leverages both a deep generative model and physics-based data consistency to further improve the mapping performance. Furthermore, our method enables additional acquisition acceleration, allowing high-quality qMRI mapping from a fourfold-accelerated MuPa-ZTE scan (approximately 1 minute). Specifically, we trained a denoising diffusion probabilistic model (DDPM) to map MuPa-ZTE image series to qMRI maps, and we incorporated the MuPa-ZTE forward signal model as an explicit data consistency (DC) constraint during inference. We compared our mapping method against a baseline dictionary matching approach and a purely data-driven diffusion model. The diffusion models were trained entirely on synthetic data generated from digital brain phantoms, eliminating the need for large real-scan datasets. We evaluated on synthetic data, a NISM/ISMRM phantom, healthy volunteers, and a patient with brain metastases. The results demonstrated that our method produces 3D qMRI maps with high accuracy, reduced noise and better preservation of structural details. Notably, it generalised well to real scans despite training on synthetic data alone. The combination of the MuPa-ZTE acquisition and our physics-informed diffusion model is termed q3-MuPa, a quick, quiet, and quantitative multi-parametric mapping framework, and our findings highlight its strong clinical potential. - oai:arXiv.org:2512.23726v1 - physics.med-ph - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shishuai Wang, Florian Wiesinger, Noemi Sgambelluri, Carolin Pirkl, Stefan Klein, Juan A. Hernandez-Tamames, Dirk H. J. Poot - - - Spike-Timing-Dependent Plasticity for Bernoulli Message Passing - https://arxiv.org/abs/2512.23728 - arXiv:2512.23728v1 Announce Type: cross -Abstract: Bayesian inference provides a principled framework for understanding brain function, while neural activity in the brain is inherently spike-based. This paper bridges these two perspectives by designing spiking neural networks that simulate Bayesian inference through message passing for Bernoulli messages. To train the networks, we employ spike-timing-dependent plasticity, a biologically plausible mechanism for synaptic plasticity which is based on the Hebbian rule. Our results demonstrate that the network's performance closely matches the true numerical solution. We further demonstrate the versatility of our approach by implementing a factor graph example from coding theory, illustrating signal transmission over an unreliable channel. - oai:arXiv.org:2512.23728v1 - q-bio.NC - cs.LG - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Sepideh Adamiat, Wouter M. Kouw, Bert de Vries - - - Leveraging Machine Learning for Early Detection of Lung Diseases - https://arxiv.org/abs/2512.23757 - arXiv:2512.23757v1 Announce Type: cross -Abstract: A combination of traditional image processing methods with advanced neural networks concretes a predictive and preventive healthcare paradigm. This study offers rapid, accurate, and non-invasive diagnostic solutions that can significantly impact patient outcomes, particularly in areas with limited access to radiologists and healthcare resources. In this project, deep learning methods apply in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays. We trained and validated various neural network models, including CNNs, VGG16, InceptionV3, and EfficientNetB0, with high accuracy, precision, recall, and F1 scores to highlight the models' reliability and potential in real-world diagnostic applications. - oai:arXiv.org:2512.23757v1 - eess.IV - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bahareh Rahmani, Harsha Reddy Bindela, Rama Kanth Reddy Gosula, Krishna Yedubati, Mohammad Amir Salari, Leslie Hinyard, Payam Norouzzadeh, Eli Snir, Martin Schoen - - - Stochastic Galerkin Method and Hierarchical Preconditioning for PDE-constrained Optimization - https://arxiv.org/abs/2512.23804 - arXiv:2512.23804v1 Announce Type: cross -Abstract: We develop efficient hierarchical preconditioners for optimal control problems governed by partial differential equations with uncertain coefficients. Adopting a discretize-then-optimize framework that integrates finite element discretization, stochastic Galerkin approximation, and advanced time-discretization schemes, the approach addresses the challenge of large-scale, ill-conditioned linear systems arising in uncertainty quantification. By exploiting the sparsity inherent in generalized polynomial chaos expansions, we derive hierarchical preconditioners based on truncated stochastic expansion that strike an effective balance between computational cost and preconditioning quality. Numerical experiments demonstrate that the proposed preconditioners significantly accelerate the convergence of iterative solvers compared to existing methods, providing robust and efficient solvers for both steady-state and time-dependent optimal control applications under uncertainty. - oai:arXiv.org:2512.23804v1 - math.OC - cs.NA - math.AP - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Zhendong Li, Akwum Onwunta, Bed\v{r}ich Soused\'ik - - - Fitted Q Evaluation Without Bellman Completeness via Stationary Weighting - https://arxiv.org/abs/2512.23805 - arXiv:2512.23805v1 Announce Type: cross -Abstract: Fitted Q-evaluation (FQE) is a central method for off-policy evaluation in reinforcement learning, but it generally requires Bellman completeness: that the hypothesis class is closed under the evaluation Bellman operator. This requirement is challenging because enlarging the hypothesis class can worsen completeness. We show that the need for this assumption stems from a fundamental norm mismatch: the Bellman operator is gamma-contractive under the stationary distribution of the target policy, whereas FQE minimizes Bellman error under the behavior distribution. We propose a simple fix: reweight each regression step using an estimate of the stationary density ratio, thereby aligning FQE with the norm in which the Bellman operator contracts. This enables strong evaluation guarantees in the absence of realizability or Bellman completeness, avoiding the geometric error blow-up of standard FQE in this setting while maintaining the practicality of regression-based evaluation. - oai:arXiv.org:2512.23805v1 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Lars van der Laan, Nathan Kallus - - - Syndrome aware mitigation of logical errors - https://arxiv.org/abs/2512.23810 - arXiv:2512.23810v1 Announce Type: cross -Abstract: Broad applications of quantum computers will require error correction (EC). However, quantum hardware roadmaps indicate that physical qubit numbers will remain limited in the foreseeable future, leading to residual logical errors that limit the size and accuracy of achievable computations. Recent work suggested logical error mitigation (LEM), which applies known error mitigation (EM) methods to logical errors, eliminating their effect at the cost of a runtime overhead. Improving the efficiency of LEM is crucial for increasing the logical circuit volumes it enables to execute. - We introduce syndrome-aware logical error mitigation (SALEM), which makes use of the syndrome data measured during error correction, when mitigating the logical errors. The runtime overhead of SALEM is exponentially lower than that of previously proposed LEM schemes, resulting in significantly increased circuit volumes that can be executed accurately. Notably, relative to the routinely used combination of error correction and syndrome rejection (post-selection), SALEM increases the size of reliably executable computations by orders of magnitude. In this practical setting in which space and time are both resources that need to be optimized, our work reveals a surprising phenomenon: SALEM, which tightly combines EC with EM, can outperform physical EM even above the standard fault-tolerance threshold. Thus, SALEM can make use of EC in regimes of physical error rates at which EC is commonly deemed useless. - oai:arXiv.org:2512.23810v1 - quant-ph - cs.CC - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Dorit Aharonov, Yosi Atia, Eyal Bairey, Zvika Brakerski, Itsik Cohen, Omri Golan, Ilya Gurwich, Netanel H. Lindner, Maor Shutman - - - Quantum Error Mitigation with Attention Graph Transformers for Burgers Equation Solvers on NISQ Hardware - https://arxiv.org/abs/2512.23817 - arXiv:2512.23817v1 Announce Type: cross -Abstract: We present a hybrid quantum-classical framework augmented with learned error mitigation for solving the viscous Burgers equation on noisy intermediate-scale quantum (NISQ) hardware. Using the Cole-Hopf transformation, the nonlinear Burgers equation is mapped to a diffusion equation, discretized on uniform grids, and encoded into a quantum state whose time evolution is approximated via Trotterized nearest-neighbor circuits implemented in Qiskit. Quantum simulations are executed on noisy Aer backends and IBM superconducting quantum devices and are benchmarked against high-accuracy classical solutions obtained using a Krylov-based solver applied to the corresponding discretized Hamiltonian. From measured quantum amplitudes, we reconstruct the velocity field and evaluate physical and numerical diagnostics, including the L2 error, shock location, and dissipation rate, both with and without zero-noise extrapolation (ZNE). To enable data-driven error mitigation, we construct a large parametric dataset by sweeping viscosity, time step, grid resolution, and boundary conditions, producing matched tuples of noisy, ZNE-corrected, hardware, and classical solutions together with detailed circuit metadata. Leveraging this dataset, we train an attention-based graph neural network that incorporates circuit structure, light-cone information, global circuit parameters, and noisy quantum outputs to predict error-mitigated solutions. Across a wide range of parameters, the learned model consistently reduces the discrepancy between quantum and classical solutions beyond what is achieved by ZNE alone. We discuss extensions of this approach to higher-dimensional Burgers systems and more general quantum partial differential equation solvers, highlighting learned error mitigation as a promising complement to physics-based noise reduction techniques on NISQ devices. - oai:arXiv.org:2512.23817v1 - quant-ph - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Seyed Mohamad Ali Tousi, Adib Bazgir, Yuwen Zhang, G. N. DeSouza - - - Energy-Tweedie: Score meets Score, Energy meets Energy - https://arxiv.org/abs/2512.23818 - arXiv:2512.23818v1 Announce Type: cross -Abstract: Denoising and score estimation have long been known to be linked via the classical Tweedie's formula. In this work, we first extend the latter to a wider range of distributions often called "energy models" and denoted elliptical distributions in this work. Next, we examine an alternative view: we consider the denoising posterior $P(X|Y)$ as the optimizer of the energy score (a scoring rule) and derive a fundamental identity that connects the (path-) derivative of a (possibly) non-Euclidean energy score to the score of the noisy marginal. This identity can be seen as an analog of Tweedie's identity for the energy score, and allows for several interesting applications; for example, score estimation, noise distribution parameter estimation, as well as using energy score models in the context of "traditional" diffusion model samplers with a wider array of noising distributions. - oai:arXiv.org:2512.23818v1 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Andrej Leban - - - The Flow-Limit of Reflect-Reflect-Relax: Existence, Stability, and Discrete-Time Behavior - https://arxiv.org/abs/2512.23843 - arXiv:2512.23843v1 Announce Type: cross -Abstract: We study the Reflect-Reflect-Relax (RRR) algorithm in its small-step (flow-limit) regime. In the smooth transversal setting, we show that the transverse dynamics form a hyperbolic sink, yielding exponential decay of a natural gap measure. Under uniform geometric assumptions, we construct a tubular neighborhood of the feasible manifold on which the squared gap defines a strict Lyapunov function, excluding recurrent dynamics and chaotic behavior within this basin. - In the discrete setting, the induced flow is piecewise constant on W-domains and supports Filippov sliding along convergent boundaries, leading to finite-time capture into a solution domain. We prove that small-step RRR is a forward-Euler discretization of this flow, so that solution times measured in rescaled units converge to a finite limit while iteration counts diverge, explaining the emergence of iteration-optimal relaxation parameters. Finally, we introduce a heuristic mesoscopic framework based on percolation and renormalization group to organize performance deterioration near the Douglas-Rachford limit. - oai:arXiv.org:2512.23843v1 - math.OC - cs.NA - math.DS - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Manish Krishan Lal - - - A Test of Lookahead Bias in LLM Forecasts - https://arxiv.org/abs/2512.23847 - arXiv:2512.23847v1 Announce Type: cross -Abstract: We develop a statistical test to detect lookahead bias in economic forecasts generated by large language models (LLMs). Using state-of-the-art pre-training data detection techniques, we estimate the likelihood that a given prompt appeared in an LLM's training corpus, a statistic we term Lookahead Propensity (LAP). We formally show that a positive correlation between LAP and forecast accuracy indicates the presence and magnitude of lookahead bias, and apply the test to two forecasting tasks: news headlines predicting stock returns and earnings call transcripts predicting capital expenditures. Our test provides a cost-efficient, diagnostic tool for assessing the validity and reliability of LLM-generated forecasts. - oai:arXiv.org:2512.23847v1 - q-fin.GN - cs.LG - q-fin.TR - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Zhenyu Gao, Wenxi Jiang, Yutong Yan - - - Autoregressive long-horizon prediction of plasma edge dynamics - https://arxiv.org/abs/2512.23884 - arXiv:2512.23884v1 Announce Type: cross -Abstract: Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1-100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices. - oai:arXiv.org:2512.23884v1 - physics.plasm-ph - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Hunor Csala, Sebastian De Pascuale, Paul Laiu, Jeremy Lore, Jae-Sun Park, Pei Zhang - - - A multimodal Transformer for InSAR-based ground deformation forecasting with cross-site generalization across Europe - https://arxiv.org/abs/2512.23906 - arXiv:2512.23906v1 Announce Type: cross -Abstract: Near-real-time regional-scale monitoring of ground deformation is increasingly required to support urban planning, critical infrastructure management, and natural hazard mitigation. While Interferometric Synthetic Aperture Radar (InSAR) and continental-scale services such as the European Ground Motion Service (EGMS) provide dense observations of past motion, predicting the next observation remains challenging due to the superposition of long-term trends, seasonal cycles, and occasional abrupt discontinuities (e.g., co-seismic steps), together with strong spatial heterogeneity. In this study we propose a multimodal patch-based Transformer for single-step, fixed-interval next-epoch nowcasting of displacement maps from EGMS time series (resampled to a 64x64 grid over 100 km x 100 km tiles). The model ingests recent displacement snapshots together with (i) static kinematic indicators (mean velocity, acceleration, seasonal amplitude) computed in a leakage-safe manner from the training window only, and (ii) harmonic day-of-year encodings. On the eastern Ireland tile (E32N34), the STGCN is strongest in the displacement-only setting, whereas the multimodal Transformer clearly outperforms CNN-LSTM, CNN-LSTM+Attn, and multimodal STGCN when all models receive the same multimodal inputs, achieving RMSE = 0.90 mm and $R^2$ = 0.97 on the test set with the best threshold accuracies. - oai:arXiv.org:2512.23906v1 - eess.SP - cs.AI - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wendong Yao, Binhua Huang, Soumyabrata Dev - - - Tensor Computing Interface: An Application-Oriented, Lightweight Interface for Portable High-Performance Tensor Network Applications - https://arxiv.org/abs/2512.23917 - arXiv:2512.23917v1 Announce Type: cross -Abstract: Tensor networks (TNs) are a central computational tool in quantum science and artificial intelligence. However, the lack of unified software interface across tensor-computing frameworks severely limits the portability of TN applications, coupling algorithmic development to specific hardware and software back ends. To address this challenge, we introduce the Tensor Computing Interface (TCI) -- an application-oriented, lightweight application programming interface designed to enable framework-independent, high-performance TN applications. TCI provides a well-defined type system that abstracts tensor objects together with a minimal yet expressive set of core functions covering essential tensor manipulations and tensor linear-algebra operations. Through numerical demonstrations on representative tensor-network applications, we show that codes written against TCI can be migrated seamlessly across heterogeneous hardware and software platforms while achieving performance comparable to native framework implementations. We further release an open-source implementation of TCI based on \textit{Cytnx}, demonstrating its practicality and ease of integration with existing tensor-computing frameworks. - oai:arXiv.org:2512.23917v1 - quant-ph - cond-mat.str-el - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rong-Yang Sun, Tomonori Shirakawa, Hidehiko Kohshiro, D. N. Sheng, Seiji Yunoki - - - Stationary Reweighting Yields Local Convergence of Soft Fitted Q-Iteration - https://arxiv.org/abs/2512.23927 - arXiv:2512.23927v1 Announce Type: cross -Abstract: Fitted Q-iteration (FQI) and its entropy-regularized variant, soft FQI, are central tools for value-based model-free offline reinforcement learning, but can behave poorly under function approximation and distribution shift. In the entropy-regularized setting, we show that the soft Bellman operator is locally contractive in the stationary norm of the soft-optimal policy, rather than in the behavior norm used by standard FQI. This geometric mismatch explains the instability of soft Q-iteration with function approximation in the absence of Bellman completeness. To restore contraction, we introduce stationary-reweighted soft FQI, which reweights each regression update using the stationary distribution of the current policy. We prove local linear convergence under function approximation with geometrically damped weight-estimation errors, assuming approximate realizability. Our analysis further suggests that global convergence may be recovered by gradually reducing the softmax temperature, and that this continuation approach can extend to the hardmax limit under a mild margin condition. - oai:arXiv.org:2512.23927v1 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Lars van der Laan, Nathan Kallus - - - Assessing generative modeling approaches for free energy estimates in condensed matter - https://arxiv.org/abs/2512.23930 - arXiv:2512.23930v1 Announce Type: cross -Abstract: The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multiple intermediate states to ensure sufficient overlap in phase space and are, consequently, computationally expensive. Several generative-model-based methods have recently addressed this challenge by learning a direct bridge between distributions, bypassing the need for intermediate states. However, it remains unclear which approaches provide the best trade-off between efficiency, accuracy, and scalability. In this work, we systematically review these methods and benchmark selected approaches with a focus on condensed-matter systems. In particular, we investigate the performance of discrete and continuous normalizing flows in the context of targeted free energy perturbation as well as FEAT (Free energy Estimators with Adaptive Transport) together with the escorted Jarzynski equality, using coarse-grained monatomic ice and Lennard-Jones solids as benchmark systems. We evaluate accuracy, data efficiency, computational cost, and scalability with system size. Our results provide a quantitative framework for selecting effective free energy estimation strategies in condensed-phase systems. - oai:arXiv.org:2512.23930v1 - cond-mat.stat-mech - cond-mat.mtrl-sci - cs.LG - physics.comp-ph - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Maximilian Schebek, Jiajun He, Emil Hoffmann, Yuanqi Du, Frank No\'e, Jutta Rogal - - - Implicit geometric regularization in flow matching via density weighted Stein operators - https://arxiv.org/abs/2512.23956 - arXiv:2512.23956v1 Announce Type: cross -Abstract: Flow Matching (FM) has emerged as a powerful paradigm for continuous normalizing flows, yet standard FM implicitly performs an unweighted $L^2$ regression over the entire ambient space. In high dimensions, this leads to a fundamental inefficiency: the vast majority of the integration domain consists of low-density ``void'' regions where the target velocity fields are often chaotic or ill-defined. In this paper, we propose {$\gamma$-Flow Matching ($\gamma$-FM)}, a density-weighted variant that aligns the regression geometry with the underlying probability flow. While density weighting is desirable, naive implementations would require evaluating the intractable target density. We circumvent this by introducing a Dynamic Density-Weighting strategy that estimates the \emph{target} density directly from training particles. This approach allows us to dynamically downweight the regression loss in void regions without compromising the simulation-free nature of FM. Theoretically, we establish that $\gamma$-FM minimizes the transport cost on a statistical manifold endowed with the $\gamma$-Stein metric. Spectral analysis further suggests that this geometry induces an implicit Sobolev regularization, effectively damping high-frequency oscillations in void regions. Empirically, $\gamma$-FM significantly improves vector field smoothness and sampling efficiency on high-dimensional latent datasets, while demonstrating intrinsic robustness to outliers. - oai:arXiv.org:2512.23956v1 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shinto Eguchi - - - Fundamental limits for weighted empirical approximations of tilted distributions - https://arxiv.org/abs/2512.23979 - arXiv:2512.23979v1 Announce Type: cross -Abstract: Consider the task of generating samples from a tilted distribution of a random vector whose underlying distribution is unknown, but samples from it are available. This finds applications in fields such as finance and climate science, and in rare event simulation. In this article, we discuss the asymptotic efficiency of a self-normalized importance sampler of the tilted distribution. We provide a sharp characterization of its accuracy, given the number of samples and the degree of tilt. Our findings reveal a surprising dichotomy: while the number of samples needed to accurately tilt a bounded random vector increases polynomially in the tilt amount, it increases at a super polynomial rate for unbounded distributions. - oai:arXiv.org:2512.23979v1 - math.ST - cs.LG - math.PR - stat.ML - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Sarvesh Ravichandran Iyer, Himadri Mandal, Dhruman Gupta, Rushil Gupta, Agniv Bandhyopadhyay, Achal Bassamboo, Varun Gupta, Sandeep Juneja - - - One-Shot Structured Pruning of Quantum Neural Networks via $q$-Group Engineering and Quantum Geometric Metrics - https://arxiv.org/abs/2512.24019 - arXiv:2512.24019v1 Announce Type: cross -Abstract: Quantum neural networks (QNNs) suffer from severe gate-level redundancy, which hinders their deployment on noisy intermediate-scale quantum (NISQ) devices. In this work, we propose q-iPrune, a one-shot structured pruning framework grounded in the algebraic structure of $q$-deformed groups and task-conditioned quantum geometry. - Unlike prior heuristic or gradient-based pruning methods, q-iPrune formulates redundancy directly at the gate level. Each gate is compared within an algebraically consistent subgroup using a task-conditioned $q$-overlap distance, which measures functional similarity through state overlaps on a task-relevant ensemble. A gate is removed only when its replacement by a subgroup representative provably induces a bounded deviation on all task observables. - We establish three rigorous theoretical guarantees. First, we prove completeness of redundancy pruning: no gate that violates the prescribed similarity threshold is removed. Second, we show that the pruned circuit is functionally equivalent up to an explicit, task-conditioned error bound, with a closed-form dependence on the redundancy tolerance and the number of replaced gates. Third, we prove that the pruning procedure is computationally feasible, requiring only polynomial-time comparisons and avoiding exponential enumeration over the Hilbert space. - To adapt pruning decisions to hardware imperfections, we introduce a noise-calibrated deformation parameter $\lambda$ that modulates the $q$-geometry and redundancy tolerance. Experiments on standard quantum machine learning benchmarks demonstrate that q-iPrune achieves substantial gate reduction while maintaining bounded task performance degradation, consistent with our theoretical guarantees. - oai:arXiv.org:2512.24019v1 - quant-ph - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Haijian Shao, Wei Liu, Xing Deng, Yingtao Jiang - - - Exposed: Shedding Blacklight on Online Privacy - https://arxiv.org/abs/2512.24041 - arXiv:2512.24041v1 Announce Type: cross -Abstract: To what extent are users surveilled on the web, by what technologies, and by whom? We answer these questions by combining passively observed, anonymized browsing data of a large, representative sample of Americans with domain-level data on tracking from Blacklight. We find that nearly all users ($ > 99\%$) encounter at least one ad tracker or third-party cookie over the observation window. More invasive techniques like session recording, keylogging, and canvas fingerprinting are less widespread, but over half of the users visited a site employing at least one of these within the first 48 hours of the start of tracking. Linking trackers to their parent organizations reveals that a single organization, usually Google, can track over $50\%$ of web activity of more than half the users. Demographic differences in exposure are modest and often attenuate when we account for browsing volume. However, disparities by age and race remain, suggesting that what users browse, not just how much, shapes their surveillance risk. - oai:arXiv.org:2512.24041v1 - stat.AP - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Lucas Shen, Gaurav Sood - - - $L^p$ Estimates for Numerical Approximation of Hamilton-Jacobi Equations - https://arxiv.org/abs/2512.24051 - arXiv:2512.24051v1 Announce Type: cross -Abstract: We establish $L^p$ error estimates for monotone numerical schemes approximating Hamilton-Jacobi equations on the $d$-dimensional torus. Using the adjoint method, we first prove a $L^1$ error bound of order one for finite-difference and semi-Lagrangian schemes under standard convexity assumptions on the Hamiltonian. By interpolation, we also obtain $L^p$ estimates for every finite $p>1$. Our analysis covers a broad class of schemes, improves several existing results, and provides a unified framework for discrete error estimates. - oai:arXiv.org:2512.24051v1 - math.AP - cs.NA - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Alessio Basti, Fabio Camilli - - - Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data - https://arxiv.org/abs/2512.24056 - arXiv:2512.24056v1 Announce Type: cross -Abstract: This paper studies the policy mirror descent (PMD) method, which is a general policy optimization framework in reinforcement learning and can cover a wide range of policy gradient methods by specifying difference mirror maps. Existing sample complexity analysis for policy mirror descent either focuses on the generative sampling model, or the Markovian sampling model but with the action values being explicitly approximated to certain pre-specified accuracy. In contrast, we consider the sample complexity of policy mirror descent with temporal difference (TD) learning under the Markovian sampling model. Two algorithms called Expected TD-PMD and Approximate TD-PMD have been presented, which are off-policy and mixed policy algorithms respectively. Under a small enough constant policy update step size, the $\tilde{O}(\varepsilon^{-2})$ (a logarithm factor about $\varepsilon$ is hidden in $\tilde{O}(\cdot)$) sample complexity can be established for them to achieve average-time $\varepsilon$-optimality. The sample complexity is further improved to $O(\varepsilon^{-2})$ (without the hidden logarithm factor) to achieve the last-iterate $\varepsilon$-optimality based on adaptive policy update step sizes. - oai:arXiv.org:2512.24056v1 - math.OC - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Wenye Li, Hongxu Chen, Jiacai Liu, Ke Wei - - - Notes on the 33-point Erd\H{o}s--Szekeres problem - https://arxiv.org/abs/2512.24061 - arXiv:2512.24061v1 Announce Type: cross -Abstract: The determination of $ES(7)$ is the first open case of the planar Erd\H{o}s--Szekeres problem, where the general conjecture predicts $ES(7)=33$. We present a SAT encoding for the 33-point case based on triple-orientation variables and a 4-set convexity criterion for excluding convex 7-gons, together with convex-layer anchoring constraints. The framework yields UNSAT certificates for a collection of anchored subfamilies. We also report pronounced runtime variability across configurations, including heavy-tailed behavior that currently dominates the computational effort and motivates further encoding refinements. - oai:arXiv.org:2512.24061v1 - math.CO - cs.CG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Bogdan Dumitru - - - Constructive Approximation of Random Process via Stochastic Interpolation Neural Network Operators - https://arxiv.org/abs/2512.24106 - arXiv:2512.24106v1 Announce Type: cross -Abstract: In this paper, we construct a class of stochastic interpolation neural network operators (SINNOs) with random coefficients activated by sigmoidal functions. We establish their boundedness, interpolation accuracy, and approximation capabilities in the mean square sense, in probability, as well as path-wise within the space of second-order stochastic (random) processes \( L^2(\Omega, \mathcal{F},\mathbb{P}) \). Additionally, we provide quantitative error estimates using the modulus of continuity of the processes. These results highlight the effectiveness of SINNOs for approximating stochastic processes with potential applications in COVID-19 case prediction. - oai:arXiv.org:2512.24106v1 - stat.ML - cs.LG - math.PR - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Sachin Saini, Uaday Singh - - - Dominion of some graphs - https://arxiv.org/abs/2512.24115 - arXiv:2512.24115v1 Announce Type: cross -Abstract: Given a graph G equals (V,E), a subset S subset of V is a dominating set if every vertex in V minus S is adjacent to some vertex in S. The dominating set with the least cardinality, gamma, is called a gamma-set which is commonly known as a minimum dominating set. The dominion of a graph G, denoted by zeta(G), is the number of its gamma-sets. Some relations between these two seemingly distinct parameters are established. In particular, we present the dominions of paths, some cycles and the join of any two graphs. - oai:arXiv.org:2512.24115v1 - math.CO - cs.DM - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - International Journal of Mathematics and Computer Science, 16 (2021), no. 4, 1709-1720 - Julian Allagan, Benkam Bobga - - - Quantitative Understanding of PDF Fits and their Uncertainties - https://arxiv.org/abs/2512.24116 - arXiv:2512.24116v1 Announce Type: cross -Abstract: Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons. As the LHC enters an era of high-precision measurements, a robust PDF determination with a reliable uncertainty quantification has become mandatory in order to match the experimental precision. The NNPDF collaboration has pioneered the use of Machine Learning (ML) techniques for PDF determinations, using Neural Networks (NNs) to parametrise the unknown PDFs in a flexible and unbiased way. The NNs are then trained on experimental data by means of stochastic gradient descent algorithms. The statistical robustness of the results is validated by extensive closure tests using synthetic data. In this work, we develop a theoretical framework based on the Neural Tangent Kernel (NTK) to analyse the training dynamics of neural networks. This approach allows us to derive, under precise assumptions, an analytical description of the neural network evolution during training, enabling a quantitative understanding of the training process. Having an analytical handle on the training dynamics allows us to clarify the role of the NN architecture and the impact of the experimental data in a transparent way. Similarly, we are able to describe the evolution of the covariance of the NN output during training, providing a quantitative description of how uncertainties are propagated from the data to the fitted function. While our results are not a substitute for PDF fitting, they do provide a powerful diagnostic tool to assess the robustness of current fitting methodologies. Beyond its relevance for particle physics phenomenology, our analysis of PDF determinations provides a testbed to apply theoretical ideas about the learning process developed in the ML community. - oai:arXiv.org:2512.24116v1 - hep-ph - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Amedeo Chiefa, Luigi Del Debbio, Richard Kenway - - - Targeted Semantic Segmentation of Himalayan Glacial Lakes Using Time-Series SAR: Towards Automated GLOF Early Warning - https://arxiv.org/abs/2512.24117 - arXiv:2512.24117v1 Announce Type: cross -Abstract: Glacial Lake Outburst Floods (GLOFs) are one of the most devastating climate change induced hazards. Existing remote monitoring approaches often prioritise maximising spatial coverage to train generalistic models or rely on optical imagery hampered by persistent cloud coverage. This paper presents an end-to-end, automated deep learning pipeline for the targeted monitoring of high-risk Himalayan glacial lakes using time-series Sentinel-1 SAR. We introduce a "temporal-first" training strategy, utilising a U-Net with an EfficientNet-B3 backbone trained on a curated dataset of a cohort of 4 lakes (Tsho Rolpa, Chamlang Tsho, Tilicho and Gokyo Lake). The model achieves an IoU of 0.9130 validating the success and efficacy of the "temporal-first" strategy required for transitioning to Early Warning Systems. Beyond the model, we propose an operational engineering architecture: a Dockerised pipeline that automates data ingestion via the ASF Search API and exposes inference results via a RESTful endpoint. This system shifts the paradigm from static mapping to dynamic and automated early warning, providing a scalable architectural foundation for future development in Early Warning Systems. - oai:arXiv.org:2512.24117v1 - eess.IV - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Pawan Adhikari, Satish Raj Regmi, Hari Ram Shrestha - - - Score-based sampling without diffusions: Guidance from a simple and modular scheme - https://arxiv.org/abs/2512.24152 - arXiv:2512.24152v1 Announce Type: cross -Abstract: Sampling based on score diffusions has led to striking empirical results, and has attracted considerable attention from various research communities. It depends on availability of (approximate) Stein score functions for various levels of additive noise. We describe and analyze a modular scheme that reduces score-based sampling to solving a short sequence of ``nice'' sampling problems, for which high-accuracy samplers are known. We show how to design forward trajectories such that both (a) the terminal distribution, and (b) each of the backward conditional distribution is defined by a strongly log concave (SLC) distribution. This modular reduction allows us to exploit \emph{any} SLC sampling algorithm in order to traverse the backwards path, and we establish novel guarantees with short proofs for both uni-modal and multi-modal densities. The use of high-accuracy routines yields $\varepsilon$-accurate answers, in either KL or Wasserstein distances, with polynomial dependence on $\log(1/\varepsilon)$ and $\sqrt{d}$ dependence on the dimension. - oai:arXiv.org:2512.24152v1 - math.ST - cs.LG - stat.ML - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - M. J. Wainwright - - - Discovering Optimal Robust Minimum Redundancy Arrays (RMRAs) through Exhaustive Search and Algebraic Formulation of a New Sub-Optimal RMRA - https://arxiv.org/abs/2512.24155 - arXiv:2512.24155v1 Announce Type: cross -Abstract: Modern sparse arrays are maximally economic in that they retain just as many sensors required to provide a specific aperture while maintaining a hole-free difference coarray. As a result, these are susceptible to the failure of even a single sensor. Contrarily, two-fold redundant sparse arrays (TFRSAs) and robust minimum redundancy arrays (RMRAs) ensure robustness against single-sensor failures due to their inherent redundancy in their coarrays. At present, optimal RMRA configurations are known only for arrays with sensor counts N=6 to N=10. To this end, this paper proposes two objectives: (i) developing a systematic algorithm to discover optimal RMRAs for N>10, and (ii) obtaining a new family of near-/sub-optimal RMRA that can be completely specified using closed-form expressions (CFEs). We solve the combinatorial optimization problem of finding RMRAs using an exhaustive search technique implemented in MATLAB. Optimal RMRAs for N = 11 to 14 were successfully found and near/sub-optimal arrays for N = 15 to 20 were determined using the proposed technique. As a byproduct of the exhaustive search, a large catalogue of valid near- and sub-optimal RMRAs was also obtained. In the second stage, CFEs for a new TFRSA were obtained by applying pattern mining and algebraic generalizations to the arrays obtained through exhaustive search. The proposed family enjoys CFEs for sensor positions, available aperture, and achievable degrees of freedom (DOFs). The CFEs have been thoroughly validated using MATLAB and are found to be valid for $N\geq8$. Hence, it can be concluded that the novelty of this work is two-fold: extending the catalogue of known optimal RMRAs and formulating a sub-optimal RMRA that abides by CFEs. - oai:arXiv.org:2512.24155v1 - eess.SP - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ashish Patwari, Sanjeeva Reddy S, G Ramachandra Reddy - - - Variational Quantum Brushes - https://arxiv.org/abs/2512.24173 - arXiv:2512.24173v1 Announce Type: cross -Abstract: Quantum brushes are computational arts software introduced by Ferreira et al (2025) that leverage quantum behavior to generate novel artistic effects. In this outreach paper, we introduce the mathematical framework and describe the implementation of two quantum brushes based on variational quantum algorithms, Steerable and Chemical. While Steerable uses quantum geometric control theory to merge two works of art, Chemical mimics variational eigensolvers for estimating molecular ground energies to evolve colors on an underlying canvas. The implementation of both brushes is available open-source at https://github.com/moth-quantum/QuantumBrush and is fully compatible with the original quantum brushes. - oai:arXiv.org:2512.24173v1 - quant-ph - cs.GR - cs.LG - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Jui-Ting Lu, Henrique Ennes, Chih-Kang Huang, Ali Abbassi - - - Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC - https://arxiv.org/abs/2512.24290 - arXiv:2512.24290v1 Announce Type: cross -Abstract: Optical-readout Time Projection Chambers (TPCs) produce megapixel-scale images whose fine-grained topological information is essential for rare-event searches, but whose size challenges real-time data selection. We present an unsupervised, reconstruction-based anomaly-detection strategy for fast Region-of-Interest (ROI) extraction that operates directly on minimally processed camera frames. A convolutional autoencoder trained exclusively on pedestal images learns the detector noise morphology without labels, simulation, or fine-grained calibration. Applied to standard data-taking frames, localized reconstruction residuals identify particle-induced structures, from which compact ROIs are extracted via thresholding and spatial clustering. Using real data from the CYGNO optical TPC prototype, we compare two pedestal-trained autoencoder configurations that differ only in their training objective, enabling a controlled study of its impact. The best configuration retains (93.0 +/- 0.2)% of reconstructed signal intensity while discarding (97.8 +/- 0.1)% of the image area, with an inference time of approximately 25 ms per frame on a consumer GPU. The results demonstrate that careful design of the training objective is critical for effective reconstruction-based anomaly detection and that pedestal-trained autoencoders provide a transparent and detector-agnostic baseline for online data reduction in optical TPCs. - oai:arXiv.org:2512.24290v1 - physics.ins-det - cs.LG - physics.data-an - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - F. D. Amaro, R. Antonietti, E. Baracchini, L. Benussi, C. Capoccia, M. Caponero, L. G. M. de Carvalho, G. Cavoto, I. A. Costa, A. Croce, M. D'Astolfo, G. D'Imperio, G. Dho, E. Di Marco, J. M. F. dos Santos, D. Fiorina, F. Iacoangeli, Z. Islam, E. Kemp, H. P. Lima Jr., G. Maccarrone, R. D. P. Mano, D. J. G. Marques, G. Mazzitelli, P. Meloni, A. Messina, V. Monno, C. M. B. Monteiro, R. A. Nobrega, G. M. Oppedisano, I. F. Pains, E. Paoletti, F. Petrucci, S. Piacentini, D. Pierluigi, D. Pinci, F. Renga, A. Russo, G. Saviano, P. A. O. C. Silva, N. J. Spooner, R. Tesauro, S. Tomassini, D. Tozzi - - - On maximum distance separable and completely regular codes - https://arxiv.org/abs/2512.24292 - arXiv:2512.24292v1 Announce Type: cross -Abstract: We investigate when a maximum distance separable ($MDS$) code over $F_q$ is also completely regular ($CR$). For lengths $n=q+1$ and $n=q+2$ we provide a complete classification of the $MDS$ codes that are $CR$ or at least uniformly packed in the wide sense ($UPWS$). For the more restricted case $n\leq q$ with $q\leq 5$ we obtain a full classification (up to equivalence) of all nontrivial $MDS$ codes: there are none for $q=2$; only the ternary Hamming code for $q=3$; four nontrivial families for $q=4$; and exactly six linear $MDS$ codes for $q=5$ (three of which are $CR$ and one admits a self-dual version). Additionally, we close two gaps left open in a previous classification of self-dual $CR$ codes with covering radius $\rho\leq 3$: we precisely determine over which finite fields the $MDS$ self-dual completely regular codes with parameters $[2,1,2]_q$ and $[4,2,3]_q$ exist. - oai:arXiv.org:2512.24292v1 - math.CO - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Joaquim Borges, Josep Rif\`a, Victor Zinoviev - - - Generative Video Compression: Towards 0.01% Compression Rate for Video Transmission - https://arxiv.org/abs/2512.24300 - arXiv:2512.24300v1 Announce Type: cross -Abstract: Whether a video can be compressed at an extreme compression rate as low as 0.01%? To this end, we achieve the compression rate as 0.02% at some cases by introducing Generative Video Compression (GVC), a new framework that redefines the limits of video compression by leveraging modern generative video models to achieve extreme compression rates while preserving a perception-centric, task-oriented communication paradigm, corresponding to Level C of the Shannon-Weaver model. Besides, How we trade computation for compression rate or bandwidth? GVC answers this question by shifting the burden from transmission to inference: it encodes video into extremely compact representations and delegates content reconstruction to the receiver, where powerful generative priors synthesize high-quality video from minimal transmitted information. Is GVC practical and deployable? To ensure practical deployment, we propose a compression-computation trade-off strategy, enabling fast inference on consume-grade GPUs. Within the AI Flow framework, GVC opens new possibility for video communication in bandwidth- and resource-constrained environments such as emergency rescue, remote surveillance, and mobile edge computing. Through empirical validation, we demonstrate that GVC offers a viable path toward a new effective, efficient, scalable, and practical video communication paradigm. - oai:arXiv.org:2512.24300v1 - eess.IV - cs.AI - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xiangyu Chen, Jixiang Luo, Jingyu Xu, Fangqiu Yi, Chi Zhang, Xuelong Li - - - Topological Spatial Graph Coarsening - https://arxiv.org/abs/2512.24327 - arXiv:2512.24327v1 Announce Type: cross -Abstract: Spatial graphs are particular graphs for which the nodes are localized in space (e.g., public transport network, molecules, branching biological structures). In this work, we consider the problem of spatial graph reduction, that aims to find a smaller spatial graph (i.e., with less nodes) with the same overall structure as the initial one. In this context, performing the graph reduction while preserving the main topological features of the initial graph is particularly relevant, due to the additional spatial information. Thus, we propose a topological spatial graph coarsening approach based on a new framework that finds a trade-off between the graph reduction and the preservation of the topological characteristics. The coarsening is realized by collapsing short edges. In order to capture the topological information required to calibrate the reduction level, we adapt the construction of classical topological descriptors made for point clouds (the so-called persistent diagrams) to spatial graphs. This construction relies on the introduction of a new filtration called triangle-aware graph filtration. Our coarsening approach is parameter-free and we prove that it is equivariant under rotations, translations and scaling of the initial spatial graph. We evaluate the performances of our method on synthetic and real spatial graphs, and show that it significantly reduces the graph sizes while preserving the relevant topological information. - oai:arXiv.org:2512.24327v1 - stat.ML - cs.CG - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Anna Calissano, Etienne Lasalle - - - OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers - https://arxiv.org/abs/2512.24334 - arXiv:2512.24334v1 Announce Type: cross -Abstract: The rapid deployment of mega-constellations is driving the long-term vision of space data centers (SDCs), where interconnected satellites form in-orbit distributed computing and learning infrastructures. Enabling distributed federated learning in such systems is challenging because iterative training requires frequent aggregation over inter-satellite links that are bandwidth- and energy-constrained, and the link conditions can be highly dynamic. In this work, we exploit over-the-air computation (AirComp) as an in-network aggregation primitive. However, conventional coherent AirComp relies on stringent phase alignment, which is difficult to maintain in space environments due to satellite jitter and Doppler effects. To overcome this limitation, we propose OptiVote, a robust and communication-efficient non-coherent free-space optical (FSO) AirComp framework for federated learning toward Space Data Centers. OptiVote integrates sign stochastic gradient descent (signSGD) with a majority-vote (MV) aggregation principle and pulse-position modulation (PPM), where each satellite conveys local gradient signs by activating orthogonal PPM time slots. The aggregation node performs MV detection via non-coherent energy accumulation, transforming phase-sensitive field superposition into phase-agnostic optical intensity combining, thereby eliminating the need for precise phase synchronization and improving resilience under dynamic impairments. To mitigate aggregation bias induced by heterogeneous FSO channels, we further develop an importance-aware, channel state information (CSI)-free dynamic power control scheme that balances received energies without additional signaling. We provide theoretical analysis by characterizing the aggregate error probability under statistical FSO channels and establishing convergence guarantees for non-convex objectives. - oai:arXiv.org:2512.24334v1 - eess.SP - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Anbang Zhang, Chenyuan Feng, Wai Ho Mow, Jia Ye, Shuaishuai Guo, Geyong Min, Tony Q. S. Quek - - - Deep Learning in Geotechnical Engineering: A Critical Assessment of PINNs and Operator Learning - https://arxiv.org/abs/2512.24365 - arXiv:2512.24365v1 Announce Type: cross -Abstract: Deep learning methods -- physics-informed neural networks (PINNs), deep operator networks (DeepONet), and graph network simulators (GNS) -- are increasingly proposed for geotechnical problems. This paper tests these methods against traditional solvers on canonical problems: wave propagation and beam-foundation interaction. PINNs run 90,000 times slower than finite difference with larger errors. DeepONet requires thousands of training simulations and breaks even only after millions of evaluations. Multi-layer perceptrons fail catastrophically when extrapolating beyond training data -- the common case in geotechnical prediction. GNS shows promise for geometry-agnostic simulation but faces scaling limits and cannot capture path-dependent soil behavior. For inverse problems, automatic differentiation through traditional solvers recovers material parameters with sub-percent accuracy in seconds. We recommend: use automatic differentiation for inverse problems; apply site-based cross-validation to account for spatial autocorrelation; reserve neural networks for problems where traditional solvers are genuinely expensive and predictions remain within the training envelope. When a method is four orders of magnitude slower with less accuracy, it is not a viable replacement for proven solvers. - oai:arXiv.org:2512.24365v1 - physics.geo-ph - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-sa/4.0/ - Krishna Kumar - - - Implicit score matching meets denoising score matching: improved rates of convergence and log-density Hessian estimation - https://arxiv.org/abs/2512.24378 - arXiv:2512.24378v1 Announce Type: cross -Abstract: We study the problem of estimating the score function using both implicit score matching and denoising score matching. Assuming that the data distribution exhibiting a low-dimensional structure, we prove that implicit score matching is able not only to adapt to the intrinsic dimension, but also to achieve the same rates of convergence as denoising score matching in terms of the sample size. Furthermore, we demonstrate that both methods allow us to estimate log-density Hessians without the curse of dimensionality by simple differentiation. This justifies convergence of ODE-based samplers for generative diffusion models. Our approach is based on Gagliardo-Nirenberg-type inequalities relating weighted $L^2$-norms of smooth functions and their derivatives. - oai:arXiv.org:2512.24378v1 - math.ST - cs.LG - stat.ML - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Konstantin Yakovlev, Anna Markovich, Nikita Puchkin - - - Finite element analysis of very large bone models based on micro-CT scans - https://arxiv.org/abs/2512.24401 - arXiv:2512.24401v1 Announce Type: cross -Abstract: High-resolution voxel-based micro-finite element ($\mu$FE) models derived from $\mu$CT imaging enable detailed investigation of bone mechanics but remain computationally challenging at anatomically relevant scales. This study presents a comprehensive $\mu$FE framework for large-scale biomechanical analysis of an intact New Zealand White (NZW) rabbit femur, integrating advanced segmentation, scalable finite element solvers, and experimental validation using predominantly open-source libraries. Bone geometries were segmented from $\mu$CT data using the MIA clustering algorithm and converted into voxel-based $\mu$FE meshes, which were solved using the open-source MFEM library with algorithms designed for large-scale linear elasticity systems. - The numerical solutions were verified by comparing with a commercial finite element solver, and by evaluating the performance of full assembly and element-by-element formulations within MFEM. Models containing over $8\times10^{8}$ DOFs were solved using moderate HPC resources, demonstrating the feasibility of anatomically realistic $\mu$FE simulations at this scale. Resolution effects were investigated by comparing models with voxel sizes of 20, 40, and 80 $\mu$m, revealing that 40 $\mu$m preserves boundary displacement and principal strain distributions with minimal bias while significantly reducing computational cost. Sensitivity analyses further showed that segmentation parameters influence the global mechanical response. - Finally, $\mu$FE predictions were coupled with Digital Image Correlation measurements on an NZW rabbit femur under compression to calibrate effective bone material properties at the micron scale. The results demonstrate that large-scale, experimentally informed $\mu$FE modeling can be achieved using open-source tools, providing a robust foundation for preclinical assessment of bone mechanics and treatment-related risks. - oai:arXiv.org:2512.24401v1 - physics.med-ph - cs.NA - math.NA - q-bio.QM - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shani Martinez-Weissberg, Will Pazner, Zohar Yosibash - - - Virasoro Symmetry in Neural Network Field Theories - https://arxiv.org/abs/2512.24420 - arXiv:2512.24420v1 Announce Type: cross -Abstract: Neural Network Field Theories (NN-FTs) can realize global conformal symmetries via embedding space architectures. These models describe Generalized Free Fields (GFFs) in the infinite width limit. However, they typically lack a local stress-energy tensor satisfying conformal Ward identities. This presents an obstruction to realizing infinite-dimensional, local conformal symmetry typifying 2d Conformal Field Theories (CFTs). We present the first construction of an NN-FT that encodes the full Virasoro symmetry of a 2d CFT. We formulate a neural free boson theory with a local stress tensor $T(z)$ by properly choosing the architecture and prior distribution of network parameters. We verify the analytical results through numerical simulation; computing the central charge and the scaling dimensions of vertex operators. We then construct an NN realization of a Majorana Fermion and an $\mathcal{N}=(1,1)$ scalar multiplet, which then enables an extension of the formalism to include super-Virasoro symmetry. Finally, we extend the framework by constructing boundary NN-FTs that preserve (super-)conformal symmetry via the method of images. - oai:arXiv.org:2512.24420v1 - hep-th - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Brandon Robinson - - - Automated Market Making for Energy Sharing - https://arxiv.org/abs/2512.24432 - arXiv:2512.24432v1 Announce Type: cross -Abstract: We develop an axiomatic theory for Automated Market Makers (AMMs) in local energy sharing markets and analyze the Markov Perfect Equilibrium of the resulting economy with a Mean-Field Game. In this game, heterogeneous prosumers solve a Bellman equation to optimize energy consumption, storage, and exchanges. Our axioms identify a class of mechanisms with linear, Lipschitz continuous payment functions, where prices decrease with the aggregate supply-to-demand ratio of energy. We prove that implementing batch execution and concentrated liquidity allows standard design conditions from decentralized finance-quasi-concavity, monotonicity, and homotheticity-to construct AMMs that satisfy our axioms. The resulting AMMs are budget-balanced and achieve ex-ante efficiency, contrasting with the strategy-proof, expost optimal VCG mechanism. Since the AMM implements a Potential Game, we solve its equilibrium by first computing the social planner's optimum and then decentralizing the allocation. Numerical experiments using data from the Paris administrative region suggest that the prosumer community can achieve gains from trade up to 40% relative to the grid-only benchmark. - oai:arXiv.org:2512.24432v1 - econ.TH - cs.GT - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Michele Fabi, Viraj Nadkarni, Leonardo Leone, Matheus X. V. Ferreira - - - Quasicrystalline Gibbs states in 4-dimensional lattice-gas models with finite-range interactions - https://arxiv.org/abs/2512.24436 - arXiv:2512.24436v1 Announce Type: cross -Abstract: We construct a four-dimensional lattice-gas model with finite-range interactions that has non-periodic, ``quasicrystalline'' Gibbs states at low temperatures. Such Gibbs states are probability measures which are small perturbations of non-periodic ground-state configurations corresponding to tilings of the plane with Ammann's aperiodic tiles. Our construction is based on the correspondence between probabilistic cellular automata and Gibbs measures on their space-time trajectories, and a classical result on noise-resilient computing with cellular automata. The cellular automaton is constructed on the basis of Ammann's tiles, which are deterministic in one direction, and has non-periodic space-time trajectories corresponding to each valid tiling. Repetitions along two extra dimensions, together with an error-correction mechanism, ensure stability of the trajectories subjected to noise. - oai:arXiv.org:2512.24436v1 - math-ph - cond-mat.stat-mech - cs.DM - math.MP - math.PR - nlin.CG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Siamak Taati, Jacek Mi\c{e}kisz - - - Towards mechanistic understanding in a data-driven weather model: internal activations reveal interpretable physical features - https://arxiv.org/abs/2512.24440 - arXiv:2512.24440v1 Announce Type: cross -Abstract: Large data-driven physics models like DeepMind's weather model GraphCast have empirically succeeded in parameterizing time operators for complex dynamical systems with an accuracy reaching or in some cases exceeding that of traditional physics-based solvers. Unfortunately, how these data-driven models perform computations is largely unknown and whether their internal representations are interpretable or physically consistent is an open question. Here, we adapt tools from interpretability research in Large Language Models to analyze intermediate computational layers in GraphCast, leveraging sparse autoencoders to discover interpretable features in the neuron space of the model. We uncover distinct features on a wide range of length and time scales that correspond to tropical cyclones, atmospheric rivers, diurnal and seasonal behavior, large-scale precipitation patterns, specific geographical coding, and sea-ice extent, among others. We further demonstrate how the precise abstraction of these features can be probed via interventions on the prediction steps of the model. As a case study, we sparsely modify a feature corresponding to tropical cyclones in GraphCast and observe interpretable and physically consistent modifications to evolving hurricanes. Such methods offer a window into the black-box behavior of data-driven physics models and are a step towards realizing their potential as trustworthy predictors and scientifically valuable tools for discovery. - oai:arXiv.org:2512.24440v1 - physics.ao-ph - cs.LG - physics.comp-ph - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Theodore MacMillan, Nicholas T. Ouellette - - - The Wigner-Ville Transform as an Information Theoretic Tool in Radio-frequency Signal Analysis - https://arxiv.org/abs/2512.24488 - arXiv:2512.24488v1 Announce Type: cross -Abstract: This paper presents novel interpretations to the field of classical signal processing of the Wigner-Ville transform as an information measurement tool. The transform's utility in detecting and localizing information-laden signals amidst noisy and cluttered backgrounds, and further providing measure of their information volumes, are detailed herein using Tsallis' entropy and information and related functionals. Example use cases in radio frequency communications are given, where Wigner-Ville-based detection measures can be seen to provide significant sensitivity advantage, for some shown contexts greater than 15~dB advantage, over energy-based measures and without extensive training routines. Such an advantage is particularly significant for applications which have limitations on observation resources including time/space integration pressures and transient and/or feeble signals, where Wigner-Ville-based methods would improve sensing effectiveness by multiple orders of magnitude. The potential for advancement of several such applications is discussed. - oai:arXiv.org:2512.24488v1 - eess.SP - cs.IT - math.IT - quant-ph - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Erik Lentz, Emily Ellwein, Bill Kay, Audun Myers, Cameron Mackenzie - - - Automated Classification of First-Trimester Fetal Heart Views Using Ultrasound-Specific Self-Supervised Learning - https://arxiv.org/abs/2512.24492 - arXiv:2512.24492v1 Announce Type: cross -Abstract: Congenital heart disease remains the most common congenital anomaly and a leading cause of neonatal morbidity and mortality. Although first-trimester fetal echocardiography offers an opportunity for earlier detection, automated analysis at this stage is challenging due to small cardiac structures, low signal-to-noise ratio, and substantial inter-operator variability. In this work, we evaluate a self-supervised ultrasound foundation model, USF-MAE, for first-trimester fetal heart view classification. USF-MAE is pretrained using masked autoencoding modelling on more than 370,000 unlabelled ultrasound images spanning over 40 anatomical regions and is subsequently fine-tuned for downstream classification. As a proof of concept, the pretrained Vision Transformer encoder was fine-tuned on an open-source dataset of 6,720 first-trimester fetal echocardiography images to classify five categories: aorta, atrioventricular flows, V sign, X sign, and Other. Model performance was benchmarked against supervised convolutional neural network baselines (ResNet-18 and ResNet-50) and a Vision Transformer (ViT-B/16) model pretrained on natural images (ImageNet-1k). All models were trained and evaluated using identical preprocessing, data splits, and optimization protocols. On an independent test set, USF-MAE achieved the highest performance across all evaluation metrics, with 90.57% accuracy, 91.15% precision, 90.57% recall, and 90.71% F1-score. This represents an improvement of +2.03% in accuracy and +1.98% in F1-score compared with the strongest baseline, ResNet-18. The proposed approach demonstrated robust performance without reliance on aggressive image preprocessing or region-of-interest cropping and showed improved discrimination of non-diagnostic frames. - oai:arXiv.org:2512.24492v1 - eess.IV - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Youssef Megahed, Aylin Erman, Robin Ducharme, Mark C. Walker, Steven Hawken, Adrian D. C. Chan - - - Improving the stability of the covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling - https://arxiv.org/abs/2512.24515 - arXiv:2512.24515v1 Announce Type: cross -Abstract: Stochastic gradient Langevin dynamics and its variants approximate the likelihood of an entire dataset, via random (and typically much smaller) subsets, in the setting of Bayesian sampling. Due to the (often substantial) improvement of the computational efficiency, they have been widely used in large-scale machine learning applications. It has been demonstrated that the so-called covariance-controlled adaptive Langevin (CCAdL) thermostat, which incorporates an additional term involving the covariance matrix of the noisy force, outperforms popular alternative methods. A moving average is used in CCAdL to estimate the covariance matrix of the noisy force, in which case the covariance matrix will converge to a constant matrix in long-time limit. Moreover, it appears in our numerical experiments that the use of a moving average could reduce the stability of the numerical integrators, thereby limiting the largest usable stepsize. In this article, we propose a modified CCAdL (i.e., mCCAdL) thermostat that uses the scaling part of the scaling and squaring method together with a truncated Taylor series approximation to the exponential to numerically approximate the exact solution to the subsystem involving the additional term proposed in CCAdL. We also propose a symmetric splitting method for mCCAdL, instead of an Euler-type discretisation used in the original CCAdL thermostat. We demonstrate in our numerical experiments that the newly proposed mCCAdL thermostat achieves a substantial improvement in the numerical stability over the original CCAdL thermostat, while significantly outperforming popular alternative stochastic gradient methods in terms of the numerical accuracy for large-scale machine learning applications. - oai:arXiv.org:2512.24515v1 - stat.ML - cs.LG - stat.CO - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Jiani Wei, Xiaocheng Shang - - - Power Analysis is Essential: High-Powered Tests Suggest Minimal to No Effect of Rounded Shapes on Click-Through Rates - https://arxiv.org/abs/2512.24521 - arXiv:2512.24521v1 Announce Type: cross -Abstract: Underpowered studies (below 50%) suffer from the winner's curse: a statistically significant result must exaggerate the true treatment effect to meet the significance threshold. A study by Dipayan Biswas, Annika Abell, and Roger Chacko published in the Journal of Consumer Research (2023) reported that in an A/B test simply rounding the corners of square buttons increased the online click-through rate by 55% (p-value 0.037)$\unicode{x2014}$a striking finding with potentially wide-ranging implications for the digital industry that is seeking to enhance consumer engagement. Drawing on our experience with tens of thousands of A/B tests, many involving similar user interface modifications, we found this dramatic claim implausibly large. To evaluate the claim, we conducted three high-powered A/B tests, each involving over two thousand times more users than the original study. All three experiments yielded effect size estimates that were approximately two orders of magnitude smaller than initially reported, with 95% confidence intervals that include zero, that is, not statistically significant at the 0.05 level. Two additional independent replications by Evidoo found similarly small effects. These findings underscore the critical importance of power analysis and experimental design to increase trust and reproducibility of results. - oai:arXiv.org:2512.24521v1 - stat.ME - cs.HC - stat.AP - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Ron Kohavi, Jakub Linowski, Lukas Vermeer, Fabrice Boisseranc, Joachim Furuseth, Andrew Gelman, Guido Imbens, Ravikiran Rajagopal - - - Proper colorings of a graph in linear time using a number of colors linear in the maximum degree of the graph - https://arxiv.org/abs/2512.24522 - arXiv:2512.24522v1 Announce Type: cross -Abstract: A new algorithm for exactly sampling from the set of proper colorings of a graph is presented. This is the first such algorithm that has an expected running time that is guaranteed to be linear in the size of a graph with maximum degree \( \Delta \) when the number of colors is greater than \( 3.637 \Delta + 1\). - oai:arXiv.org:2512.24522v1 - math.PR - cs.CC - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kritika Bhandari, Mark Huber - - - Generative AI-enhanced Sector-based Investment Portfolio Construction - https://arxiv.org/abs/2512.24526 - arXiv:2512.24526v1 Announce Type: cross -Abstract: This paper investigates how Large Language Models (LLMs) from leading providers (OpenAI, Google, Anthropic, DeepSeek, and xAI) can be applied to quantitative sector-based portfolio construction. We use LLMs to identify investable universes of stocks within S&P 500 sector indices and evaluate how their selections perform when combined with classical portfolio optimization methods. Each model was prompted to select and weight 20 stocks per sector, and the resulting portfolios were compared with their respective sector indices across two distinct out-of-sample periods: a stable market phase (January-March 2025) and a volatile phase (April-June 2025). - Our results reveal a strong temporal dependence in LLM portfolio performance. During stable market conditions, LLM-weighted portfolios frequently outperformed sector indices on both cumulative return and risk-adjusted (Sharpe ratio) measures. However, during the volatile period, many LLM portfolios underperformed, suggesting that current models may struggle to adapt to regime shifts or high-volatility environments underrepresented in their training data. Importantly, when LLM-based stock selection is combined with traditional optimization techniques, portfolio outcomes improve in both performance and consistency. - This study contributes one of the first multi-model, cross-provider evaluations of generative AI algorithms in investment management. It highlights that while LLMs can effectively complement quantitative finance by enhancing stock selection and interpretability, their reliability remains market-dependent. The findings underscore the potential of hybrid AI-quantitative frameworks, integrating LLM reasoning with established optimization techniques, to produce more robust and adaptive investment strategies. - oai:arXiv.org:2512.24526v1 - q-fin.PM - cs.AI - cs.CE - q-fin.CP - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Alina Voronina, Oleksandr Romanko, Ruiwen Cao, Roy H. Kwon, Rafael Mendoza-Arriaga - - - Probabilistic Computers for Neural Quantum States - https://arxiv.org/abs/2512.24558 - arXiv:2512.24558v1 Announce Type: cross -Abstract: Neural quantum states efficiently represent many-body wavefunctions with neural networks, but the cost of Monte Carlo sampling limits their scaling to large system sizes. Here we address this challenge by combining sparse Boltzmann machine architectures with probabilistic computing hardware. We implement a probabilistic computer on field programmable gate arrays (FPGAs) and use it as a fast sampler for energy-based neural quantum states. For the two-dimensional transverse-field Ising model at criticality, we obtain accurate ground-state energies for lattices up to 80 $\times$ 80 (6400 spins) using a custom multi-FPGA cluster. Furthermore, we introduce a dual-sampling algorithm to train deep Boltzmann machines, replacing intractable marginalization with conditional sampling over auxiliary layers. This enables the training of sparse deep models and improves parameter efficiency relative to shallow networks. Using this algorithm, we train deep Boltzmann machines for a system with 35 $\times$ 35 (1225 spins). Together, these results demonstrate that probabilistic hardware can overcome the sampling bottleneck in variational simulation of quantum many-body systems, opening a path to larger system sizes and deeper variational architectures. - oai:arXiv.org:2512.24558v1 - quant-ph - cond-mat.dis-nn - cs.ET - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Shuvro Chowdhury, Jasper Pieterse, Navid Anjum Aadit, Johan H. Mentink, Kerem Y. Camsari - - - Robust Bayesian Dynamic Programming for On-policy Risk-sensitive Reinforcement Learning - https://arxiv.org/abs/2512.24580 - arXiv:2512.24580v1 Announce Type: cross -Abstract: We propose a novel framework for risk-sensitive reinforcement learning (RSRL) that incorporates robustness against transition uncertainty. We define two distinct yet coupled risk measures: an inner risk measure addressing state and cost randomness and an outer risk measure capturing transition dynamics uncertainty. Our framework unifies and generalizes most existing RL frameworks by permitting general coherent risk measures for both inner and outer risk measures. Within this framework, we construct a risk-sensitive robust Markov decision process (RSRMDP), derive its Bellman equation, and provide error analysis under a given posterior distribution. We further develop a Bayesian Dynamic Programming (Bayesian DP) algorithm that alternates between posterior updates and value iteration. The approach employs an estimator for the risk-based Bellman operator that combines Monte Carlo sampling with convex optimization, for which we prove strong consistency guarantees. Furthermore, we demonstrate that the algorithm converges to a near-optimal policy in the training environment and analyze both the sample complexity and the computational complexity under the Dirichlet posterior and CVaR. Finally, we validate our approach through two numerical experiments. The results exhibit excellent convergence properties while providing intuitive demonstrations of its advantages in both risk-sensitivity and robustness. Empirically, we further demonstrate the advantages of the proposed algorithm through an application on option hedging. - oai:arXiv.org:2512.24580v1 - q-fin.RM - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shanyu Han, Yangbo He, Yang Liu - - - On Circular Threshold Words and Other Stronger Versions of Dejean's conjecture - https://arxiv.org/abs/2512.24581 - arXiv:2512.24581v1 Announce Type: cross -Abstract: Let the root of the word $w$ be the smallest prefix $v$ of $w$ such that $w$ is a prefix of $vvv...$. $per(w)$ is the length of the root of $w$. For any $n\ge5$, an $n$-ary threshold word is a word $w$ such that for any factor (subword) $v$ of $w$ the condition $\frac{|v|}{per(v)}\le\frac{n}{n-1}$ holds. Dejean conjecture (completely proven in 2009) states for $n\ge5$ that exists infinitely many of $n$-ary TWs. - This manuscript is based on the author's student works (diplomas of 2011 (bachelor's thesis) and 2013 (master's thesis) years) and presents an edited version (in Russian) of these works with some improvements. - In a 2011 work proposed new methods of proving of the Dejean conjecture for some odd cases $n\ge5$, using computer verification in polynomial time (depending on $n$). Moreover, the constructed threshold words (TWs) are ciclic/ring TWs (any cyclic shift is a TW). - In the 2013 work, the proof method (of 2011) was improved by reducing the verification conditions. A solution for some even cases $n\ge6$ is also proposed. A 2013 work also proposed a method to construct stronger TWs, using a TW tree with regular exponential growth. Namely, the TWs, where all long factors have an exponent close to 1. - oai:arXiv.org:2512.24581v1 - math.CO - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Igor N. Tunev - - - MultiRisk: Multiple Risk Control via Iterative Score Thresholding - https://arxiv.org/abs/2512.24587 - arXiv:2512.24587v1 Announce Type: cross -Abstract: As generative AI systems are increasingly deployed in real-world applications, regulating multiple dimensions of model behavior has become essential. We focus on test-time filtering: a lightweight mechanism for behavior control that compares performance scores to estimated thresholds, and modifies outputs when these bounds are violated. We formalize the problem of enforcing multiple risk constraints with user-defined priorities, and introduce two efficient dynamic programming algorithms that leverage this sequential structure. The first, MULTIRISK-BASE, provides a direct finite-sample procedure for selecting thresholds, while the second, MULTIRISK, leverages data exchangeability to guarantee simultaneous control of the risks. Under mild assumptions, we show that MULTIRISK achieves nearly tight control of all constraint risks. The analysis requires an intricate iterative argument, upper bounding the risks by introducing several forms of intermediate symmetrized risk functions, and carefully lower bounding the risks by recursively counting jumps in symmetrized risk functions between appropriate risk levels. We evaluate our framework on a three-constraint Large Language Model alignment task using the PKU-SafeRLHF dataset, where the goal is to maximize helpfulness subject to multiple safety constraints, and where scores are generated by a Large Language Model judge and a perplexity filter. Our experimental results show that our algorithm can control each individual risk at close to the target level. - oai:arXiv.org:2512.24587v1 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Sunay Joshi, Yan Sun, Hamed Hassani, Edgar Dobriban - - - A Uniform Pilot and Data Payload Optimization Framework for OTFS-Based ISAC - https://arxiv.org/abs/2512.24624 - arXiv:2512.24624v1 Announce Type: cross -Abstract: The orthogonal time frequency space (OTFS) signal is considered a promising solution for high-mobility wireless environments. It manages Doppler effects by utilizing delay-Doppler (DD) domain processing. However, the relatively long OTFS frame duration could introduce considerable sensing or communication latency when radar and communication are performed separately. By operating in a dual-functional radar and communication (DFRC) mode, the OTFS system performs sensing and data transmission simultaneously, thereby reducing the resulting latency. Nevertheless, the optimal OTFS DFRC signal strategy remains insufficiently explored. This paper investigates the optimal signal design for OTFS DFRC systems, focusing on pilot symbol design and data symbol power allocation. Specifically, we derive a channel capacity lower bound metric for communication that considers channel estimation errors in OTFS. For sensing, we derive an integrated sidelobe level (ISL), accounting for the randomness of the data symbols alongside the deterministic pilot symbols. Leveraging the above metrics, we formulate an optimization problem that balances radar and communication performance, and then solve it using an alternating optimization framework. We validate the proposed signal through numerical analysis and Monte Carlo simulations. Our analysis shows that OTFS DFRC enforces a deterministic pilot signal that is characterized by a concentrated peak in the DD domain, which furnishes a common structure in the DD domain facilitating sensing and channel estimation, with data multiplexed in other DD grids, thereby unifying sensing and communication within a single OTFS signal. Compared with conventional OTFS signals, the proposed OTFS DFRC signal expands the achievable sensing-communication performance region, delivering at least a 9.45 dB ISL suppression for sensing and a 4.82 dB SINR ratio gain for communication. - oai:arXiv.org:2512.24624v1 - eess.SP - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Borui Du, Yumeng Zhang, Christos Masouros, Bruno Clerckx - - - Soliton profiles: Classical Numerical Schemes vs. Neural Network - Based Solvers - https://arxiv.org/abs/2512.24634 - arXiv:2512.24634v1 Announce Type: cross -Abstract: We present a comparative study of classical numerical solvers, such as Petviashvili's method or finite difference with Newton iterations, and neural network-based methods for computing ground states or profiles of solitary-wave solutions to the one-dimensional dispersive PDEs that include the nonlinear Schr\"odinger, the nonlinear Klein-Gordon and the generalized KdV equations. We confirm that classical approaches retain high-order accuracy and strong computational efficiency for single-instance problems in the one-dimensional setting. Physics-informed neural networks (PINNs) are also able to reproduce qualitative solutions but are generally less accurate and less efficient in low dimensions than classical solvers due to expensive training and slow convergence. We also investigate the operator-learning methods, which, although computationally intensive during training, can be reused across many parameter instances, providing rapid inference after pretraining, making them attractive for applications involving repeated simulations or real-time predictions. For single-instance computations, however, the accuracy of operator-learning methods remains lower than that of classical methods or PINNs, in general. - oai:arXiv.org:2512.24634v1 - nlin.PS - cs.LG - cs.NA - math.AP - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chandler Haight, Svetlana Roudenko, Zhongming Wang - - - A unified spatiotemporal formulation with physics-preserving structure for time-dependent convection-diffusion problems - https://arxiv.org/abs/2512.24650 - arXiv:2512.24650v1 Announce Type: cross -Abstract: We propose a unified four-dimensional (4D) spatiotemporal formulation for time-dependent convection-diffusion problems that preserves underlying physical structures. By treating time as an additional space-like coordinate, the evolution problem is reformulated as a stationary convection-diffusion equation on a 4D space-time domain. Using exterior calculus, we extend this framework to the full family of convection-diffusion problems posed on $H(\textbf{grad})$, $H(\textbf{curl})$, and $H(\text{div})$. The resulting formulation is based on a 4D Hodge-Laplacian operator with a spatiotemporal diffusion tensor and convection field, augmented by a small temporal perturbation to ensure nondegeneracy. This formulation naturally incorporates fundamental physical constraints, including divergence-free and curl-free conditions. We further introduce an exponentially-fitted 4D spatiotemporal flux operator that symmetrizes the convection-diffusion operator and enables a well-posed variational formulation. Finally, we prove that the temporally-perturbed formulation converges to the original time-dependent convection-diffusion model as the perturbation parameter tends to zero. - oai:arXiv.org:2512.24650v1 - math.AP - cs.NA - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - James H. Adler, Xiaozhe Hu, Seulip Lee - - - An Adaptive, Disentangled Representation for Multidimensional MRI Reconstruction - https://arxiv.org/abs/2512.24674 - arXiv:2512.24674v1 Announce Type: cross -Abstract: We present a new approach for representing and reconstructing multidimensional magnetic resonance imaging (MRI) data. Our method builds on a novel, learned feature-based image representation that disentangles different types of features, such as geometry and contrast, into distinct low-dimensional latent spaces, enabling better exploitation of feature correlations in multidimensional images and incorporation of pre-learned priors specific to different feature types for reconstruction. More specifically, the disentanglement was achieved via an encoderdecoder network and image transfer training using large public data, enhanced by a style-based decoder design. A latent diffusion model was introduced to impose stronger constraints on distinct feature spaces. New reconstruction formulations and algorithms were developed to integrate the learned representation with a zero-shot selfsupervised learning adaptation and subspace modeling. The proposed method has been evaluated on accelerated T1 and T2 parameter mapping, achieving improved performance over state-of-the-art reconstruction methods, without task-specific supervised training or fine-tuning. This work offers a new strategy for learning-based multidimensional image reconstruction where only limited data are available for problem-specific or task-specific training. - oai:arXiv.org:2512.24674v1 - eess.IV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ruiyang Zhao, Fan Lam - - - A New Decomposition Paradigm for Graph-structured Nonlinear Programs via Message Passing - https://arxiv.org/abs/2512.24676 - arXiv:2512.24676v1 Announce Type: cross -Abstract: We study finite-sum nonlinear programs whose decision variables interact locally according to a graph or hypergraph. We propose MP-Jacobi (Message Passing-Jacobi), a graph-compliant decentralized framework that couples min-sum message passing with Jacobi block updates. The (hyper)graph is partitioned into tree clusters. At each iteration, agents update in parallel by solving a cluster subproblem whose objective decomposes into (i) an intra-cluster term evaluated by a single min-sum sweep on the cluster tree (cost-to-go messages) and (ii) inter-cluster couplings handled via a Jacobi correction using neighbors' latest iterates. This design uses only single-hop communication and yields a convergent message-passing method on loopy graphs. - For strongly convex objectives we establish global linear convergence and explicit rates that quantify how curvature, coupling strength, and the chosen partition affect scalability and provide guidance for clustering. To mitigate the computation and communication cost of exact message updates, we develop graph-compliant surrogates that preserve convergence while reducing per-iteration complexity. We further extend MP-Jacobi to hypergraphs; in heavily overlapping regimes, a surrogate-based hyperedge-splitting scheme restores finite-time intra-cluster message updates and maintains convergence. Experiments validate the theory and show consistent improvements over decentralized gradient baselines. - oai:arXiv.org:2512.24676v1 - math.OC - cs.IT - cs.LG - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kuangyu Ding, Marie Maros, Gesualdo Scutari - - - Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model - https://arxiv.org/abs/2512.24687 - arXiv:2512.24687v1 Announce Type: cross -Abstract: Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to the challenge of semantic uncertainty, glosses from different sources inevitably carry semantic biases, which can lead to biased disambiguation results. Inspired by quantum superposition in modeling uncertainty, this paper proposes a Quantum Inference Model for Unsupervised Visual Word Sense Disambiguation (Q-VWSD). It encodes multiple glosses of the target word into a superposition state to mitigate semantic biases. Then, the quantum circuit is executed, and the results are observed. By formalizing our method, we find that Q-VWSD is a quantum generalization of the method based on classical probability. Building on this, we further designed a heuristic version of Q-VWSD that can run more efficiently on classical computing. The experiments demonstrate that our method outperforms state-of-the-art classical methods, particularly by effectively leveraging non-specialized glosses from large language models, which further enhances performance. Our approach showcases the potential of quantum machine learning in practical applications and provides a case for leveraging quantum modeling advantages on classical computers while quantum hardware remains immature. - oai:arXiv.org:2512.24687v1 - quant-ph - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wenbo Qiao, Peng Zhang, Qinghua Hu - - - Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach - https://arxiv.org/abs/2512.24747 - arXiv:2512.24747v1 Announce Type: cross -Abstract: Machine learning improves predictive accuracy in insurance pricing but exacerbates trade-offs between competing fairness criteria across different discrimination measures, challenging regulators and insurers to reconcile profitability with equitable outcomes. While existing fairness-aware models offer partial solutions under GLM and XGBoost estimation methods, they remain constrained by single-objective optimization, failing to holistically navigate a conflicting landscape of accuracy, group fairness, individual fairness, and counterfactual fairness. To address this, we propose a novel multi-objective optimization framework that jointly optimizes all four criteria via the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a diverse Pareto front of trade-off solutions. We use a specific selection mechanism to extract a premium on this front. Our results show that XGBoost outperforms GLM in accuracy but amplifies fairness disparities; the Orthogonal model excels in group fairness, while Synthetic Control leads in individual and counterfactual fairness. Our method consistently achieves a balanced compromise, outperforming single-model approaches. - oai:arXiv.org:2512.24747v1 - q-fin.RM - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tim J. Boonen, Xinyue Fan, Zixiao Quan - - - AstroReview: An LLM-driven Multi-Agent Framework for Telescope Proposal Peer Review and Refinement - https://arxiv.org/abs/2512.24754 - arXiv:2512.24754v1 Announce Type: cross -Abstract: Competitive access to modern observatories has intensified as proposal volumes outpace available telescope time, making timely, consistent, and transparent peer review a critical bottleneck for the advancement of astronomy. Automating parts of this process is therefore both scientifically significant and operationally necessary to ensure fair allocation and reproducible decisions at scale. We present AstroReview, an open-source, agent-based framework that automates proposal review in three stages: (i) novelty and scientific merit, (ii) feasibility and expected yield, and (iii) meta-review and reliability verification. Task isolation and explicit reasoning traces curb hallucinations and improve transparency. Without any domain specific fine tuning, AstroReview used in our experiments only for the last stage, correctly identifies genuinely accepted proposals with an accuracy of 87%. The AstroReview in Action module replicates the review and refinement loop; with its integrated Proposal Authoring Agent, the acceptance rate of revised drafts increases by 66% after two iterations, showing that iterative feedback combined with automated meta-review and reliability verification delivers measurable quality gains. Together, these results point to a practical path toward scalable, auditable, and higher throughput proposal review for resource limited facilities. - oai:arXiv.org:2512.24754v1 - astro-ph.IM - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yutong Wang, Yunxiang Xiao, Yonglin Tian, Junyong Li, Jing Wang, Yisheng Lv - - - Sparse Offline Reinforcement Learning with Corruption Robustness - https://arxiv.org/abs/2512.24768 - arXiv:2512.24768v1 Announce Type: cross -Abstract: We investigate robustness to strong data corruption in offline sparse reinforcement learning (RL). In our setting, an adversary may arbitrarily perturb a fraction of the collected trajectories from a high-dimensional but sparse Markov decision process, and our goal is to estimate a near optimal policy. The main challenge is that, in the high-dimensional regime where the number of samples $N$ is smaller than the feature dimension $d$, exploiting sparsity is essential for obtaining non-vacuous guarantees but has not been systematically studied in offline RL. We analyse the problem under uniform coverage and sparse single-concentrability assumptions. While Least Square Value Iteration (LSVI), a standard approach for robust offline RL, performs well under uniform coverage, we show that integrating sparsity into LSVI is unnatural, and its analysis may break down due to overly pessimistic bonuses. To overcome this, we propose actor-critic methods with sparse robust estimator oracles, which avoid the use of pointwise pessimistic bonuses and provide the first non-vacuous guarantees for sparse offline RL under single-policy concentrability coverage. Moreover, we extend our results to the contaminated setting and show that our algorithm remains robust under strong contamination. Our results provide the first non-vacuous guarantees in high-dimensional sparse MDPs with single-policy concentrability coverage and corruption, showing that learning a near-optimal policy remains possible in regimes where traditional robust offline RL techniques may fail. - oai:arXiv.org:2512.24768v1 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Nam Phuong Tran, Andi Nika, Goran Radanovic, Long Tran-Thanh, Debmalya Mandal - - - Structured Production Systems: Viability - https://arxiv.org/abs/2512.24777 - arXiv:2512.24777v1 Announce Type: cross -Abstract: This paper introduces a novel framework for analysing equilibrium in structured production systems incorporating a static social division of labour by distinguishing between consumption goods traded in competitive markets and intermediate goods exchanged through bilateral relationships. We develop the concept of viability -- the requirement that all producers earn positive incomes -- as a foundational equilibrium prerequisite. - Our main theoretical contribution establishes that acyclic production systems -- those without circular conversion processes among goods -- are always viable, a condition that implies coherence. We characterise completely viable systems through input restrictions demonstrating that prohibiting consumption goods as inputs for other consumption goods is necessary for ensuring viable prices exist for all consumption good price vectors. The analysis reveals fundamental relationships between production system architectural design and economic sustainability. - The introduced framework bridges Leontief-Sraffa production theory with modern network economics while capturing institutional realities of contemporary production systems. This also results in a contribution of the literature on the existence of a positive output price system and the Hawkins-Simon condition. - oai:arXiv.org:2512.24777v1 - econ.TH - cs.SI - physics.soc-ph - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Robert P. Gilles, Marialaura Pesce - - - Limits of quantum generative models with classical sampling hardness - https://arxiv.org/abs/2512.24801 - arXiv:2512.24801v1 Announce Type: cross -Abstract: Sampling tasks have been successful in establishing quantum advantages both in theory and experiments. This has fueled the use of quantum computers for generative modeling to create samples following the probability distribution underlying a given dataset. In particular, the potential to build generative models on classically hard distributions would immediately preclude classical simulability, due to theoretical separations. In this work, we study quantum generative models from the perspective of output distributions, showing that models that anticoncentrate are not trainable on average, including those exhibiting quantum advantage. In contrast, models outputting data from sparse distributions can be trained. We consider special cases to enhance trainability, and observe that this opens the path for classical algorithms for surrogate sampling. This observed trade-off is linked to verification of quantum processes. We conclude that quantum advantage can still be found in generative models, although its source must be distinct from anticoncentration. - oai:arXiv.org:2512.24801v1 - quant-ph - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Sabrina Herbst, Ivona Brandi\'c, Adri\'an P\'erez-Salinas - - - Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models - https://arxiv.org/abs/2512.24813 - arXiv:2512.24813v1 Announce Type: cross -Abstract: We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots, demonstrated on a 2D Kolmogorov Flow, and a 3D Kelvin-Helmholtz Instability (KHI). We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent kinetic energy spectra over generated sequences, and temporal decay of turbulent structures. For the non-stationary Kelvin-Helmholtz Instability, we assess the ability of the proposed method to capture evolving flow statistics across the most strongly time-varying flow regime. We additionally examine instantaneous fields and physically motivated metrics at key stages of the KHI flow evolution. - oai:arXiv.org:2512.24813v1 - physics.flu-dyn - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-sa/4.0/ - Mohammed Sardar, Ma{\l}gorzata J. Zimo\'n, Samuel Draycott, Alistair Revell, Alex Skillen - - - Advances in Agentic AI: Back to the Future - https://arxiv.org/abs/2512.24856 - arXiv:2512.24856v1 Announce Type: cross -Abstract: In light of the recent convergence between Agentic AI and our field of Algorithmization, this paper seeks to restore conceptual clarity and provide a structured analytical framework for an increasingly fragmented discourse. First, (a) it examines the contemporary landscape and proposes precise definitions for the key notions involved, ranging from intelligence to Agentic AI. Second, (b) it reviews our prior body of work to contextualize the evolution of methodologies and technological advances developed over the past decade, highlighting their interdependencies and cumulative trajectory. Third, (c) by distinguishing Machine and Learning efforts within the field of Machine Learning (d) it introduces the first Machine in Machine Learning (M1) as the underlying platform enabling today's LLM-based Agentic AI, conceptualized as an extension of B2C information-retrieval user experiences now being repurposed for B2B transformation. Building on this distinction, (e) the white paper develops the notion of the second Machine in Machine Learning (M2) as the architectural prerequisite for holistic, production-grade B2B transformation, characterizing it as Strategies-based Agentic AI and grounding its definition in the structural barriers-to-entry that such systems must overcome to be operationally viable. Further, (f) it offers conceptual and technical insight into what appears to be the first fully realized implementation of an M2. Finally, drawing on the demonstrated accuracy of the two previous decades of professional and academic experience in developing the foundational architectures of Algorithmization, (g) it outlines a forward-looking research and transformation agenda for the coming two decades. - oai:arXiv.org:2512.24856v1 - econ.TH - cs.AR - cs.CE - cs.ET - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Sergio Alvarez-Telena, Marta Diez-Fernandez - - - Approximate Computation via Le Cam Simulability - https://arxiv.org/abs/2512.24860 - arXiv:2512.24860v1 Announce Type: cross -Abstract: We propose a decision-theoretic framework for computational complexity, complementary to classical theory: moving from syntactic exactness (Turing / Shannon) to semantic simulability (Le Cam). While classical theory classifies problems by the cost of exact solution, modern computation often seeks only decision-valid approximations. We introduce a framework where "computation" is viewed as the efficient simulation of a target statistical experiment within a bounded risk distortion (Le Cam deficiency). - We formally define computational deficiency ($\delta_{\text{poly}}$) and use it to construct the complexity class LeCam-P (Decision-Robust Polynomial Time), characterizing problems that may be syntactically hard but semantically easy to approximate. We show that classical Karp reductions can be viewed as zero-deficiency simulations, and that approximate reductions correspond to bounded deficiency. Furthermore, we establish the No-Free-Transfer Inequality, showing that strictly invariant representations inevitably destroy decision-relevant information. This framework offers a statistical perspective on approximation theory, bridging the gap between algorithmic complexity and decision theory. - oai:arXiv.org:2512.24860v1 - math.ST - cs.CC - cs.IT - math.IT - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Deniz Akdemir - - - On Prime Matrix Product Factorizations - https://arxiv.org/abs/2512.24864 - arXiv:2512.24864v1 Announce Type: cross -Abstract: A graph $G$ factors into graphs $H$ and $K$ via a matrix product if $A = BC$, where $A$, $B$, and $C$ are the adjacency matrices of $G$, $H$, and $K$, respectively. The graph $G$ is prime if, in every such factorization, one of the factors is a perfect matching that is, it corresponds to a permutation matrix. We characterize all prime graphs, then using this result we classify all factorable forests, answering a question of Akbari et al. [\emph{Linear Algebra and its Applications} (2025)]. We prove that every torus is factorable, and we characterize all possible factorizations of grids, addressing two questions posed by Maghsoudi et al. [\emph{Journal of Algebraic Combinatorics} (2025)]. - oai:arXiv.org:2512.24864v1 - math.CO - cs.DM - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Saieed Akbari, Mohamad Parsa Elahimanes, Bobby Miraftab - - - Towards autonomous time-calibration of large quantum-dot devices: Detection, real-time feedback, and noise spectroscopy - https://arxiv.org/abs/2512.24894 - arXiv:2512.24894v1 Announce Type: cross -Abstract: The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration becomes impractical, creating a need for autonomous stabilization frameworks. Here, we introduce a method that uses the full network of charge-transition lines in repeatedly acquired double-quantum-dot charge stability diagrams (CSDs) as a multidimensional probe of the local electrostatic environment. By accurately tracking the motion of selected transitions in time, we detect voltage drifts, identify abrupt charge reconfigurations, and apply compensating updates to maintain stable operating conditions. We demonstrate our approach on a 10-QD device, showing robust stabilization and real-time diagnostic access to dot-specific noise processes. The high acquisition rate of radio-frequency reflectometry CSD measurements also enables time-domain noise spectroscopy, allowing the extraction of noise power spectral densities, the identification of two-level fluctuators, and the analysis of spatial noise correlations across the array. From our analysis, we find that the background noise at 100~$\mu$\si{\hertz} is dominated by drift with a power law of $1/f^2$, accompanied by a few dominant two-level fluctuators and an average linear correlation length of $(188 \pm 38)$~\si{\nano\meter} in the device. These capabilities form the basis of a scalable, autonomous calibration and characterization module for QD-based quantum processors, providing essential feedback for long-duration, high-fidelity qubit operations. - oai:arXiv.org:2512.24894v1 - cond-mat.mes-hall - cs.CV - cs.ET - quant-ph - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Anantha S. Rao, Barnaby van Straaten, Valentin John, C\'ecile X. Yu, Stefan D. Oosterhout, Lucas Stehouwer, Giordano Scappucci, M. D. Stewart, Jr., Menno Veldhorst, Francesco Borsoi, Justyna P. Zwolak - - - Adaptive Resource Orchestration for Distributed Quantum Computing Systems - https://arxiv.org/abs/2512.24902 - arXiv:2512.24902v1 Announce Type: cross -Abstract: Scaling quantum computing beyond a single device requires networking many quantum processing units (QPUs) into a coherent quantum-HPC system. We propose the Modular Entanglement Hub (ModEn-Hub) architecture: a hub-and-spoke photonic interconnect paired with a real-time quantum network orchestrator. ModEn-Hub centralizes entanglement sources and shared quantum memory to deliver on-demand, high-fidelity Bell pairs across heterogeneous QPUs, while the control plane schedules teleportation-based non-local gates, launches parallel entanglement attempts, and maintains a small ebit cache. To quantify benefits, we implement a lightweight, reproducible Monte Carlo study under realistic loss and tight round budgets, comparing a naive sequential baseline to an orchestrated policy with logarithmically scaled parallelism and opportunistic caching. Across 1-128 QPUs and 2,500 trials per point, ModEn-Hub-style orchestration sustains about 90% teleportation success while the baseline degrades toward about 30%, at the cost of higher average entanglement attempts (about 10-12 versus about 3). These results provide clear, high-level evidence that adaptive resource orchestration in the ModEn-Hub enables scalable and efficient quantum-HPC operation on near-term hardware. - oai:arXiv.org:2512.24902v1 - quant-ph - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Kuan-Cheng Chen, Felix Burt, Nitish K. Panigrahy, Kin K. Leung - - - No Vision, No Wearables: 5G-based 2D Human Pose Recognition with Integrated Sensing and Communications - https://arxiv.org/abs/2512.24923 - arXiv:2512.24923v1 Announce Type: cross -Abstract: With the increasing maturity of contactless human pose recognition (HPR) technology, indoor interactive applications have raised higher demands for natural, controller-free interaction methods. However, current mainstream HPR solutions relying on vision or radio-frequency (RF) (including WiFi, radar) still face various challenges in practical deployment, such as privacy concerns, susceptibility to occlusion, dedicated equipment and functions, and limited sensing resolution and range. 5G-based integrated sensing and communication (ISAC) technology, by merging communication and sensing functions, offers a new approach to address these challenges in contactless HPR. We propose a practical 5G-based ISAC system capable of inferring 2D HPR from uplink sounding reference signals (SRS). Specifically, rich features are extracted from multiple domains and employ an encoder to achieve unified alignment and representation in a latent space. Subsequently, low-dimensional features are fused to output the human pose state. Experimental results demonstrate that in typical indoor environments, our proposed 5G-based ISAC HPR system significantly outperforms current mainstream baseline solutions in HPR performance, providing a solid technical foundation for universal human-computer interaction. - oai:arXiv.org:2512.24923v1 - eess.SP - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haojin Li, Dongzhe Li, Anbang Zhang, Wenqi Zhang, Chen Sun, Haijun Zhang - - - Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach - https://arxiv.org/abs/2512.24927 - arXiv:2512.24927v1 Announce Type: cross -Abstract: Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inherently slower and that increasing discretization order is the primary path to faster generation. This paper challenges this belief and revisits acceleration from a complementary angle: beyond solver order, the placement of DPM evaluations along the reverse-time dynamics can substantially affect sampling accuracy in the low-neural function evaluation (NFE) regime. - We propose a novel training-free, first-order sampler whose leading discretization error has the opposite sign to that of DDIM. Algorithmically, the method approximates the forward-value evaluation via a cheap one-step lookahead predictor. We provide theoretical guarantees showing that the resulting sampler provably approximates the ideal forward-value trajectory while retaining first-order convergence. Empirically, across standard image generation benchmarks (CIFAR-10, ImageNet, FFHQ, and LSUN), the proposed sampler consistently improves sample quality under the same NFE budget and can be competitive with, and sometimes outperform, state-of-the-art higher-order samplers. Overall, the results suggest that the placement of DPM evaluations provides an additional and largely independent design angle for accelerating diffusion sampling. - oai:arXiv.org:2512.24927v1 - stat.ML - cs.LG - math.ST - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Yuchen Jiao, Na Li, Changxiao Cai, Gen Li - - - Geometric characterisation of structural and regular equivalences in undirected (hyper)graphs - https://arxiv.org/abs/2512.24961 - arXiv:2512.24961v1 Announce Type: cross -Abstract: Similarity notions between vertices in a graph, such as structural and regular equivalence, are one of the main ingredients in clustering tools in complex network science. We generalise structural and regular equivalences for undirected hypergraphs and provide a characterisation of structural and regular equivalences of undirected graphs and hypergraphs through neighbourhood graphs and Ollivier-Ricci curvature. Our characterisation sheds new light on these similarity notions opening a new avenue for their exploration. These characterisations also enable the construction of a possibly wide family of regular partitions, thereby offering a new route to a task that has so far been computationally challenging. - oai:arXiv.org:2512.24961v1 - math.CO - cs.DM - cs.SI - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Marzieh Eidi, Nina Otter - - - The Impact of LLMs on Online News Consumption and Production - https://arxiv.org/abs/2512.24968 - arXiv:2512.24968v1 Announce Type: cross -Abstract: Large language models (LLMs) change how consumers acquire information online; their bots also crawl news publishers' websites for training data and to answer consumer queries; and they provide tools that can lower the cost of content creation. These changes lead to predictions of adverse impact on news publishers in the form of lowered consumer demand, reduced demand for newsroom employees, and an increase in news "slop." Consequently, some publishers strategically responded by blocking LLM access to their websites using the robots.txt file standard. - Using high-frequency granular data, we document four effects related to the predicted shifts in news publishing following the introduction of generative AI (GenAI). First, we find a consistent and moderate decline in traffic to news publishers occurring after August 2024. Second, using a difference-in-differences approach, we find that blocking GenAI bots can have adverse effects on large publishers by reducing total website traffic by 23% and real consumer traffic by 14% compared to not blocking. Third, on the hiring side, we do not find evidence that LLMs are replacing editorial or content-production jobs yet. The share of new editorial and content-production job listings increases over time. Fourth, regarding content production, we find no evidence that large publishers increased text volume; instead, they significantly increased rich content and use more advertising and targeting technologies. - Together, these findings provide early evidence of some unforeseen impacts of the introduction of LLMs on news production and consumption. - oai:arXiv.org:2512.24968v1 - econ.GN - cs.AI - cs.CY - q-fin.EC - stat.AP - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hangcheng Zhao, Ron Berman - - - Large language models and the entropy of English - https://arxiv.org/abs/2512.24969 - arXiv:2512.24969v1 Announce Type: cross -Abstract: We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interactions across these distances. A corollary is that there are small but significant correlations between characters at these separations, as we show from the data independent of models. The distribution of code lengths reveals an emergent certainty about an increasing fraction of characters at large $N$. Over the course of model training, we observe different dynamics at long and short context lengths, suggesting that long-ranged structure is learned only gradually. Our results constrain efforts to build statistical physics models of LLMs or language itself. - oai:arXiv.org:2512.24969v1 - cond-mat.stat-mech - cs.CL - physics.bio-ph - q-bio.NC - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Colin Scheibner, Lindsay M. Smith, William Bialek - - - SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets - https://arxiv.org/abs/2512.24977 - arXiv:2512.24977v1 Announce Type: cross -Abstract: Sequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property of tasks to which we would like to apply artificial intelligence. It is therefore of great importance to develop frameworks that allow us to evaluate sequence learning and processing in a domain agnostic fashion, whilst simultaneously providing a link to formal theories of computation and computability. To address this need, we introduce two complementary software tools: SymSeq, designed to rigorously generate and analyze structured symbolic sequences, and SeqBench, a comprehensive benchmark suite of rule-based sequence processing tasks to evaluate the performance of artificial learning systems in cognitively relevant domains. In combination, SymSeqBench offers versatility in investigating sequential structure across diverse knowledge domains, including experimental psycholinguistics, cognitive psychology, behavioral analysis, neuromorphic computing and artificial intelligence. Due to its basis in Formal Language Theory (FLT), SymSeqBench provides researchers in multiple domains with a convenient and practical way to apply the concepts of FLT to conceptualize and standardize their experiments, thus advancing our understanding of cognition and behavior through shared computational frameworks and formalisms. The tool is modular, openly available and accessible to the research community. - oai:arXiv.org:2512.24977v1 - q-bio.NC - cs.AI - cs.LG - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Barna Zajzon, Younes Bouhadjar, Maxime Fabre, Felix Schmidt, Noah Ostendorf, Emre Neftci, Abigail Morrison, Renato Duarte - - - Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis - https://arxiv.org/abs/2512.24999 - arXiv:2512.24999v1 Announce Type: cross -Abstract: We introduce \textit{basic inequalities} for first-order iterative optimization algorithms, forming a simple and versatile framework that connects implicit and explicit regularization. While related inequalities appear in the literature, we isolate and highlight a specific form and develop it as a well-rounded tool for statistical analysis. Let $f$ denote the objective function to be optimized. Given a first-order iterative algorithm initialized at $\theta_0$ with current iterate $\theta_T$, the basic inequality upper bounds $f(\theta_T)-f(z)$ for any reference point $z$ in terms of the accumulated step sizes and the distances between $\theta_0$, $\theta_T$, and $z$. The bound translates the number of iterations into an effective regularization coefficient in the loss function. We demonstrate this framework through analyses of training dynamics and prediction risk bounds. In addition to revisiting and refining known results on gradient descent, we provide new results for mirror descent with Bregman divergence projection, for generalized linear models trained by gradient descent and exponentiated gradient descent, and for randomized predictors. We illustrate and supplement these theoretical findings with experiments on generalized linear models. - oai:arXiv.org:2512.24999v1 - math.ST - cs.LG - cs.NA - math.NA - math.OC - stat.ML - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - cross - http://creativecommons.org/licenses/by/4.0/ - Seunghoon Paik, Kangjie Zhou, Matus Telgarsky, Ryan J. Tibshirani - - - Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions - https://arxiv.org/abs/2010.11450 - arXiv:2010.11450v2 Announce Type: replace -Abstract: A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input. Our goal is to identify the optimal approximation-smoothness tradeoffs for different measures of approximation and smoothness. This leads to novel soft-max functions, each of which is optimal for a different application. The most commonly used soft-max function, called exponential mechanism, has optimal tradeoff between approximation measured in terms of expected additive approximation and smoothness measured with respect to R\'enyi Divergence. We introduce a soft-max function, called "piecewise linear soft-max", with optimal tradeoff between approximation, measured in terms of worst-case additive approximation and smoothness, measured with respect to $\ell_q$-norm. The worst-case approximation guarantee of the piecewise linear mechanism enforces sparsity in the output of our soft-max function, a property that is known to be important in Machine Learning applications [Martins et al. '16, Laha et al. '18] and is not satisfied by the exponential mechanism. Moreover, the $\ell_q$-smoothness is suitable for applications in Mechanism Design and Game Theory where the piecewise linear mechanism outperforms the exponential mechanism. Finally, we investigate another soft-max function, called power mechanism, with optimal tradeoff between expected \textit{multiplicative} approximation and smoothness with respect to the R\'enyi Divergence, which provides improved theoretical and practical results in differentially private submodular optimization. - oai:arXiv.org:2010.11450v2 - cs.LG - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Manolis Zampetakis - - - Pointwise Distance Distributions for detecting near-duplicates in large materials databases - https://arxiv.org/abs/2108.04798 - arXiv:2108.04798v4 Announce Type: replace -Abstract: Many real objects are modeled as discrete sets of points, such as corners or other salient features. For our main applications in chemistry, points represent atomic centers in a molecule or a solid material. We study the problem of classifying discrete (finite and periodic) sets of unordered points under isometry, which is any transformation preserving distances in a metric space. - Experimental noise motivates the new practical requirement to make such invariants Lipschitz continuous so that perturbing every point in its epsilon-neighborhood changes the invariant up to a constant multiple of epsilon in a suitable distance satisfying all metric axioms. Since the given points are unordered, the key challenge is to compute all invariants and metrics in a near-linear time of the input size. - We define the Pointwise Distance Distribution (PDD) for any discrete set and prove, in addition to the properties above, the completeness of PDD for all periodic sets in general position. The PDD can compare nearly 2 million crystals from the world's five largest databases within 2 hours on a modest desktop computer. The impact is upholding data integrity in crystallography because the PDD will not allow anyone to claim a `new' material as a noisy disguise of a known crystal. - oai:arXiv.org:2108.04798v4 - cs.CG - math.MG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1137/25M1736657 - Daniel Widdowson, Vitaliy Kurlin - - - To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online - https://arxiv.org/abs/2203.17259 - arXiv:2203.17259v4 Announce Type: replace -Abstract: Double-blind conferences have engaged in debates over whether to allow authors to post their papers online on arXiv or elsewhere during the review process. Independently, some authors of research papers face the dilemma of whether to put their papers on arXiv due to its pros and cons. We conduct a study to substantiate this debate and dilemma via quantitative measurements. Specifically, we conducted surveys of reviewers in two top-tier double-blind computer science conferences -- ICML 2021 (5361 submissions and 4699 reviewers) and EC 2021 (498 submissions and 190 reviewers). Our three main findings are as follows. First, more than a third of the reviewers self-report searching online for a paper they are assigned to review. Second, conference policies restricting authors from publicising their work on social media or posting preprints before the review process may have only limited effectiveness in maintaining anonymity. Third, outside the review process, we find that preprints from better-ranked institutions experience a very small increase in visibility compared to preprints from other institutions. - oai:arXiv.org:2203.17259v4 - cs.DL - stat.AP - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Charvi Rastogi, Ivan Stelmakh, Xinwei Shen, Marina Meila, Federico Echenique, Shuchi Chawla, Nihar B. Shah - - - Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition - https://arxiv.org/abs/2206.13356 - arXiv:2206.13356v3 Announce Type: replace -Abstract: Online exams conducted via video conferencing platforms such as Zoom have become widespread, yet ensuring exam integrity remains challenging due to the difficulty of monitoring multiple video feeds in real time. We present iExam, an online exam proctoring and analysis system that combines lightweight real-time face detection with deep face recognition for postexam analysis. iExam assists invigilators by monitoring student presence during exams and identifies abnormal behaviors, such as face disappearance, face rotation, and identity substitution, from recorded videos. The system addresses three key challenges: (i)efficient real-time video capture and analysis, (ii) automated student identity labeling using enhanced OCR on dynamic Zoom name tags, and (iii) resource-efficient training and inference on standard teacher devices. Extensive experiments show that iExam achieves 90.4% accuracy in real-time face detection and 98.4% accuracy in post-exam recognition with low overhead, demonstrating its practicality and effectiveness for online exam proctoring. - oai:arXiv.org:2206.13356v3 - cs.CV - eess.IV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xu Yang, Juantao Zhong, Daoyuan Wu, Xiao Yi, Jimmy H. M. Lee, Tan Lee, Peng Han - - - A Nonparametric Framework for Online Stochastic Matching with Correlated Arrivals - https://arxiv.org/abs/2208.02229 - arXiv:2208.02229v5 Announce Type: replace -Abstract: The design of online algorithms for matching markets and revenue management settings is usually bound by the assumption that the demand process is formed by a fixed-length sequence of queries with unknown types, each drawn independently. This notion of serial independence implies that the demand of each type, i.e., the number of queries of a given type, has low variance and is approximately Poisson-distributed. - This paper proposes a nonparametric framework for modeling arrival sequences in online stochastic matching that departs from the serial independent assumption. We propose two models, INDEP and CORREL, that capture different forms of serial correlations by combining a nonparametric distribution for the demand with standard assumptions on the arrival patterns -- adversarial or random order. The INDEP model can capture arbitrary serial correlations within each customer type but assumes cross-sectional independence across types, whereas the CORREL model captures common shocks across customer types. We demonstrate that fluid relaxations, which rely solely on demand expectations, have arbitrarily bad performance guarantees. In contrast, we develop new algorithms that achieve optimal (constant-factor) performance guarantees in each model. Our mathematical analysis includes tighter linear programming (LP) relaxations that leverage distribution knowledge, and a new lossless randomized LP rounding scheme for INDEP. We test our new LP relaxations and rounding scheme in simulations on real and synthetic data, and find that they consistently outperform well-established matching algorithms, especially on real data sequences that exhibit greater demand variance. - oai:arXiv.org:2208.02229v5 - cs.DS - cs.DM - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Ali Aouad, Will Ma - - - Active Learning with Neural Networks: Insights from Nonparametric Statistics - https://arxiv.org/abs/2210.08367 - arXiv:2210.08367v2 Announce Type: replace -Abstract: Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, however, rigorous label complexity guarantees of deep active learning have remained elusive. This constitutes a significant gap between theory and practice. This paper tackles this gap by providing the first near-optimal label complexity guarantees for deep active learning. The key insight is to study deep active learning from the nonparametric classification perspective. Under standard low noise conditions, we show that active learning with neural networks can provably achieve the minimax label complexity, up to disagreement coefficient and other logarithmic terms. When equipped with an abstention option, we further develop an efficient deep active learning algorithm that achieves $\mathsf{polylog}(\frac{1}{\epsilon})$ label complexity, without any low noise assumptions. We also provide extensions of our results beyond the commonly studied Sobolev/H\"older spaces and develop label complexity guarantees for learning in Radon $\mathsf{BV}^2$ spaces, which have recently been proposed as natural function spaces associated with neural networks. - oai:arXiv.org:2210.08367v2 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yinglun Zhu, Robert Nowak - - - The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing - https://arxiv.org/abs/2302.01186 - arXiv:2302.01186v4 Announce Type: replace -Abstract: We propose $\textsf{ScaledGD($\lambda$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned. Using overparametrized factor representations, $\textsf{ScaledGD($\lambda$)}$ starts from a small random initialization, and proceeds by gradient descent with a specific form of damped preconditioning to combat bad curvatures induced by overparameterization and ill-conditioning. At the expense of light computational overhead incurred by preconditioners, $\textsf{ScaledGD($\lambda$)}$ is remarkably robust to ill-conditioning compared to vanilla gradient descent ($\textsf{GD}$) even with overprameterization. Specifically, we show that, under the Gaussian design, $\textsf{ScaledGD($\lambda$)}$ converges to the true low-rank matrix at a constant linear rate after a small number of iterations that scales only logarithmically with respect to the condition number and the problem dimension. This significantly improves over the convergence rate of vanilla $\textsf{GD}$ which suffers from a polynomial dependency on the condition number. Our work provides evidence on the power of preconditioning in accelerating the convergence without hurting generalization in overparameterized learning. - oai:arXiv.org:2302.01186v4 - cs.LG - eess.SP - math.OC - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xingyu Xu, Yandi Shen, Yuejie Chi, Cong Ma - - - Maximum Independent Set when excluding an induced minor: $K_1 + tK_2$ and $tC_3 \uplus C_4$ - https://arxiv.org/abs/2302.08182 - arXiv:2302.08182v2 Announce Type: replace -Abstract: Dallard, Milani\v{c}, and \v{S}torgel [arXiv '22] ask if for every class excluding a fixed planar graph $H$ as an induced minor, Maximum Independent Set can be solved in polynomial time, and show that this is indeed the case when $H$ is any planar complete bipartite graph, or the 5-vertex clique minus one edge, or minus two disjoint edges. A positive answer would constitute a far-reaching generalization of the state-of-the-art, when we currently do not know if a polynomial-time algorithm exists when $H$ is the 7-vertex path. Relaxing tractability to the existence of a quasipolynomial-time algorithm, we know substantially more. Indeed, quasipolynomial-time algorithms were recently obtained for the $t$-vertex cycle, $C_t$ [Gartland et al., STOC '21] and the disjoint union of $t$ triangles, $tC_3$ [Bonamy et al., SODA '23]. - We give, for every integer $t$, a polynomial-time algorithm running in $n^{O(t^5)}$ when $H$ is the friendship graph $K_1 + tK_2$ ($t$ disjoint edges plus a vertex fully adjacent to them), and a quasipolynomial-time algorithm running in $n^{O(t^2 \log n)+f(t)}$, with $f$ a single-exponential function, when $H$ is $tC_3 \uplus C_4$ (the disjoint union of $t$ triangles and a 4-vertex cycle). The former extends a classical result on graphs excluding $tK_2$ as an induced subgraph [Alekseev, DAM '07], while the latter extends Bonamy et al.'s result. - oai:arXiv.org:2302.08182v2 - cs.DS - cs.DM - math.CO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1007/s00453-025-01356-2 - \'Edouard Bonnet, Julien Duron, Colin Geniet, St\'ephan Thomass\'e, Alexandra Wesolek - - - SymX: Energy-based Simulation from Symbolic Expressions - https://arxiv.org/abs/2303.02156 - arXiv:2303.02156v2 Announce Type: replace -Abstract: Optimization time integrators are effective at solving complex multi-physics problems including deformable solids with non-linear material models, contact with friction, strain limiting, etc. For challenging problems, Newton-type optimizers are often used, which necessitates first- and second-order derivatives of the global non-linear objective function. Manually differentiating, implementing, testing, optimizing, and maintaining the resulting code is extremely time-consuming, error-prone, and precludes quick changes to the model, even when using tools that assist with parts of such pipeline. - We present SymX, an open source framework that computes the required derivatives of the different energy contributions by symbolic differentiation, generates optimized code, compiles it on-the-fly, and performs the global assembly. The user only has to provide the symbolic expression of each energy for a single representative element in its corresponding discretization and our system will determine the assembled derivatives for the whole simulation. We demonstrate the versatility of SymX in complex simulations featuring different non-linear materials, high-order finite elements, rigid body systems, adaptive discretizations, frictional contact, and coupling of multiple interacting physical systems. - SymX's derivatives offer performance on par with SymPy, an established off-the-shelf symbolic engine, and produces simulations at least one order of magnitude faster than TinyAD, an alternative state-of-the-art integral solution. - oai:arXiv.org:2303.02156v2 - cs.CE - cs.GR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.1145/3764928 - ACM Trans. Graph., Vol. 45, No. 1, Article 5. Pages 1 - 19. Publication date: October 2025 - Jos\'e Antonio Fern\'andez-Fern\'andez, Fabian L\"oschner, Lukas Westhofen, Andreas Longva, Jan Bender - - - CascadeNS: Confidence-Cascaded Neurosymbolic Model for Sarcasm Detection - https://arxiv.org/abs/2304.01424 - arXiv:2304.01424v2 Announce Type: replace -Abstract: Sarcasm detection in product reviews requires balancing domain-specific symbolic pattern recognition with deep semantic understanding. Symbolic representations capture explicit linguistic phenomena that are often decisive for sarcasm detection. Existing work either favors interpretable symbolic representation or semantic neural modeling, but rarely achieves both effectively. Prior hybrid methods typically combine these paradigms through feature fusion or ensembling, which can degrade performance. We propose CascadeNS, a confidence-calibrated neurosymbolic architecture that integrates symbolic and neural reasoning through selective activation rather than fusion. A symbolic semigraph handles pattern-rich instances with high confidence, while semantically ambiguous cases are delegated to a neural module based on pre-trained LLM embeddings. At the core of CascadeNS is a calibrated confidence measure derived from polarity-weighted semigraph scores. This measure reliably determines when symbolic reasoning is sufficient and when neural analysis is needed. Experiments on product reviews show that CascadeNS outperforms the strong baselines by 7.44%. - oai:arXiv.org:2304.01424v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Swapnil Mane, Vaibhav Khatavkar - - - HiGen: Hierarchical Graph Generative Networks - https://arxiv.org/abs/2305.19337 - arXiv:2305.19337v3 Announce Type: replace -Abstract: Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. To address this limitation, we propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. At each level of hierarchy, this model generates communities in parallel, followed by the prediction of cross-edges between communities using separate neural networks. This modular approach enables scalable graph generation for large and complex graphs. Moreover, we model the output distribution of edges in the hierarchical graph with a multinomial distribution and derive a recursive factorization for this distribution. This enables us to generate community graphs with integer-valued edge weights in an autoregressive manner. Empirical studies demonstrate the effectiveness and scalability of our proposed generative model, achieving state-ofthe-art performance in terms of graph quality across various benchmark datasets. The code is available at https://github.com/Karami-m/HiGen_main. - oai:arXiv.org:2305.19337v3 - cs.LG - cs.SI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mahdi Karami - - - HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising - https://arxiv.org/abs/2306.17797 - arXiv:2306.17797v2 Announce Type: replace -Abstract: Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only restore one clean HSI from the given noisy HSI with a deterministic mapping, thus ignoring the ill-posed issue and always resulting in an over-smoothing problem. Additionally, these DL-based methods often neglect that noise is part of the high-frequency component and their network architectures fail to decouple the learning of low-frequency and high-frequency. To alleviate these issues, this paper proposes a flow-based HSI denoising network (HIDFlowNet) to directly learn the conditional distribution of the clean HSI given the noisy HSI and thus diverse clean HSIs can be sampled from the conditional distribution. Overall, our HIDFlowNet is induced from the generative flow model and is comprised of an invertible decoder and a conditional encoder, which can explicitly decouple the learning of low-frequency and high-frequency information of HSI. Specifically, the invertible decoder is built by staking a succession of invertible conditional blocks (ICBs) to capture the local high-frequency details. The conditional encoder utilizes down-sampling operations to obtain low-resolution images and uses transformers to capture correlations over a long distance so that global low-frequency information can be effectively extracted. Extensive experiments on simulated and real HSI datasets verify that our proposed HIDFlowNet can obtain better or comparable results compared with other state-of-the-art methods. - oai:arXiv.org:2306.17797v2 - cs.CV - eess.IV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qizhou Wang, Li Pang, Xiangyong Cao, Zhiqiang Tian, Deyu Meng - - - An analysis on stochastic Lanczos quadrature with asymmetric quadrature nodes - https://arxiv.org/abs/2307.00847 - arXiv:2307.00847v3 Announce Type: replace -Abstract: The stochastic Lanczos quadrature method has garnered significant attention recently. Upon examination of the error analyses given by Ubaru, Chen and Saad and Cortinovis and Kressner, certain notable inconsistencies arise. It turns out that the former's results are valid for cases with symmetric quadrature nodes and may not be adequate for many practical cases such as estimating log determinant of matrices. This paper analyzes probabilistic error bound of the stochastic Lanczos quadrature method for cases with asymmetric quadrature nodes. Besides, an optimized error allocation technique is employed to minimize the overall number of matrix vector multiplications required by the stochastic Lanczos quadrature method. - oai:arXiv.org:2307.00847v3 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wenhao Li, Zongyuan Han, Yixuan Huang, Shengxin Zhu - - - Almost perfect nonlinear power functions with exponents expressed as fractions - https://arxiv.org/abs/2307.15657 - arXiv:2307.15657v2 Announce Type: replace -Abstract: Let $F$ be a finite field, let $f$ be a function from $F$ to $F$, and let $a$ be a nonzero element of $F$. The discrete derivative of $f$ in direction $a$ is $\Delta_a f \colon F \to F$ with $(\Delta_a f)(x)=f(x+a)-f(x)$. The differential spectrum of $f$ is the multiset of cardinalities of all the fibers of all the derivatives $\Delta_a f$ as $a$ runs through $F^*$. An almost perfect nonlinear (APN) function is one for which the largest cardinality in its differential spectrum is $2$. Almost perfect nonlinear functions are of interest as cryptographic primitives. If $d$ is a positive integer, then the power function over $F$ with exponent $d$ is the function $f \colon F \to F$ with $f(x)=x^d$ for every $x \in F$. There is a small number of known infinite families of APN power functions. In this paper, we re-express the exponents for one such family in a more convenient form. This enables us not only to obtain the differential spectrum of each power function $f$ with an exponent in our family, but also to determine the elements that lie in an arbitrary fiber of the discrete derivative of $f$. This differential analysis, which is far more detailed than previous results, is achieved by composing the discrete derivative of $f$ with some permutations and a double covering of its domain to obtain a function whose fibers can more readily be analyzed. - oai:arXiv.org:2307.15657v2 - cs.IT - cs.CR - cs.DM - math.CO - math.IT - math.NT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Daniel J. Katz, Kathleen R. O'Connor, Kyle Pacheco, Yakov Sapozhnikov - - - Content-based Recommendation Engine for Video Streaming Platform - https://arxiv.org/abs/2308.08406 - arXiv:2308.08406v2 Announce Type: replace -Abstract: Recommendation engines suggest content, products, or services to the user by using machine learning algorithms. This paper proposes a content-based recommendation engine that provides personalized video suggestions based on users' previous interactions and preferences. The engine uses TF-IDF (Term Frequency-Inverse Document Frequency) text vectorization technique to evaluate the relevance of words in video descriptions, followed by the computation of cosine similarity between content items to determine their degree of similarity. The system's performance is evaluated using precision, recall, and F1-score metrics. Experimental results demonstrate the effectiveness of content-based filtering in delivering relevant and personalized video recommendations to users. This approach can enhance user engagement on video streaming platforms and reduce search time, providing a more intuitive, preference-based viewing experience. - oai:arXiv.org:2308.08406v2 - cs.IR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Puskal Khadka, Prabhav Lamichhane - - - Enumeration and updates for conjunctive linear algebra queries through expressibility - https://arxiv.org/abs/2310.04118 - arXiv:2310.04118v5 Announce Type: replace -Abstract: Due to the importance of linear algebra and matrix operations in data analytics, there is significant interest in using relational query optimization and processing techniques for evaluating (sparse) linear algebra programs. In particular, in recent years close connections have been established between linear algebra programs and relational algebra that allow transferring optimization techniques of the latter to the former. In this paper, we ask ourselves which linear algebra programs in MATLANG correspond to the free-connex and q-hierarchical fragments of conjunctive first-order logic. Both fragments have desirable query processing properties: free-connex conjunctive queries support constant-delay enumeration after a linear-time preprocessing phase, and q-hierarchical conjunctive queries further allow constant-time updates. By characterizing the corresponding fragments of MATLANG, we hence identify the fragments of linear algebra programs that one can evaluate with constant-delay enumeration after linear-time preprocessing and with constant-time updates. To derive our results, we improve and generalize previous correspondences between MATLANG and relational algebra evaluated over semiring-annotated relations. In addition, we identify properties on semirings that allow to generalize the complexity bounds for free-connex and q-hierarchical conjunctive queries from Boolean annotations to general semirings. - oai:arXiv.org:2310.04118v5 - cs.CC - cs.DB - cs.DS - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Thomas Mu\~noz, Cristian Riveros, Stijn Vansummeren - - - Multi-fidelity Bayesian Optimization: A Review - https://arxiv.org/abs/2311.13050 - arXiv:2311.13050v3 Announce Type: replace -Abstract: Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive review of this advanced optimization technique. In this paper, we survey recent developments of two essential ingredients of MF BO: Gaussian process (GP) based MF surrogates and acquisition functions. We first categorize the existing MF modeling methods and MFO strategies to locate MF BO in a large family of surrogate-based optimization and MFO algorithms. We then exploit the common properties shared between the methods from each ingredient of MF BO to describe important GP-based MF surrogate models and review various acquisition functions. By doing so, we expect to provide a structured understanding of MF BO. Finally, we attempt to reveal important aspects that require further research for applications of MF BO in solving intricate yet important design optimization problems, including constrained optimization, high-dimensional optimization, optimization under uncertainty, and multi-objective optimization. - oai:arXiv.org:2311.13050v3 - cs.CE - cs.LG - math.OC - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.2514/1.J063812 - AIAA Journal 63:6 (2025) 2286-2322 - Bach Do, Ruda Zhang - - - Ricci-Notation Tensor Framework for Model-based Approaches to Imaging - https://arxiv.org/abs/2312.04018 - arXiv:2312.04018v4 Announce Type: replace -Abstract: Model-based approaches to imaging, like specialized image enhancements in astronomy, facilitate explanations of relationships between observed inputs and computed outputs. These models may be expressed with extended matrix-vector (EMV) algebra, especially when they involve only scalars, vectors, and matrices, and with n-mode or index notations, when they involve multidimensional arrays, also called numeric tensors or, simply, tensors. While this paper features an example, inspired by exoplanet imaging, that employs tensors to reveal (inverse) 2D fast Fourier transforms in an image enhancement model, the work is actually about the tensor algebra and software, or tensor frameworks, available for model-based imaging. The paper proposes a Ricci-notation tensor (RT) framework, comprising a dual-variant index notation, with Einstein summation convention, and codesigned object-oriented software, called the RTToolbox for MATLAB. Extensions to Ricci notation offer novel representations for entrywise, pagewise, and broadcasting operations popular in EMV frameworks for imaging. Complementing the EMV algebra computable with MATLAB, the RTToolbox demonstrates programmatic and computational efficiency via careful design of numeric tensor and dual-variant index classes. Compared to its closest competitor, also a numeric tensor framework that uses index notation, the RT framework enables superior ways to model imaging problems and, thereby, to develop solutions. - oai:arXiv.org:2312.04018v4 - cs.MS - astro-ph.IM - eess.IV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.2352/J.ImagingSci.Technol.2024.68.4.040504 - Journal of Imaging Science and Technology, 68(4), 2024 - Dileepan Joseph (Electrical,Computer Engineering, University of Alberta) - - - ODIN: Object Density Aware Index for CkNN Queries over Moving Objects on Road Networks - https://arxiv.org/abs/2312.12688 - arXiv:2312.12688v2 Announce Type: replace -Abstract: We study the problem of processing continuous k nearest neighbor (CkNN) queries over moving objects on road networks, which is an essential operation in a variety of applications. We are particularly concerned with scenarios where the object densities in different parts of the road network evolve over time as the objects move. Existing methods on CkNN query processing are ill-suited for such scenarios as they utilize index structures with fixed granularities and are thus unable to keep up with the evolving object densities. In this paper, we directly address this problem and propose an object density aware index structure called ODIN that is an elastic tree built on a hierarchical partitioning of the road network. It is equipped with the unique capability of dynamically folding/unfolding its nodes, thereby adapting to varying object densities. We further present the ODIN-KNN-Init and ODIN-KNN-Inc algorithms for the initial identification of the kNNs and the incremental update of query result as objects move. Thorough experiments on both real and synthetic datasets confirm the superiority of our proposal over several baseline methods. - oai:arXiv.org:2312.12688v2 - cs.DB - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1109/TKDE.2023.3344662 - Ziqiang Yu, Xiaohui Yu, Tao Zhou, Yueting Chen, Yang Liu, Bohan Li - - - Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling - https://arxiv.org/abs/2402.18508 - arXiv:2402.18508v3 Announce Type: replace -Abstract: In the rapidly evolving field of deep learning, the demand for models that are both expressive and computationally efficient has never been more critical. This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional attention mechanisms without compromising the ability to capture long-range dependencies and in-context learning. At the core of this architecture lies a new data-dependent global convolution layer, which contextually adapts its kernel conditioned on input sequence using a dedicated conditioning neural network. We design two simple conditioning networks that maintain shift equivariance in our data-dependent convolution operation. The dynamic nature of the proposed convolution kernel grants Orchid high expressivity while maintaining quasilinear scalability for long sequences. We evaluate the proposed model across multiple domains, including language modeling and image classification, to highlight its performance and generality. Our experiments demonstrate that this architecture not only outperforms traditional attention-based architectures such as BERT and Vision Transformers with smaller model sizes, but also extends the feasible sequence length beyond the limitations of the dense attention layers. This achievement represents a significant step towards more efficient and scalable deep learning models for sequence modeling. The code is available at https://github.com/Karami-m/orchid. - oai:arXiv.org:2402.18508v3 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Advances in Neural Information Processing Systems 37 (2024): 76991-77022 - Mahdi Karami, Ali Ghodsi - - - Subsequence Matching and LCS under Cartesian-Tree Equivalence - https://arxiv.org/abs/2402.19146 - arXiv:2402.19146v4 Announce Type: replace -Abstract: Two strings of the same length are said to Cartesian-tree match (CT-match) if their Cartesian-trees are isomorphic [Park et al., TCS 2020]. Cartesian-tree matching is a natural model that allows for capturing similarities of numerical sequences. Oizumi et al. [CPM 2022] showed that subsequence pattern matching under CT-matching model (CT-MSeq) can be solved in $O(nm \log \log n)$ time, where $n$ and $m$ are text and pattern lengths, respectively. This current article follows this line of research, and gives the following new results: (1) An $O(nm)$-time CT-MSeq algorithm for binary alphabets; (2) An $O((nm)^{1-\epsilon})$-time conditional lower bound for the CT-MSeq problem on alphabets of size 4, for any constant $\epsilon > 0$, under the Orthogonal Vector Hypothesis (OVH). Further, we introduce the new problem of longest common subsequence under CT-matching (CT-LCS) for two given strings $S$ and $T$ of length $n$, and present the following results: (3) An $O(n^6)$-time CT-LCS algorithm for general ordered alphabets; (4) An $O(n^2 / \log n)$-time CT-LCS algorithm for binary alphabets; (5) An $O(n^{2-\epsilon})$-time conditional lower bound for the CT-LCS problem on alphabets of size 5, for any constant $\epsilon > 0$, under OVH. - oai:arXiv.org:2402.19146v4 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Taketo Tsujimoto, Yuki Yonemoto, Hiroki Shibata, Takuya Mieno, Yuto Nakashima, Shunsuke Inenaga - - - Structuring Concept Space with the Musical Circle of Fifths by Utilizing Music Grammar Based Activations - https://arxiv.org/abs/2403.00790 - arXiv:2403.00790v4 Announce Type: replace -Abstract: We propose a neural coding framework harmonic toroidal codes in which abstract cognitive operations are implemented through dynamical activity on manifolds derived from music theoretic structures. - oai:arXiv.org:2403.00790v4 - cs.SD - cs.AI - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Tofara Moyo, Panashe Chiurunge - - - Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons - https://arxiv.org/abs/2403.07688 - arXiv:2403.07688v2 Announce Type: replace -Abstract: When training neural networks, dying neurons -- units becoming inactive or saturated -- are traditionally seen as harmful. This paper sheds new light on this phenomenon. By exploring the impact of various hyperparameter configurations on dying neurons during training, we gather insights on how to improve upon sparse training approaches to pruning. We introduce Demon Pruning (DemP), a method that controls the proliferation of dead neurons through a combination of noise injection on active units and a one-cycle schedule regularization strategy, dynamically leading to network sparsity. Experiments on CIFAR-10 and ImageNet datasets demonstrate that DemP outperforms existing dense-to-sparse structured pruning methods, achieving better accuracy-sparsity tradeoffs and accelerating training by up to 3.56$\times$. These findings provide a novel perspective on dying neurons as a resource for efficient model compression and optimization. - oai:arXiv.org:2403.07688v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Transactions on Machine Learning Research 2835-8856 (2025) - Simon Dufort-Labb\'e, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin - - - Matching Semantically Similar Non-Identical Objects - https://arxiv.org/abs/2403.08227 - arXiv:2403.08227v4 Announce Type: replace -Abstract: Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. This study addresses a novel task of matching such non-identical objects at the pixel level. We propose a weighting scheme of descriptors, Semantic Enhancement Weighting (SEW), that incorporates semantic information from object detectors into existing sparse feature matching methods, extending their targets from identical objects captured from different perspectives to semantically similar objects. The experiments show successful matching between non-identical objects in various cases, including in-class design variations, class discrepancy, and domain shifts (e.g., photo vs. drawing and image corruptions). The code is available at https://github.com/Circ-Leaf/NIOM . - oai:arXiv.org:2403.08227v4 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yusuke Marumo, Kazuhiko Kawamoto, Satomi Tanaka, Shigenobu Hirano, Hiroshi Kera - - - Reconstructing Hand-Held Objects in 3D from Images and Videos - https://arxiv.org/abs/2404.06507 - arXiv:2404.06507v4 Announce Type: replace -Abstract: Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from Internet videos. Not only does the hand occlude much of the object, but also the object is often only visible in a small number of image pixels. At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is small relative to all possible objects. With these insights in mind, we present a scalable paradigm for hand-held object reconstruction that builds on recent breakthroughs in large language/vision models and 3D object datasets. Given a monocular RGB video, we aim to reconstruct hand-held object geometry in 3D, over time. In order to obtain the best performing single frame model, we first present MCC-Hand-Object (MCC-HO), which jointly reconstructs hand and object geometry given a single RGB image and inferred 3D hand as inputs. Subsequently, we prompt a text-to-3D generative model using GPT-4(V) to retrieve a 3D object model that matches the object in the image(s); we call this alignment Retrieval-Augmented Reconstruction (RAR). RAR provides unified object geometry across all frames, and the result is rigidly aligned with both the input images and 3D MCC-HO observations in a temporally consistent manner. Experiments demonstrate that our approach achieves state-of-the-art performance on lab and Internet image/video datasets. We make our code and models available on the project website: https://janehwu.github.io/mcc-ho - oai:arXiv.org:2404.06507v4 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jane Wu, Georgios Pavlakos, Georgia Gkioxari, Jitendra Malik - - - Spectral Convolutional Conditional Neural Processes - https://arxiv.org/abs/2404.13182 - arXiv:2404.13182v5 Announce Type: replace -Abstract: Neural Processes (NPs) are meta-learning models that learn to map sets of observations to approximations of the corresponding posterior predictive distributions. By accommodating variable-sized, unstructured collections of observations and enabling probabilistic predictions at arbitrary query points, NPs provide a flexible framework for modeling functions over continuous domains. Since their introduction, numerous variants have emerged; however, early formulations shared a fundamental limitation: they compressed the observed data into finite-dimensional global representations via aggregation operations such as mean pooling. This strategy induces an intrinsic mismatch with the infinite-dimensional nature of the stochastic processes that NPs intend to model. Convolutional conditional neural processes (ConvCNPs) address this limitation by constructing infinite-dimensional functional embeddings processed through convolutional neural networks (CNNs) to enforce translation equivariance. Yet CNNs with local spatial kernels struggle to capture long-range dependencies without resorting to large kernels, which impose significant computational costs. To overcome this limitation, we propose spectral ConvCNPs (SConvCNPs), which perform global convolution in the frequency domain. Inspired by Fourier neural operators (FNOs) for learning solution operators of partial differential equations (PDEs), our approach directly parameterizes convolution kernels in the frequency domain, leveraging the relatively compact yet global Fourier representation of many natural signals. We validate the effectiveness of SConvCNPs on both synthetic and real-world datasets, demonstrating how ideas from operator learning can advance the capabilities of NPs. - oai:arXiv.org:2404.13182v5 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Peiman Mohseni, Nick Duffield - - - FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol - https://arxiv.org/abs/2404.15834 - arXiv:2404.15834v2 Announce Type: replace -Abstract: The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, with thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR applications include but are not limited to direct messaging, file sharing, audio/video streaming, collaborative writing, blogging and data processing through distributed AI directories. In this work, we propose an approach that builds upon the existing protocol structure with end goal a decentralized marketplace for federated learning and LLM training. In this proposed design there are two parties: on one side there are customers who provide a dataset that they want to use for training an AI model. On the other side, there are service providers, who receive (parts of) the dataset, train the AI model, and for a payment as an exchange, they return the optimized AI model. To demonstrate viability, we present a proof-of-concept implementation over public NOSTR relays. The decentralized and censorship resistant features of the NOSTR enable the possibility of designing a fair and open marketplace for training AI models and LLMs. - oai:arXiv.org:2404.15834v2 - cs.DC - cs.AI - cs.CR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Konstantinos E. Nikolakakis, George Chantzialexiou, Dionysis Kalogerias - - - Myopically Verifiable Probabilistic Certificates for Safe Control and Learning - https://arxiv.org/abs/2404.16883 - arXiv:2404.16883v2 Announce Type: replace -Abstract: This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit significant long-term risks due to cumulative uncertainties/risks. On the other hand, reachability-based approaches that account for the long-term future may require prohibitive computation in real-time decision making. To overcome this challenge involving stringent long-term safety vs. computation tradeoffs, we first introduce a novel technique termed `probabilistic invariance'. This technique characterizes the invariance conditions of the probability of interest. When the target probability is defined using long-term trajectories, this technique can be used to design myopic conditions/controllers with assured long-term safe probability. Then, we integrate this technique into safe control and learning. The proposed control methods efficiently assure long-term safety using neural networks or model predictive controllers with short outlook horizons. The proposed learning methods can be used to guarantee long-term safety during and after training. Finally, we demonstrate the performance of the proposed techniques in numerical simulations. - oai:arXiv.org:2404.16883v2 - eess.SY - cs.LG - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhuoyuan Wang, Haoming Jing, Christian Kurniawan, Albert Chern, Yorie Nakahira - - - Are Biological Systems More Intelligent Than Artificial Intelligence? - https://arxiv.org/abs/2405.02325 - arXiv:2405.02325v5 Announce Type: replace -Abstract: Are biological self-organising systems more `intelligent' than artificial intelligence (AI)? If so, why? I explore this through a mathematical lens which frames intelligence in terms of adaptability. I model systems as stacks of abstraction layers (\emph{Stack Theory}) and compare them by how they delegate agentic control down their stacks, illustrating with examples of computational, biological, human military, governmental and economic systems. Contemporary AI rests on a static, human-engineered stack in which lower layers are static during deployment. Put provocatively, static stacks resemble inflexible bureaucracies, adapting only top-down. Biological stacks are more `intelligent' because they delegate adaptation. Formally, I prove a theorem (\emph{The Law of the Stack}) showing adaptability in higher layers requires sufficient adaptability in lower layers. Generalising bio-electric explanations of cancer as isolation from collective informational structures, I explore how cancer-like failures occur in non-biological systems when delegation is inadequate. This helps explain how to build more robust systems, by delegating control like the military doctrine of mission command. It also provides a design perspective on hybrid agents (e.g. organoids, systems involving both humans and AI): hybrid creation is a boundary-condition design problem in which human-imposed constraints prune low-level policy spaces to yield desired collective behaviour while preserving collective identity. - oai:arXiv.org:2405.02325v5 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Michael Timothy Bennett - - - Finding Diverse Solutions Parameterized by Cliquewidth - https://arxiv.org/abs/2405.20931 - arXiv:2405.20931v2 Announce Type: replace -Abstract: Finding a few solutions for a given problem that are diverse, as opposed to finding a single best solution to solve the problem, has recently become a notable topic in theoretical computer science. Recently, Baste, Fellows, Jaffke, Masa\v{r}\'ik, Oliveira, Philip, and Rosamond showed that under a standard structural parameterization by treewidth, one can find a set of diverse solutions for many problems with only a very small additional cost [Artificial Intelligence 2022]. In this paper, we investigate a much stronger graph parameter, the cliquewidth, which can additionally describe some dense graph classes. Broadly speaking, it describes graphs that can be recursively constructed by a few operations defined on graphs whose vertices are divided into a bounded number of groups while each such group behaves uniformly with respect to any operation. - We show that for any vertex problem, if we are given a dynamic program solving that problem on cliquewidth decomposition, we can modify it to produce a few solutions that are as diverse as possible with as little overhead as in the above-mentioned treewidth paper. As a consequence, we prove that a diverse version of any MSO$_1$ expressible problem can be solved in linear FPT time parameterized by the cliquewidth, the number of sought solutions, and the number of quantifiers in the formula, which was a natural missing piece in the complexity landscape of structural graph parameters and logic for the diverse problems. We prove our results allowing for a more general natural collection of diversity functions compared to only two mostly studied diversity functions previously. That might be of independent interest as a larger pool of different diversity functions can highlight various aspects of different solutions to a problem. - oai:arXiv.org:2405.20931v2 - cs.DS - cs.DM - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Karolina Drabik, Tom\'a\v{s} Masa\v{r}\'ik - - - Jacobian-Enhanced Neural Networks - https://arxiv.org/abs/2406.09132 - arXiv:2406.09132v3 Announce Type: replace -Abstract: Jacobian-Enhanced Neural Networks (JENN) are densely connected multi-layer perceptrons, whose training process is modified to predict partial derivatives accurately. Their main benefit is better accuracy with fewer training points compared to standard neural networks. These attributes are particularly desirable in the field of computer-aided design, where there is often the need to replace computationally expensive, physics-based models with fast running approximations, known as surrogate models or meta-models. Since a surrogate emulates the original model accurately in near-real time, it yields a speed benefit that can be used to carry out orders of magnitude more function calls quickly. However, in the special case of gradient-enhanced methods, there is the additional value proposition that partial derivatives are accurate, which is a critical property for one important use-case: surrogate-based optimization. This work derives the complete theory and exemplifies its superiority over standard neural nets for surrogate-based optimization. - oai:arXiv.org:2406.09132v3 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Steven H. Berguin - - - ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts - https://arxiv.org/abs/2406.10973 - arXiv:2406.10973v5 Announce Type: replace -Abstract: Parameter-efficient fine-tuning (PEFT) techniques such as low-rank adaptation (LoRA) can effectively adapt large pre-trained foundation models to downstream tasks using only a small fraction (0.1%-10%) of the original trainable weights. An under-explored question of PEFT is in extending the pre-training phase without supervised labels; that is, can we adapt a pre-trained foundation model to a new domain via efficient self-supervised pre-training on this domain? In this work, we introduce ExPLoRA, a highly effective technique to improve transfer learning of pre-trained vision transformers (ViTs) under domain shifts. Initializing a ViT with pre-trained weights on large, natural-image datasets such as from DinoV2 or MAE, ExPLoRA continues the unsupervised pre-training objective on a new domain, unfreezing 1-2 pre-trained ViT blocks and tuning all other layers with LoRA. We then fine-tune the resulting model only with LoRA on this new domain for supervised learning. Our experiments demonstrate state-of-the-art results on satellite imagery, even outperforming fully pre-training and fine-tuning ViTs. Using the DinoV2 training objective, we demonstrate up to 8% improvement in linear probing top-1 accuracy on downstream tasks while using <10% of the number of parameters that are used in prior fully-tuned state-of-the-art approaches. Our ablation studies confirm the efficacy of our approach over other baselines such as PEFT. Code is available on the project website: https://samar-khanna.github.io/ExPLoRA/ - oai:arXiv.org:2406.10973v5 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon - - - MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs - https://arxiv.org/abs/2406.17126 - arXiv:2406.17126v2 Announce Type: replace -Abstract: Spurious bias, a tendency to exploit spurious correlations between superficial input attributes and prediction targets, has revealed a severe robustness pitfall in classical machine learning problems. Multimodal Large Language Models (MLLMs), which leverage pretrained vision and language models, have recently demonstrated strong capability in joint vision-language understanding. However, both the presence and severity of spurious biases in MLLMs remain poorly understood. In this work, we address this gap by analyzing the spurious biases in the multimodal setting and uncovering the specific inference-time data patterns that can manifest this problem. To support this analysis, we introduce MM-SpuBench, a comprehensive, human-verified benchmark dataset consisting of image-class pairs annotated with core and spurious attributes, grounded in our taxonomy of nine distinct types of spurious correlations. The benchmark is constructed using human-interpretable attribute information to capture a wide range of spurious patterns reflective of real-world knowledge. Leveraging this benchmark, we conduct a comprehensive evaluation of the state-of-the-art open-source and proprietary MLLMs with both standard accuracy and the proposed Conditional Generation Likelihood Advantage (CGLA). Our findings highlight the persistence of reliance on spurious correlations and the difficulty of mitigation on our benchmark. We hope this work can inspire new technical strides to mitigate these biases. Our benchmark is publicly available at https://huggingface.co/datasets/mmbench/MM-SpuBench. - oai:arXiv.org:2406.17126v2 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.1145/3770854.3785678 - Wenqian Ye, Bohan Liu, Guangtao Zheng, Di Wang, Yunsheng Ma, Xu Cao, Bolin Lai, James M. Rehg, Aidong Zhang - - - DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models - https://arxiv.org/abs/2407.01519 - arXiv:2407.01519v5 Announce Type: replace -Abstract: We present DiffIR2VR-Zero, a zero-shot framework that enables any pre-trained image restoration diffusion model to perform high-quality video restoration without additional training. While image diffusion models have shown remarkable restoration capabilities, their direct application to video leads to temporal inconsistencies, and existing video restoration methods require extensive retraining for different degradation types. Our approach addresses these challenges through two key innovations: a hierarchical latent warping strategy that maintains consistency across both keyframes and local frames, and a hybrid token merging mechanism that adaptively combines optical flow and feature matching. Through extensive experiments, we demonstrate that our method not only maintains the high-quality restoration of base diffusion models but also achieves superior temporal consistency across diverse datasets and degradation conditions, including challenging scenarios like 8$\times$ super-resolution and severe noise. Importantly, our framework works with any image restoration diffusion model, providing a versatile solution for video enhancement without task-specific training or modifications. Project page: https://jimmycv07.github.io/DiffIR2VR_web/ - oai:arXiv.org:2407.01519v5 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Chang-Han Yeh, Hau-Shiang Shiu, Chin-Yang Lin, Zhixiang Wang, Chi-Wei Hsiao, Ting-Hsuan Chen, Yu-Lun Liu - - - The Fr\'echet Distance Unleashed: Approximating a Dog with a Frog - https://arxiv.org/abs/2407.03101 - arXiv:2407.03101v4 Announce Type: replace -Abstract: We show that a variant of the continuous Frechet distance between polygonal curves can be computed using essentially the same algorithm used to solve the discrete version. The new variant is not necessarily monotone, but this shortcoming can be easily handled via refinement. - Combined with a Dijkstra/Prim type algorithm, this leads to a realization of the Frechet distance (i.e., a morphing) that is locally optimal (aka locally correct), that is both easy to compute, and in practice, takes near linear time on many inputs. The new morphing has the property that the leash is always as short as possible. These matchings/morphings are more natural and are better than the ones computed by standard algorithms -- in particular, they handle noise more graciously. This approach should make the Frechet distance more useful for real-world applications. - We implemented the new algorithm and various strategies to obtain reasonably fast practical performance. We performed extensive experiments on our new algorithm, and released publicly available (and easily installable and usable) Julia and Python packages. Our algorithms can be used to compute the almost-exact Frechet distance between polygonal curves. - Implementations and numerous examples are available here: https://frechet.xyz. - We emphasize, however, that the existing state-of-the-art algorithm/implementation in C++ is faster, by several orders of magnitude, than our current algorithm/implementation. - oai:arXiv.org:2407.03101v4 - cs.CG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Sariel Har-Peled, Benjamin Raichel, Eliot W. Robson - - - LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language Models - https://arxiv.org/abs/2407.05434 - arXiv:2407.05434v2 Announce Type: replace -Abstract: Temporal Reasoning (TR) is a critical ability for LLMs to understand and reason over temporal information and relationships between events. To study the TR ability in LLMs, prior works provide different ways for evaluating various aspects of TR ability. In this work, we propose an alternative perspective for evaluating TR ability by leveraging Linear Temporal Logic (LTL), and develop a pipeline to automatically synthesize challenges for assessing the TR ability of LLMs. Based on this pipeline, we construct a dataset, namely \LTL, consisting of $2000$ TR challenges, and benchmark 12 LLMs across 5 different methods. Furthermore, we conduct additional experiments to investigate the impact of increasing the number of formula operators and events on both LLM performance and the complexity of TR problems. We also perform qualitative analyses of their reasoning processes and the effects of varying the number of events and formula operators, which reveal 3 main issues in their temporal reasoning processes and the unexpected performance changes observed as problem complexity increases. We expect this work to provide valuable insights into the TR ability of LLMs. - oai:arXiv.org:2407.05434v2 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Weizhi Tang, Kwabena Nuamah, Vaishak Belle - - - UnPaSt: unsupervised patient stratification by biclustering of omics data - https://arxiv.org/abs/2408.00200 - arXiv:2408.00200v2 Announce Type: replace -Abstract: Unsupervised patient stratification is essential for disease subtype discovery, yet, despite growing evidence of molecular heterogeneity of non-oncological diseases, popular methods are benchmarked primarily using cancers with mutually exclusive molecular subtypes well-differentiated by numerous biomarkers. Evaluating 22 unsupervised methods, including clustering and biclustering, using simulated and real transcriptomics data revealed their inefficiency in scenarios with non-mutually exclusive subtypes or subtypes discriminated only by few biomarkers. To address these limitations and advance precision medicine, we developed UnPaSt, a novel biclustering algorithm for unsupervised patient stratification based on differentially expressed biclusters. UnPaSt outperformed widely used patient stratification approaches in the de novo identification of known subtypes of breast cancer and asthma. In addition, it detected many biologically insightful patterns across bulk transcriptomics, proteomics, single-cell, spatial transcriptomics, and multi-omics datasets, enabling a more nuanced and interpretable view of high-throughput data heterogeneity than traditionally used methods. - oai:arXiv.org:2408.00200v2 - cs.LG - q-bio.GN - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Michael Hartung, Andreas Maier, Yuliya Burankova, Fernando Delgado-Chaves, Olga I. Isaeva, Alexey Savchik, F\'abio Malta de S\'a Patroni, Jens J. G. Lohmann, Daniel He, Casey Shannon, Jan-Ole Schulze, Katharina Kaufmann, Zoe Chervontseva, Farzaneh Firoozbakht, Anne Hartebrodt, Niklas Probul, Olga Tsoy, Alexandra Abisheva, Evgenia Zotova, Kavya Singh, Kristel Van Steen, Malte Kuehl, Victor G. Puelles, David B. Blumenthal, Martin Ester, Tanja Laske, Jan Baumbach, Olga Zolotareva - - - Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems - https://arxiv.org/abs/2408.05992 - arXiv:2408.05992v3 Announce Type: replace -Abstract: This paper presents a novel online transfer learning approach in state-based potential games (TL-SbPGs) for distributed self-optimization in manufacturing systems. The approach targets practical industrial scenarios where knowledge sharing among similar players enhances learning in large-scale and decentralized environments. TL-SbPGs enable players to reuse learned policies from others, which improves learning outcomes and accelerates convergence. To accomplish this goal, we develop transfer learning concepts and similarity criteria for players, which offer two distinct settings: (a) predefined similarities between players and (b) dynamically inferred similarities between players during training. The applicability of the SbPG framework to transfer learning is formally established. Furthermore, we present a method to optimize the timing and weighting of knowledge transfer. Experimental results from a laboratory-scale testbed show that TL-SbPGs improve production efficiency and reduce power consumption compared to vanilla SbPGs. - oai:arXiv.org:2408.05992v3 - cs.LG - cs.AI - cs.GT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1016/j.compind.2025.104376 - Steve Yuwono, Dorothea Schwung, Andreas Schwung - - - [Draft] High-order estimation-based properties and high-order observers for labeled finite-state automata - https://arxiv.org/abs/2408.06141 - arXiv:2408.06141v3 Announce Type: replace -Abstract: In this paper, we consider labeled finite-state automata (LFSAs), extend some state estimation-based properties from a single agent to a finite ordered set of agents. We also extend the notion of observer to \emph{high-order observer} using our \emph{concurrent composition}. As a result, a general framework for characterizing high-order estimation-based properties is built, in which each agent infers its preceding agent's estimation via all agents in front. The high-order observer plays the role of a basic tool to verify such properties. - In more detail, in our general framework, the system's structure is publicly known to all agents $A_1,\dots,A_n$; each agent $A_i$ has its own observable event set $E_i$, and additionally knows all its preceding agents' observable events but can only observe its own observable events. The intuitive meaning of our high-order observer is to characterize what agent $A_n$ knows about what $A_{n-1}$ knows about \dots what $A_2$ knows about $A_1$'s state estimate of the system. This general framework can be regarded as an automata representation of dynamic epistemic logic. Compared with the classical representation of dynamic epistemic logic based on fragments of logic, our representation has advantages in property verification and flexibly changing agents to enforce properties. As case studies, this general framework applies to basic properties such as current-state opacity, strong current-state opacity, regular-language-based opacity, critical observability, high-order opacity, etc. Special cases for which verification can be done more efficiently are also discussed. - oai:arXiv.org:2408.06141v3 - cs.FL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kuize Zhang, Xiaoguang Han, Alessandro Giua, Carla Seatzu - - - Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities - https://arxiv.org/abs/2408.07666 - arXiv:2408.07666v5 Announce Type: replace -Abstract: Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive computation. As model merging becomes increasingly prevalent across various fields, it is crucial to understand the available model merging techniques comprehensively. However, there is a significant gap in the literature regarding a systematic and thorough review of these techniques. This survey provides a comprehensive overview of model merging methods and theories, their applications in various domains and settings, and future research directions. Specifically, we first propose a new taxonomic approach that exhaustively discusses existing model merging methods. Secondly, we discuss the application of model merging techniques in large language models, multimodal large language models, and more than ten machine learning subfields, including continual learning, multi-task learning, few-shot learning, etc. Finally, we highlight the remaining challenges of model merging and discuss future research directions. A comprehensive list of papers about model merging is available at https://github.com/EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications. - oai:arXiv.org:2408.07666v5 - cs.LG - cs.AI - cs.CL - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, Dacheng Tao - - - Stock Price Responses to Firm-Level News in Supply Chain Networks - https://arxiv.org/abs/2409.06255 - arXiv:2409.06255v4 Announce Type: replace -Abstract: This study examines how positive and negative news about firms are associated with stock prices and whether these associations extend to suppliers and clients linked via supply chain relationships, using large samples of publicly listed firms worldwide and in Japan. News sentiment is measured using FinBERT, a natural language processing model fine-tuned for financial text, and supply chain links are identified from financial statements for global firms and from large-scale firm-level surveys for Japanese firms. We find that stock prices exhibit systematic associations with positive and negative news even before public disclosure. These associations are also observed for suppliers and clients before and after disclosure. In general, post-disclosure associations are larger than pre-disclosure associations, with the difference concentrated around the time of public news disclosure relative to the pre-disclosure period. However, for Japanese firms, the post-disclosure associations for suppliers and clients are smaller than the pre-disclosure associations, in contrast to the pattern observed for firms outside Japan. - oai:arXiv.org:2409.06255v4 - cs.SI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Hiroyasu Inoue, Yasuyuki Todo - - - Proactive Recommendation in Social Networks: Steering User Interest with Causal Inference - https://arxiv.org/abs/2409.08934 - arXiv:2409.08934v2 Announce Type: replace -Abstract: Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct steering might not align perfectly with the evolution of users' interests, detrimentally affecting the target users' experience. - To avoid this issue, we propose a new task named Proactive Recommendation in Social Networks (PRSN) that indirectly steers users' interest by utilizing the influence of social neighbors, i.e., indirect steering by adjusting the exposure of a target item to target users' neighbors. The key to PRSN lies in answering an interventional question: what would a target user' s feedback be on a target item if the item is exposed to the user' s different neighbors? To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item' s exposure to the user' s neighbors; and (2) adjusting the exposure of a target item to target users' neighbors to trade off steering performance and the damage to the neighbors' experience. To this end, we propose a Neighbor Interference Recommendation (NIRec) framework with two modules: (1) an interference representation-based estimation module for modeling potential feedback; (2) a post-learning-based optimization module for adjusting a target item' s exposure to trade off steering performance and the neighbors' experience through greedy search. We conduct extensive semi-simulation experiments on real-world datasets, validating the steering effectiveness of NIRec. - oai:arXiv.org:2409.08934v2 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hang Pan, Shuxian Bi, Wenjie Wang, Haoxuan Li, Peng Wu, Fuli Feng - - - Semantic Parsing with Candidate Expressions for Knowledge Base Question Answering - https://arxiv.org/abs/2410.00414 - arXiv:2410.00414v4 Announce Type: replace -Abstract: Semantic parsers convert natural language to logical forms, which can be evaluated on knowledge bases (KBs) to produce denotations. Recent semantic parsers have been developed with sequence-to-sequence (seq2seq) pre-trained language models (PLMs) or large language models, where the models treat logical forms as sequences of tokens. For syntactic and semantic validity, the semantic parsers use grammars that enable constrained decoding. However, the grammars lack the ability to utilize large information of KBs, although logical forms contain representations of KB elements, such as entities or relations. In this work, we propose a grammar augmented with candidate expressions for semantic parsing on a large KB with a seq2seq PLM. The grammar defines actions as production rules, and our semantic parser predicts actions during inference under the constraints by types and candidate expressions. We apply the grammar to knowledge base question answering, where the constraints by candidate expressions assist a semantic parser to generate valid KB elements. We also introduce two special rules, sub-type inference and union types, and a mask caching algorithm. In particular, sub-type inference and the mask caching algorithm greatly increase the decoding speed of our semantic parser. We experimented on two benchmarks, KQA Pro and Overnight, where the constraints by candidate expressions increased the accuracy of our semantic parser, whether it was trained with strong supervision or weak supervision. In addition, our semantic parser had a fast decoding speed in the experiments. Our source code is publicly available at https://github.com/daehwannam/candexpr-sp.git. - oai:arXiv.org:2410.00414v4 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1016/j.eswa.2025.130564 - Expert Syst. Appl. 306 (2026) 130564 - Daehwan Nam, Gary Geunbae Lee - - - Beyond Firms and Industries: Shock Propagation through Establishment- and Product-Level Supply Chains - https://arxiv.org/abs/2410.05595 - arXiv:2410.05595v4 Announce Type: replace -Abstract: This paper investigates how the granularity of supply-chain data affects the propagation of economic shocks through production networks. Using newly constructed establishment-level supply chains with product-level information links for Japan, we simulate disruption dynamics under alternative definitions of network nodes and input classifications. We show that defining inputs at the product level generates substantially larger propagation effects than industry-based classifications, indicating that coarse industry measures overstate input substitutability and underestimate systemic vulnerability. While establishment-level networks generally amplify shock propagation relative to firm-level networks, this effect is quantitatively modest, reflecting opposing forces of increased network complexity and greater substitution possibilities. We further demonstrate that incorporating establishment-level geographic information is critical for assessing region-specific shocks, as firm-level networks tend to overstate the impact of shocks originating in major metropolitan areas. Overall, our results highlight the importance of granular information on products, establishments, and geography for accurately evaluating supply-chain resilience and systemic risk. - oai:arXiv.org:2410.05595v4 - cs.SI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Hiroyasu Inoue, Yasuyuki Todo - - - Faster and Simpler Online Computation of String Net Frequency - https://arxiv.org/abs/2410.06837 - arXiv:2410.06837v3 Announce Type: replace -Abstract: An occurrence of a repeated substring $u$ in a string $S$ is called a net occurrence if extending the occurrence to the left or to the right decreases the number of occurrences to 1. The net frequency (NF) of a repeated substring $u$ in a string $S$ is the number of net occurrences of $u$ in $S$. Very recently, Guo et al. [SPIRE 2024] proposed an online $O(n \log \sigma)$-time algorithm that maintains a data structure of $O(n)$ space which answers Single-NF queries in $O(m\log \sigma + \sigma^2)$ time and reports all answers of the All-NF problem in $O(n\sigma^2)$ time. Here, $n$ is the length of the input string $S$, $m$ is the query pattern length, and $\sigma$ is the alphabet size. The $\sigma^2$ term is a major drawback of their method since computing string net frequencies is originally motivated for Chinese language processing where $\sigma$ can be thousands large. This paper presents an improved online $O(n \log \sigma)$-time algorithm, which answers Single-NF queries in $O(m \log \sigma)$ time and reports all answers to the All-NF problem in output-optimal $O(|\mathsf{NF}^+(S)|)$ time, where $\mathsf{NF}^+(S)$ is the set of substrings of $S$ paired with their positive NF values. We note that $|\mathsf{NF}^+(S)| = O(n)$ always holds. In contract to Guo et al.'s algorithm that is based on Ukkonen's suffix tree construction, our algorithm is based on Weiner's suffix tree construction. - oai:arXiv.org:2410.06837v3 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Shunsuke Inenaga - - - SwitchFS: Asynchronous Metadata Updates for Distributed Filesystems with In-Network Coordination - https://arxiv.org/abs/2410.08618 - arXiv:2410.08618v3 Announce Type: replace -Abstract: Distributed filesystem metadata updates are typically synchronous. This creates inherent challenges for access efficiency, load balancing, and directory contention, especially under dynamic and skewed workloads. This paper argues that synchronous updates are overly conservative. We propose SwitchFS with asynchronous metadata updates that allow operations to return early and defer directory updates until reads, both hiding latency and amortizing overhead. The key challenge lies in efficiently maintaining the synchronous POSIX semantics of metadata updates. To address this, SwitchFS is co-designed with a programmable switch, leveraging the limited on-switch resources to track directory states with negligible overhead. This allows SwitchFS to aggregate and apply delayed updates efficiently, using batching and consolidation before directory reads. Evaluation shows that SwitchFS achieves up to 13.34$\times$ and 3.85$\times$ higher throughput, and 61.6% and 57.3% lower latency than two state-of-the-art distributed filesystems, Emulated-InfiniFS and Emulated-CFS, respectively, under skewed workloads. For real-world workloads, SwitchFS improves end-to-end throughput by 21.1$\times$, 1.1$\times$, and 0.3$\times$ over CephFS, Emulated-InfiniFS, and Emulated-CFS, respectively. - oai:arXiv.org:2410.08618v3 - cs.DC - cs.OS - cs.PF - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jingwei Xu, Mingkai Dong, Qiulin Tian, Ziyi Tian, Tong Xin, Haibo Chen - - - A Systematic Survey on Large Language Models for Algorithm Design - https://arxiv.org/abs/2410.14716 - arXiv:2410.14716v4 Announce Type: replace -Abstract: Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. In just a few years, this integration has yielded remarkable progress in areas ranging from combinatorial optimization to scientific discovery. Despite this rapid expansion, a holistic understanding of the field is hindered by the lack of a systematic review, as existing surveys either remain limited to narrow sub-fields or with different objectives. This paper seeks to provide a systematic review of algorithm design with LLMs. We introduce a taxonomy that categorises the roles of LLMs as optimizers, predictors, extractors and designers, analyzing the progress, advantages, and limitations within each category. We further synthesize literature across the three phases of the algorithm design pipeline and across diverse algorithmic applications that define the current landscape. Finally, we outline key open challenges and opportunities to guide future research. - oai:arXiv.org:2410.14716v4 - cs.LG - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Fei Liu, Yiming Yao, Ping Guo, Zhiyuan Yang, Zhe Zhao, Xi Lin, Xialiang Tong, Kun Mao, Zhichao Lu, Zhenkun Wang, Mingxuan Yuan, Qingfu Zhang - - - Automatic identification of diagnosis from hospital discharge letters via weakly-supervised Natural Language Processing - https://arxiv.org/abs/2410.15051 - arXiv:2410.15051v2 Announce Type: replace -Abstract: Identifying patient diagnoses from discharge letters is essential to enable large-scale cohort selection and epidemiological research, but traditional supervised approaches rely on extensive manual annotation, which is often impractical for large textual datasets. In this study, we present a novel weakly-supervised Natural Language Processing pipeline designed to classify Italian discharge letters without requiring manual labelling. After extracting diagnosis-related sentences, the method leverages a transformer-based model with an additional pre-training on Italian medical documents to generate semantic embeddings. A two-level clustering procedure is applied to these embeddings, and the resulting clusters are mapped to the diseases of interest to derive weak labels for a subset of data, eventually used to train a transformer-based classifier. We evaluate the approach on a real-world case study on bronchiolitis in a corpus of 33,176 Italian discharge letters of children admitted to 44 emergency rooms or hospitals in the Veneto Region between 2017 and 2020. The pipeline achieves an area under the curve (AUC) of 77.68% ($\pm 4.30\%)$ and an F1-score of 78.14% ($\pm 4.89\%$) against manual annotations. Its performance surpasses other unsupervised methods and approaches fully supervised models, maintaining robustness to cluster selection and promising generalizability across different disease types. It allows saving approximately 3 minutes of expert time per discharge letter, resulting in more than 1,500 hours for a dataset like ours. This study demonstrates the feasibility of a weakly-supervised strategy for identifying diagnoses from Italian discharge letters. The pipeline achieves strong performance, is adaptable to various diseases, and offers a scalable solution for clinical text classification, reducing the need for manual annotation while maintaining reliable accuracy. - oai:arXiv.org:2410.15051v2 - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Vittorio Torri, Elisa Barbieri, Anna Cantarutti, Carlo Giaquinto, Francesca Ieva - - - EON: A practical energy-preserving rough diffuse BRDF - https://arxiv.org/abs/2410.18026 - arXiv:2410.18026v3 Announce Type: replace -Abstract: We introduce the "Energy-preserving Oren--Nayar" (EON) model for reflection from rough surfaces. Unlike the popular qualitative Oren--Nayar model (QON) and its variants, our model is energy-preserving via analytical energy compensation. We include self-contained GLSL source code for efficient evaluation of the new model and importance sampling based on a novel technique we term "Clipped Linearly Transformed Cosine" (CLTC) sampling. - oai:arXiv.org:2410.18026v3 - cs.GR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Journal of Computer Graphics Techniques (JCGT), vol. 14, no. 1, 116-139, 2025 - Jamie Portsmouth, Peter Kutz, Stephen Hill - - - Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation - https://arxiv.org/abs/2410.18565 - arXiv:2410.18565v2 Announce Type: replace -Abstract: We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adaptive Learning Rate, which dynamically adjusts the learning rate based on training progress. To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench, novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field. - oai:arXiv.org:2410.18565v2 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.7494/csci.2025.26.4.7689 - Computer Science 26(4) (2025) 131-161 - Krzysztof Ociepa, {\L}ukasz Flis, Krzysztof Wr\'obel, Adrian Gwo\'zdziej, Remigiusz Kinas - - - Computing the bridge length: the key ingredient in a continuous isometry classification of periodic point sets - https://arxiv.org/abs/2410.23288 - arXiv:2410.23288v2 Announce Type: replace -Abstract: The fundamental model of any periodic crystal is a periodic set of points at all atomic centres. Since crystal structures are determined in a rigid form, their strongest equivalence is rigid motion (composition of translations and rotations) or isometry (also including reflections). The recent classification of periodic point sets under rigid motion used a complete invariant isoset whose size essentially depends on the bridge length, defined as the minimum `jump' that suffices to connect any points in the given set. - We propose a practical algorithm to compute the bridge length of any periodic point set given by a motif of points in a periodically translated unit cell. The algorithm has been tested on a large crystal dataset and is required for an efficient continuous classification of all periodic crystals. The exact computation of the bridge length is a key step to realising the inverse design of materials from new invariant values. - oai:arXiv.org:2410.23288v2 - cs.CG - cond-mat.mtrl-sci - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1107/S2053273325008253 - Jonathan McManus, Vitaliy Kurlin - - - Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching - https://arxiv.org/abs/2411.00759 - arXiv:2411.00759v3 Announce Type: replace -Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unlike continuous flow matching, the rectification strategy cannot be applied due to the stochasticity of discrete paths, necessitating alternative methods to minimize state transitions. We propose a dynamic-optimal-transport-like minimization objective and derive its Kantorovich formulation for discrete flows with convex interpolants, where transport cost depends solely on inter-state similarity and can be optimized via minibatch strategies. In the case of bag-of-words (BoW) sourced flows, we show that such methods can reduce the number of transitions up to 8 times (1024 to 128) to reach the same generative perplexity without compromising diversity. Additionally, path nondeterminism in discrete flows precludes an instantaneous change-of-variables analogue, preventing precise probability estimation available to continuous flows. We therefore propose two upper bounds on perplexity, enabling principled training, evaluation and model comparison. Finally, we introduce Multimask Flows which outperform masked flows in generative perplexity, particularly when utilizing minibatch Optimal Transport, without sacrificing diversity. - oai:arXiv.org:2411.00759v3 - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Etrit Haxholli, Yeti Z. G\"urb\"uz, O\u{g}ul Can, Eli Waxman - - - Two-Stage Robust Optimal Operation of Distribution Networks Considering Renewable Energy and Demand Asymmetric Uncertainties - https://arxiv.org/abs/2411.10166 - arXiv:2411.10166v3 Announce Type: replace -Abstract: This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing asymmetric uncertainties related to renewable energy and load demands. Building on conventional IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To account for the probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties, reducing reliance on precise probability distributions. Consequently, a two-stage robust optimal operation model for distribution networks using CL-DIGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing method, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%. - oai:arXiv.org:2411.10166v3 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhisheng Xiong, Bo Zeng, Peter Palensky, Pedro P. Vergara - - - The Generalization Error of Supervised Machine Learning Algorithms - https://arxiv.org/abs/2411.12030 - arXiv:2411.12030v2 Announce Type: replace -Abstract: In this paper, the method of gaps, a technique for deriving closed-form expressions in terms of information measures for the generalization error of supervised machine learning algorithms is introduced. The method relies on the notion of \emph{gaps}, which characterize the variation of the expected empirical risk (when either the model or dataset is kept fixed) with respect to changes in the probability measure on the varying parameter (either the dataset or the model, respectively). This distinction results in two classes of gaps: Algorithm-driven gaps (fixed dataset) and data-driven gaps (fixed model). In general, the method relies on two central observations: $(i)$~The generalization error is the expectation of an algorithm-driven gap or a data-driven gap. In the first case, the expectation is with respect to a measure on the datasets; and in the second case, with respect to a measure on the models. $(ii)$~Both, algorithm-driven gaps and data-driven gaps exhibit closed-form expressions in terms of relative entropies. In particular, algorithm-driven gaps involve a Gibbs probability measure on the set of models, which represents a supervised Gibbs algorithm. Alternatively, data-driven gaps involve a worst-case data-generating (WCDG) probability measure on the set of data points, which is also a Gibbs probability measure. Interestingly, such Gibbs measures, which are exogenous to the analysis of generalization, place both the supervised Gibbs algorithm and the WCDG probability measure as natural references for the analysis of supervised learning algorithms. All existing exact expressions for the generalization error of supervised machine learning algorithms can be obtained with the proposed method. Also, this method allows obtaining numerous new exact expressions, which allows establishing connections with other areas in statistics. - oai:arXiv.org:2411.12030v2 - cs.LG - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Samir M. Perlaza, Xinying Zou - - - Strong Linearizability without Compare&Swap: The Case of Bags - https://arxiv.org/abs/2411.19365 - arXiv:2411.19365v3 Announce Type: replace -Abstract: Because strongly-linearizable objects provide stronger guarantees than linearizability, they serve as valuable building blocks for the design of concurrent data structures. Yet, many objects that have linearizable implementations from base objects weaker than compare&swap objects do not have strongly-linearizable implementations from the same base objects. We focus on one such object: the bag, a multiset from which processes can take unspecified elements. - We present the first lock-free, strongly-linearizable implementation of a bag from interfering objects (specifically, registers, and test&set objects). This may be surprising, since there are provably no such implementations of stacks or queues. - Since a bag can contain arbitrarily many elements, an unbounded amount of space must be used to implement it. Hence, it makes sense to also consider a bag with a bound on its capacity. However, like stacks and queues, a bag with capacity $b$ shared by more than $2b$ processes has no lock-free, strongly-linearizable implementation from interfering objects. If we further restrict a bounded bag so that only one process can insert into it, we are able to obtain a lock-free, strongly-linearizable implementation from $O(b + n)$ interfering objects, where $n$ is the number of processes. - Our goal is to understand the circumstances under which strongly-linearizable implementations of bags exist and, more generally, to understand the power of interfering objects. - oai:arXiv.org:2411.19365v3 - cs.DC - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.4230/LIPIcs.DISC.2025.29 - Faith Ellen, Gal Sela - - - Explaining Object Detectors via Collective Contribution of Pixels - https://arxiv.org/abs/2412.00666 - arXiv:2412.00666v3 Announce Type: replace -Abstract: Visual explanations for object detectors are crucial for enhancing their reliability. Object detectors identify and localize instances by assessing multiple visual features collectively. When generating explanations, overlooking these collective influences in detections may lead to missing compositional cues or capturing spurious correlations. However, existing methods typically focus solely on individual pixel contributions, neglecting the collective contribution of multiple pixels. To address this limitation, we propose a game-theoretic method based on Shapley values and interactions to explicitly capture both individual and collective pixel contributions. Our method provides explanations for both bounding box localization and class determination, highlighting regions crucial for detection. Extensive experiments demonstrate that the proposed method identifies important regions more accurately than state-of-the-art methods. The code is available at https://github.com/tttt-0814/VX-CODE - oai:arXiv.org:2412.00666v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Toshinori Yamauchi, Hiroshi Kera, Kazuhiko Kawamoto - - - Private Linear Regression with Differential Privacy and PAC Privacy - https://arxiv.org/abs/2412.02578 - arXiv:2412.02578v2 Announce Type: replace -Abstract: Linear regression is a fundamental tool for statistical analysis, which has motivated the development of linear regression methods that satisfy provable privacy guarantees so that the learned model reveals little about any one data point used to construct it. Most existing privacy-preserving linear regression methods rely on the well-established framework of differential privacy, while the newly proposed PAC Privacy has not yet been explored in this context. In this paper, we systematically compare linear regression models trained with differential privacy and PAC privacy across three real-world datasets, observing several key findings that impact the performance of privacy-preserving linear regression. - oai:arXiv.org:2412.02578v2 - cs.LG - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Hillary Yang, Yuntao Du - - - SoundnessBench: A Soundness Benchmark for Neural Network Verifiers - https://arxiv.org/abs/2412.03154 - arXiv:2412.03154v3 Announce Type: replace -Abstract: Neural network (NN) verification aims to formally verify properties of NNs, which is crucial for ensuring the behavior of NN-based models in safety-critical applications. In recent years, the community has developed many NN verifiers and benchmarks to evaluate them. However, existing benchmarks typically lack ground-truth for hard instances where no current verifier can verify the property and no counterexample can be found. This makes it difficult to validate the soundness of a verifier, when it claims verification on such challenging instances that no other verifier can handle. In this work, we develop a new benchmark for NN verification, named SoundnessBench, specifically for testing the soundness of NN verifiers. SoundnessBench consists of instances with deliberately inserted counterexamples that are hidden from adversarial attacks commonly used to find counterexamples. Thereby, it can identify false verification claims when hidden counterexamples are known to exist. We design a training method to produce NNs with hidden counterexamples and systematically construct our SoundnessBench with instances across various model architectures, activation functions, and input data. We demonstrate that our training effectively produces hidden counterexamples and our SoundnessBench successfully identifies bugs in state-of-the-art NN verifiers. Our code is available at https://github.com/mvp-harry/SoundnessBench and our dataset is available at https://huggingface.co/datasets/SoundnessBench/SoundnessBench. - oai:arXiv.org:2412.03154v3 - cs.LG - cs.AI - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xingjian Zhou, Keyi Shen, Andy Xu, Hongji Xu, Cho-Jui Hsieh, Huan Zhang, Zhouxing Shi - - - INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning - https://arxiv.org/abs/2412.03565 - arXiv:2412.03565v2 Announce Type: replace -Abstract: Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a holistic level, they still struggle with instance-level understanding that requires a more fine-grained comprehension and alignment. Instance-level understanding is crucial for LMMs, as it focuses on the specific elements that we are most interested in. Excitingly, existing works find that the SOTA LMMs exhibit strong instance understanding capabilities when provided with explicit visual cues. Motivated by this, we proposed Inst-IT, a solution to enhance LMMs in Instance understanding via explicit visual prompt Instruction Tuning for instance guidance. Inst-IT consists of a benchmark to diagnose multimodal instance-level understanding, a large-scale instruction-tuning dataset, and a continuous instruction-tuning training paradigm to effectively enhance spatial-temporal instance understanding capabilities of existing LMMs. Experimental results show that, enhanced by Inst-IT, our models not only achieve outstanding performance on Inst-IT Bench and other instance understanding benchmarks, but also demonstrate significant improvements across various generic image and video understanding benchmarks. This highlights that our method not only boosts instance-level understanding but also strengthens the overall capabilities of generic image and video comprehension. - oai:arXiv.org:2412.03565v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Wujian Peng, Lingchen Meng, Yitong Chen, Yiweng Xie, Yang Liu, Tao Gui, Hang Xu, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang - - - Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots - https://arxiv.org/abs/2412.04235 - arXiv:2412.04235v3 Announce Type: replace -Abstract: I combine detection and mitigation techniques to addresses hallucinations in Large Language Models (LLMs). Mitigation is achieved in a question-answering Retrieval-Augmented Generation (RAG) framework while detection is obtained by introducing the Negative Missing Information Scoring System (NMISS), which accounts for contextual relevance in responses. While RAG mitigates hallucinations by grounding answers in external data, NMISS refines the evaluation by identifying cases where traditional metrics incorrectly flag contextually accurate responses as hallucinations. I use Italian health news articles as context to evaluate LLM performance. Results show that Gemma2 and GPT-4 outperform the other models, with GPT-4 producing answers closely aligned with reference responses. Mid-tier models, such as Llama2, Llama3, and Mistral benefit significantly from NMISS, highlighting their ability to provide richer contextual information. This combined approach offers new insights into the reduction and more accurate assessment of hallucinations in LLMs, with applications in real-world healthcare tasks and other domains. - oai:arXiv.org:2412.04235v3 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Maria Paola Priola - - - The Oracle Complexity of Simplex-based Matrix Games: Linear Separability and Nash Equilibria - https://arxiv.org/abs/2412.06990 - arXiv:2412.06990v3 Announce Type: replace -Abstract: We study the problem of solving matrix games of the form $\max_{\mathbf{w}\in\mathcal{W}}\min_{\mathbf{p}\in\Delta}\mathbf{p}^{\top}A\mathbf{w}$, where $A$ is some matrix and $\Delta$ is the probability simplex. This problem encapsulates canonical tasks such as finding a linear separator and computing Nash equilibria in zero-sum games. However, perhaps surprisingly, its inherent complexity (as formalized in the standard framework of oracle complexity [Nemirovski and Yudin, 1983]) is not well-understood. In this work, we first identify different oracle models which are implicitly used by prior algorithms, amounting to multiplying the matrix $A$ by a vector from either one or both sides. We then prove complexity lower bounds for algorithms under both access models, which in particular imply a separation between them. Specifically, we start by showing that algorithms for linear separability based on one-sided multiplications must require $\Omega(\gamma_A^{-2})$ iterations, where $\gamma_A$ is the margin, as matched by the Perceptron algorithm. We then prove that accelerated algorithms for this task, which utilize multiplications from both sides, must require $\tilde{\Omega}(\gamma_{A}^{-2/3})$ iterations, establishing the first oracle complexity barrier for such algorithms. Finally, by adapting our lower bound to $\ell_1$ geometry, we prove that computing an $\epsilon$-approximate Nash equilibrium requires $\tilde{\Omega}(\epsilon^{-2/3})$ iterations, which is an exponential improvement over the previously best-known lower bound due to Hadiji et al. [2024]. - oai:arXiv.org:2412.06990v3 - cs.GT - cs.LG - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Guy Kornowski, Ohad Shamir - - - Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning - https://arxiv.org/abs/2412.07454 - arXiv:2412.07454v3 Announce Type: replace -Abstract: Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often address these issues separately, sacrificing either system robustness or model accuracy. This work introduces Tazza, a secure and efficient federated learning framework that simultaneously addresses both challenges. By leveraging the permutation equivariance and invariance properties of neural networks via weight shuffling and shuffled model validation, Tazza enhances resilience against diverse poisoning attacks, while ensuring data confidentiality and high model accuracy. Comprehensive evaluations on various datasets and embedded platforms show that Tazza achieves robust defense with up to 6.7x improved computational efficiency compared to alternative schemes, without compromising performance. - oai:arXiv.org:2412.07454v3 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Kichang Lee, Jaeho Jin, JaeYeon Park, Songkuk Kim, JeongGil Ko - - - Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction - https://arxiv.org/abs/2412.08243 - arXiv:2412.08243v2 Announce Type: replace -Abstract: Camera-based 3D Semantic Occupancy Prediction (SOP) is crucial for understanding complex 3D scenes from limited 2D image observations. Existing SOP methods typically aggregate contextual features to assist the occupancy representation learning, alleviating issues like occlusion or ambiguity. However, these solutions often face misalignment issues wherein the corresponding features at the same position across different frames may have different semantic meanings during the aggregation process, which leads to unreliable contextual fusion results and an unstable representation learning process. To address this problem, we introduce a new Hierarchical context alignment paradigm for a more accurate SOP (Hi-SOP). Hi-SOP first disentangles the geometric and temporal context for separate alignment, which two branches are then composed to enhance the reliability of SOP. This parsing of the visual input into a local-global alignment hierarchy includes: (I) disentangled geometric and temporal separate alignment, within each leverages depth confidence and camera pose as prior for relevant feature matching respectively; (II) global alignment and composition of the transformed geometric and temporal volumes based on semantics consistency. Our method outperforms SOTAs for semantic scene completion on the SemanticKITTI & NuScenes-Occupancy datasets and LiDAR semantic segmentation on the NuScenes dataset. The project website is available at https://arlo0o.github.io/hisop.github.io/. - oai:arXiv.org:2412.08243v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Bohan Li, Jiajun Deng, Yasheng Sun, Xiaofeng Wang, Xin Jin, Wenjun Zeng - - - Lagrangian Index Policy for Restless Bandits with Average Reward - https://arxiv.org/abs/2412.12641 - arXiv:2412.12641v3 Announce Type: replace -Abstract: We study the Lagrangian Index Policy (LIP) for restless multi-armed bandits with long-run average reward. In particular, we compare the performance of LIP with the performance of the Whittle Index Policy (WIP), both heuristic policies known to be asymptotically optimal under certain natural conditions. Even though in most cases their performances are very similar, in the cases when WIP shows bad performance, LIP continues to perform very well. We then propose reinforcement learning algorithms, both tabular and NN-based, to obtain online learning schemes for LIP in the model-free setting. The proposed reinforcement learning schemes for LIP require significantly less memory than the analogous schemes for WIP. We calculate analytically the Lagrangian index for the restart model, which applies to the optimal web crawling and the minimization of the weighted age of information. We also give a new proof of asymptotic optimality in case of homogeneous arms as the number of arms goes to infinity, based on exchangeability and de Finetti's theorem. - oai:arXiv.org:2412.12641v3 - cs.LG - cs.AI - math.OC - math.PR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Konstantin Avrachenkov, Vivek S. Borkar, Pratik Shah - - - OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization - https://arxiv.org/abs/2412.15159 - arXiv:2412.15159v2 Announce Type: replace -Abstract: Video diffusion models (VDMs) have demonstrated remarkable capabilities in text-to-video (T2V) generation. Despite their success, VDMs still suffer from degraded image quality and flickering artifacts. To address these issues, some approaches have introduced preference learning to exploit human feedback to enhance the video generation. However, these methods primarily adopt the routine in the image domain without an in-depth investigation into video-specific preference optimization. In this paper, we reexamine the design of the video preference learning from two key aspects: feedback source and feedback tuning methodology, and present OnlineVPO, a more efficient preference learning framework tailored specifically for VDMs. On the feedback source, we found that the image-level reward model commonly used in existing methods fails to provide a human-aligned video preference signal due to the modality gap. In contrast, video quality assessment (VQA) models show superior alignment with human perception of video quality. Building on this insight, we propose leveraging VQA models as a proxy of humans to provide more modality-aligned feedback for VDMs. Regarding the preference tuning methodology, we introduce an online DPO algorithm tailored for VDMs. It not only enjoys the benefits of superior scalability in optimizing videos with higher resolution and longer duration compared with the existing method, but also mitigates the insufficient optimization issue caused by off-policy learning via online preference generation and curriculum preference update designs. Extensive experiments on the open-source video-diffusion model demonstrate OnlineVPO as a simple yet effective and, more importantly, scalable preference learning algorithm for video diffusion models. - oai:arXiv.org:2412.15159v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jiacheng Zhang, Jie Wu, Weifeng Chen, Yatai Ji, Xuefeng Xiao, Weilin Huang, Kai Han - - - Quantifying Positional Biases in Text Embedding Models - https://arxiv.org/abs/2412.15241 - arXiv:2412.15241v4 Announce Type: replace -Abstract: Embedding models are crucial for tasks in Information Retrieval (IR) and semantic similarity measurement, yet their handling of longer texts and associated positional biases remains underexplored. In this study, we investigate the impact of content position and input size on text embeddings. Our experiments reveal that embedding models, irrespective of their positional encoding mechanisms, disproportionately prioritize the beginning of an input. Ablation studies demonstrate that insertion of irrelevant text or removal at the start of a document reduces cosine similarity between altered and original embeddings by up to 12.3% more than ablations at the end. Regression analysis further confirms this bias, with sentence importance declining as position moves further from the start, even with with content-agnosticity. We hypothesize that this effect arises from pre-processing strategies and chosen positional encoding techniques. These findings quantify the sensitivity of retrieval systems and suggest a new lens towards embedding model robustness. - oai:arXiv.org:2412.15241v4 - cs.CL - cs.AI - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Reagan J. Lee, Samarth Goel, Kannan Ramchandran - - - Quantum $(r,\delta)$-locally recoverable codes - https://arxiv.org/abs/2412.16590 - arXiv:2412.16590v3 Announce Type: replace -Abstract: Classical $(r,\delta)$-locally recoverable codes are designed for avoiding loss of information in large scale distributed and cloud storage systems. We introduce the quantum counterpart of those codes by defining quantum $(r,\delta)$-locally recoverable codes which are quantum error-correcting codes capable of correcting $\delta -1$ qudit erasures from sets of at most $r+ \delta -1$ qudits. - We give a necessary and sufficient condition for a quantum stabilizer code $Q(C)$ to be $(r,\delta)$-locally recoverable. Our condition depends only on the puncturing and shortening at suitable sets of both the symplectic self-orthogonal code $C$ used for constructing $Q(C)$ and its symplectic dual $C^{\perp_s}$. When $Q(C)$ comes from a Hermitian or Euclidean dual-containing code, and under an extra condition, we show that there is an equivalence between the classical and quantum concepts of $(r,\delta)$-local recoverability. A Singleton-like bound is stated in this case and examples attaining the bound are given. - oai:arXiv.org:2412.16590v3 - cs.IT - math.IT - quant-ph - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1016/j.ffa.2025.102785 - Finite Fields and Their Applications, vol. 111, article ID 102785, March 2026 - Carlos Galindo, Fernando Hernando, Helena Mart\'in-Cruz, Ryutaroh Matsumoto - - - A Gas-Kinetic Scheme for Maxwell Equations - https://arxiv.org/abs/2412.16845 - arXiv:2412.16845v2 Announce Type: replace -Abstract: The Gas-Kinetic Scheme (GKS), widely used in computational fluid dynamics for simulating hypersonic and other complicated flow phenomena, is extended in this work to electromagnetic problems by solving Maxwell's equations. In contrast to the classical GKS formulation, the proposed scheme employs a discrete rather than a continuous velocity space. By evaluating a time-accurate numerical flux at cell interfaces, the proposed scheme attains second-order accuracy within a single step. Its kinetic formulation provides an inherently multidimensional framework, while the finite-volume formulation ensures straightforward extension to unstructured meshes. Through the incorporation of a collision process, the scheme exhibits lower numerical dissipation than classical flux-vector splitting (FVS) methods. Furthermore, the kinetic decomposition enables direct implementation of non-reflecting boundary conditions. The proposed scheme is validated against several benchmark problems and compared with established methods, including the Finite-Difference Time-Domain (FDTD) method and FVS. A lattice Boltzmann method (LBM) implementation is also included for comparative analysis. Finally, the technique is applied to simulate electromagnetic wave propagation in a realistic aircraft configuration, demonstrating its ability to model complex geometries. - oai:arXiv.org:2412.16845v2 - math.NA - cs.NA - physics.comp-ph - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhigang Pu, Wenpei Long, Kun Xu - - - Distributed Graph Algorithms with Predictions - https://arxiv.org/abs/2501.05267 - arXiv:2501.05267v2 Announce Type: replace -Abstract: We initiate the study of deterministic distributed graph algorithms with predictions in synchronous message passing systems. The process at each node in the graph is given a prediction, which is some extra information about the problem instance that may be incorrect. The processes may use the predictions to help them solve the problem. The overall goal is to develop algorithms that both work faster when predictions are good and do not work much worse than algorithms without predictions when predictions are bad. Concepts from the more general area of algorithms with predictions, such as error measures, consistency, robustness, and smoothness, are adapted to distributed graph algorithms with predictions. - We consider algorithms with predictions for distributed graph problems, where each node is given a prediction for its output. We present a framework for evaluating distributed graph algorithms with predictions and methods for transforming existing algorithms without predictions to effectively use predictions. Our approach is illustrated by developing algorithms with predictions for the Maximal Independent Set problem. - We also include a discussion of error measures and demonstrate how fine-tuning an error measure towards a particular problem can yield stronger results about the performance of algorithms for that problem. - oai:arXiv.org:2501.05267v2 - cs.DC - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Joan Boyar, Faith Ellen, Kim S. Larsen - - - EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model - https://arxiv.org/abs/2501.05710 - arXiv:2501.05710v3 Announce Type: replace -Abstract: Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emotional image generation relies on discrete emotion categories, making it challenging to capture complex and subtle emotional nuances accurately. Additionally, these methods struggle to control the specific content of generated images based on text prompts. In this work, we introduce the new task of continuous emotional image content generation (C-EICG) and present EmotiCrafter, an emotional image generation model that generates images based on text prompts and Valence-Arousal values. Specifically, we propose a novel emotion-embedding mapping network that embeds Valence-Arousal values into textual features, enabling the capture of specific emotions in alignment with intended input prompts. Additionally, we introduce a loss function to enhance emotion expression. The experimental results show that our method effectively generates images representing specific emotions with the desired content and outperforms existing techniques. - oai:arXiv.org:2501.05710v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shengqi Dang, Yi He, Long Ling, Ziqing Qian, Nanxuan Zhao, Nan Cao - - - Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models - https://arxiv.org/abs/2501.07033 - arXiv:2501.07033v3 Announce Type: replace -Abstract: This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated. Traditional security systems struggle to identify these sophisticated forms of fraud. This research proposes a novel GAN-based model that enhances online payment security by identifying subtle manipulations in payment images. The model is trained on a dataset consisting of real-world online payment images and deepfake images generated using advanced GAN architectures, such as StyleGAN and DeepFake. The results demonstrate that the proposed model can accurately distinguish between legitimate transactions and deepfakes, achieving a high detection rate above 95%. This approach significantly improves the robustness of payment systems against AI-driven fraud. The paper contributes to the growing field of digital security, offering insights into the application of GANs for fraud detection in financial services. Keywords- Payment Security, Image Recognition, Generative Adversarial Networks, AI Deepfake, Fraudulent Activities - oai:arXiv.org:2501.07033v3 - cs.LG - cs.CR - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/ICAACE65325.2025.11020513 - 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) - Zong Ke, Shicheng Zhou, Yining Zhou, Chia Hong Chang, Rong Zhang - - - Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone - https://arxiv.org/abs/2501.12073 - arXiv:2501.12073v4 Announce Type: replace -Abstract: Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. In dense forests, relying on the Global Navigation Satellite System (GNSS) for localization is not feasible. In addition, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests. Specifically, the study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera. The autonomous flight capability of the prototype was evaluated through multiple test flights in boreal forests. The tree parameter estimation capability was studied by performing diameter at breast height (DBH) estimation. The prototype successfully carried out flights in selected challenging forest environments, and the experiments showed promising performance in forest 3D modeling with a miniaturized stereoscopic photogrammetric system. The DBH estimation achieved a root mean square error (RMSE) of 3.33 - 3.97 cm (10.69 - 12.98 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 - 2.56 cm (5.74 - 12.47 %). The results provide valuable insights into autonomous under-canopy forest mapping and highlight the critical next steps for advancing lightweight robotic drone systems for mapping complex forest environments. - oai:arXiv.org:2501.12073v4 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.1080/01431161.2025.2579803 - V\"ain\"o Karjalainen, Niko Koivum\"aki, Teemu Hakala, Jesse Muhojoki, Eric Hyypp\"a, Anand George, Juha Suomalainen, Eija Honkavaara - - - Knowledge-Driven Federated Graph Learning on Model Heterogeneity - https://arxiv.org/abs/2501.12624 - arXiv:2501.12624v4 Announce Type: replace -Abstract: Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning, enabling multiple parties to jointly train models while preserving data privacy. However, most existing approaches assume homogeneous client models and largely overlook the challenge of model-centric heterogeneous FGL (MHtFGL), which frequently arises in practice when organizations employ graph neural networks (GNNs) of different scales and architectures.Such architectural diversity not only undermines smooth server-side aggregation, which presupposes a unified representation space shared across clients' updates, but also further complicates the transfer and integration of structural knowledge across clients. To address this issue, we propose the Federated Graph Knowledge Collaboration (FedGKC) framework. FedGKC introduces a lightweight Copilot Model on each client to facilitate knowledge exchange while local architectures are heterogeneous across clients, and employs two complementary mechanisms: Client-side Self-Mutual Knowledge Distillation, which transfers effective knowledge between local and copilot models through bidirectional distillation with multi-view perturbation; and Server-side Knowledge-Aware Model Aggregation, which dynamically assigns aggregation weights based on knowledge provided by clients. Extensive experiments on eight benchmark datasets demonstrate that FedGKC achieves an average accuracy gain of 3.88% over baselines in MHtFGL scenarios, while maintaining excellent performance in homogeneous settings. - oai:arXiv.org:2501.12624v4 - cs.LG - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhengyu Wu, Guang Zeng, Huilin Lai, Daohan Su, Jishuo Jia, Yinlin Zhu, Xunkai Li, Rong-Hua Li, Guoren Wang, Chenghu Zhou - - - Illusions of Relevance: Arbitrary Content Injection Attacks Deceive Retrievers, Rerankers, and LLM Judges - https://arxiv.org/abs/2501.18536 - arXiv:2501.18536v2 Announce Type: replace -Abstract: This work considers a black-box threat model in which adversaries attempt to propagate arbitrary non-relevant content in search. We show that retrievers, rerankers, and LLM relevance judges are all highly vulnerable to attacks that enable arbitrary content to be promoted to the top of search results and to be assigned perfect relevance scores. We investigate how attackers may achieve this via content injection, injecting arbitrary sentences into relevant passages or query terms into arbitrary passages. Our study analyzes how factors such as model class and size, the balance between relevant and non-relevant content, injection location, toxicity and severity of injected content, and the role of LLM-generated content influence attack success, yielding novel, concerning, and often counterintuitive results. Our results reveal a weakness in embedding models, LLM-based scoring models, and generative LLMs, raising concerns about the general robustness, safety, and trustworthiness of language models regardless of the type of model or the role in which they are employed. We also emphasize the challenges of robust defenses against these attacks. Classifiers and more carefully prompted LLM judges often fail to recognize passages with content injection, especially when considering diverse text topics and styles. Our findings highlight the need for further research into arbitrary content injection attacks. We release our code for further study. - oai:arXiv.org:2501.18536v2 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Manveer Singh Tamber, Jimmy Lin - - - MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding - https://arxiv.org/abs/2502.03183 - arXiv:2502.03183v3 Announce Type: replace -Abstract: Modern Video Large Language Models (VLLMs) often rely on uniform frame sampling for video understanding, but this approach frequently fails to capture critical information due to frame redundancy and variations in video content. We propose MaxInfo, the first training-free method based on the maximum volume principle, which is available in Fast and Slow versions and a Chunk-based version that selects and retains the most representative frames from a video. By maximizing the geometric volume formed by selected embeddings, MaxInfo ensures that the chosen frames cover the most informative regions of the embedding space, effectively reducing redundancy while preserving diversity. This method enhances the quality of input representations and improves long video comprehension performance across benchmarks. For instance, MaxInfo achieves a 3.28% improvement on LongVideoBench and a 6.4% improvement on EgoSchema for LLaVA-Video-7B. Moreover, MaxInfo boosts LongVideoBench performance by 3.47% on LLaVA-Video-72B and 3.44% on MiniCPM4.5. The approach is simple to implement and works with existing VLLMs without the need for additional training and very lower latency, making it a practical and effective alternative to traditional uniform sampling methods. Our code are available at https://github.com/FusionBrainLab/MaxInfo.git - oai:arXiv.org:2502.03183v3 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Pengyi Li, Irina Abdullaeva, Alexander Gambashidze, Andrey Kuznetsov, Ivan Oseledets - - - An Empirical Study of Methods for Small Object Detection from Satellite Imagery - https://arxiv.org/abs/2502.03674 - arXiv:2502.03674v2 Announce Type: replace -Abstract: This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detection from urban satellite images and bee box detection from satellite images of agricultural lands as application scenarios. Drawing from the existing surveys and literature, we identify several top-performing methods for the empirical study. Public, high-resolution satellite image datasets are used in our experiments. - oai:arXiv.org:2502.03674v2 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Xiaohui Yuan, Aniv Chakravarty, Lichuan Gu, Zhenchun Wei, Elinor Lichtenberg, Tian Chen - - - Large Multimodal Models for Low-Resource Languages: A Survey - https://arxiv.org/abs/2502.05568 - arXiv:2502.05568v2 Announce Type: replace -Abstract: In this survey, we systematically analyze techniques used to adapt large multimodal models (LMMs) for low-resource (LR) languages, examining approaches ranging from visual enhancement and data creation to cross-modal transfer and fusion strategies. Through a comprehensive analysis of 117 studies across 96 LR languages, we identify key patterns in how researchers tackle the challenges of limited data and computational resources. We categorize works into resource-oriented and method-oriented contributions, further dividing contributions into relevant sub-categories. We compare method-oriented contributions in terms of performance and efficiency, discussing benefits and limitations of representative studies. We find that visual information often serves as a crucial bridge for improving model performance in LR settings, though significant challenges remain in areas such as hallucination mitigation and computational efficiency. In summary, we provide researchers with a clear understanding of current approaches and remaining challenges in making LMMs more accessible to speakers of LR (understudied) languages. We complement our survey with an open-source repository available at: https://github.com/marianlupascu/LMM4LRL-Survey. - oai:arXiv.org:2502.05568v2 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Marian Lupascu, Ana-Cristina Rogoz, Mihai Sorin Stupariu, Radu Tudor Ionescu - - - Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings - https://arxiv.org/abs/2502.11007 - arXiv:2502.11007v4 Announce Type: replace -Abstract: Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multiple dialogues and multi-modal data sources. These unique characteristics of LLMs, together with their large model size, make their deployment more challenging. Specifically, (i) deploying LLMs on local devices faces computational, memory, and energy resource issues, while (ii) deploying them in the cloud cannot guarantee real-time service and incurs communication/usage costs. In this paper, we design TMO, a local-cloud LLM inference system with Three-M Offloading: Multi-modal, Multi-task, and Multi-dialogue. TMO incorporates (i) a lightweight local LLM that can process simple tasks at high speed and (ii) a large-scale cloud LLM that can handle multi-modal data sources. We develop a resource-constrained reinforcement learning (RCRL) strategy for TMO that optimizes the inference location (i.e., local vs. cloud) and multi-modal data sources to use for each task/dialogue, aiming to maximize the long-term reward (response quality, latency, and usage cost) while adhering to resource constraints. We also contribute M4A1, a new dataset we curated that contains reward and cost metrics across multiple modality, task, dialogue, and LLM configurations, enabling evaluation of offloading decisions. We demonstrate the effectiveness of TMO compared to several exploration-decision and LLM-as-Agent baselines, showing significant improvements in latency, cost, and response quality. - oai:arXiv.org:2502.11007v4 - cs.LG - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Liangqi Yuan, Dong-Jun Han, Shiqiang Wang, Christopher G. Brinton - - - Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification - https://arxiv.org/abs/2502.14433 - arXiv:2502.14433v2 Announce Type: replace -Abstract: Many real-world applications rely on land surface temperature (LST) data at high spatiotemporal resolution. In complex urban areas, LST exhibits significant variations, fluctuating dramatically within and across city blocks. Landsat provides high spatial resolution data at 100 meters but is limited by long revisit time, with cloud cover further disrupting data collection. Here, we propose DELAG, a deep ensemble learning method that integrates annual temperature cycles and Gaussian processes, to reconstruct Landsat LST in complex urban areas. Leveraging the cross-track characteristics and dual-satellite operation of Landsat since 2021, we further enhance data availability to 4 scenes every 16 days. We select New York City, London and Hong Kong from three different continents as study areas. Experiments show that DELAG successfully reconstructed LST in the three cities under clear-sky (RMSE = 0.73-0.96 K) and heavily-cloudy (RMSE = 0.84-1.62 K) situations, superior to existing methods. Additionally, DELAG can quantify uncertainty that enhances LST reconstruction reliability. We further tested the reconstructed LST to estimate near-surface air temperature, achieving results (RMSE = 1.48-2.11 K) comparable to those derived from clear-sky LST (RMSE = 1.63-2.02 K). The results demonstrate the successful reconstruction through DELAG and highlight the broader applications of LST reconstruction for estimating accurate air temperature. Our study thus provides a novel and practical method for Landsat LST reconstruction, particularly suited for complex urban areas within Landsat cross-track areas, taking one step toward addressing complex climate events at high spatiotemporal resolution. Code and data are available at https://skrisliu.com/delag - oai:arXiv.org:2502.14433v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/TGRS.2025.3643985 - Shengjie Liu, Siqin Wang, Lu Zhang - - - ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting - https://arxiv.org/abs/2502.14780 - arXiv:2502.14780v2 Announce Type: replace -Abstract: Efficient and privacy-preserving multimodal interaction is essential as AR, VR, and modern smartphones with powerful cameras become primary interfaces for human-computer communication. Existing powerful large vision-language models (VLMs) enabling multimodal interaction often rely on cloud-based processing, raising significant concerns about (1) visual privacy by transmitting sensitive vision data to servers, and (2) their limited real-time, on-device usability. This paper explores Visual Instruction Rewriting, a novel approach that transforms multimodal instructions into text-only commands, allowing seamless integration of lightweight on-device instruction rewriter VLMs (250M parameters) with existing conversational AI systems, enhancing vision data privacy. To achieve this, we present a dataset of over 39,000 examples across 14 domains and develop a compact VLM, pretrained on image captioning datasets and fine-tuned for instruction rewriting. Experimental results, evaluated through NLG metrics such as BLEU, METEOR, and ROUGE, along with semantic parsing analysis, demonstrate that even a quantized version of the model (<500MB storage footprint) can achieve effective instruction rewriting, thus enabling privacy-focused, multimodal AI applications. - oai:arXiv.org:2502.14780v2 - cs.CL - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Abhijit Mishra, Mingda Li, Hsiang Fu, Richard Noh, Minji Kim - - - CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems - https://arxiv.org/abs/2502.17821 - arXiv:2502.17821v3 Announce Type: replace -Abstract: Multi-modal learning has emerged as a key technique for improving performance across domains such as autonomous driving, robotics, and reasoning. However, in certain scenarios, particularly in resource-constrained environments, some modalities available during training may be absent during inference. While existing frameworks effectively utilize multiple data sources during training and enable inference with reduced modalities, they are primarily designed for single-agent settings. This poses a critical limitation in dynamic environments such as connected autonomous vehicles (CAV), where incomplete data coverage can lead to decision-making blind spots. Conversely, some works explore multi-agent collaboration but without addressing missing modality at test time. To overcome these limitations, we propose Collaborative Auxiliary Modality Learning (CAML), a novel multi-modal multi-agent framework that enables agents to collaborate and share multi-modal data during training, while allowing inference with reduced modalities during testing. Experimental results in collaborative decision-making for CAV in accident-prone scenarios demonstrate that CAML achieves up to a 58.1% improvement in accident detection. Additionally, we validate CAML on real-world aerial-ground robot data for collaborative semantic segmentation, achieving up to a 10.6% improvement in mIoU. - oai:arXiv.org:2502.17821v3 - cs.RO - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Rui Liu, Yu Shen, Peng Gao, Pratap Tokekar, Ming Lin - - - SciceVPR: Stable Cross-Image Correlation Enhanced Model for Visual Place Recognition - https://arxiv.org/abs/2502.20676 - arXiv:2502.20676v2 Announce Type: replace -Abstract: Visual Place Recognition (VPR) is a major challenge for robotics and autonomous systems, with the goal of predicting the location of an image based solely on its visual features. State-of-the-art (SOTA) models extract global descriptors using the powerful foundation model DINOv2 as backbone. These models either explore the cross-image correlation or propose a time-consuming two-stage re-ranking strategy to achieve better performance. However, existing works only utilize the final output of DINOv2, and the current cross-image correlation causes unstable retrieval results. To produce both discriminative and constant global descriptors, this paper proposes stable cross-image correlation enhanced model for VPR called SciceVPR. This model explores the full potential of DINOv2 in providing useful feature representations that implicitly encode valuable contextual knowledge. Specifically, SciceVPR first uses a multi-layer feature fusion module to capture increasingly detailed task-relevant channel and spatial information from the multi-layer output of DINOv2. Secondly, SciceVPR considers the invariant correlation between images within a batch as valuable knowledge to be distilled into the proposed self-enhanced encoder. In this way, SciceVPR can acquire fairly robust global features regardless of domain shifts (e.g., changes in illumination, weather and viewpoint between pictures taken in the same place). Experimental results demonstrate that the base variant, SciceVPR-B, outperforms SOTA one-stage methods with single input on multiple datasets with varying domain conditions. The large variant, SciceVPR-L, performs on par with SOTA two-stage models, scoring over 3% higher in Recall@1 compared to existing models on the challenging Tokyo24/7 dataset. Our code will be released at https://github.com/shuimushan/SciceVPR. - oai:arXiv.org:2502.20676v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1016/j.neucom.2025.132539 - Shanshan Wan, Yingmei Wei, Lai Kang, Tianrui Shen, Haixuan Wang, Yee-Hong Yang - - - Recent Advances in Numerical Solutions for Hamilton-Jacobi PDEs - https://arxiv.org/abs/2502.20833 - arXiv:2502.20833v2 Announce Type: replace -Abstract: Hamilton-Jacobi partial differential equations (HJ PDEs) play a central role in many applications such as economics, physics, and engineering. These equations describe the evolution of a value function which encodes valuable information about the system, such as action, cost, or level sets of a dynamic process. Their importance lies in their ability to model diverse phenomena, ranging from the propagation of fronts in computational physics to optimal decision-making in control systems. This paper provides a review of some recent advances in numerical methods to address challenges such as high-dimensionality, nonlinearity, and computational efficiency. By examining these developments, this paper sheds light on important techniques and emerging directions in the numerical solution of HJ PDEs. - oai:arXiv.org:2502.20833v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Tingwei Meng, Siting Liu, Samy Wu Fung, Stanley Osher - - - OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction - https://arxiv.org/abs/2503.03734 - arXiv:2503.03734v4 Announce Type: replace -Abstract: Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visionlanguage models (VLMs) as visual and language features are independently fed into downstream policies, degrading the pre-trained semantic alignments. We propose OTTER, a novel VLA architecture that leverages these existing alignments through explicit, text-aware visual feature extraction. Instead of processing all visual features, OTTER selectively extracts and passes only task-relevant visual features that are semantically aligned with the language instruction to the policy transformer. This allows OTTER to keep the pre-trained vision-language encoders frozen. Thereby, OTTER preserves and utilizes the rich semantic understanding learned from large-scale pre-training, enabling strong zero-shot generalization capabilities. In simulation and real-world experiments, OTTER significantly outperforms existing VLA models, demonstrating strong zeroshot generalization to novel objects and environments. Video, code, checkpoints, and dataset: https://ottervla.github.io/. - oai:arXiv.org:2503.03734v4 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Huang Huang, Fangchen Liu, Letian Fu, Tingfan Wu, Mustafa Mukadam, Jitendra Malik, Ken Goldberg, Pieter Abbeel - - - Simple Self Organizing Map with Visual Transformer - https://arxiv.org/abs/2503.04121 - arXiv:2503.04121v2 Announce Type: replace -Abstract: Vision Transformers (ViTs) have demonstrated exceptional performance in various vision tasks. However, they tend to underperform on smaller datasets due to their inherent lack of inductive biases. Current approaches address this limitation implicitly-often by pairing ViTs with pretext tasks or by distilling knowledge from convolutional neural networks (CNNs) to strengthen the prior. In contrast, Self-Organizing Maps (SOMs), a widely adopted self-supervised framework, are inherently structured to preserve topology and spatial organization, making them a promising candidate to directly address the limitations of ViTs in limited or small training datasets. Despite this potential, equipping SOMs with modern deep learning architectures remains largely unexplored. In this study, we conduct a novel exploration on how Vision Transformers (ViTs) and Self-Organizing Maps (SOMs) can empower each other, aiming to bridge this critical research gap. Our findings demonstrate that these architectures can synergistically enhance each other, leading to significantly improved performance in both unsupervised and supervised tasks. Code is publicly available on GitHub. - oai:arXiv.org:2503.04121v2 - cs.CV - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/LSP.2025.3643388 - IEEE Signal Processing Letters, 2025, pp. 1-5 - Alan Luo, Kaiwen Yuan - - - Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild - https://arxiv.org/abs/2503.06898 - arXiv:2503.06898v3 Announce Type: replace -Abstract: Single-shot low-light image enhancement (SLLIE) remains challenging due to the limited availability of diverse, real-world paired datasets. To bridge this gap, we introduce the Low-Light Smartphone Dataset (LSD), a large-scale, high-resolution (4K+) dataset collected in the wild across a wide range of challenging lighting conditions (0.1 to 200 lux). LSD contains 6,425 precisely aligned low and normal-light image pairs, selected from over 8,000 dynamic indoor and outdoor scenes through multi-frame acquisition and expert evaluation. To evaluate generalization and aesthetic quality, we collect 2,117 unpaired low-light images from previously unseen devices. To fully exploit LSD, we propose TFFormer, a hybrid model that encodes luminance and chrominance (LC) separately to reduce color-structure entanglement. We further propose a cross-attention-driven joint decoder for context-aware fusion of LC representations, along with LC refinement and LC-guided supervision to significantly enhance perceptual fidelity and structural consistency. TFFormer achieves state-of-the-art results on LSD (+2.45 dB PSNR) and substantially improves downstream vision tasks, such as low-light object detection (+6.80 mAP on ExDark). - oai:arXiv.org:2503.06898v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - S M A Sharif, Abdur Rehman, Zain Ul Abidin, Fayaz Ali Dharejo, Radu Timofte, Rizwan Ali Naqvi - - - Effective and Efficient Jailbreaks of Black-Box LLMs with Cross-Behavior Attacks - https://arxiv.org/abs/2503.08990 - arXiv:2503.08990v2 Announce Type: replace -Abstract: Despite recent advancements in Large Language Models (LLMs) and their alignment, they can still be jailbroken, i.e., harmful and toxic content can be elicited from them. While existing red-teaming methods have shown promise in uncovering such vulnerabilities, these methods struggle with limited success and high computational and monetary costs. To address this, we propose a black-box Jailbreak method with Cross-Behavior attacks (JCB), that can automatically and efficiently find successful jailbreak prompts. JCB leverages successes from past behaviors to help jailbreak new behaviors, thereby significantly improving the attack efficiency. Moreover, JCB does not rely on time- and/or cost-intensive calls to auxiliary LLMs to discover/optimize the jailbreak prompts, making it highly efficient and scalable. Comprehensive experimental evaluations show that JCB significantly outperforms related baselines, requiring up to 94% fewer queries while still achieving 12.9% higher average attack success. JCB also achieves a notably high 37% attack success rate on Llama-2-7B, one of the most resilient LLMs, and shows promising zero-shot transferability across different LLMs. - oai:arXiv.org:2503.08990v2 - cs.CR - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Vasudev Gohil - - - Revisiting Agnostic Boosting - https://arxiv.org/abs/2503.09384 - arXiv:2503.09384v3 Announce Type: replace -Abstract: Boosting is a key method in statistical learning, allowing for converting weak learners into strong ones. While well studied in the realizable case, the statistical properties of weak-to-strong learning remain less understood in the agnostic setting, where there are no assumptions on the distribution of the labels. In this work, we propose a new agnostic boosting algorithm with substantially improved sample complexity compared to prior works under very general assumptions. Our approach is based on a reduction to the realizable case, followed by a margin-based filtering of high-quality hypotheses. Furthermore, we show a nearly-matching lower bound, settling the sample complexity of agnostic boosting up to logarithmic factors. - oai:arXiv.org:2503.09384v3 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Arthur da Cunha, Mikael M{\o}ller H{\o}gsgaard, Andrea Paudice, Yuxin Sun - - - Adjusted Count Quantification Learning on Graphs - https://arxiv.org/abs/2503.09395 - arXiv:2503.09395v2 Announce Type: replace -Abstract: Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem has only been addressed via node clustering methods. In this paper, we extend the popular Adjusted Classify & Count (ACC) method to graphs. We show that the prior probability shift assumption upon which ACC relies is often not applicable to graph quantification problems. To address this issue, we propose structural importance sampling (SIS), the first graph quantification method that is applicable under (structural) covariate shift. Additionally, we propose Neighborhood-aware ACC, which improves quantification in the presence of non-homophilic edges. We show the effectiveness of our techniques on multiple graph quantification tasks. - oai:arXiv.org:2503.09395v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Clemens Damke, Eyke H\"ullermeier - - - Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributions - https://arxiv.org/abs/2503.14553 - arXiv:2503.14553v4 Announce Type: replace -Abstract: Federated Learning (FL) has emerged as one of the prominent paradigms for distributed machine learning (ML). However, it is well-established that its performance can degrade significantly under non-IID (non-independent and identically distributed) data distributions across clients. To study this effect, the existing works predominantly emulate data heterogeneity by imposing label distribution skew across clients. In this paper, we show that label distribution skew fails to fully capture the data heterogeneity in computer vision tasks beyond classification, exposing an overlooked gap in the literature. Motivated by this, by utilizing pre-trained deep neural networks to extract task-specific data embeddings, we define task-specific data heterogeneity through the lens of each vision task and introduce a new level of data heterogeneity called embedding-based data heterogeneity. Our methodology involves clustering data points based on embeddings and distributing them among clients using the Dirichlet distribution. Through extensive experiments, we evaluate the performance of different FL methods under our revamped notion of data heterogeneity, introducing new benchmark performance measures to the literature. For instance, across seven representative computer vision tasks, our embedding-based heterogeneity formulation leads to up to around 60% increase in the observed loss under FedAvg, indicating that it more accurately exposes the performance degradation caused by data heterogeneity. We further unveil a series of open research directions that can be pursued. - oai:arXiv.org:2503.14553v4 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kasra Borazjani, Payam Abdisarabshali, Naji Khosravan, Seyyedali Hosseinalipour - - - A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond - https://arxiv.org/abs/2503.21614 - arXiv:2503.21614v2 Announce Type: replace -Abstract: Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. However, a growing concern lies in their tendency to produce excessively long reasoning traces, which are often filled with redundant content (e.g., repeated definitions), over-analysis of simple problems, and superficial exploration of multiple reasoning paths for harder tasks. This inefficiency introduces significant challenges for training, inference, and real-world deployment (e.g., in agent-based systems), where token economy is critical. In this survey, we provide a comprehensive overview of recent efforts aimed at improving reasoning efficiency in LRMs, with a particular focus on the unique challenges that arise in this new paradigm. We identify common patterns of inefficiency, examine methods proposed across the LRM lifecycle, i.e., from pretraining to inference, and discuss promising future directions for research. To support ongoing development, we also maintain a real-time GitHub repository tracking recent progress in the field. We hope this survey serves as a foundation for further exploration and inspires innovation in this rapidly evolving area. - oai:arXiv.org:2503.21614v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xiaoye Qu, Yafu Li, Zhao-Chen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, Bowen Zhou, Yu Cheng - - - AINav: Large Language Model-Based Adaptive Interactive Navigation - https://arxiv.org/abs/2503.22942 - arXiv:2503.22942v2 Announce Type: replace -Abstract: Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive interactive navigation approach that proactively interacts with environments to create feasible paths to achieve originally unreachable goals. Specifically, we present a primitive skill tree for task planning with large language models (LLMs), facilitating effective reasoning to determine interaction objects and sequences. To ensure robust subtask execution, we adopt reinforcement learning to pre-train a comprehensive skill library containing versatile locomotion and interaction behaviors for motion planning. Furthermore, we introduce an adaptive replanning approach featuring two LLM-based modules: an advisor serving as a flexible replanning trigger and an arborist for autonomous plan adjustment. Integrated with the tree structure, the replanning mechanism allows for convenient node addition and pruning, enabling rapid plan adaptation in a priori unknown environments. Comprehensive simulations and experiments have demonstrated AINav's effectiveness and adaptivity in diverse scenarios. The supplementary video is available at: https://youtu.be/CjXm5KFx9AI. - oai:arXiv.org:2503.22942v2 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/MRA.2025.3639793 - Kangjie Zhou, Yao Mu, Haoyang Song, Yi Zeng, Pengying Wu, Han Gao, Chang Liu - - - Lattice: Learning to Efficiently Compress the Memory - https://arxiv.org/abs/2504.05646 - arXiv:2504.05646v2 Announce Type: replace -Abstract: Attention mechanisms have revolutionized sequence learning but suffer from quadratic computational complexity. This paper introduces \model, a novel recurrent neural network (RNN) mechanism that leverages the inherent low-rank structure of K-V matrices to efficiently compress the cache into a fixed number of memory slots, achieving sub-quadratic complexity. We formulate this compression as an online optimization problem and derive a dynamic memory update rule based on a single gradient descent step. The resulting recurrence features a state- and input-dependent gating mechanism, offering an interpretable memory update process. The core innovation is the orthogonal update: each memory slot is updated exclusively with information orthogonal to its current state, hence incorporating only novel, non-redundant data to minimize interference with previously stored information. We derive an efficient computation for this orthogonal update rule and further approximate it with chunk-wise parallelization to ensure training scalability. Empirically, Lattice outperforms strong baselines on language modeling and associative recall tasks across diverse context lengths and model sizes, achieving superior memory efficiency with significantly reduced memory sizes. - oai:arXiv.org:2504.05646v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mahdi Karami, Razvan Pascanu, Vahab Mirrokni - - - Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities - https://arxiv.org/abs/2504.06313 - arXiv:2504.06313v4 Announce Type: replace -Abstract: This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompted to generate images of individuals engaging in common everyday activities. Five scenarios were developed, and 2,060 images were generated using input prompts that specified nationalities across five activities. When aggregating across activities and models, results showed that 28.4% of the images depicted individuals wearing traditional attire, including attire that is impractical for the specified activities in several cases. This pattern was statistically significantly associated with regions, with the Middle East & North Africa and Sub-Saharan Africa disproportionately affected, and was also associated with World Bank income groups. Similar region- and income-linked patterns were observed for images labeled as depicting impractical attire in two athletics-related activities. To assess image-text alignment, CLIP, ALIGN, and GPT-4.1 mini were used to score 9,270 image-prompt pairs. Images labeled as featuring traditional attire received statistically significantly higher alignment scores when prompts included country names, and this pattern weakened or reversed when country names were removed. Revised prompt analysis showed that one model frequently inserted the word "traditional" (50.3% for traditional-labeled images vs. 16.6% otherwise). These results indicate that these representational patterns can be shaped by several components of the pipeline, including image generator, evaluation models, and prompt revision. - oai:arXiv.org:2504.06313v4 - cs.CV - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Abdulkareem Alsudais - - - Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration - https://arxiv.org/abs/2504.09973 - arXiv:2504.09973v3 Announce Type: replace -Abstract: All-in-One Image Restoration (AiOIR), which addresses diverse degradation types with a unified model, presents significant challenges in designing task-aware prompts that effectively guide restoration across multiple degradation scenarios. While adaptive prompt learning enables end-to-end optimization, it often yields overlapping or redundant task representations. Conversely, explicit prompts derived from pretrained classifiers enhance discriminability but discard critical visual information needed for reconstruction. To address these limitations, we introduce Contrastive Prompt Learning (CPL), a framework that aims to improve prompt-task alignment through two complementary components: a Sparse Prompt Module (SPM) that efficiently captures degradation-aware representations while reducing redundancy, and a Contrastive Prompt Regularization (CPR) that explicitly strengthens task boundaries by incorporating negative prompt samples across different degradation types. Unlike previous approaches that focus primarily on degradation classification, CPL directly optimizes the interaction between prompts and the restoration model. Extensive experiments across five benchmarks show that CPL consistently boosts the performance of strong AiOIR baselines across diverse scenarios. Our approach achieves state-of-the-art average performance on these benchmarks, providing a general and robust solution for AiOIR. The code is available at https://github.com/Aitical/CPLIR - oai:arXiv.org:2504.09973v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, Liqiang Nie - - - xVerify: Efficient Answer Verifier for Reasoning Model Evaluations - https://arxiv.org/abs/2504.10481 - arXiv:2504.10481v2 Announce Type: replace -Abstract: With the release of OpenAI's o1 model, reasoning models that adopt slow-thinking strategies have become increasingly common. Their outputs often contain complex reasoning, intermediate steps, and self-reflection, making existing evaluation methods and reward models inadequate. In particular, they struggle to judge answer equivalence and to reliably extract final answers from long, complex responses. To address this challenge, we propose xVerify, an efficient answer verifier for evaluating reasoning models. xVerify shows strong equivalence judgment capabilities, enabling accurate comparison between model outputs and reference answers across diverse question types. To train and evaluate xVerify, we construct the VAR dataset, which consists of question-answer pairs generated by multiple LLMs across various datasets. The dataset incorporates multiple reasoning models and challenging evaluation sets specifically designed for reasoning assessment, with a multi-round annotation process to ensure label quality. Based on VAR, we train xVerify models at different scales. Experimental results on both test and generalization sets show that all xVerify variants achieve over 95% F1 score and accuracy. Notably, the smallest model, xVerify-0.5B-I, outperforms all evaluation methods except GPT-4o, while xVerify-3B-Ib surpasses GPT-4o in overall performance. In addition, reinforcement learning experiments using xVerify as the reward model yield an 18.4% improvement for Qwen2.5-7B compared with direct generation, exceeding the gains achieved with Math Verify as the reward. These results demonstrate the effectiveness and generalizability of xVerify. All xVerify resources are available on \href{https://github.com/IAAR-Shanghai/xVerify}{GitHub}. - oai:arXiv.org:2504.10481v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ding Chen, Qingchen Yu, Pengyuan Wang, Mengting Hu, Wentao Zhang, Zhengren Wang, Bo Tang, Feiyu Xiong, Xinchi Li, Chao Wang, Minchuan Yang, Zhiyu Li - - - Pre-DPO: Improving Data Utilization in Direct Preference Optimization Using a Guiding Reference Model - https://arxiv.org/abs/2504.15843 - arXiv:2504.15843v3 Announce Type: replace -Abstract: Direct Preference Optimization (DPO) simplifies reinforcement learning from human feedback (RLHF) for large language models (LLMs) by directly optimizing human preferences without an explicit reward model. We find that during DPO training, the reference model plays the role of a data weight adjuster. However, the common practice of initializing the policy and reference models identically in DPO can lead to inefficient data utilization and impose a performance ceiling. Meanwhile, the lack of a reference model in Simple Preference Optimization (SimPO) reduces training robustness and necessitates stricter conditions to prevent catastrophic forgetting. In this work, we propose Pre-DPO, a simple yet effective DPO-based training paradigm that enhances preference optimization performance by leveraging a guiding reference model. This reference model provides foresight into the optimal policy state achievable through the training preference data, serving as a guiding mechanism that adaptively assigns higher weights to samples more suitable for the model and lower weights to those less suitable. Extensive experiments on AlpacaEval 2.0 and Arena-Hard v0.1 benchmarks demonstrate that Pre-DPO consistently improves the performance of both DPO and SimPO, without relying on external models or additional data. - oai:arXiv.org:2504.15843v3 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Junshu Pan, Wei Shen, Shulin Huang, Qiji Zhou, Yue Zhang - - - ParetoHqD: Fast Offline Multiobjective Alignment of Large Language Models using Pareto High-quality Data - https://arxiv.org/abs/2504.16628 - arXiv:2504.16628v3 Announce Type: replace -Abstract: Aligning large language models with multiple human expectations and values is crucial for ensuring that they adequately serve a variety of user needs. To this end, offline multiobjective alignment algorithms such as the Rewards-in-Context algorithm have shown strong performance and efficiency. However, inappropriate preference representations and training with imbalanced reward scores limit the performance of such algorithms. In this work, we introduce ParetoHqD that addresses the above issues by representing human preferences as preference directions in the objective space and regarding data near the Pareto front as "high-quality" data. For each preference, ParetoHqD follows a two-stage supervised fine-tuning process, where each stage uses an individual Pareto high-quality training set that best matches its preference direction. The experimental results have demonstrated the superiority of ParetoHqD over five baselines on two multiobjective alignment tasks. - oai:arXiv.org:2504.16628v3 - cs.LG - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haoran Gu, Handing Wang, Yi Mei, Mengjie Zhang, Yaochu Jin - - - Dynamic Approximate Maximum Matching in the Distributed Vertex Partition Model - https://arxiv.org/abs/2504.17338 - arXiv:2504.17338v3 Announce Type: replace -Abstract: We initiate the study of approximate maximum matching in the vertex partition model, for graphs subject to dynamic changes. We assume that the $n$ vertices of the graph are partitioned among $k$ players, who execute a distributed algorithm and communicate via message passing. An adaptive adversary may perform dynamic updates to the graph topology by inserting or removing edges between the nodes, and the algorithm needs to respond to these changes by adapting the output of the players, with the goal of maintaining an approximate maximum matching. The main performance metric in this setting is the algorithm's update time, which corresponds to the number of rounds required for updating the solution upon an adversarial change. For the standard setting of single-edge insertions and deletions, we give a randomized Las Vegas algorithm with an expected update time of $O( \lceil \frac{\sqrt{m}}{\beta k} \rceil )$ rounds that maintains a $\frac{2}{3}$-approximate maximum matching that is also maximal, where $m$ is the number of edges in the graph and $\beta$ is the available link bandwidth. For batch-dynamic updates, where the adversary may insert up to $\ell\ge 1$ edges at once, we prove the following. There is a randomized algorithm that succeeds with high probability in maintaining a $\frac{2}{3}$-approximate maximum matching and has a worst case update time of $O(\lceil\frac{\ell\log n}{\sqrt{\beta k}}\rceil )$ rounds. Any algorithm for maintaining a maximal matching without 3-augmenting paths under batches of $\ell$-edge insertions has an update time of $\Omega( \frac{\ell}{\beta k \log n} )$ rounds in the worst case. - oai:arXiv.org:2504.17338v3 - cs.DC - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Peter Robinson, Xianbin Zhu - - - ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding - https://arxiv.org/abs/2504.18785 - arXiv:2504.18785v3 Announce Type: replace -Abstract: We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video, and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representations that capture both content and behavioral patterns. Our model achieves state-of-the-art performance on critical tasks including fraud detection, policy violation identification, and advertiser similarity matching. In production deployment, ALF demonstrates significant real-world impact by delivering simultaneous gains in both precision and recall, for instance boosting recall by over 40 percentage points on one critical policy and increasing precision to 99.8% on another. The architecture's effectiveness stems from its novel combination of multi-modal transformations, inter-sample attention mechanism, spectrally normalized projections, and calibrated probabilistic outputs. - oai:arXiv.org:2504.18785v3 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1145/3770854.3783927 - Santosh Rajagopalan, Jonathan Vronsky, Songbai Yan, S. Alireza Golestaneh, Shubhra Chandra, Min Zhou - - - Neurosymbolic Association Rule Mining from Tabular Data - https://arxiv.org/abs/2504.19354 - arXiv:2504.19354v5 Announce Type: replace -Abstract: Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimensional datasets often result in an excessive number of rules, increasing execution time and negatively impacting downstream task performance. Managing this rule explosion remains a central challenge in ARM research. To address this, we introduce Aerial+, a novel neurosymbolic ARM method. Aerial+ leverages an under-complete autoencoder to create a neural representation of the data, capturing associations between features. It extracts rules from this neural representation by exploiting the model's reconstruction mechanism. Extensive evaluations on five datasets against seven baselines demonstrate that Aerial+ achieves state-of-the-art results by learning more concise, high-quality rule sets with full data coverage. When integrated into rule-based interpretable machine learning models, Aerial+ significantly reduces execution time while maintaining or improving accuracy. - oai:arXiv.org:2504.19354v5 - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning, PMLR 284:565-588, 2025 - Erkan Karabulut, Paul Groth, Victoria Degeler - - - Analysis of Errors in Robotic Surgical Skill Acquisition with Video-Based Detection - https://arxiv.org/abs/2504.19571 - arXiv:2504.19571v2 Announce Type: replace -Abstract: Robot-assisted minimally invasive surgeries offer many advantages but require complex motor tasks that take surgeons years to master. There is currently a lack of knowledge on how surgeons acquire these robotic surgical skills. Toward bridging this gap, a previous study followed surgical residents learning complex surgical dry lab tasks on a surgical robot over six months. Errors are an important measure for training and skill evaluation, but unlike in virtual simulations, in dry lab training, errors are difficult to monitor automatically. Here, we analyzed errors in the ring tower transfer task, in which surgical residents moved a ring along a curved wire as quickly and accurately as possible. We developed an image-processing algorithm using color and size thresholds, optical flow and short time Fourier transforms to detect collision errors and achieved a detection accuracy of approximately 95%. Using the detected errors and task completion time, we found that the residents reduced their completion time and number of errors over the six months, while the percentage of task time spent making errors remained relatively constant on average. This analysis sheds light on the learning process of the residents and can serve as a step towards providing error-related feedback to robotic surgeons. - oai:arXiv.org:2504.19571v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Hanna Kossowsky Lev, Yarden Sharon, Alex Geftler, Ilana Nisky - - - Adapting In-Domain Few-Shot Segmentation to New Domains without Source Domain Retraining - https://arxiv.org/abs/2504.21414 - arXiv:2504.21414v4 Announce Type: replace -Abstract: Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains, which is often challenging due to the diverse characteristics of target domains and the limited availability of support data. Most CD-FSS methods redesign and retrain in-domain FSS models using abundant base data from the source domain, which are effective but costly to train. To address these issues, we propose adapting informative model structures of the well-trained FSS model for target domains by learning domain characteristics from few-shot labeled support samples during inference, thereby eliminating the need for source domain retraining. Specifically, we first adaptively identify domain-specific model structures by measuring parameter importance using a novel structure Fisher score in a data-dependent manner. Then, we progressively train the selected informative model structures with hierarchically constructed training samples, progressing from fewer to more support shots. The resulting Informative Structure Adaptation (ISA) method effectively addresses domain shifts and equips existing well-trained in-domain FSS models with flexible adaptation capabilities for new domains, eliminating the need to redesign or retrain CD-FSS models on base data. Extensive experiments validate the effectiveness of our method, demonstrating superior performance across multiple CD-FSS benchmarks. Codes are at https://github.com/fanq15/ISA. - oai:arXiv.org:2504.21414v4 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qi Fan, Kaiqi Liu, Nian Liu, Hisham Cholakkal, Rao Muhammad Anwer, Wenbin Li, Yang Gao - - - Zoomer: Adaptive Image Focus Optimization for Black-box MLLM - https://arxiv.org/abs/2505.00742 - arXiv:2505.00742v2 Announce Type: replace -Abstract: Multimodal large language models (MLLMs) such as GPT-4o, Gemini Pro, and Claude 3.5 have enabled unified reasoning over text and visual inputs, yet they often hallucinate in real world scenarios especially when small objects or fine spatial context are involved. We pinpoint two core causes of this failure: the absence of region-adaptive attention and inflexible token budgets that force uniform downsampling, leading to critical information loss. To overcome these limitations, we introduce Zoomer, a visual prompting framework that delivers token-efficient, detail-preserving image representations for black-box MLLMs. Zoomer integrates (1) a prompt-aware emphasis module to highlight semantically relevant regions, (2) a spatial-preserving orchestration schema to maintain object relationships, and (3) a budget-aware strategy to adaptively allocate tokens between global context and local details. Extensive experiments on nine benchmarks and three commercial MLLMs demonstrate that Zoomer boosts accuracy by up to 27% while cutting image token usage by up to 67%. Our approach establishes a principled methodology for robust, resource-aware multimodal understanding in settings where model internals are inaccessible. - oai:arXiv.org:2505.00742v2 - cs.CV - cs.AI - eess.IV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jiaxu Qian, Chendong Wang, Yifan Yang, Chaoyun Zhang, Huiqiang Jiang, Xufang Luo, Yu Kang, Qingwei Lin, Anlan Zhang, Shiqi Jiang, Ting Cao, Tianjun Mao, Suman Banerjee, Guyue Liu, Saravan Rajmohan, Dongmei Zhang, Yuqing Yang, Qi Zhang, Lili Qiu - - - Multi-Antenna Users in Cell-Free Massive MIMO: Stream Allocation and Necessity of Downlink Pilots - https://arxiv.org/abs/2505.02951 - arXiv:2505.02951v2 Announce Type: replace -Abstract: We consider a cell-free massive multiple-input multiple-output (MIMO) system with multiple antennas on the users and access points (APs). In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well for single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent when having multi-antenna users. The reason is that the effective channel matrix does not harden. We propose a pilot-based downlink estimation scheme, derive a new SE expression, and show numerically that it yields substantially higher performance when having correlated Rayleigh fading channels. - In cases with multi-antenna users, the APs can either transmit the same or different data streams. The latter reduces the fronthaul signaling but comes with a SE loss. We propose precoding and combining schemes for these cases and consider whether channel knowledge is shared between the APs. Finally, we show numerically how the number of users, APs, and the number of antennas on users and APs affect the SE. - oai:arXiv.org:2505.02951v2 - cs.IT - eess.SP - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1109/TCOMM.2025.3647734 - Eren Berk Kama, Junbeom Kim, Emil Bj\"ornson - - - An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation - https://arxiv.org/abs/2505.03452 - arXiv:2505.03452v3 Announce Type: replace -Abstract: Optimizing Retrieval-Augmented Generation (RAG) configurations for specific tasks is a complex and resource-intensive challenge. Motivated by this challenge, frameworks for RAG hyper-parameter optimization (HPO) have recently emerged, yet their effectiveness has not been rigorously benchmarked. To fill this gap, we present a comprehensive study involving five HPO algorithms over five datasets from diverse domains, including a newly curated real-world product documentation dataset. Our study explores the largest RAG HPO search space to date that includes full grid-search evaluations, and uses three evaluation metrics as optimization targets. Analysis of the results shows that RAG HPO can be done efficiently, either greedily or with random search, and that it significantly boosts RAG performance for all datasets. For greedy HPO approaches, we show that optimizing model selection first is preferable to the common practice of following the RAG pipeline order during optimization. - oai:arXiv.org:2505.03452v3 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Matan Orbach, Ohad Eytan, Benjamin Sznajder, Ariel Gera, Odellia Boni, Yoav Kantor, Gal Bloch, Omri Levy, Hadas Abraham, Nitzan Barzilay, Eyal Shnarch, Michael E. Factor, Shila Ofek-Koifman, Paula Ta-Shma, Assaf Toledo - - - Unveiling the Role of ChatGPT in Software Development: Insights from Developer-ChatGPT Interactions on GitHub - https://arxiv.org/abs/2505.03901 - arXiv:2505.03901v3 Announce Type: replace -Abstract: The advent of Large Language Models (LLMs) has introduced a new paradigm in Software Engineering (SE), with generative AI tools like ChatGPT gaining widespread adoption among developers. While ChatGPT's potential has been extensively discussed, empirical evidence about how developers actually use LLMs' assistance in real-world practices remains limited. To bridge this gap, we conducted a large-scale empirical analysis of ChatGPT usage on GitHub, and we presented DevChat, a curated dataset of 2,547 publicly shared ChatGPT conversation links collected from GitHub between May 2023 and June 2024. Through comprehensively analyzing DevChat, we explored the characteristics of developer-ChatGPT interaction patterns and identified five key categories of developers' purposes for sharing developer-ChatGPT conversations during software development. Additionally, we investigated the dominant development-related activities in which ChatGPT is used, and presented a mapping framework that links GitHub data sources, development-related activities, and SE tasks. The findings show that interactions are typically short and task-focused (most are 1-3 turns); developers share conversations mainly to delegate tasks, resolve problems, and acquire knowledge, revealing five purpose categories; ChatGPT is most frequently engaged for Software Implementation and Maintenance & Evolution; we identified 39 fine-grained SE tasks supported by ChatGPT, with Code Generation & Completion as well as Code modification & Optimization being the most prominent. Our study offers a comprehensive mapping of ChatGPT's applications in real-world software development scenarios and provides a foundation for understanding LLMs' practical roles in software development. - oai:arXiv.org:2505.03901v3 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Ruiyin Li, Peng Liang, Yifei Wang, Yangxiao Cai, Weisong Sun, Zengyang Li - - - Selfish, Local and Online Scheduling via Vector Fitting - https://arxiv.org/abs/2505.10082 - arXiv:2505.10082v3 Announce Type: replace -Abstract: We provide a dual fitting technique on a semidefinite program yielding simple proofs of tight bounds for the robust price of anarchy of several congestion and scheduling games under the sum of weighted completion times objective. The same approach also allows to bound the approximation ratio of local search algorithms and the competitive ratio of online algorithms for the scheduling problem $R || \sum w_j C_j$. All of our results are obtained through a simple unified dual fitting argument on the same semidefinite programming relaxation, which can essentially be obtained through the first round of the Lasserre/Sum of Squares hierarchy. - As our main application, we show that the known coordination ratio bounds of respectively $4, (3 + \sqrt{5})/2 \approx 2.618,$ and $32/15 \approx 2.133$ for the scheduling game $R || \sum w_j C_j$ under the coordination mechanisms Smith's Rule, Proportional Sharing and Rand (STOC 2011) can be extended to congestion games and obtained through this approach. For the natural restriction where the weight of each player is proportional to its processing time on every resource, we show that the last bound can be improved from 2.133 to 2. This improvement can also be made for general instances when considering the price of anarchy of the game, rather than the coordination ratio. As a further application of this technique, we show that it recovers the tight bound of $(3 + \sqrt{5})/2$ for the price of anarchy of weighted affine congestion games and the Kawaguchi-Kyan bound of $(1+ \sqrt{2})/2$ for the pure price of anarchy of $P || \sum w_j C_j$. Moreover, this approach can analyze a simple local search algorithm for $R || \sum w_j C_j$, the best currently known combinatorial approximation algorithm for this problem achieving an approximation ratio of $(5 + \sqrt{5})/4 + \varepsilon$ and an online greedy algorithm which is $4$-competitive. - oai:arXiv.org:2505.10082v3 - cs.GT - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Danish Kashaev - - - Neural Field Equations with random data - https://arxiv.org/abs/2505.16343 - arXiv:2505.16343v3 Announce Type: replace -Abstract: We study neural field equations, which are prototypical models of large-scale cortical activity, subject to random data. We view this spatially-extended, nonlocal evolution equation as a Cauchy problem on abstract Banach spaces, with randomness in the synaptic kernel, firing rate function, external stimuli, and initial conditions. We determine conditions on the random data that guarantee existence, uniqueness, and measurability of the solution in an appropriate Banach space, and examine the regularity of the solution in relation to the regularity of the inputs. We present results for linear and nonlinear neural fields, and for the two most common functional setups in the numerical analysis of this problem. In addition to the continuous problem, we analyse in abstract form neural fields that have been spatially discretised, setting the foundations for analysing uncertainty quantification (UQ) schemes. - oai:arXiv.org:2505.16343v3 - math.NA - cs.NA - math.DS - math.PR - nlin.PS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Daniele Avitabile, Francesca Cavallini, Svetlana Dubinkina, Gabriel J. Lord - - - MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding - https://arxiv.org/abs/2505.20298 - arXiv:2505.20298v2 Announce Type: replace -Abstract: Manga, or Japanese comics, is a richly multimodal narrative form that blends images and text in complex ways. Teaching large multimodal models (LMMs) to understand such narratives at a human-like level could help manga creators reflect on and refine their stories. To this end, we introduce two benchmarks for multimodal manga understanding: MangaOCR, which targets in-page text recognition, and MangaVQA, a novel benchmark designed to evaluate contextual understanding through visual question answering. MangaVQA consists of 526 high-quality, manually constructed question-answer pairs, enabling reliable evaluation across diverse narrative and visual scenarios. Building on these benchmarks, we develop MangaLMM, a manga-specialized model finetuned from the open-source LMM Qwen2.5-VL to jointly handle both tasks. Through extensive experiments, including comparisons with proprietary models such as GPT-4o and Gemini 2.5, we assess how well LMMs understand manga. Our benchmark and model provide a comprehensive foundation for evaluating and advancing LMMs in the richly narrative domain of manga. - oai:arXiv.org:2505.20298v2 - cs.CL - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jeonghun Baek, Kazuki Egashira, Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Hikaru Ikuta, Kiyoharu Aizawa - - - OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation - https://arxiv.org/abs/2505.20425 - arXiv:2505.20425v2 Announce Type: replace -Abstract: Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos. In the one-shot setting, the agent generates a policy after observing a single expert demonstration without additional fine-tuning. Existing approaches typically train and evaluate on the same set of tasks, varying only object configurations, and struggle to generalize to unseen tasks with different semantic or structural requirements. While some recent methods attempt to address this, they exhibit low success rates on hard test tasks that, despite being visually similar to some training tasks, differ in context and require distinct responses. Additionally, most existing methods lack an explicit model of environment dynamics, limiting their ability to reason about future states. To address these limitations, we propose a novel framework for one-shot visual imitation learning via world-model-guided trajectory generation. Given an expert demonstration video and the agent's initial observation, our method leverages a learned world model to predict a sequence of latent states and actions. This latent trajectory is then decoded into physical waypoints that guide the agent's execution. Our method is evaluated on two simulated benchmarks and three real-world robotic platforms, where it consistently outperforms prior approaches, with over 30% improvement in some cases. The code is available at https://github.com/raktimgg/osvi-wm. - oai:arXiv.org:2505.20425v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Raktim Gautam Goswami, Prashanth Krishnamurthy, Yann LeCun, Farshad Khorrami - - - Do LLMs Understand Collaborative Signals? Diagnosis and Repair - https://arxiv.org/abs/2505.20730 - arXiv:2505.20730v3 Announce Type: replace -Abstract: Collaborative information from user-item interactions is a fundamental source of signal in successful recommender systems. Recently, researchers have attempted to incorporate this knowledge into large language model-based recommender approaches (LLMRec) to enhance their performance. However, there has been little fundamental analysis of whether LLMs can effectively reason over collaborative information. In this paper, we analyze the ability of LLMs to reason about collaborative information in recommendation tasks, comparing their performance to traditional matrix factorization (MF) models. We propose a simple and effective method to improve LLMs' reasoning capabilities using retrieval-augmented generation (RAG) over the user-item interaction matrix with four different prompting strategies. Our results show that the LLM outperforms the MF model whenever we provide relevant information in a clear and easy-to-follow format, and prompt the LLM to reason based on it. We observe that with this strategy, in almost all cases, the more information we provide, the better the LLM performs. - oai:arXiv.org:2505.20730v3 - cs.IR - cs.AI - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shahrooz Pouryousef, Ali Montazeralghaem - - - GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking - https://arxiv.org/abs/2505.22228 - arXiv:2505.22228v2 Announce Type: replace -Abstract: Video text spotting (VTS) extends image text spotting (ITS) by adding text tracking, significantly increasing task complexity. Despite progress in VTS, existing methods still fall short of the performance seen in ITS. This paper identifies a key limitation in current video text spotters: limited recognition capability, even after extensive end-to-end training. To address this, we propose GoMatching++, a parameter- and data-efficient method that transforms an off-the-shelf image text spotter into a video specialist. The core idea lies in freezing the image text spotter and introducing a lightweight, trainable tracker, which can be optimized efficiently with minimal training data. Our approach includes two key components: (1) a rescoring mechanism to bridge the domain gap between image and video data, and (2) the LST-Matcher, which enhances the frozen image text spotter's ability to handle video text. We explore various architectures for LST-Matcher to ensure efficiency in both parameters and training data. As a result, GoMatching++ sets new performance records on challenging benchmarks such as ICDAR15-video, DSText, and BOVText, while significantly reducing training costs. To address the lack of curved text datasets in VTS, we introduce ArTVideo, a new benchmark featuring over 30% curved text with detailed annotations. We also provide a comprehensive statistical analysis and experimental results for ArTVideo. We believe that GoMatching++ and the ArTVideo benchmark will drive future advancements in video text spotting. The source code, models and dataset are publicly available at https://github.com/Hxyz-123/GoMatching. - oai:arXiv.org:2505.22228v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haibin He, Jing Zhang, Maoyuan Ye, Juhua Liu, Bo Du, Dacheng Tao - - - Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs - https://arxiv.org/abs/2505.24830 - arXiv:2505.24830v3 Announce Type: replace -Abstract: Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, such as Retrieval Augmented Generation, partially address these issues by grounding answers in source documents, but hallucinations and low fact-level explainability persist. In this work, we introduce a novel atomic fact-checking framework designed to enhance the reliability and explainability of LLMs used in medical long-form question answering. This method decomposes LLM-generated responses into discrete, verifiable units called atomic facts, each of which is independently verified against an authoritative knowledge base of medical guidelines. This approach enables targeted correction of errors and direct tracing to source literature, thereby improving the factual accuracy and explainability of medical Q&A. Extensive evaluation using multi-reader assessments by medical experts and an automated open Q&A benchmark demonstrated significant improvements in factual accuracy and explainability. Our framework achieved up to a 40% overall answer improvement and a 50% hallucination detection rate. The ability to trace each atomic fact back to the most relevant chunks from the database provides a granular, transparent explanation of the generated responses, addressing a major gap in current medical AI applications. This work represents a crucial step towards more trustworthy and reliable clinical applications of LLMs, addressing key prerequisites for clinical application and fostering greater confidence in AI-assisted healthcare. - oai:arXiv.org:2505.24830v3 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Juraj Vladika, Annika Domres, Mai Nguyen, Rebecca Moser, Jana Nano, Felix Busch, Lisa C. Adams, Keno K. Bressem, Denise Bernhardt, Stephanie E. Combs, Kai J. Borm, Florian Matthes, Jan C. Peeken - - - TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection - https://arxiv.org/abs/2505.24866 - arXiv:2505.24866v2 Announce Type: replace -Abstract: The rapid advancement of talking-head deepfake generation fueled by advanced generative models has elevated the realism of synthetic videos to a level that poses substantial risks in domains such as media, politics, and finance. However, current benchmarks for deepfake talking-head detection fail to reflect this progress, relying on outdated generators and offering limited insight into model robustness and generalization. We introduce TalkingHeadBench, a comprehensive multi-model multi-generator benchmark and curated dataset designed to evaluate the performance of state-of-the-art detectors on the most advanced generators. Our dataset includes deepfakes synthesized by leading academic and commercial models and features carefully constructed protocols to assess generalization under distribution shifts in identity and generator characteristics. We benchmark a diverse set of existing detection methods, including CNNs, vision transformers, and temporal models, and analyze their robustness and generalization capabilities. In addition, we provide error analysis using Grad-CAM visualizations to expose common failure modes and detector biases. TalkingHeadBench is hosted on https://huggingface.co/datasets/luchaoqi/TalkingHeadBench with open access to all data splits and protocols. Our benchmark aims to accelerate research towards more robust and generalizable detection models in the face of rapidly evolving generative techniques. - oai:arXiv.org:2505.24866v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xinqi Xiong, Prakrut Patel, Qingyuan Fan, Amisha Wadhwa, Sarathy Selvam, Xiao Guo, Luchao Qi, Xiaoming Liu, Roni Sengupta - - - Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task? - https://arxiv.org/abs/2506.01482 - arXiv:2506.01482v2 Announce Type: replace -Abstract: Stage lighting is a vital component in live music performances, shaping an engaging experience for both musicians and audiences. In recent years, Automatic Stage Lighting Control (ASLC) has attracted growing interest due to the high costs of hiring or training professional lighting engineers. However, most existing ASLC solutions only classify music into limited categories and map them to predefined light patterns, resulting in formulaic and monotonous outcomes that lack rationality. To address this gap, this paper presents Skip-BART, an end-to-end model that directly learns from experienced lighting engineers and predict vivid, human-like stage lighting. To the best of our knowledge, this is the first work to conceptualize ASLC as a generative task rather than merely a classification problem. Our method adapts the BART model to take audio music as input and produce light hue and value (intensity) as output, incorporating a novel skip connection mechanism to enhance the relationship between music and light within the frame grid. To address the lack of available datasets, we create the first stage lighting dataset, along with several pre-training and transfer learning techniques to improve model training with limited data. We validate our method through both quantitative analysis and an human evaluation, demonstrating that Skip-BART outperforms conventional rule-based methods across all evaluation metrics and shows only a limited gap compared to real lighting engineers. To support further research, we have made our self-collected dataset, code, and trained model parameters available at https://github.com/RS2002/Skip-BART . - oai:arXiv.org:2506.01482v2 - cs.LG - cs.AI - cs.MM - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zijian Zhao, Dian Jin, Zijing Zhou, Xiaoyu Zhang - - - Controllable Human-centric Keyframe Interpolation with Generative Prior - https://arxiv.org/abs/2506.03119 - arXiv:2506.03119v2 Announce Type: replace -Abstract: Existing interpolation methods use pre-trained video diffusion priors to generate intermediate frames between sparsely sampled keyframes. In the absence of 3D geometric guidance, these methods struggle to produce plausible results for complex, articulated human motions and offer limited control over the synthesized dynamics. In this paper, we introduce PoseFuse3D Keyframe Interpolator (PoseFuse3D-KI), a novel framework that integrates 3D human guidance signals into the diffusion process for Controllable Human-centric Keyframe Interpolation (CHKI). To provide rich spatial and structural cues for interpolation, our PoseFuse3D, a 3D-informed control model, features a novel SMPL-X encoder that transforms 3D geometry and shape into the 2D latent conditioning space, alongside a fusion network that integrates these 3D cues with 2D pose embeddings. For evaluation, we build CHKI-Video, a new dataset annotated with both 2D poses and 3D SMPL-X parameters. We show that PoseFuse3D-KI consistently outperforms state-of-the-art baselines on CHKI-Video, achieving a 9% improvement in PSNR and a 38% reduction in LPIPS. Comprehensive ablations demonstrate that our PoseFuse3D model improves interpolation fidelity. - oai:arXiv.org:2506.03119v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Zujin Guo, Size Wu, Zhongang Cai, Wei Li, Chen Change Loy - - - Not All Tokens Are Meant to Be Forgotten - https://arxiv.org/abs/2506.03142 - arXiv:2506.03142v2 Announce Type: replace -Abstract: Large Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they tend to memorize unwanted information, such as private or copyrighted content, raising significant privacy and legal concerns. Unlearning has emerged as a promising solution, but existing methods face a significant challenge of over-forgetting. This issue arises because they indiscriminately suppress the generation of all the tokens in forget samples, leading to a substantial loss of model utility. To overcome this challenge, we introduce the Targeted Information Forgetting (TIF) framework, which consists of (1) a flexible targeted information identifier designed to differentiate between unwanted words (UW) and general words (GW) in the forget samples, and (2) a novel Targeted Preference Optimization approach that leverages Logit Preference Loss to unlearn unwanted information associated with UW and Preservation Loss to retain general information in GW, effectively improving the unlearning process while mitigating utility degradation. Extensive experiments on the TOFU and MUSE benchmarks demonstrate that the proposed TIF framework enhances unlearning effectiveness while preserving model utility and achieving state-of-the-art results. - oai:arXiv.org:2506.03142v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xiangyu Zhou, Yao Qiang, Saleh Zare Zade, Douglas Zytko, Prashant Khanduri, Dongxiao Zhu - - - Contextual Integrity in LLMs via Reasoning and Reinforcement Learning - https://arxiv.org/abs/2506.04245 - arXiv:2506.04245v4 Announce Type: replace -Abstract: As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) -- what is the appropriate information to share while carrying out a certain task -- becomes a central question to the field. We posit that CI demands a form of reasoning where the agent needs to reason about the context in which it is operating. To test this, we first prompt LLMs to reason explicitly about CI when deciding what information to disclose. We then extend this approach by developing a reinforcement learning (RL) framework that further instills in models the reasoning necessary to achieve CI. Using a synthetic, automatically created, dataset of only $\sim700$ examples but with diverse contexts and information disclosure norms, we show that our method substantially reduces inappropriate information disclosure while maintaining task performance across multiple model sizes and families. Importantly, improvements transfer from this synthetic dataset to established CI benchmarks such as PrivacyLens that has human annotations and evaluates privacy leakage of AI assistants in actions and tool calls. Our code is available at: https://github.com/EricGLan/CI-RL - oai:arXiv.org:2506.04245v4 - cs.AI - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Guangchen Lan, Huseyin A. Inan, Sahar Abdelnabi, Janardhan Kulkarni, Lukas Wutschitz, Reza Shokri, Christopher G. Brinton, Robert Sim - - - BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning - https://arxiv.org/abs/2506.05762 - arXiv:2506.05762v3 Announce Type: replace -Abstract: Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints on pre-collected datasets. However, such static datasets often exhibit distribution bias, resulting in limited generalizability. To address this limitation, a straightforward solution is data augmentation (DA), which leverages generative models to enrich data distribution. Despite the promising results, current DA techniques focus solely on reconstructing future trajectories from given states, while ignoring the exploration of history transitions that reach them. This single-direction paradigm inevitably hinders the discovery of diverse behavior patterns, especially those leading to critical states that may have yielded high-reward outcomes. In this work, we introduce Bidirectional Trajectory Diffusion (BiTrajDiff), a novel DA framework for offline RL that models both future and history trajectories from any intermediate states. Specifically, we decompose the trajectory generation task into two independent yet complementary diffusion processes: one generating forward trajectories to predict future dynamics, and the other generating backward trajectories to trace essential history transitions.BiTrajDiff can efficiently leverage critical states as anchors to expand into potentially valuable yet underexplored regions of the state space, thereby facilitating dataset diversity. Extensive experiments on the D4RL benchmark suite demonstrate that BiTrajDiff achieves superior performance compared to other advanced DA methods across various offline RL backbones. - oai:arXiv.org:2506.05762v3 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yunpeng Qing, Yixiao Chi, Shuo Chen, Shunyu Liu, Kelu Yao, Sixu Lin, Litao Liu, Changqing Zou - - - Guiding Cross-Modal Representations with MLLM Priors via Preference Alignment - https://arxiv.org/abs/2506.06970 - arXiv:2506.06970v3 Announce Type: replace -Abstract: Despite Contrastive Language-Image Pretraining (CLIP)'s remarkable capability to retrieve content across modalities, a substantial modality gap persists in its feature space. Intriguingly, we discover that off-the-shelf MLLMs (Multimodal Large Language Models) demonstrate powerful inherent modality alignment properties. While recent MLLM-based retrievers with unified architectures partially mitigate this gap, their reliance on coarse modality alignment mechanisms fundamentally limits their potential. In this work, We introduce MAPLE (Modality-Aligned Preference Learning for Embeddings), a novel framework that leverages the fine grained alignment priors inherent in MLLM to guide cross modal representation learning. MAPLE formulates the learning process as reinforcement learning with two key components: (1) Automatic preference data construction using off-the-shelf MLLM, and (2) a new Relative Preference Alignment (RPA) loss, which adapts Direct Preference Optimization (DPO) to the embedding learning setting. Experimental results show that our preference-guided alignment achieves substantial gains in fine-grained cross-modal retrieval, underscoring its effectiveness in handling nuanced semantic distinctions. - oai:arXiv.org:2506.06970v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Pengfei Zhao, Rongbo Luan, Wei Zhang, Peng Wu, Sifeng He - - - Reproducibility in the Control of Autonomous Mobility-on-Demand Systems - https://arxiv.org/abs/2506.07345 - arXiv:2506.07345v2 Announce Type: replace -Abstract: Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD offers fast and personalized travel services by leveraging centralized control of autonomous vehicle fleets to optimize operations and enhance service performance. However, the rapid growth of this field has outpaced the development of standardized practices for evaluating and reporting results, leading to significant challenges in reproducibility. As AMoD control algorithms become increasingly complex and data-driven, a lack of transparency in modeling assumptions, experimental setups, and algorithmic implementation hinders scientific progress and undermines confidence in the results. This paper presents a systematic study of reproducibility in AMoD research. We identify key components across the research pipeline, spanning system modeling, control problems, simulation design, algorithm specification, and evaluation, and analyze common sources of irreproducibility. We survey prevalent practices in the literature, highlight gaps, and propose a structured framework to assess and improve reproducibility. Specifically, concrete guidelines are offered, along with a "reproducibility checklist", to support future work in achieving replicable, comparable, and extensible results. While focused on AMoD, the principles and practices we advocate generalize to a broader class of cyber-physical systems that rely on networked autonomy and data-driven control. This work aims to lay the foundation for a more transparent and reproducible research culture in the design and deployment of intelligent mobility systems. - oai:arXiv.org:2506.07345v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xinling Li, Meshal Alharbi, Daniele Gammelli, James Harrison, Filipe Rodrigues, Maximilian Schiffer, Marco Pavone, Emilio Frazzoli, Jinhua Zhao, Gioele Zardini - - - Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs - https://arxiv.org/abs/2506.08899 - arXiv:2506.08899v3 Announce Type: replace -Abstract: We present a comprehensive approach to the automated formalization of legal texts using large language models (LLMs), targeting their transformation into Defeasible Deontic Logic (DDL). Our method employs a structured pipeline that segments complex normative language into atomic snippets, extracts deontic rules, and evaluates them for syntactic and semantic coherence. We introduce a refined success metric that more precisely captures the completeness of formalizations, and a novel two-stage pipeline with a dedicated refinement step to improve logical consistency and coverage. The evaluation procedure has been strengthened with stricter error assessment, and we provide comparative results across multiple LLM configurations, including newly released models and various prompting and fine-tuning strategies. Experiments on legal norms from the Australian Telecommunications Consumer Protections Code demonstrate that, when guided effectively, LLMs can produce formalizations that align closely with expert-crafted representations, underscoring their potential for scalable legal informatics. - oai:arXiv.org:2506.08899v3 - cs.CL - cs.AI - cs.CY - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Elias Horner, Cristinel Mateis, Guido Governatori, Agata Ciabattoni - - - Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning - https://arxiv.org/abs/2506.08987 - arXiv:2506.08987v3 Announce Type: replace -Abstract: Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond, partly due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and limitations of current individualized planning strategies. In a step toward an in-silico approach, we develop two geometric deep learning models, based on graph neural network (GNN) and geometry-informed neural operator (GINO), to predict activation time maps on left ventricular (LV) geometries in real time. Trained on a large dataset generated from finite-element simulations spanning a wide range of synthetic LV shapes, pacing site configurations, and tissue conductivities, the GINO model outperforms the GNN on synthetic cases (1.38% vs 2.44% error), while both demonstrate comparable performance on real-world LV geometries (GINO: 4.79% vs GNN: 4.07%). Using the trained models, we develop a workflow to identify an optimal pacing site on the LV from a given activation time map and show that both models can robustly recover ground-truth subject-specific parameters from noisy inputs. In conjunction with an interactive web-based interface (https://dcsim.egr.msu.edu/), this study shows potential and motivates future extension toward a clinical decision-support tool for personalized pre-procedural CRT optimization. - oai:arXiv.org:2506.08987v3 - cs.CE - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ehsan Naghavi, Haifeng Wang, Vahid Ziaei Rad, Julius Guccione, Ghassan Kassab, Vishnu Boddeti, Seungik Baek, Lik-Chuan Lee - - - The Rise of AI Companions: How Human-Chatbot Relationships Influence Well-Being - https://arxiv.org/abs/2506.12605 - arXiv:2506.12605v4 Announce Type: replace -Abstract: As large language models (LLMs)-enhanced chatbots grow increasingly expressive and socially responsive, many users are beginning to form companionship-like bonds with them, particularly with simulated AI partners designed to mimic emotionally attuned interlocutors. These emerging AI companions raise critical questions: Can such systems fulfill social needs typically met by human relationships? How do they shape psychological well-being? And what new risks arise as users develop emotional ties to non-human agents? This study investigates how people interact with AI companions, especially simulated partners on CharacterAI, and how this use is associated with users' psychological well-being. We analyzed survey data from 1,131 users and 4,363 chat sessions (413,509 messages) donated by 244 participants, focusing on three dimensions of use: nature of the interaction, interaction intensity, and self-disclosure. By triangulating self-reports primary motivation, open-ended relationship descriptions, and annotated chat transcripts, we identify patterns in how users engage with AI companions and its associations with well-being. Findings suggest that people with smaller social networks are more likely to turn to chatbots for companionship, but that companionship-oriented chatbot usage is consistently associated with lower well-being, particularly when people use the chatbots more intensively, engage in higher levels of self-disclosure, and lack strong human social support. Even though some people turn to chatbots to fulfill social needs, these uses of chatbots do not fully substitute for human connection. As a result, the psychological benefits may be limited, and the relationship could pose risks for more socially isolated or emotionally vulnerable users. - oai:arXiv.org:2506.12605v4 - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yutong Zhang, Dora Zhao, Jeffrey T. Hancock, Robert Kraut, Diyi Yang - - - A Geometric Multigrid Preconditioner for Discontinuous Galerkin Shifted Boundary Method - https://arxiv.org/abs/2506.12899 - arXiv:2506.12899v2 Announce Type: replace -Abstract: This paper introduces a geometric multigrid preconditioner for the Shifted Boundary Method (SBM) designed to solve PDEs on complex geometries. While SBM simplifies mesh generation by using a non-conforming background grid, it often results in non-symmetric and potentially ill-conditioned linear systems that are challenging to solve efficiently. Standard multigrid methods with pointwise smoothers prove ineffective for such systems due to the localized perturbations introduced by the shifted boundary conditions. To address this challenge, we introduce a Discontinuous Galerkin (DG) formulation for SBM that enables the design of a cell-wise multiplicative smoother within an $hp$-multigrid framework. The element-local nature of DG methods naturally facilitates cell-wise correction, which can effectively handle the local complexities arising from the boundary treatment. Numerical results for the Poisson equation demonstrate favorable performance with mesh refinement for linear ($p=1$) and quadratic ($p=2$) elements in both 2D and 3D, with iteration counts showing mild growth. However, challenges emerge for cubic ($p=3$) elements, particularly in 3D, where the current smoother shows reduced effectiveness. - oai:arXiv.org:2506.12899v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Michal Wichrowski - - - ChartBlender: An Interactive System for Authoring and Synchronizing Visualization Charts in Video - https://arxiv.org/abs/2506.13129 - arXiv:2506.13129v2 Announce Type: replace -Abstract: Embedding data visualizations in video can enhance the communication of complex information. However, this process is often labor-intensive, requiring designers to adjust visualizations frame by frame manually. In this work, we present ChartBlender, a novel system that streamlines this process by enabling users to create data visualizations, embed them seamlessly into video scenes, and automatically synchronize them with both camera motion and moving objects. Particularly, ChartBlender incorporates a tracking algorithm that supports both object and camera tracking, ensuring robust alignment of visualizations with dynamic video content. To maintain visual clarity and aesthetic coherence, we also explore the design space of video-suited visualizations and develop a library of customizable templates optimized for video embedding. We evaluate \oursName\ChartBlender through two controlled experiments and expert interviews with five domain experts. Results show that our system enables accurate synchronization and accelerates the production of data-driven videos. - oai:arXiv.org:2506.13129v2 - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yi He, Yuqi Liu, Chenpu Li, Ruoyan Chen, Chuer Chen, Shengqi Dang, Nan Cao - - - A Survey on LLM-Assisted Clinical Trial Recruitment - https://arxiv.org/abs/2506.15301 - arXiv:2506.15301v3 Announce Type: replace -Abstract: Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are designed in natural language and patient data is represented as both structured and unstructured text, the task of matching trials and patients benefits from knowledge aggregation and reasoning abilities of LLMs. Classical approaches are trial-specific and LLMs with their ability to consolidate distributed knowledge hold the potential to build a more general solution. Yet recent applications of LLM-assisted methods rely on proprietary models and weak evaluation benchmarks. In this survey, we are the first to analyze the task of trial-patient matching and contextualize emerging LLM-based approaches in clinical trial recruitment. We critically examine existing benchmarks, approaches and evaluation frameworks, the challenges to adopting LLM technologies in clinical research and exciting future directions. - oai:arXiv.org:2506.15301v3 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Shrestha Ghosh, Moritz Schneider, Carina Reinicke, Carsten Eickhoff - - - Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis - https://arxiv.org/abs/2506.20049 - arXiv:2506.20049v2 Announce Type: replace -Abstract: We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We implement SceneSense, a diffusion model designed and trained for predicting 3D occupancy maps given partial observations. Our proposed approach probabilistically fuses these predictions into a running occupancy map in real-time, resulting in significant improvements in map quality and traversability. We deploy SceneSense on a quadruped robot and validate its performance with real-world experiments to demonstrate the effectiveness of the model. In these experiments we show that occupancy maps enhanced with SceneSense predictions better estimate the distribution of our fully observed ground truth data ($24.44\%$ FID improvement around the robot and $75.59\%$ improvement at range). We additionally show that integrating SceneSense enhanced maps into our robotic exploration stack as a ``drop-in'' map improvement, utilizing an existing off-the-shelf planner, results in improvements in robustness and traversability time. Finally, we show results of full exploration evaluations with our proposed system in two dissimilar environments and find that locally enhanced maps provide more consistent exploration results than maps constructed only from direct sensor measurements. - oai:arXiv.org:2506.20049v2 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1007/s10514-025-10229-0 - Auton Robot 50, 8 (2026) - Lorin Achey, Alec Reed, Brendan Crowe, Bradley Hayes, Christoffer Heckman - - - OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions - https://arxiv.org/abs/2506.23361 - arXiv:2506.23361v3 Announce Type: replace -Abstract: Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging problem that how to use the signals such as depth, mask, camera, and text prompts to control and edit the subject in the customized video is still less explored. In this paper, we first propose a data construction pipeline, VideoCus-Factory, to produce training data pairs for multi-subject customization from raw videos without labels and control signals such as depth-to-video and mask-to-video pairs. Based on our constructed data, we develop an Image-Video Transfer Mixed (IVTM) training with image editing data to enable instructive editing for the subject in the customized video. Then we propose a diffusion Transformer framework, OmniVCus, with two embedding mechanisms, Lottery Embedding (LE) and Temporally Aligned Embedding (TAE). LE enables inference with more subjects by using the training subjects to activate more frame embeddings. TAE encourages the generation process to extract guidance from temporally aligned control signals by assigning the same frame embeddings to the control and noise tokens. Experiments demonstrate that our method significantly surpasses state-of-the-art methods in both quantitative and qualitative evaluations. Video demos are at our project page: https://caiyuanhao1998.github.io/project/OmniVCus/. Our code, models, data are released at https://github.com/caiyuanhao1998/Open-OmniVCus - oai:arXiv.org:2506.23361v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuanhao Cai, He Zhang, Xi Chen, Jinbo Xing, Yiwei Hu, Yuqian Zhou, Kai Zhang, Zhifei Zhang, Soo Ye Kim, Tianyu Wang, Yulun Zhang, Xiaokang Yang, Zhe Lin, Alan Yuille - - - Passage-traversing optimal path planning with sampling-based algorithms - https://arxiv.org/abs/2506.23614 - arXiv:2506.23614v2 Announce Type: replace -Abstract: This paper introduces a new paradigm of optimal path planning, i.e., passage-traversing optimal path planning (PTOPP), that optimizes paths' traversed passages for specified optimization objectives. In particular, PTOPP is utilized to find the path with optimal accessible free space along its entire length, which represents a basic requirement for paths in robotics. As passages are places where free space shrinks and becomes constrained, the core idea is to leverage the path's passage traversal status to characterize its accessible free space comprehensively. To this end, a novel passage detection and free space decomposition method using proximity graphs is proposed, enabling fast detection of sparse but informative passages and environment decompositions. Based on this preprocessing, optimal path planning with accessible free space objectives or constraints is formulated as PTOPP problems compatible with sampling-based optimal planners. Then, sampling-based algorithms for PTOPP, including their dependent primitive procedures, are developed leveraging partitioned environments for fast passage traversal check. All these methods are implemented and thoroughly tested for effectiveness and efficiency validation. Compared to existing approaches, such as clearance-based methods, PTOPP demonstrates significant advantages in configurability, solution optimality, and efficiency, addressing prior limitations and incapabilities. It is believed to provide an efficient and versatile solution to accessible free space optimization over conventional avenues and more generally, to a broad class of path planning problems that can be formulated as PTOPP. - oai:arXiv.org:2506.23614v2 - cs.RO - cs.CG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jing Huang, Hao Su, Kwok Wai Samuel Au - - - Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation - https://arxiv.org/abs/2507.01347 - arXiv:2507.01347v2 Announce Type: replace -Abstract: We introduce Generalized Test-Time Augmentation (GTTA), a highly effective method for improving the performance of a trained model, which unlike other existing Test-Time Augmentation approaches from the literature is general enough to be used off-the-shelf for many vision and non-vision tasks, such as classification, regression, image segmentation and object detection. By applying a new general data transformation, that randomly perturbs multiple times the PCA subspace projection of a test input, GTTA creates valid augmented samples from the data distribution with high diversity, properties we theoretically show that are essential for a Test-Time Augmentation method to be effective. Different from other existing methods, we also propose a final self-supervised learning stage in which the ensemble output, acting as an unsupervised teacher, is used to train the initial single student model, thus reducing significantly the test time computational cost. Our comparisons to strong TTA approaches and SoTA models on various vision and non-vision well-known datasets and tasks, such as image classification and segmentation, pneumonia detection, speech recognition and house price prediction, validate the generality of the proposed GTTA. Furthermore, we also prove its effectiveness on the more specific real-world task of salmon segmentation and detection in low-visibility underwater videos, for which we introduce DeepSalmon, the largest dataset of its kind in the literature. - oai:arXiv.org:2507.01347v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu - - - MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining - https://arxiv.org/abs/2507.01785 - arXiv:2507.01785v2 Announce Type: replace -Abstract: Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single rater for 17 target languages. MuRating aggregates multiple English "raters" via pairwise comparisons to learn unified document-quality scores,then projects these judgments through translation to train a multilingual evaluator on monolingual, cross-lingual, and parallel text pairs. Applied to web data, MuRating selects balanced subsets of English and multilingual content to pretrain a 1.2 B-parameter LLaMA model. Compared to strong baselines, including QuRater, AskLLM, DCLM and so on, our approach boosts average accuracy on both English benchmarks and multilingual evaluations, with especially large gains on knowledge-intensive tasks. We further analyze translation fidelity, selection biases, and underrepresentation of narrative material, outlining directions for future work. - oai:arXiv.org:2507.01785v2 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhixun Chen, Ping Guo, Wenhan Han, Yifan Zhang, Binbin Liu, Haobin Lin, Fengze Liu, Yan Zhao, Bingni Zhang, Taifeng Wang, Yin Zheng, Meng Fang - - - Large Language Model-Driven Closed-Loop UAV Operation with Semantic Observations - https://arxiv.org/abs/2507.01930 - arXiv:2507.01930v5 Announce Type: replace -Abstract: Recent advances in large Language Models (LLMs) have revolutionized mobile robots, including unmanned aerial vehicles (UAVs), enabling their intelligent operation within Internet of Things (IoT) ecosystems. However, LLMs still face challenges from logical reasoning and complex decision-making, leading to concerns about the reliability of LLM-driven UAV operations in IoT applications. In this paper, we propose a closed-loop LLM-driven UAV operation code generation framework that enables reliable UAV operations powered by effective feedback and refinement using two LLM modules, i.e., a Code Generator and an Evaluator. Our framework transforms numerical state observations from UAV operations into semantic trajectory descriptions to enhance the evaluator LLM's understanding of UAV dynamics for precise feedback generation. Our framework also enables a simulation-based refinement process, and hence eliminates the risks to physical UAVs caused by incorrect code execution during the refinement. Extensive experiments on UAV control tasks with different complexities are conducted. The experimental results show that our framework can achieve reliable UAV operations using LLMs, which significantly outperforms baseline methods in terms of success rate and completeness with the increase of task complexity. - oai:arXiv.org:2507.01930v5 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - 10.1109/JIOT.2025.3649376 - Wenhao Wang, Yanyan Li, Long Jiao, Jiawei Yuan - - - Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models - https://arxiv.org/abs/2507.02002 - arXiv:2507.02002v4 Announce Type: replace -Abstract: Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents continuously adapt to each other's behavior, as in cooperative gameplay settings. In this paper, we bridge this gap by combining LLM-driven agents with strategic reasoning and real-time adaptation in cooperative, multi-agent environments grounded in game-theoretic principles such as belief consistency and Nash equilibrium. The proposed framework applies broadly to dynamic scenarios in which agents coordinate, communicate, and make decisions in response to continuously changing conditions. We provide real-time strategy refinement and adaptive feedback mechanisms that enable agents to dynamically adjust policies based on immediate contextual interactions, in contrast to previous efforts that evaluate LLM capabilities in static or turn-based settings. Empirical results show that our method achieves up to a 26\% improvement in return over PPO baselines in high-noise environments, while maintaining real-time latency under 1.05 milliseconds. Our approach improves collaboration efficiency, task completion rates, and flexibility, illustrating that game-theoretic guidance integrated with real-time feedback enhances LLM performance, ultimately fostering more resilient and flexible strategic multi-agent systems. - oai:arXiv.org:2507.02002v4 - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Shaurya Mallampati, Rashed Shelim, Walid Saad, Naren Ramakrishnan - - - Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verification - https://arxiv.org/abs/2507.05405 - arXiv:2507.05405v2 Announce Type: replace -Abstract: We present $\textbf{P}$robabilistically $\textbf{T}$ightened $\textbf{Li}$near $\textbf{R}$elaxation-based $\textbf{P}$erturbation $\textbf{A}$nalysis ($\texttt{PT-LiRPA}$), a novel framework that combines over-approximation techniques from LiRPA-based approaches with a sampling-based method to compute tight intermediate reachable sets. In detail, we show that with negligible computational overhead, $\texttt{PT-LiRPA}$ exploiting the estimated reachable sets, significantly tightens the lower and upper linear bounds of a neural network's output, reducing the computational cost of formal verification tools while providing probabilistic guarantees on verification soundness. Extensive experiments on standard formal verification benchmarks, including the International Verification of Neural Networks Competition, show that our $\texttt{PT-LiRPA}$-based verifier improves robustness certificates, i.e., the certified lower bound of $\varepsilon$ perturbation tolerated by the models, by up to 3.31X and 2.26X compared to related work. Importantly, our probabilistic approach results in a valuable solution for challenging competition entries where state-of-the-art formal verification methods fail, allowing us to provide answers with high confidence (i.e., at least 99%). - oai:arXiv.org:2507.05405v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.1613/jair.1.20808 - Journal of Artificial Intelligence Research 84, Article 30 (December 2025), 34 pages - Luca Marzari, Ferdinando Cicalese, Alessandro Farinelli - - - PERK: Long-Context Reasoning as Parameter-Efficient Test-Time Learning - https://arxiv.org/abs/2507.06415 - arXiv:2507.06415v2 Announce Type: replace -Abstract: Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. Previous research shows that using test-time learning to encode context directly into model parameters can effectively enable reasoning over noisy information. However, meta-learning methods for enabling test-time learning are prohibitively memory-intensive, preventing their application to long context settings. In this work, we propose PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for learning to encode long input contexts using gradient updates to a lightweight model adapter at test time. Specifically, PERK employs two nested optimization loops in a meta-training phase. The inner loop rapidly encodes contexts into a low-rank adapter (LoRA) that serves as a parameter-efficient memory module for the base model. Concurrently, the outer loop learns to use the updated adapter to accurately recall and reason over relevant information from the encoded long context. Our evaluations on several long-context reasoning tasks show that PERK significantly outperforms the standard prompt-based long-context baseline, achieving average absolute performance gains of up to 90% for smaller models (GPT-2) and up to 27% for our largest evaluated model, Qwen-2.5-0.5B. In general, PERK is more robust to reasoning complexity, length extrapolation, and the locations of relevant information in contexts. Finally, we show that while PERK is memory-intensive during training, it scales more efficiently at inference time than prompt-based long-context inference. - oai:arXiv.org:2507.06415v2 - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Zeming Chen, Angelika Romanou, Gail Weiss, Antoine Bosselut - - - Mathematical artificial data for operator learning - https://arxiv.org/abs/2507.06752 - arXiv:2507.06752v2 Announce Type: replace -Abstract: Machine learning has emerged as a transformative tool for solving differential equations (DEs), yet prevailing methodologies remain constrained by dual limitations: data-driven methods demand costly labeled datasets while model-driven techniques face efficiency-accuracy trade-offs. We present the Mathematical Artificial Data (MAD) framework, a new paradigm that integrates physical laws with data-driven learning to facilitate large-scale operator discovery. By exploiting DEs' intrinsic mathematical structure to generate physics-embedded analytical solutions and associated synthetic data, MAD fundamentally eliminates dependence on experimental or simulated training data. This enables computationally efficient operator learning across multi-parameter systems while maintaining mathematical rigor. Through numerical demonstrations spanning 2D parametric problems where both the boundary values and source term are functions, we showcase MAD's generalizability and superior efficiency/accuracy across various DE scenarios. This physics-embedded-data-driven framework and its capacity to handle complex parameter spaces gives it the potential to become a universal paradigm for physics-informed machine intelligence in scientific computing. - oai:arXiv.org:2507.06752v2 - cs.LG - cs.NA - math.NA - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Heng Wu, Benzhuo Lu - - - One Graph to Track Them All: Dynamic GNNs for Single- and Multi-View Tracking - https://arxiv.org/abs/2507.08494 - arXiv:2507.08494v2 Announce Type: replace -Abstract: This work presents a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories without relying on pre-computed tracklets. The model builds a dynamic spatiotemporal graph that aggregates spatial, contextual, and temporal information, enabling seamless information propagation across entire sequences. To improve occlusion handling, the graph can also encode scene-specific information. We also introduce a new large-scale dataset with 25 partially overlapping views, detailed scene reconstructions, and extensive occlusions. Experiments show the model achieves state-of-the-art performance on public benchmarks and the new dataset, with flexibility across diverse conditions. Both the dataset and approach will be publicly released to advance research in multi-people tracking. - oai:arXiv.org:2507.08494v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Martin Engilberge, Ivan Vrkic, Friedrich Wilke Grosche, Julien Pilet, Engin Turetken, Pascal Fua - - - Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices - https://arxiv.org/abs/2507.09627 - arXiv:2507.09627v2 Announce Type: replace -Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity. Extremely Large-Scale MIMO (XL-MIMO) and Reconfigurable Intelligent Surface (RIS) are key enablers, with XL-MIMO boosting spectral and energy efficiency through numerous antennas, and RIS offering dynamic control over the wireless environment via passive reflective elements. However, realizing their full potential depends on accurate Channel State Information (CSI). Recent advances in deep learning have facilitated efficient cascaded channel estimation. However, the scalability and practical deployment of existing estimation models in XL-MIMO systems remain limited. The growing number of antennas and RIS elements introduces a significant barrier to real-time and efficient channel estimation, drastically increasing data volume, escalating computational complexity, requiring advanced hardware, and resulting in substantial energy consumption. To address these challenges, we propose a lightweight deep learning framework for efficient cascaded channel estimation in XL-MIMO systems, designed to minimize computational complexity and make it suitable for deployment on resource-constrained edge devices. Using spatial correlations in the channel, we introduce a patch-based training mechanism that reduces the dimensionality of input to patch-level representations while preserving essential information, allowing scalable training for large-scale systems. Simulation results under diverse conditions demonstrate that our framework significantly improves estimation accuracy and reduces computational complexity, regardless of the increasing number of antennas and RIS elements in XL-MIMO systems. - oai:arXiv.org:2507.09627v2 - cs.IT - cs.CV - cs.LG - cs.NI - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Muhammad Kamran Saeed, Ashfaq Khokhar, Shakil Ahmed - - - Compressed data structures for Heegaard splittings - https://arxiv.org/abs/2507.11406 - arXiv:2507.11406v2 Announce Type: replace -Abstract: Heegaard splittings provide a natural representation of closed 3-manifolds by gluing two handlebodies along a common surface. These splittings can be equivalently given by two finite sets of meridians lying on the surface, which define a Heegaard diagram. We present a data structure to effectively represent Heegaard diagrams as normal curves with respect to triangulations of a surface, where the complexity is measured by the space required to express the normal coordinates' vectors in binary. This structure can be significantly more compact than triangulations of 3-manifolds, yielding exponential gains for certain families. Even with this succinct definition of complexity, we establish polynomial-time algorithms for comparing and manipulating diagrams, performing stabilizations, detecting trivial stabilizations and reductions, and computing topological invariants of the underlying manifolds, such as their fundamental and homology groups. We also contrast early implementations of our techniques with standard software programs for 3-manifolds, achieving faster algorithms for the average cases and exponential gains in speed for some particular presentations of the inputs. - oai:arXiv.org:2507.11406v2 - cs.CG - cs.DS - math.GT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Henrique Ennes, Cl\'ement Maria - - - An Ecosystem for Ontology Interoperability - https://arxiv.org/abs/2507.12311 - arXiv:2507.12311v5 Announce Type: replace -Abstract: Ontology interoperability is one of the complicated issues that restricts the use of ontologies in knowledge graphs (KGs). Different ontologies with conflicting and overlapping concepts make it difficult to design, develop, and deploy an interoperable ontology for downstream tasks. We propose an ecosystem for ontology interoperability. The ecosystem employs three state-of-the-art semantic techniques in different phases of the ontology engineering life cycle: ontology design patterns (ODPs) in the design phase, ontology matching and versioning (OM\&OV) in the develop phase, and data-driven ontology validation (DOVA) in the deploy phase, to achieve better ontology interoperability and data integration in real-world applications. A case study of sensor observation in the building domain validates the usefulness of the proposed ecosystem. - oai:arXiv.org:2507.12311v5 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhangcheng Qiang - - - Sampling from Gaussian Processes: A Tutorial and Applications in Global Sensitivity Analysis and Optimization - https://arxiv.org/abs/2507.14746 - arXiv:2507.14746v2 Announce Type: replace -Abstract: High-fidelity simulations and physical experiments are essential for engineering analysis and design, yet their high cost often makes two critical tasks--global sensitivity analysis (GSA) and optimization--prohibitively expensive. This limitation motivates the common use of Gaussian processes (GPs) as proxy regression models that provide uncertainty-aware predictions from a limited number of high-quality observations. GPs naturally enable efficient sampling strategies that support informed decision-making under uncertainty by extracting information from a subset of possible functions for the model of interest. However, direct sampling from GPs is inefficient due to their infinite-dimensional nature and the high cost associated with large covariance matrix operations. Despite their popularity in machine learning and statistics communities, sampling from GPs has received little attention in the community of engineering optimization. In this paper, we present the formulation and detailed implementation of two notable sampling methods--random Fourier features and pathwise conditioning--for generating posterior samples from GPs at reduced computational cost. Alternative approaches are briefly described. Importantly, we detail how the generated samples can be applied in GSA, single-objective optimization, and multi-objective optimization. We show successful applications of these sampling methods through a series of numerical examples. - oai:arXiv.org:2507.14746v2 - cs.LG - math.OC - stat.AP - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bach Do, Nafeezat A. Ajenifuja, Taiwo A. Adebiyi, Ruda Zhang - - - One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms - https://arxiv.org/abs/2507.15351 - arXiv:2507.15351v2 Announce Type: replace -Abstract: Order dispatch is a critical task in ride-sharing systems with Autonomous Vehicles (AVs), directly influencing efficiency and profits. Recently, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising solution to this problem by decomposing the large state and action spaces among individual agents, effectively addressing the Curse of Dimensionality (CoD) in transportation market, which is caused by the substantial number of vehicles, passengers, and orders. However, conventional MARL-based approaches heavily rely on accurate estimation of the value function, which becomes problematic in large-scale, highly uncertain environments. To address this issue, we propose two novel methods that bypass value function estimation, leveraging the homogeneous property of AV fleets. First, we draw an analogy between AV fleets and groups in Group Relative Policy Optimization (GRPO), adapting it to the order dispatch task. By replacing the Proximal Policy Optimization (PPO) baseline with the group average reward-to-go, GRPO eliminates critic estimation errors and reduces training bias. Inspired by this baseline replacement, we further propose One-Step Policy Optimization (OSPO), demonstrating that the optimal policy can be trained using only one-step group rewards under a homogeneous fleet. Experiments on a real-world ride-hailing dataset show that both GRPO and OSPO achieve promising performance across all scenarios, efficiently optimizing pickup times and the number of served orders using simple Multilayer Perceptron (MLP) networks. Furthermore, OSPO outperforms GRPO in all scenarios, attributed to its elimination of bias caused by the bounded time horizon of GRPO. Our code, trained models, and processed data are provided at https://github.com/RS2002/OSPO . - oai:arXiv.org:2507.15351v2 - cs.AI - cs.ET - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zijian Zhao, Sen Li - - - Natural Language Processing for Tigrinya: Current State and Future Directions - https://arxiv.org/abs/2507.17974 - arXiv:2507.17974v3 Announce Type: replace -Abstract: Despite being spoken by millions of people, Tigrinya remains severely underrepresented in Natural Language Processing (NLP) research. This work presents a comprehensive survey of NLP research for Tigrinya, analyzing over 50 studies from 2011 to 2025. We systematically review the current state of computational resources, models, and applications across fifteen downstream tasks, including morphological processing, part-of-speech tagging, named entity recognition, machine translation, question-answering, speech recognition, and synthesis. Our analysis reveals a clear trajectory from foundational, rule-based systems to modern neural architectures, with progress consistently driven by milestones in resource creation. We identify key challenges rooted in Tigrinya's morphological properties and resource scarcity, and highlight promising research directions, including morphology-aware modeling, cross-lingual transfer, and community-centered resource development. This work serves both as a reference for researchers and as a roadmap for advancing Tigrinya NLP. An anthology of surveyed studies and resources is publicly available. - oai:arXiv.org:2507.17974v3 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Fitsum Gaim, Jong C. Park - - - GestureHYDRA: Semantic Co-speech Gesture Synthesis via Hybrid Modality Diffusion Transformer and Cascaded-Synchronized Retrieval-Augmented Generation - https://arxiv.org/abs/2507.22731 - arXiv:2507.22731v2 Announce Type: replace -Abstract: While increasing attention has been paid to co-speech gesture synthesis, most previous works neglect to investigate hand gestures with explicit and essential semantics. In this paper, we study co-speech gesture generation with an emphasis on specific hand gesture activation, which can deliver more instructional information than common body movements. To achieve this, we first build a high-quality dataset of 3D human body movements including a set of semantically explicit hand gestures that are commonly used by live streamers. Then we present a hybrid-modality gesture generation system GestureHYDRA built upon a hybrid-modality diffusion transformer architecture with novelly designed motion-style injective transformer layers, which enables advanced gesture modeling ability and versatile gesture operations. To guarantee these specific hand gestures can be activated, we introduce a cascaded retrieval-augmented generation strategy built upon a semantic gesture repository annotated for each subject and an adaptive audio-gesture synchronization mechanism, which substantially improves semantic gesture activation and production efficiency. Quantitative and qualitative experiments demonstrate that our proposed approach achieves superior performance over all the counterparts. The project page can be found at https://mumuwei.github.io/GestureHYDRA/. - oai:arXiv.org:2507.22731v2 - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Quanwei Yang, Luying Huang, Kaisiyuan Wang, Jiazhi Guan, Shengyi He, Fengguo Li, Hang Zhou, Lingyun Yu, Yingying Li, Haocheng Feng, Hongtao Xie - - - Learning Network Dismantling Without Handcrafted Inputs - https://arxiv.org/abs/2508.00706 - arXiv:2508.00706v2 Announce Type: replace -Abstract: The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces bias into the otherwise purely data-driven network representations. Here, we eliminate the need for handcrafted features by introducing an attention mechanism and utilizing message-iteration profiles, in addition to an effective algorithmic approach to generate a structurally diverse training set of small synthetic networks. Thereby, we build an expressive message-passing framework and use it to efficiently solve the NP-hard problem of Network Dismantling, virtually equivalent to vital node identification, with significant real-world applications. Trained solely on diversified synthetic networks, our proposed model -- MIND: Message Iteration Network Dismantler -- generalizes to large, unseen real networks with millions of nodes, outperforming state-of-the-art network dismantling methods. Increased efficiency and generalizability of the proposed model can be leveraged beyond dismantling in a range of complex network problems. - oai:arXiv.org:2508.00706v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haozhe Tian, Pietro Ferraro, Robert Shorten, Mahdi Jalili, Homayoun Hamedmoghadam - - - Multi-step retrieval and reasoning improves radiology question answering with large language models - https://arxiv.org/abs/2508.00743 - arXiv:2508.00743v4 Announce Type: replace -Abstract: Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-augmented generation (RAG) systems for radiology question answering (QA) typically rely on single-step retrieval, limiting their ability to handle complex clinical reasoning tasks. Here we propose radiology Retrieval and Reasoning (RaR), a multi-step retrieval and reasoning framework designed to improve diagnostic accuracy, factual consistency, and clinical reliability of LLMs in radiology question answering. We evaluated 25 LLMs spanning diverse architectures, parameter scales (0.5B to >670B), and training paradigms (general-purpose, reasoning-optimized, clinically fine-tuned), using 104 expert-curated radiology questions from previously established RSNA-RadioQA and ExtendedQA datasets. To assess generalizability, we additionally tested on an unseen internal dataset of 65 real-world radiology board examination questions. RaR significantly improved mean diagnostic accuracy over zero-shot prompting and conventional online RAG. The greatest gains occurred in small-scale models, while very large models (>200B parameters) demonstrated minimal changes (<2% improvement). Additionally, RaR retrieval reduced hallucinations (mean 9.4%) and retrieved clinically relevant context in 46% of cases, substantially aiding factual grounding. Even clinically fine-tuned models showed gains from RaR (e.g., MedGemma-27B), indicating that retrieval remains beneficial despite embedded domain knowledge. These results highlight the potential of RaR to enhance factuality and diagnostic accuracy in radiology QA, warranting future studies to validate their clinical utility. All datasets, code, and the full RaR framework are publicly available to support open research and clinical translation. - oai:arXiv.org:2508.00743v4 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - 10.1038/s41746-025-02250-5 - npj Digit. Med. 8, 790 (2025) - Sebastian Wind, Jeta Sopa, Daniel Truhn, Mahshad Lotfinia, Tri-Thien Nguyen, Keno Bressem, Lisa Adams, Mirabela Rusu, Harald K\"ostler, Gerhard Wellein, Andreas Maier, Soroosh Tayebi Arasteh - - - SplatSSC: Decoupled Depth-Guided Gaussian Splatting for Semantic Scene Completion - https://arxiv.org/abs/2508.02261 - arXiv:2508.02261v3 Announce Type: replace -Abstract: Monocular 3D Semantic Scene Completion (SSC) is a challenging yet promising task that aims to infer dense geometric and semantic descriptions of a scene from a single image. While recent object-centric paradigms significantly improve efficiency by leveraging flexible 3D Gaussian primitives, they still rely heavily on a large number of randomly initialized primitives, which inevitably leads to 1) inefficient primitive initialization and 2) outlier primitives that introduce erroneous artifacts. In this paper, we propose SplatSSC, a novel framework that resolves these limitations with a depth-guided initialization strategy and a principled Gaussian aggregator. Instead of random initialization, SplatSSC utilizes a dedicated depth branch composed of a Group-wise Multi-scale Fusion (GMF) module, which integrates multi-scale image and depth features to generate a sparse yet representative set of initial Gaussian primitives. To mitigate noise from outlier primitives, we develop the Decoupled Gaussian Aggregator (DGA), which enhances robustness by decomposing geometric and semantic predictions during the Gaussian-to-voxel splatting process. Complemented with a specialized Probability Scale Loss, our method achieves state-of-the-art performance on the Occ-ScanNet dataset, outperforming prior approaches by over 6.3% in IoU and 4.1% in mIoU, while reducing both latency and memory cost by more than 9.3%. - oai:arXiv.org:2508.02261v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Rui Qian, Haozhi Cao, Tianchen Deng, Shenghai Yuan, Lihua Xie - - - BadBlocks: Lightweight and Stealthy Backdoor Threat in Text-to-Image Diffusion Models - https://arxiv.org/abs/2508.03221 - arXiv:2508.03221v4 Announce Type: replace -Abstract: Diffusion models have recently achieved remarkable success in image generation, yet growing evidence shows their vulnerability to backdoor attacks, where adversaries implant covert triggers to manipulate outputs. While existing defenses can detect many such attacks via visual inspection and neural network-based analysis, we identify a more lightweight and stealthy threat, termed BadBlocks. BadBlocks selectively contaminates specific blocks within the UNet architecture while preserving the normal behavior of the remaining components. Compared with prior methods, it requires only about 30% of the computation and 20% of the GPU time, yet achieves high attack success rates with minimal perceptual degradation. Extensive experiments demonstrate that BadBlocks can effectively evade state-of-the-art defenses, particularly attention-based detection frameworks. Ablation studies further reveal that effective backdoor injection does not require fine-tuning the entire network and highlight the critical role of certain layers in backdoor mapping. Overall, BadBlocks substantially lowers the barrier for backdooring large-scale diffusion models, even on consumer-grade GPUs. - oai:arXiv.org:2508.03221v4 - cs.CR - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yu Pan, Jiahao Chen, Wenjie Wang, Bingrong Dai, Junjun Yang - - - evTransFER: A Transfer Learning Framework for Event-based Facial Expression Recognition - https://arxiv.org/abs/2508.03609 - arXiv:2508.03609v2 Announce Type: replace -Abstract: Event-based cameras are bio-inspired sensors that asynchronously capture pixel intensity changes with microsecond latency, high temporal resolution, and high dynamic range, providing information on the spatiotemporal dynamics of a scene. We propose evTransFER, a transfer learning-based framework for facial expression recognition using event-based cameras. The main contribution is a feature extractor designed to encode facial spatiotemporal dynamics, built by training an adversarial generative method on facial reconstruction and transferring the encoder weights to the facial expression recognition system. We demonstrate that the proposed transfer learning method improves facial expression recognition compared to training a network from scratch. We propose an architecture that incorporates an LSTM to capture longer-term facial expression dynamics and introduces a new event-based representation called TIE. We evaluated the framework using both the synthetic event-based facial expression database e-CK+ and the real neuromorphic dataset NEFER. On e-CK+, evTransFER achieved a recognition rate of 93.6\%, surpassing state-of-the-art methods. For NEFER, which comprises event sequence with real sensor noise and sparse activity, the proposed transfer learning strategy achieved an accuracy of up to 76.7\%. In both datasets, the outcomes surpassed current methodologies and exceeded results when compared with models trained from scratch. - oai:arXiv.org:2508.03609v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rodrigo Verschae, Ignacio Bugueno-Cordova - - - Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning - https://arxiv.org/abs/2508.04329 - arXiv:2508.04329v4 Announce Type: replace -Abstract: Supervised fine-tuning (SFT) plays a critical role for pretrained large language models (LLMs), notably enhancing their capacity to acquire domain-specific knowledge while preserving or potentially augmenting their general-purpose capabilities. However, the efficacy of SFT hinges on data quality as well as data volume, otherwise it may result in limited performance gains or even degradation relative to the associated baselines. To mitigate such reliance, we suggest categorizing tokens within each corpus into two parts -- positive and negative tokens -- based on whether they are useful to improve model performance. Positive tokens can be trained in common ways, whereas negative tokens, which may lack essential semantics or be misleading, should be explicitly forgotten. Overall, the token categorization facilitate the model to learn less informative message, and the forgetting process shapes a knowledge boundary to guide the model on what information to learn more precisely. We conduct experiments across diverse and well-established benchmarks using various model architectures, demonstrating that this forgetting mechanism enhances model performance. - oai:arXiv.org:2508.04329v4 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ali Taheri, Alireza Taban, Shanshan Ye, Abdolreza Mirzaei, Tongliang Liu, Bo Han - - - ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations - https://arxiv.org/abs/2508.05667 - arXiv:2508.05667v2 Announce Type: replace -Abstract: Large language models (LLMs) have demonstrated outstanding performance in natural language processing tasks. However, in the field of recommender systems, due to the inherent structural discrepancy between user behavior data and natural language, LLMs struggle to effectively model the associations between user preferences and items. Although prompt-based methods can generate recommendation results, their inadequate understanding of recommendation tasks leads to constrained performance. To address this gap, we construct a comprehensive instruction tuning dataset, ITDR, which encompasses seven subtasks across two root tasks: user-item interaction and user-item understanding. The dataset integrates data from 13 public recommendation datasets and is built using manually crafted standardized templates, comprising approximately 200,000 instances. Experimental results demonstrate that ITDR significantly enhances the performance of mainstream open-source LLMs such as GLM-4, Qwen2.5, Qwen2.5-Instruct and LLaMA-3.2 on recommendation tasks. Furthermore, we analyze the correlations between tasks and explore the impact of task descriptions and data scale on instruction tuning effectiveness. Finally, we perform comparative experiments against closed-source LLMs with massive parameters. Our tuning dataset ITDR, the fine-tuned large recommendation models, all LoRA modules, and the complete experimental results are available at https://github.com/hellolzk/ITDR. - oai:arXiv.org:2508.05667v2 - cs.IR - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zekun Liu, Xiaowen Huang, Jitao Sang - - - MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark - https://arxiv.org/abs/2508.07307 - arXiv:2508.07307v3 Announce Type: replace -Abstract: Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Multimodal Large Language Models (MLLMs) brings new challenges in Multimodal Continual Learning (MCL), where models are expected to address both catastrophic forgetting and cross-modal coordination. To advance research in this area, we present MCITlib, a comprehensive library for Multimodal Continual Instruction Tuning. MCITlib currently implements 8 representative algorithms and conducts evaluations on 3 benchmarks under 2 backbone models. The library will be continuously updated to support future developments in MCL. The codebase is released at https://github.com/Ghy0501/MCITlib. - oai:arXiv.org:2508.07307v3 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haiyang Guo, Fei Zhu, Hongbo Zhao, Fanhu Zeng, Wenzhuo Liu, Shijie Ma, Da-Han Wang, Xu-Yao Zhang - - - Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications - https://arxiv.org/abs/2508.07473 - arXiv:2508.07473v2 Announce Type: replace -Abstract: In Online Convex Optimization (OCO), when the stochastic gradient has a finite variance, many algorithms provably work and guarantee a sublinear regret. However, limited results are known if the gradient estimate has a heavy tail, i.e., the stochastic gradient only admits a finite $\mathsf{p}$-th central moment for some $\mathsf{p}\in\left(1,2\right]$. Motivated by it, this work examines different old algorithms for OCO (e.g., Online Gradient Descent) in the more challenging heavy-tailed setting. Under the standard bounded domain assumption, we establish new regrets for these classical methods without any algorithmic modification. Remarkably, these regret bounds are fully optimal in all parameters (can be achieved even without knowing $\mathsf{p}$), suggesting that OCO with heavy tails can be solved effectively without any extra operation (e.g., gradient clipping). Our new results have several applications. A particularly interesting one is the first provable and optimal convergence result for nonsmooth nonconvex optimization under heavy-tailed noise without gradient clipping. Furthermore, we explore broader settings (e.g., smooth OCO) and extend our ideas to optimistic algorithms to handle different cases simultaneously. - oai:arXiv.org:2508.07473v2 - cs.LG - math.OC - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zijian Liu - - - Fast weight programming and linear transformers: from machine learning to neurobiology - https://arxiv.org/abs/2508.08435 - arXiv:2508.08435v3 Announce Type: replace -Abstract: Recent advances in artificial neural networks for machine learning, and language modeling in particular, have established a family of recurrent neural network (RNN) architectures that, unlike conventional RNNs with vector-form hidden states, use two-dimensional (2D) matrix-form hidden states. Such 2D-state RNNs, known as Fast Weight Programmers (FWPs), can be interpreted as a neural network whose synaptic weights (called fast weights) dynamically change over time as a function of input observations, and serve as short-term memory storage; corresponding synaptic weight modifications are controlled or programmed by another network (the programmer) whose parameters are trained (e.g., by gradient descent). In this Primer, we review the technical foundations of FWPs, their computational characteristics, and their connections to transformers and state space models. We also discuss connections between FWPs and models of synaptic plasticity in the brain, suggesting a convergence of natural and artificial intelligence. - oai:arXiv.org:2508.08435v3 - cs.LG - cs.AI - q-bio.NC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Kazuki Irie, Samuel J. Gershman - - - Generalising Traffic Forecasting to Regions without Traffic Observations - https://arxiv.org/abs/2508.08947 - arXiv:2508.08947v2 Announce Type: replace -Abstract: Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high deployment and maintenance costs, not all regions are equipped with such sensors. This paper aims to forecast for regions without traffic sensors, where the lack of historical traffic observations challenges the generalisability of existing models. We propose a model named GenCast, the core idea of which is to exploit external knowledge to compensate for the missing observations and to enhance generalisation. We integrate physics-informed neural networks into GenCast, enabling physical principles to regularise the learning process. We introduce an external signal learning module to explore correlations between traffic states and external signals such as weather conditions, further improving model generalisability. Additionally, we design a spatial grouping module to filter localised features that hinder model generalisability. Extensive experiments show that GenCast consistently reduces forecasting errors on multiple real-world datasets. - oai:arXiv.org:2508.08947v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xinyu Su, Majid Sarvi, Feng Liu, Egemen Tanin, Jianzhong Qi - - - CLF-RL: Control Lyapunov Function Guided Reinforcement Learning - https://arxiv.org/abs/2508.09354 - arXiv:2508.09354v2 Announce Type: replace -Abstract: Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly shaped objectives. In this work, we propose a structured reward shaping framework that leverages model-based trajectory generation and control Lyapunov functions (CLFs) to guide policy learning. We explore two model-based planners for generating reference trajectories: a reduced-order linear inverted pendulum (LIP) model for velocity-conditioned motion planning, and a precomputed gait library based on hybrid zero dynamics (HZD) using full-order dynamics. These planners define desired end-effector and joint trajectories, which are used to construct CLF-based rewards that penalize tracking error and encourage rapid convergence. This formulation provides meaningful intermediate rewards, and is straightforward to implement once a reference is available. Both the reference trajectories and CLF shaping are used only during training, resulting in a lightweight policy at deployment. We validate our method both in simulation and through extensive real-world experiments on a Unitree G1 robot. CLF-RL demonstrates significantly improved robustness relative to the baseline RL policy and better performance than a classic tracking reward RL formulation. - oai:arXiv.org:2508.09354v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Kejun Li, Zachary Olkin, Yisong Yue, Aaron D. Ames - - - RAJ-PGA: Reasoning-Activated Jailbreak and Principle-Guided Alignment Framework for Large Reasoning Models - https://arxiv.org/abs/2508.12897 - arXiv:2508.12897v2 Announce Type: replace -Abstract: Large Reasoning Models (LRMs) face a distinct safety vulnerability: their internal reasoning chains may generate harmful content even when the final output appears benign. To address this overlooked risk, we first propose a novel attack paradigm, Reasoning-Activated Jailbreak (RAJ) via Concretization, which demonstrates that refining malicious prompts to be more specific can trigger step-by-step logical reasoning that overrides the model's safety protocols. To systematically mitigate this vulnerability, we further develop a scalable framework for constructing high-quality safety alignment datasets. This framework first leverages the RAJ attack to elicit challenging harmful reasoning chains from LRMs, then transforms these high-risk traces into safe, constructive, and educational responses through a tailored Principle-Guided Alignment (PGA) mechanism. Then, we introduce the PGA dataset, a verified alignment dataset containing 3,989 samples using our proposed method. Extensive experiments show that fine-tuning LRMs with PGA dataset significantly enhances model safety, achieving up to a 29.5% improvement in defense success rates across multiple jailbreak benchmarks. Critically, our approach not only defends against sophisticated reasoning-based attacks but also preserves, even enhances, the model's general reasoning capabilities. This work provides a scalable and effective pathway for safety alignment in reasoning-intensive AI systems, addressing the core trade-off between safety and functional performance. - oai:arXiv.org:2508.12897v2 - cs.AI - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jianhao Chen, Mayi Xu, Haoyang Chen, Xiaohu Li, Xiangyu Zhang, Jianjie Huang, Zheng Wang, Xiaochun Cao, Tieyun Qian - - - Holistic Evaluation of Multimodal LLMs on Spatial Intelligence - https://arxiv.org/abs/2508.13142 - arXiv:2508.13142v5 Announce Type: replace -Abstract: Multimodal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, the very capability that anchors artificial general intelligence in the physical world. With the recent release of GPT-5, allegedly the most powerful AI model to date, it is timely to examine where the leading models (GPT, Gemini, Grok, Seed, Qwen, and Intern) stand on the path toward spatial intelligence (SI). We thus propose EASI for holistic Evaluation of multimodAl LLMs on Spatial Intelligence. EASI conceptualizes a comprehensive taxonomy of spatial tasks that unifies existing benchmarks and a growing collection of newly curated ones, enabling systematic evaluation of state-of-the-art models. In this report, we conduct the study across eight key benchmarks, at a cost exceeding ten billion total tokens. Our empirical study then reveals that (1) GPT-5 demonstrates unprecedented strength in SI, yet (2) still falls short of human performance significantly across a broad spectrum of SI-tasks. Moreover, we (3) show that SI-tasks expose greater model capability deficiency than non-SI tasks, to the extent that (4) proprietary models do not exhibit a decisive advantage when facing the most difficult ones. In addition, we conduct a qualitative evaluation across a diverse set of scenarios that are intuitive for humans, yet fail the most advanced multimodal models. EASI is an ongoing community effort: we have open-sourced the EASI codebase that provides a one-stop and reproducible solution with standardized interfaces, integrated protocols and prompts that significantly reduce the friction of configuring and running multiple benchmarks; we have also launched an accompanying EASI leaderboard to provide a continually updated snapshot of model performance across the full SI spectrum, accelerating collective progress toward robust SI. - oai:arXiv.org:2508.13142v5 - cs.CV - cs.CL - cs.LG - cs.MM - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhongang Cai, Yubo Wang, Qingping Sun, Ruisi Wang, Chenyang Gu, Wanqi Yin, Zhiqian Lin, Zhitao Yang, Chen Wei, Oscar Qian, Hui En Pang, Xuanke Shi, Kewang Deng, Xiaoyang Han, Zukai Chen, Jiaqi Li, Xiangyu Fan, Hanming Deng, Lewei Lu, Bo Li, Ziwei Liu, Quan Wang, Dahua Lin, Lei Yang - - - Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions - https://arxiv.org/abs/2508.14556 - arXiv:2508.14556v2 Announce Type: replace -Abstract: We introduce a new music source separation model tailored for accurate vocal isolation. Unlike Transformer-based approaches, which often fail to capture intermittently occurring vocals, our model leverages Mamba2, a recent state space model, to better capture long-range temporal dependencies. To handle long input sequences efficiently, we combine a band-splitting strategy with a dual-path architecture. Experiments show that our approach outperforms recent state-of-the-art models, achieving a cSDR of 11.03 dB-the best reported to date-and delivering substantial gains in uSDR. Moreover, the model exhibits stable and consistent performance across varying input lengths and vocal occurrence patterns. These results demonstrate the effectiveness of Mamba-based models for high-resolution audio processing and open up new directions for broader applications in audio research. - oai:arXiv.org:2508.14556v2 - cs.SD - cs.AI - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Euiyeon Kim, Yong-Hoon Choi - - - MedQARo: A Large-Scale Benchmark for Evaluating Large Language Models on Medical Question Answering in Romanian - https://arxiv.org/abs/2508.16390 - arXiv:2508.16390v3 Announce Type: replace -Abstract: Question answering (QA) is an actively studied topic, being a core natural language processing (NLP) task that needs to be addressed before achieving Artificial General Intelligence (AGI). However, the lack of QA datasets in specific domains and languages hinders the development of robust AI models able to generalize across various domains and languages. To this end, we introduce MedQARo, the first large-scale medical QA benchmark in Romanian, alongside a comprehensive evaluation of state-of-the-art (SOTA) large language models (LLMs). We construct a high-quality and large-scale dataset comprising 105,880 QA pairs related to cancer patients from two medical centers. The questions regard medical case summaries of 1,242 patients, requiring either keyword extraction or reasoning to be answered correctly. MedQARo is the result of a time-consuming manual annotation process carried out by seven physicians specialized in oncology or radiotherapy, who spent a total of about 3,000 work hours to generate the QA pairs. Our benchmark contains both in-domain and cross-domain (cross-center and cross-cancer) test collections, enabling a precise assessment of generalization capabilities. We experiment with four open-source LLMs from distinct families of models on MedQARo. Each model is employed in two scenarios, namely one based on zero-shot prompting and one based on supervised fine-tuning. We also evaluate two state-of-the-art LLMs exposed only through APIs, namely GPT-5.2 and Gemini 3 Flash. Our results show that fine-tuned models significantly outperform zero-shot models, clearly indicating that pretrained models fail to generalize on MedQARo. Our findings demonstrate the importance of both domain-specific and language-specific fine-tuning for reliable clinical QA in Romanian. We publicly release our dataset and code at https://github.com/ana-rogoz/MedQARo. - oai:arXiv.org:2508.16390v3 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Ana-Cristina Rogoz, Radu Tudor Ionescu, Alexandra-Valentina Anghel, Ionut-Lucian Antone-Iordache, Simona Coniac, Andreea Iuliana Ionescu - - - CrystalDiT: A Diffusion Transformer for Crystal Generation - https://arxiv.org/abs/2508.16614 - arXiv:2508.16614v3 Announce Type: replace -Abstract: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting. - oai:arXiv.org:2508.16614v3 - cs.LG - cond-mat.mtrl-sci - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xiaohan Yi, Guikun Xu, Xi Xiao, Zhong Zhang, Liu Liu, Yatao Bian, Peilin Zhao - - - STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data - https://arxiv.org/abs/2508.16620 - arXiv:2508.16620v2 Announce Type: replace -Abstract: Next location prediction is a critical task in human mobility modeling, enabling applications like travel planning and urban mobility management. Existing methods mainly rely on historical spatiotemporal trajectory data to train sequence models that directly forecast future locations. However, they often overlook the importance of the future spatiotemporal contexts, which are highly informative for the future locations. For example, knowing how much time and distance a user will travel could serve as a critical clue for predicting the user's next location. Against this background, we propose \textbf{STRelay}, a universal \textbf{\underline{S}}patio\textbf{\underline{T}}emporal \textbf{\underline{Relay}}ing framework explicitly modeling the future spatiotemporal context given a human trajectory, to boost the performance of different location prediction models. Specifically, STRelay models future spatiotemporal contexts in a relaying manner, which is subsequently integrated with the encoded historical representation from a base location prediction model, enabling multi-task learning by simultaneously predicting the next time interval, next moving distance interval, and finally the next location. We evaluate STRelay integrated with five state-of-the-art location prediction base models on four real-world trajectory datasets. Results demonstrate that STRelay consistently improves prediction performance across all cases by 2.49\%-11.30\%. Additionally, we find that the future spatiotemporal contexts are particularly helpful for entertainment-related locations and also for user groups who prefer traveling longer distances. The performance gain on such non-daily-routine activities, which often suffer from higher uncertainty, is indeed complementary to the base location prediction models that often excel at modeling regular daily routine patterns. - oai:arXiv.org:2508.16620v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bangchao Deng, Lianhua Ji, Chunhua Chen, Xin Jing, Ling Ding, Bingqing QU, Pengyang Wang, Dingqi Yang - - - RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction - https://arxiv.org/abs/2508.16623 - arXiv:2508.16623v2 Announce Type: replace -Abstract: Traffic prediction is a cornerstone of modern intelligent transportation systems and a critical task in spatio-temporal forecasting. Although advanced Spatio-temporal Graph Neural Networks (STGNNs) and pre-trained models have achieved significant progress in traffic prediction, two key challenges remain: (i) limited contextual capacity when modeling complex spatio-temporal dependencies, and (ii) low predictability at fine-grained spatio-temporal points due to heterogeneous patterns. Inspired by Retrieval-Augmented Generation (RAG), we propose RAST, a universal framework that integrates retrieval-augmented mechanisms with spatio-temporal modeling to address these challenges. Our framework consists of three key designs: 1) Decoupled Encoder and Query Generator to capture decoupled spatial and temporal features and construct a fusion query via residual fusion; 2) Spatio-temporal Retrieval Store and Retrievers to maintain and retrieve vectorized fine-grained patterns; and 3) Universal Backbone Predictor that flexibly accommodates pre-trained STGNNs or simple MLP predictors. Extensive experiments on six real-world traffic networks, including large-scale datasets, demonstrate that RAST achieves superior performance while maintaining computational efficiency. - oai:arXiv.org:2508.16623v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Weilin Ruan, Xilin Dang, Ziyu Zhou, Sisuo Lyu, Yuxuan Liang - - - Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature - https://arxiv.org/abs/2508.18717 - arXiv:2508.18717v2 Announce Type: replace -Abstract: Modern multi-class image classification relies on high-dimensional CNN feature vectors, which are computationally expensive and obscure the underlying data geometry. Conventional graph-based classifiers degrade on natural multi-class images because typical graphs fail to preserve separability on feature manifolds with complex topology. We address this with a physics-inspired pipeline frozen MobileNetV2 embeddings are treated as Ising spins on a sparse Multi-Edge Type QC-LDPC graph forming a Random Bond Ising Model. The system is tuned to its Nishimori temperature identified where the smallest Bethe-Hessian eigenvalue vanishes. Our method rests on two innovations: we prove a spectral-topological correspondence linking graph trapping sets to invariants via the Ihara-Bass zeta function removing these structures boosts top-1 accuracy over four-fold in multi-class settings; we develop a quadratic-Newton estimator for the Nishimori temperature converging in around 9 Arnoldi iterations for a 6-times speedup enabling spectral embedding on scales like ImageNet-100. The resulting graphs compress 1280-dimensional MobileNetV2 features to 32 dimensions for ImageNet10 and 64 for ImageNet-100 We achieve 98.7% top-1 accuracy on ImageNet-10 and 84.92% on ImageNet-100 with a three-graph soft ensemble Versus MobileNetV2 our hard ensemble increases top-1 by 0.1% while cutting FLOPs by 2.67-times compared to ResNet50 the soft ensemble drops top1 by only 1.09% yet reduces FLOPs by 29-times. Novelty lies in (a) rigorously linking trapping sets to topological defects, (b) an efficient Nishimori temperature estimator and (c) demonstrating that topology-guided LDPC embedding produces highly compressed accurate classifiers for resource-constrained deployment - oai:arXiv.org:2508.18717v2 - cs.LG - cs.CV - cs.IT - math.AT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - V. S. Usatyuk, D. A. Sapoznikov, S. I. Egorov - - - CVBench: Benchmarking Cross-Video Synergies for Complex Multimodal Reasoning - https://arxiv.org/abs/2508.19542 - arXiv:2508.19542v3 Announce Type: replace -Abstract: While multimodal large language models (MLLMs) exhibit strong performance on single-video tasks (e.g., video question answering), their capability for spatiotemporal pattern reasoning across multiple videos remains a critical gap in pattern recognition research. However, this capability is essential for real-world applications, including multi-camera surveillance and cross-video procedural learning. To bridge this gap, we present CVBench, the first diagnostic benchmark designed to assess cross-video relational reasoning rigorously. CVBench comprises 1,000 question-answer pairs spanning three hierarchical tiers: cross-video object association (identifying shared entities), cross-video event association (linking temporal or causal event chains), and cross-video complex reasoning (integrating commonsense and domain knowledge). Built from five domain-diverse video clusters (e.g., sports, life records), the benchmark challenges models to analyze and integrate spatiotemporal patterns from dynamic visual streams. Extensive evaluation of 10+ leading MLLMs (including GPT-4o, Gemini-2.0-flash, Qwen2.5-VL) under zero-shot or chain-of-thought prompting paradigms. Key findings reveal stark performance gaps: even top models, such as GPT-4o, achieve only 63.5% accuracy on causal reasoning tasks, compared to the 91.3% accuracy of human performance. Crucially, our analysis reveals fundamental bottlenecks inherent in current MLLMs architectures, notably deficient inter-video context retention and poor disambiguation of overlapping entities. CVBench establishes a rigorous framework for advancing pattern recognition methodologies in multi-video scenarios, providing architectural insights for next-generation models. The data and evaluation code are available at: https://github.com/Hokhim2/CVBench. - oai:arXiv.org:2508.19542v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Nannan Zhu, Yonghao Dong, Teng Wang, Xueqian Li, Shengjun Deng, Yijia Wang, Zheng Hong, Tiantian Geng, Guo Niu, Hanyan Huang, Xiongfei Yao, Shuaiwei Jiao - - - Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum - https://arxiv.org/abs/2508.21740 - arXiv:2508.21740v2 Announce Type: replace -Abstract: Large Language Models (LLMs) enable generative social simulations that can capture culturally informed, norm-guided interaction on online social platforms. We build a technology community simulation modeled on Voat, a Reddit-like alt-right news aggregator and discussion platform active from 2014 to 2020. Using the YSocial framework, we seed the simulation with a fixed catalog of technology links sampled from Voat's shared URLs (covering 30+ domains) and calibrate parameters to Voat's v/technology using samples from the MADOC dataset. Agents use a base, uncensored model (Dolphin 3.0, based on Llama 3.1 8B) and concise personas (demographics, political leaning, interests, education, toxicity propensity) to generate posts, replies, and reactions under platform rules for link and text submissions, threaded replies and daily activity cycles. We run a 30-day simulation and evaluate operational validity by comparing distributions and structures with matched Voat data: activity patterns, interaction networks, toxicity, and topic coverage. Results indicate familiar online regularities: similar activity rhythms, heavy-tailed participation, sparse low-clustering interaction networks, core-periphery structure, topical alignment with Voat, and elevated toxicity. Limitations of the current study include the stateless agent design and evaluation based on a single 30-day run, which constrains external validity and variance estimates. The simulation generates realistic discussions, often featuring toxic language, primarily centered on technology topics such as Big Tech and AI. This approach offers a valuable method for examining toxicity dynamics and testing moderation strategies within a controlled environment. - oai:arXiv.org:2508.21740v2 - cs.CY - cs.SI - physics.soc-ph - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Aleksandar Toma\v{s}evi\'c, Darja Cvetkovi\'c, Sara Major, Slobodan Maleti\'c, Miroslav An{\dj}elkovi\'c, Ana Vrani\'c, Boris Stupovski, Du\v{s}an Vudragovi\'c, Aleksandar Bogojevi\'c, Marija Mitrovi\'c Dankulov - - - Aligned Anchor Groups Guided Line Segment Detector - https://arxiv.org/abs/2509.00786 - arXiv:2509.00786v2 Announce Type: replace -Abstract: This paper introduces a novel line segment detector, the Aligned Anchor Groups guided Line Segment Detector (AAGLSD), designed to detect line segments from images with high precision and completeness. The algorithm employs a hierarchical approach to extract candidate pixels with different saliency levels, including regular anchors and aligned anchor groups. AAGLSD initiates from these aligned anchor groups, sequentially linking anchors and updating the currently predicted line segment simultaneously. The final predictions are derived through straightforward validation and merging of adjacent line segments, avoiding complex refinement strategies. AAGLSD is evaluated on various datasets and quantitative experiments demonstrate that the proposed method can effectively extract complete line segments from input images compared to other advanced line segment detectors. The implementation is available at https://github.com/zyl0609/AAGLSD. - oai:arXiv.org:2509.00786v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zeyu Li, Annan Shu - - - Bidirectional Sparse Attention for Faster Video Diffusion Training - https://arxiv.org/abs/2509.01085 - arXiv:2509.01085v4 Announce Type: replace -Abstract: Video diffusion Transformer (DiT) models excel in generative quality but hit major computational bottlenecks when producing high-resolution, long-duration videos. The quadratic complexity of full attention leads to prohibitively high training and inference costs. Full attention inefficiency stems from two key challenges: excessive computation due to the inherent sparsity of Queries and Key-Value pairs, and redundant computation as fixed sparse patterns fail to leverage DiT's dynamic attention. To overcome this limitation, we propose a Bidirectional Sparse Attention (BSA) framework for faster video DiT training, the first to dynamically sparsify both Queries and Key-Value pairs within 3D full attention, thereby substantially improving training and inference efficiency. BSA addresses these issues through two key components. Query sparsity is optimized by selecting the most informative query tokens via semantic similarity and with a dynamic spatial-time training strategy, while KV sparsity is achieved by computing a statistical dynamic threshold to retain only the most salient KV blocks for computation. Extensive experiments demonstrate that BSA significantly accelerates DiT training across long sequences, reducing FLOPs by up to 20x and achieving 17.79x faster attention training, while preserving or even surpassing the generative quality of full attention. - oai:arXiv.org:2509.01085v4 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chenlu Zhan, Wen Li, Chuyu Shen, Jun Zhang, Suhui Wu, Hao Zhang - - - Towards Data-Driven Metrics for Social Robot Navigation Benchmarking - https://arxiv.org/abs/2509.01251 - arXiv:2509.01251v2 Announce Type: replace -Abstract: This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization for ground robots. We compiled a dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presented it to human raters, yielding a total of 4402 rated trajectories after data quality assurance. Notably, we provide the first all-encompassing learned social robot navigation metric, along qualitative and quantitative results, including the test loss achieved, a comparison against hand-crafted metrics, and an ablation study. All data, software, and model weights are publicly available. - oai:arXiv.org:2509.01251v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Pilar Bachiller-Burgos, Ulysses Bernardet, Luis V. Calderita, Pranup Chhetri, Anthony Francis, Noriaki Hirose, No\'e P\'erez, Dhruv Shah, Phani T. Singamaneni, Xuesu Xiao, Luis J. Manso - - - In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents - https://arxiv.org/abs/2509.01560 - arXiv:2509.01560v3 Announce Type: replace -Abstract: Tool agents--LLM-based systems that interact with external APIs--offer a way to execute real-world tasks. However, as tasks become increasingly complex, these agents struggle to identify and call the correct APIs in the proper order. To tackle this problem, we investigate converting API documentation into a structured API graph that captures API dependencies and leveraging it for multi-tool queries that require compositional API calls. To support this, we introduce In-N-Out, the first expert-annotated dataset of API graphs built from two real-world API benchmarks and their documentation. Using In-N-Out significantly improves performance on both tool retrieval and multi-tool query generation, nearly doubling that of LLMs using documentation alone. Moreover, graphs generated by models fine-tuned on In-N-Out close 90% of this gap, showing that our dataset helps models learn to comprehend API documentation and parameter relationships. Our findings highlight the promise of using explicit API graphs for tool agents and the utility of In-N-Out as a valuable resource. We release our dataset and code at https://github.com/holi-lab/In-N-Out-API-Graph. - oai:arXiv.org:2509.01560v3 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Seungkyu Lee, Nalim Kim, Yohan Jo - - - Plan Verification for LLM-Based Embodied Task Completion Agents - https://arxiv.org/abs/2509.02761 - arXiv:2509.02761v4 Announce Type: replace -Abstract: Large language model (LLM) based task plans and corresponding human demonstrations for embodied AI may be noisy, with unnecessary actions, redundant navigation, and logical errors that reduce policy quality. We propose an iterative verification framework in which a Judge LLM critiques action sequences and a Planner LLM applies the revisions, yielding progressively cleaner and more spatially coherent trajectories. Unlike rule-based approaches, our method relies on natural language prompting, enabling broad generalization across error types including irrelevant actions, contradictions, and missing steps. On a set of manually annotated actions from the TEACh embodied AI dataset, our framework achieves up to 90% recall and 100% precision across four state-of-the-art LLMs (GPT o4-mini, DeepSeek-R1, Gemini 2.5, LLaMA 4 Scout). The refinement loop converges quickly, with 96.5% of sequences requiring at most three iterations, while improving both temporal efficiency and spatial action organization. Crucially, the method preserves human error-recovery patterns rather than collapsing them, supporting future work on robust corrective behavior. By establishing plan verification as a reliable LLM capability for spatial planning and action refinement, we provide a scalable path to higher-quality training data for imitation learning in embodied AI. - oai:arXiv.org:2509.02761v4 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Ananth Hariharan, Vardhan Dongre, Dilek Hakkani-T\"ur, Gokhan Tur - - - Hybrid dynamical systems modeling of power systems - https://arxiv.org/abs/2509.02822 - arXiv:2509.02822v2 Announce Type: replace -Abstract: The increasing integration of renewable energy sources has introduced complex dynamic behavior in power systems that challenge the adequacy of traditional continuous-time modeling approaches. These developments call for modeling frameworks that can capture the intricate interplay between continuous dynamics and discrete events characterizing modern grid operations. Hybrid dynamical systems offer a rigorous foundation for representing such mixed dynamics and have emerged as a valuable tool in power system analysis. Despite their potential, existing studies remain focused on isolated applications or case-specific implementations, offering limited generalizability and guidance for model selection. This paper addresses that gap by providing a comprehensive overview of hybrid modeling approaches relevant to power systems. It critically examines key formalisms, including hybrid automata, switched systems, and piecewise affine models, evaluating their respective strengths, limitations, and suitability across control, stability, and system design tasks. In doing so, the paper identifies open challenges and outlines future research directions to support the systematic application of hybrid methods in renewable-rich, converter-dominated power systems - oai:arXiv.org:2509.02822v2 - eess.SY - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - B. G. Odunlami, M. Netto, Y. Susuki - - - STSR: High-Fidelity Speech Super-Resolution via Spectral-Transient Context Modeling - https://arxiv.org/abs/2509.03913 - arXiv:2509.03913v4 Announce Type: replace -Abstract: Speech super-resolution (SR) reconstructs high-fidelity wideband speech from low-resolution inputs-a task that necessitates reconciling global harmonic coherence with local transient sharpness. While diffusion-based generative models yield impressive fidelity, their practical deployment is often stymied by prohibitive computational demands. Conversely, efficient time-domain architectures lack the explicit frequency representations essential for capturing long-range spectral dependencies and ensuring precise harmonic alignment. We introduce STSR, a unified end-to-end framework formulated in the MDCT domain to circumvent these limitations. STSR employs a Spectral-Contextual Attention mechanism that harnesses hierarchical windowing to adaptively aggregate non-local spectral context, enabling consistent harmonic reconstruction up to 48 kHz. Concurrently, a sparse-aware regularization strategy is employed to mitigate the suppression of transient components inherent in compressed spectral representations. STSR consistently outperforms state-of-the-art baselines in both perceptual fidelity and zero-shot generalization, providing a robust, real-time paradigm for high-quality speech restoration. - oai:arXiv.org:2509.03913v4 - cs.SD - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jiajun Yuan, Xiaochen Wang, Yuhang Xiao, Yulin Wu, Chenhao Hu, Xueyang Lv - - - ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning - https://arxiv.org/abs/2509.04903 - arXiv:2509.04903v3 Announce Type: replace -Abstract: Long-form generation has become a critical and challenging application for Large Language Models (LLMs). Existing studies are limited by their reliance on scarce, high-quality long-form response data and their focus on coarse-grained, general-purpose metrics (e.g., coherence and helpfulness), overlooking the nuanced, scenario-specific requirements of real-world tasks. To address these limitations, we propose a framework utilizing Adaptive Constraint-Enhanced reward for long-form generation Reinforcement Learning (ACE-RL). ACE-RL first decomposes each instruction into a set of fine-grained, adaptive constraint criteria spanning key dimensions of long-form generation tasks. Subsequently, we design a reward mechanism to quantify the response quality based on their satisfaction over corresponding constraints, converting subjective quality evaluation into constraint verification. Finally, we leverage reinforcement learning to optimize LLMs using these fine-grained signals. Experimental results show that ACE-RL significantly outperforms existing SFT and RL baselines by 18.63% and 7.61% on WritingBench, and our top-performing model even surpasses proprietary systems like GPT-4o by 8.76%, providing a more effective training paradigm in long-form generation scenarios. - oai:arXiv.org:2509.04903v3 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/publicdomain/zero/1.0/ - Jianghao Chen, Wei Sun, Qixiang Yin, Zhixing Tan, Jiajun Zhang - - - Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison - https://arxiv.org/abs/2509.09009 - arXiv:2509.09009v3 Announce Type: replace -Abstract: We introduce open-sci-ref, a family of dense transformer models trained as research baselines across multiple model (0.13B to 1.7B parameters) and token scales (up to 1T) on 8 recent open reference datasets. Evaluating the models on various standardized benchmarks, our training runs set establishes reference points that enable researchers to assess the sanity and quality of alternative training approaches across scales and datasets. Intermediate checkpoints allow comparison and studying of the training dynamics. The established reference baselines allow training procedures to be compared through their scaling trends, aligning them on a common compute axis. Comparison of open reference datasets reveals that training on NemoTron-CC HQ consistently outperforms other reference datasets, followed by DCLM-baseline and FineWeb-Edu. In addition to intermediate training checkpoints, the release includes logs, code, and downstream evaluations to simplify reproduction, standardize comparison, and facilitate future research. - oai:arXiv.org:2509.09009v3 - cs.LG - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Marianna Nezhurina, J\"org Franke, Taishi Nakamura, Timur Carstensen, Niccol\`o Ajroldi, Ville Komulainen, David Salinas, Jenia Jitsev - - - Vital Signs Monitoring with mmWave OFDM JCAS System - https://arxiv.org/abs/2509.11767 - arXiv:2509.11767v2 Announce Type: replace -Abstract: Wireless techniques for monitoring human vital signs, such as heart and breathing rates, offer a promising solution in the context of joint communication and sensing (JCAS) with applications in medicine, sports, safety, security, and even the military. This paper reports experimental results obtained at the Fraunhofer Institute for Integrated Circuits in Ilmenau, demonstrating the effectiveness of an indoor orthogonal frequency-division multiplexing (OFDM) JCAS system for detecting human heart and breathing rates. The system operated in a bistatic configuration at an FR2 frequency of 26.5 GHz with a variable bandwidth of up to 1 GHz. Measurements were taken under various scenarios, including a subject lying down, sitting, or walking, in both line-of-sight and non-line-of-sight conditions, and with one or two subjects present simultaneously. The results indicate that while vital sign detection is generally feasible, its effectiveness is influenced by several factors, such as the subjects clothing, activity, as well as the distance and angle relative to the sensing system. In addition, no significant influence of bandwidth was detected since the vital signs information is encoded in the phase of the signal. - oai:arXiv.org:2509.11767v2 - cs.ET - cs.AR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jakub Dobosz, Maximilian Engelhardt, Diego Dupleich, Maciej Stapor, Pawel Kulakowski - - - A Novel Compression Framework for YOLOv8: Achieving Real-Time Aerial Object Detection on Edge Devices via Structured Pruning and Channel-Wise Distillation - https://arxiv.org/abs/2509.12918 - arXiv:2509.12918v3 Announce Type: replace -Abstract: Efficient deployment of deep learning models for aerial object detection on resource-constrained devices requires significant compression without com-promising performance. In this study, we propose a novel three-stage compression pipeline for the YOLOv8 object detection model, integrating sparsity-aware training, structured channel pruning, and Channel-Wise Knowledge Distillation (CWD). First, sparsity-aware training introduces dynamic sparsity during model optimization, effectively balancing parameter reduction and detection accuracy. Second, we apply structured channel pruning by leveraging batch normalization scaling factors to eliminate redundant channels, significantly reducing model size and computational complexity. Finally, to mitigate the accuracy drop caused by pruning, we employ CWD to transfer knowledge from the original model, using an adjustable temperature and loss weighting scheme tailored for small and medium object detection. Extensive experiments on the VisDrone dataset demonstrate the effectiveness of our approach across multiple YOLOv8 variants. For YOLOv8m, our method reduces model parameters from 25.85M to 6.85M (a 73.51% reduction), FLOPs from 49.6G to 13.3G, and MACs from 101G to 34.5G, while reducing AP50 by only 2.7%. The resulting compressed model achieves 47.9 AP50 and boosts inference speed from 26 FPS (YOLOv8m baseline) to 45 FPS, enabling real-time deployment on edge devices. We further apply TensorRT as a lightweight optimization step. While this introduces a minor drop in AP50 (from 47.9 to 47.6), it significantly improves inference speed from 45 to 68 FPS, demonstrating the practicality of our approach for high-throughput, re-source-constrained scenarios. - oai:arXiv.org:2509.12918v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Melika Sabaghian, Mohammad Ali Keyvanrad, Seyyedeh Mahila Moghadami - - - Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking - https://arxiv.org/abs/2509.14603 - arXiv:2509.14603v2 Announce Type: replace -Abstract: Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. However, exchanging of intermediate activations and model updates introduces significant privacy risks, especially from data reconstruction attacks that recover original inputs from intermediate representations. Existing defenses using noise injection often degrade model performance. To overcome these challenges, we present PM-SFL, a scalable and privacy-preserving SFL framework that incorporates Probabilistic Mask training to add structured randomness without relying on explicit noise. This mitigates data reconstruction risks while maintaining model utility. To address data heterogeneity, PM-SFL employs personalized mask learning that tailors submodel structures to each client's local data. For system heterogeneity, we introduce a layer-wise knowledge compensation mechanism, enabling clients with varying resources to participate effectively under adaptive model splitting. Theoretical analysis confirms its privacy protection, and experiments on image and wireless sensing tasks demonstrate that PM-SFL consistently improves accuracy, communication efficiency, and robustness to privacy attacks, with particularly strong performance under data and system heterogeneity. - oai:arXiv.org:2509.14603v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xingchen Wang, Feijie Wu, Chenglin Miao, Tianchun Li, Haoyu Hu, Qiming Cao, Jing Gao, Lu Su - - - Chunk Based Speech Pre-training with High Resolution Finite Scalar Quantization - https://arxiv.org/abs/2509.15579 - arXiv:2509.15579v2 Announce Type: replace -Abstract: Low latency speech human-machine communication is becoming increasingly necessary as speech technology advances quickly in the last decade. One of the primary factors behind the advancement of speech technology is self-supervised learning. Most self-supervised learning algorithms are designed with full utterance assumption and compromises have to made if partial utterances are presented, which are common in the streaming applications. In this work, we propose a chunk based self-supervised learning (Chunk SSL) algorithm as an unified solution for both streaming and offline speech pre-training. Chunk SSL is optimized with the masked prediction loss and an acoustic encoder is encouraged to restore indices of those masked speech frames with help from unmasked frames in the same chunk and preceding chunks. A copy and append data augmentation approach is proposed to conduct efficient chunk based pre-training. Chunk SSL utilizes a finite scalar quantization (FSQ) module to discretize input speech features and our study shows a high resolution FSQ codebook, i.e., a codebook with vocabulary size up to a few millions, is beneficial to transfer knowledge from the pre-training task to the downstream tasks. A group masked prediction loss is employed during pre-training to alleviate the high memory and computation cost introduced by the large codebook. The proposed approach is examined in two speech to text tasks, i.e., speech recognition and speech translation. Experimental results on the \textsc{Librispeech} and \textsc{Must-C} datasets show that the proposed method could achieve very competitive results for speech to text tasks at both streaming and offline modes. - oai:arXiv.org:2509.15579v2 - cs.CL - cs.SD - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yun Tang, Cindy Tseng - - - Personalized Enhanced Federated Multi-View Clustering via Heat-Kernel Tensor Decomposition - https://arxiv.org/abs/2509.16101 - arXiv:2509.16101v3 Announce Type: replace -Abstract: This paper introduces mathematical frameworks that address the challenges of multi-view clustering in federated learning environments. The objective is to integrate optimization techniques based on new objective functions employing heat-kernel coefficients to replace conventional distance metrics with quantum-inspired measures. The proposed frameworks utilize advanced tensor decomposition methods, specifically, PARAFAC2 and Tucker decomposition to efficiently represent high-dimensional, multi-view data while preserving inter-view relationships. The research has yielded the development of four novel algorithms, an efficient federated kernel multi-view clustering (E-FKMVC) model, FedHK-PARAFAC2, FedHK-Tucker, and FedHK-MVC-Person with PARAFAC2 Decomposition (Personalized FedHK-PARAFAC2). The primary objective of these algorithms is to enhance the efficacy of clustering processes while ensuring the confidentiality and efficient communication in federated learning environments. Theoretical analyses of convergence guarantees, privacy bounds, and complexity are provided to validate the effectiveness of the proposed methods. In essence, this paper makes a significant academic contribution to the field of federated multi-view clustering through its innovative integration of mathematical modeling and algorithm design. This approach addresses the critical challenges of data heterogeneity and privacy concerns, paving the way for enhanced data management and analytics in various contexts. - oai:arXiv.org:2509.16101v3 - cs.LG - cs.DC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Kristina P. Sinaga - - - Audio Super-Resolution with Latent Bridge Models - https://arxiv.org/abs/2509.17609 - arXiv:2509.17609v3 Announce Type: replace -Abstract: Audio super-resolution (SR), i.e., upsampling the low-resolution (LR) waveform to the high-resolution (HR) version, has recently been explored with diffusion and bridge models, while previous methods often suffer from sub-optimal upsampling quality due to their uninformative generation prior. Towards high-quality audio super-resolution, we present a new system with latent bridge models (LBMs), where we compress the audio waveform into a continuous latent space and design an LBM to enable a latent-to-latent generation process that naturally matches the LR-toHR upsampling process, thereby fully exploiting the instructive prior information contained in the LR waveform. To further enhance the training results despite the limited availability of HR samples, we introduce frequency-aware LBMs, where the prior and target frequency are taken as model input, enabling LBMs to explicitly learn an any-to-any upsampling process at the training stage. Furthermore, we design cascaded LBMs and present two prior augmentation strategies, where we make the first attempt to unlock the audio upsampling beyond 48 kHz and empower a seamless cascaded SR process, providing higher flexibility for audio post-production. Comprehensive experimental results evaluated on the VCTK, ESC-50, Song-Describer benchmark datasets and two internal testsets demonstrate that we achieve state-of-the-art objective and perceptual quality for any-to-48kHz SR across speech, audio, and music signals, as well as setting the first record for any-to-192kHz audio SR. Demo at https://AudioLBM.github.io/. - oai:arXiv.org:2509.17609v3 - cs.SD - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chang Li, Zehua Chen, Liyuan Wang, Jun Zhu - - - SiDiaC: Sinhala Diachronic Corpus - https://arxiv.org/abs/2509.17912 - arXiv:2509.17912v2 Announce Type: replace -Abstract: SiDiaC, the first comprehensive Sinhala Diachronic Corpus, covers a historical span from the 5th to the 20th century CE. SiDiaC comprises 58k words across 46 literary works, annotated carefully based on the written date, after filtering based on availability, authorship, copyright compliance, and data attribution. Texts from the National Library of Sri Lanka were digitised using Google Document AI OCR, followed by post-processing to correct formatting and modernise the orthography. The construction of SiDiaC was informed by practices from other corpora, such as FarPaHC, particularly in syntactic annotation and text normalisation strategies, due to the shared characteristics of low-resourced language status. This corpus is categorised based on genres into two layers: primary and secondary. Primary categorisation is binary, classifying each book into Non-Fiction or Fiction, while the secondary categorisation is more specific, grouping texts under Religious, History, Poetry, Language, and Medical genres. Despite challenges including limited access to rare texts and reliance on secondary date sources, SiDiaC serves as a foundational resource for Sinhala NLP, significantly extending the resources available for Sinhala, enabling diachronic studies in lexical change, neologism tracking, historical syntax, and corpus-based lexicography. - oai:arXiv.org:2509.17912v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Nevidu Jayatilleke, Nisansa de Silva - - - AuthGlass: Enhancing Voice Authentication on Smart Glasses via Air-Bone Acoustic Features - https://arxiv.org/abs/2509.20799 - arXiv:2509.20799v3 Announce Type: replace -Abstract: With the rapid advancement of smart glasses, voice interaction has become widely deployed due to its naturalness and convenience. However, its practicality is often undermined by the vulnerability to spoofing attacks and interference from surrounding sounds, making seamless voice authentication crucial for smart glasses usage. To address this challenge, we propose AuthGlass, a voice authentication approach that leverages both air- and bone-conducted speech features to enhance accuracy and liveness detection. Aiming to gain comprehensive knowledge on speech-related acoustic and vibration features, we built a smart glasses prototype with redundant synchronized microphones: 14 air-conductive microphones and 2 bone-conductive units. In a study with 42 participants, we validated that combining sound-field and vibration features significantly improves authentication robustness and attack resistance. Furthermore, experiments demonstrated that AuthGlass maintains competitive accuracy even under various practical scenarios, highlighting its applicability and scalability for real-world deployment. - oai:arXiv.org:2509.20799v3 - cs.HC - cs.SD - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-sa/4.0/ - Weiye Xu, Zhang Jiang, Siqi Zheng, Xiyuxing Zhang, Yankai Zhao, Changhao Zhang, Jian Liu, Weiqiang Wang, Yuntao Wang - - - Secure and Efficient Access Control for Computer-Use Agents via Context Space - https://arxiv.org/abs/2509.22256 - arXiv:2509.22256v3 Announce Type: replace -Abstract: Large language model (LLM)-based computer-use agents represent a convergence of AI and OS capabilities, enabling natural language to control system- and application-level functions. However, due to LLMs' inherent uncertainty issues, granting agents control over computers poses significant security risks. When agent actions deviate from user intentions, they can cause irreversible consequences. Existing mitigation approaches, such as user confirmation and LLM-based dynamic action validation, still suffer from limitations in usability, security, and performance. To address these challenges, we propose CSAgent, a system-level, static policy-based access control framework for computer-use agents. To bridge the gap between static policy and dynamic context and user intent, CSAgent introduces intent- and context-aware policies, and provides an automated toolchain to assist developers in constructing and refining them. CSAgent enforces these policies through an optimized OS service, ensuring that agent actions can only be executed under specific user intents and contexts. CSAgent supports protecting agents that control computers through diverse interfaces, including API, CLI, and GUI. We implement and evaluate CSAgent, which successfully defends against more than 99.56% of attacks while introducing only 1.99% performance overhead. - oai:arXiv.org:2509.22256v3 - cs.CR - cs.AI - cs.OS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haochen Gong, Chenxiao Li, Rui Chang, Wenbo Shen - - - Dynamical feedback control with operator learning for the Vlasov-Poisson system - https://arxiv.org/abs/2509.23063 - arXiv:2509.23063v2 Announce Type: replace -Abstract: To meet the demands of instantaneous control of instabilities over long time horizons in plasma fusion, we design a dynamic feedback control strategy for the Vlasov-Poisson system by constructing an operator that maps state perturbations to an external control field. In the first part of the paper, we propose learning such an operator using a neural network. Inspired by optimal control theory for linearized dynamics, we introduce a low-rank neural operator architecture and train it via adjoint state method. The resulting controller is effective at suppressing instabilities well beyond the training time horizon. To generalize control across varying initial data, we further introduce a novel cancellation-based control strategy that removes the destabilizing component of the electric field. This approach naturally defines an operator without requiring any training, ensures perturbation decay over infinite time, and demonstrates strong robustness under noisy feedback. Numerical experiments confirm the effectiveness of the method in both one- and multidimensional settings. - oai:arXiv.org:2509.23063v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jingcheng Lu, Li Wang, Jeff Calder - - - Towards Comprehensive Interactive Change Understanding in Remote Sensing: A Large-scale Dataset and Dual-granularity Enhanced VLM - https://arxiv.org/abs/2509.23105 - arXiv:2509.23105v2 Announce Type: replace -Abstract: Remote sensing change understanding (RSCU) is essential for analyzing remote sensing images and understanding how human activities affect the environment. However, existing datasets lack deep understanding and interactions in the diverse change captioning, counting, and localization tasks. To tackle these gaps, we construct ChangeIMTI, a new large-scale interactive multi-task instruction dataset that encompasses four complementary tasks including change captioning, binary change classification, change counting, and change localization. Building upon this new dataset, we further design a novel vision-guided vision-language model (ChangeVG) with dual-granularity awareness for bi-temporal remote sensing images (i.e., two remote sensing images of the same area at different times). The introduced vision-guided module is a dual-branch architecture that synergistically combines fine-grained spatial feature extraction with high-level semantic summarization. These enriched representations further serve as the auxiliary prompts to guide large vision-language models (VLMs) (e.g., Qwen2.5-VL-7B) during instruction tuning, thereby facilitating the hierarchical cross-modal learning. We extensively conduct experiments across four tasks to demonstrate the superiority of our approach. Remarkably, on the change captioning task, our method outperforms the strongest method Semantic-CC by 1.39 points on the comprehensive S*m metric, which integrates the semantic similarity and descriptive accuracy to provide an overall evaluation of change caption. Moreover, we also perform a series of ablation studies to examine the critical components of our method. The source code and associated data for this work are publicly available at Github. - oai:arXiv.org:2509.23105v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Junxiao Xue, Quan Deng, Xuecheng Wu, Kelu Yao, Xinyi Yin, Fei Yu, Wei Zhou, Yanfei Zhong, Yang Liu, Dingkang Yang - - - Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss - https://arxiv.org/abs/2509.23194 - arXiv:2509.23194v2 Announce Type: replace -Abstract: Unsupervised online 3D instance segmentation is a fundamental yet challenging task, as it requires maintaining consistent object identities across LiDAR scans without relying on annotated training data. Existing methods, such as UNIT, have made progress in this direction but remain constrained by limited training diversity, rigid temporal sampling, and heavy dependence on noisy pseudo-labels. We propose a new framework that enriches the training distribution through synthetic point cloud sequence generation, enabling greater diversity without relying on manual labels or simulation engines. To better capture temporal dynamics, our method incorporates a flexible sampling strategy that leverages both adjacent and non-adjacent frames, allowing the model to learn from long-range dependencies as well as short-term variations. In addition, a dynamic-weighting loss emphasizes confident and informative samples, guiding the network toward more robust representations. Through extensive experiments on SemanticKITTI, nuScenes, and PandaSet, our method consistently outperforms UNIT and other unsupervised baselines, achieving higher segmentation accuracy and more robust temporal associations. The code will be publicly available at github.com/Eaphan/SFT3D. - oai:arXiv.org:2509.23194v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yifan Zhang, Wei Zhang, Chuangxin He, Zhonghua Miao, Junhui Hou - - - Adversarial Reinforcement Learning Framework for ESP Cheater Simulation - https://arxiv.org/abs/2509.24274 - arXiv:2509.24274v2 Announce Type: replace -Abstract: Extra-Sensory Perception (ESP) cheats, which reveal hidden in-game information such as enemy locations, are difficult to detect because their effects are not directly observable in player behavior. The lack of observable evidence makes it difficult to collect reliably labeled data, which is essential for training effective anti-cheat systems. Furthermore, cheaters often adapt their behavior by limiting or disguising their cheat usage, which further complicates detection and detector development. To address these challenges, we propose a simulation framework for controlled modeling of ESP cheaters, non-cheaters, and trajectory-based detectors. We model cheaters and non-cheaters as reinforcement learning agents with different levels of observability, while detectors classify their behavioral trajectories. Next, we formulate the interaction between the cheater and the detector as an adversarial game, allowing both players to co-adapt over time. To reflect realistic cheater strategies, we introduce a structured cheater model that dynamically switches between cheating and non-cheating behaviors based on detection risk. Experiments demonstrate that our framework successfully simulates adaptive cheater behaviors that strategically balance reward optimization and detection evasion. This work provides a controllable and extensible platform for studying adaptive cheating behaviors and developing effective cheat detectors. - oai:arXiv.org:2509.24274v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Inkyu Park, Jeong-Gwan Lee, Taehwan Kwon, Juheon Choi, Seungku Kim, Junsu Kim, Kimin Lee - - - Deep Learning Accelerated Algebraic Multigrid Methods for Polytopal Discretizations of Second-Order Differential Problems - https://arxiv.org/abs/2510.01442 - arXiv:2510.01442v2 Announce Type: replace -Abstract: Algebraic Multigrid (AMG) methods are state-of-the-art algebraic solvers for partial differential equations. Still, their efficiency depends heavily on the choice of suitable parameters and/or ingredients. Paradigmatic examples include the so-called strong threshold parameter $\theta$, which controls the algebraic coarse-grid hierarchy, as well as the smoother, i.e., the relaxation methods used on the fine grid to damp out high-frequency errors. In AMG, since the coarse grids are constructed algebraically (without geometric intuition), the smoother's performance is even more critical. For the linear systems stemming from polytopal discretizations, such as Polytopal Discontinuous Galerkin (PolyDG) and Virtual Element Methods (VEM), AMG sensitivity to such choices is even more critical due to the significant variability of the underlying meshes, which results in algebraic systems with different sparsity patterns. We propose a novel deep learning approach that automatically tunes the strong threshold parameter, as well as the smoother choice in AMG solvers, for linear systems of equations arising from polytopal discretizations, thereby maximizing AMG performance. We interpret the sparse matrix resulting from polytopal discretization as a grayscale image, and by applying pooling, our neural network extracts compact features that preserve the necessary information at a low computational cost. We test various differential problems in both two- and three-dimensional settings, with heterogeneous coefficients and polygonal/polyhedral meshes, and demonstrate that the proposed approach generalizes well. In practice, we demonstrate that we can reduce AMG solver time by up to $27\%$ with minimal changes to existing PolyDG and VEM codes. - oai:arXiv.org:2510.01442v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Paola F. Antonietti, Matteo Caldana, Lorenzo Gentile, Marco Verani - - - Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms? - https://arxiv.org/abs/2510.03257 - arXiv:2510.03257v2 Announce Type: replace -Abstract: On-demand ride-sharing platforms, such as Uber and Lyft, face the intricate real-time challenge of bundling and matching passengers-each with distinct origins and destinations-to available vehicles, all while navigating significant system uncertainties. Due to the extensive observation space arising from the large number of drivers and orders, order dispatching, though fundamentally a centralized task, is often addressed using Multi-Agent Reinforcement Learning (MARL). However, independent MARL methods fail to capture global information and exhibit poor cooperation among workers, while Centralized Training Decentralized Execution (CTDE) MARL methods suffer from the curse of dimensionality. To overcome these challenges, we propose Triple-BERT, a centralized Single Agent Reinforcement Learning (MARL) method designed specifically for large-scale order dispatching on ride-sharing platforms. Built on a variant TD3, our approach addresses the vast action space through an action decomposition strategy that breaks down the joint action probability into individual driver action probabilities. To handle the extensive observation space, we introduce a novel BERT-based network, where parameter reuse mitigates parameter growth as the number of drivers and orders increases, and the attention mechanism effectively captures the complex relationships among the large pool of driver and orders. We validate our method using a real-world ride-hailing dataset from Manhattan. Triple-BERT achieves approximately an 11.95% improvement over current state-of-the-art methods, with a 4.26% increase in served orders and a 22.25% reduction in pickup times. Our code, trained model parameters, and processed data are publicly available at the repository https://github.com/RS2002/Triple-BERT . - oai:arXiv.org:2510.03257v2 - cs.LG - cs.AI - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zijian Zhao, Sen Li - - - Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication - https://arxiv.org/abs/2510.04000 - arXiv:2510.04000v3 Announce Type: replace -Abstract: Semantic communication shifts the focus from bit-level accuracy to task-relevant semantic delivery, enabling efficient and intelligent communication for next-generation networks. However, existing multi-modal solutions often process all available data modalities indiscriminately, ignoring that their contributions to downstream tasks are often unequal. This not only leads to severe resource inefficiency but also degrades task inference performance due to irrelevant or redundant information. To tackle this issue, we propose a novel task-aware distributed information bottleneck (TADIB) framework, which quantifies the contribution of any set of modalities to given tasks. Based on this theoretical framework, we design a practical coding scheme that intelligently selects and compresses only the most task-relevant modalities at the transmitter. To find the optimal selection and the codecs in the network, we adopt the probabilistic relaxation of discrete selection, enabling distributed encoders to make coordinated decisions with score function estimation and common randomness. Extensive experiments on public datasets demonstrate that our solution matches or surpasses the inference quality of full-modal baselines while significantly reducing communication and computational costs. - oai:arXiv.org:2510.04000v3 - cs.IT - cs.LG - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yujie Zhou, Cheng Peng, Rulong Wang, Yong Xiao, Yingyu Li, Guangming Shi, Ping Zhang - - - LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation - https://arxiv.org/abs/2510.05138 - arXiv:2510.05138v3 Announce Type: replace -Abstract: The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing. - oai:arXiv.org:2510.05138v3 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Gregory Hok Tjoan Go, Khang Ly, Anders S{\o}gaard, Amin Tabatabaei, Maarten de Rijke, Xinyi Chen - - - Smoother-type a posteriori error estimates for finite element methods - https://arxiv.org/abs/2510.07677 - arXiv:2510.07677v2 Announce Type: replace -Abstract: This work develops user-friendly a posteriori error estimates of finite element methods, based on smoothers of linear iterative solvers. The proposed method employs simple smoothers, such as Jacobi or Gauss--Seidel iteration, on an auxiliary finer mesh to process the finite element residual for a posteriori error control. The implementation has linear complexity and requires only a coarse-to-fine prolongation operator. For symmetric problems, we prove the reliability and efficiency of smoother-type error estimators under a saturation assumption. Numerical experiments for various PDEs demonstrate that the proposed smoother-type error estimators outperform residual-type estimators in accuracy and exhibit robustness with respect to parameters and polynomial degrees. - oai:arXiv.org:2510.07677v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yuwen Li, Han Shui - - - Large Language Model Sourcing: A Survey - https://arxiv.org/abs/2510.10161 - arXiv:2510.10161v2 Announce Type: replace -Abstract: Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspectives is essential. This survey presents a systematic investigation organized around four interrelated dimensions: Model Sourcing, Model Structure Sourcing, Training Data Sourcing, and External Data Sourcing. Moreover, a unified dual-paradigm taxonomy is proposed that classifies existing sourcing methods into prior-based (proactive traceability embedding) and posterior-based (retrospective inference) approaches. Traceability across these dimensions enhances the transparency, accountability, and trustworthiness of LLMs deployment in real-world applications. - oai:arXiv.org:2510.10161v2 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Liang Pang, Jia Gu, Sunhao Dai, Zihao Wei, Zenghao Duan, Kangxi Wu, Zhiyi Yin, Jun Xu, Huawei Shen, Xueqi Cheng - - - Bringing The Consistency Gap: Explicit Structured Memory for Interleaved Image-Text Generation - https://arxiv.org/abs/2510.10969 - arXiv:2510.10969v3 Announce Type: replace -Abstract: Existing Vision Language Models (VLMs) often struggle to preserve logic, entity identity, and artistic style during extended, interleaved image-text interactions. We identify this limitation as "Multimodal Context Drift", which stems from the inherent tendency of implicit neural representations to decay or become entangled over long sequences. To bridge this gap, we propose IUT-Plug, a model-agnostic Neuro-Symbolic Structured State Tracking mechanism. Unlike purely neural approaches that rely on transient attention maps, IUT-Plug introduces the Image Understanding Tree (IUT) as an explicit, persistent memory module. The framework operates by (1) parsing visual scenes into hierarchical symbolic structures (entities, attributes, and relationships); (2) performing incremental state updates to logically lock invariant properties while modifying changing elements; and (3) guiding generation through topological constraints. We evaluate our approach on a novel benchmark comprising 3,000 human-annotated samples. Experimental results demonstrate that IUT-Plug effectively mitigates context drift, achieving significantly higher consistency scores compared to unstructured text-prompting baselines. This confirms that explicit symbolic grounding is essential for maintaining robust long-horizon consistency in multimodal generation. - oai:arXiv.org:2510.10969v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zeteng Lin, Xingxing Li, Wen You, Xiaoyang Li, Zehan Lu, Yujun Cai, Jing Tang - - - Invisible Languages of the LLM Universe - https://arxiv.org/abs/2510.11557 - arXiv:2510.11557v2 Announce Type: replace -Abstract: Large Language Models are trained on massive multilingual corpora, yet this abundance masks a profound crisis: of the world's 7,613 living languages, approximately 2,000 languages with millions of speakers remain effectively invisible in digital ecosystems. We propose a critical framework connecting empirical measurements of language vitality (real world demographic strength) and digitality (online presence) with postcolonial theory and epistemic injustice to explain why linguistic inequality in AI systems is not incidental but structural. Analyzing data across all documented human languages, we identify four categories: Strongholds (33%, high vitality and digitality), Digital Echoes (6%, high digitality despite declining vitality), Fading Voices (36%, low on both dimensions), and critically, Invisible Giants (27%, high vitality but near-zero digitality) - languages spoken by millions yet absent from the LLM universe. We demonstrate that these patterns reflect continuities from colonial-era linguistic hierarchies to contemporary AI development, constituting digital epistemic injustice. Our analysis reveals that English dominance in AI is not a technical necessity but an artifact of power structures that systematically exclude marginalized linguistic knowledge. We conclude with implications for decolonizing language technology and democratizing access to AI benefits. - oai:arXiv.org:2510.11557v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Saurabh Khanna, Xinxu Li - - - Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention - https://arxiv.org/abs/2510.13940 - arXiv:2510.13940v2 Announce Type: replace -Abstract: Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized-only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI includes: (i) Selective CFG intervention, applying classifier-free guidance only at uncertain positions; and (ii) Lightweight negative-prompt guidance, reusing the main model's KV cache to approximate unconditional decoding efficiently. MTI yields consistent gains across general, coding, and STEM tasks-e.g., +9.28% average improvement on six benchmarks for DeepSeek-R1-7B and +11.25% on AIME2024 using Ling-mini-2.0-while remaining highly efficient. - oai:arXiv.org:2510.13940v2 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhen Yang, Mingyang Zhang, Feng Chen, Ganggui Ding, Liang Hou, Xin Tao, Pengfei Wan, Ying-Cong Chen - - - CoT-PL: Visual Chain-of-Thought Reasoning Meets Pseudo-Labeling for Open-Vocabulary Object Detection - https://arxiv.org/abs/2510.14792 - arXiv:2510.14792v2 Announce Type: replace -Abstract: Open-vocabulary object detection (OVD) seeks to recognize and localize object categories beyond those seen during training. Recent approaches typically leverage vision-language models (VLMs) to generate pseudo-labels using image-text alignment, allowing detectors to generalize to unseen classes without explicit supervision. However, these methods depend heavily on direct image-text matching, neglecting the intermediate reasoning steps essential for interpreting semantically complex scenes. This results in limited robustness when confronted with crowded or occluded visual contexts. In this paper, we introduce CoT-PL, a new framework that employs structured visual chain-of-thought (CoT) reasoning into the pseudo-labeling process. CoT-PL decomposes object understanding into three interpretable steps: (1) region perception even for unseen objects, (2) category recognition via zero-shot reasoning, and (3) background grounding to separate semantically complex objects. Crucially, the third step naturally motivates our contrastive background learning (CBL) that uses the pre-computed background cues as negatives to promote feature disentanglement between objects and background. In this way, CoT reasoning and CBL form an integrated pipeline tailored to robust pseudo-labeling in crowded or occluded scenes. Notably, in these two settings, our novel-class pseudo-label quality achieves relative improvements of 103.4% and 168.4% over the best prior, respectively. Our extensive experiments demonstrate that CoT-PL achieves +7.7 AP50 on open-vocabulary COCO and +2.9 mask AP on LVIS for novel classes, setting a new state of the art. Code and models are available at https://github.com/hchoi256/cotpl. - oai:arXiv.org:2510.14792v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Hojun Choi, Youngsun Lim, Jaeyo Shin, Hyunjung Shim - - - Graph Learning is Suboptimal in Causal Bandits - https://arxiv.org/abs/2510.16811 - arXiv:2510.16811v2 Announce Type: replace -Abstract: We study regret minimization in causal bandits under causal sufficiency where the underlying causal structure is not known to the agent. Previous work has focused on identifying the reward's parents and then applying classic bandit methods to them, or jointly learning the parents while minimizing regret. We investigate whether such strategies are optimal. Somewhat counterintuitively, our results show that learning the parent set is suboptimal. We do so by proving that there exist instances where regret minimization and parent identification are fundamentally conflicting objectives. We further analyze both the known and unknown parent set size regimes, establish novel regret lower bounds that capture the combinatorial structure of the action space. Building on these insights, we propose nearly optimal algorithms that bypass graph and parent recovery, demonstrating that parent identification is indeed unnecessary for regret minimization. Experiments confirm that there exists a large performance gap between our method and existing baselines in various environments. - oai:arXiv.org:2510.16811v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash - - - When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking - https://arxiv.org/abs/2510.17884 - arXiv:2510.17884v3 Announce Type: replace -Abstract: The remarkable capabilities of Large Language Models (LLMs) in natural language understanding and generation have sparked interest in their potential for cybersecurity applications, including password guessing. In this study, we conduct an empirical investigation into the efficacy of pre-trained LLMs for password cracking using synthetic user profiles. Specifically, we evaluate the performance of state-of-the-art open-source LLMs such as TinyLLaMA, Falcon-RW-1B, and Flan-T5 by prompting them to generate plausible passwords based on structured user attributes (e.g., name, birthdate, hobbies). Our results, measured using Hit@1, Hit@5, and Hit@10 metrics under both plaintext and SHA-256 hash comparisons, reveal consistently poor performance, with all models achieving less than 1.5% accuracy at Hit@10. In contrast, traditional rule-based and combinator-based cracking methods demonstrate significantly higher success rates. Through detailed analysis and visualization, we identify key limitations in the generative reasoning of LLMs when applied to the domain-specific task of password guessing. Our findings suggest that, despite their linguistic prowess, current LLMs lack the domain adaptation and memorization capabilities required for effective password inference, especially in the absence of supervised fine-tuning on leaked password datasets. This study provides critical insights into the limitations of LLMs in adversarial contexts and lays the groundwork for future efforts in secure, privacy-preserving, and robust password modeling. - oai:arXiv.org:2510.17884v3 - cs.CR - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Mohammad Abdul Rehman, Syed Imad Ali Shah, Abbas Anwar, Noor Islam, Hamid Khan - - - Space Object Detection using Multi-frame Temporal Trajectory Completion Method - https://arxiv.org/abs/2510.19220 - arXiv:2510.19220v3 Announce Type: replace -Abstract: Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%. - oai:arXiv.org:2510.19220v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xiaoqing Lan, Biqiao Xin, Bingshu Wang, Han Zhang, Rui Zhu, Laixian Zhang - - - A Unified Approach to Submodular Maximization Under Noise - https://arxiv.org/abs/2510.21128 - arXiv:2510.21128v2 Announce Type: replace -Abstract: We consider the problem of maximizing a submodular function with access to a noisy value oracle for the function instead of an exact value oracle. Similar to prior work, we assume that the noisy oracle is persistent in that multiple calls to the oracle for a specific set always return the same value. In this model, Hassidim and Singer (2017) design a $(1-1/e)$-approximation algorithm for monotone submodular maximization subject to a cardinality constraint, and Huang et al (2022) design a $(1-1/e)/2$-approximation algorithm for monotone submodular maximization subject to any arbitrary matroid constraint. In this paper, we design a meta-algorithm that allows us to take any "robust" algorithm for exact submodular maximization as a black box and transform it into an algorithm for the noisy setting while retaining the approximation guarantee. By using the meta-algorithm with the measured continuous greedy algorithm, we obtain a $(1-1/e)$-approximation (resp. $1/e$-approximation) for monotone (resp. non-monotone) submodular maximization subject to a matroid constraint under noise. Furthermore, by using the meta-algorithm with the double greedy algorithm, we obtain a $1/2$-approximation for unconstrained (non-monotone) submodular maximization under noise. - oai:arXiv.org:2510.21128v2 - cs.DS - cs.CC - cs.DM - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kshipra Bhawalkar, Yang Cai, Zhe Feng, Christopher Liaw, Tao Lin - - - VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree - https://arxiv.org/abs/2510.22693 - arXiv:2510.22693v3 Announce Type: replace -Abstract: Video anomaly detection (VAD) focuses on identifying anomalies in videos. Supervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and language interactivity of large pre-trained models to detect anomalies. However, the current fixed-length temporal window sampling approaches struggle to accurately capture anomalies with varying temporal spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularityaware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and redundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMs) to enhance the node-wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments. The code will be available at https://github.com/wenlongli10/VADTree. - oai:arXiv.org:2510.22693v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wenlong Li, Yifei Xu, Yuan Rao, Zhenhua Wang, Shuiguang Deng - - - Towards Generalisable Foundation Models for Brain MRI - https://arxiv.org/abs/2510.23415 - arXiv:2510.23415v3 Announce Type: replace -Abstract: Foundation models in artificial intelligence (AI) are transforming medical imaging by enabling general-purpose feature learning from large-scale, unlabeled datasets. In this work, we introduce BrainFound, a self-supervised foundation model for brain MRI, built by extending DINO-v2, a vision transformer originally designed for 2D natural images. BrainFound adapts DINO-v2 to model full 3D brain anatomy by incorporating volumetric information from sequential MRI slices, moving beyond conventional single-slice paradigms. It supports both single- and multimodal inputs, enabling a broad range of downstream tasks, including disease detection and image segmentation, while generalising across varied imaging protocols and clinical scenarios. We show that BrainFound consistently outperforms existing self-supervised pretraining strategies and supervised baselines, particularly in label-scarce and multi-contrast settings. By integrating information from diverse 3D MRI modalities (e.g., T1, T2, FLAIR), it enhances diagnostic accuracy and reduces dependency on extensive expert annotations. This flexibility makes BrainFound a scalable and practical solution for 3D neuroimaging pipelines, with significant potential for clinical deployment and research innovation. - oai:arXiv.org:2510.23415v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Moona Mazher, Geoff J. M. Parker, Daniel C. Alexander - - - Human- vs. AI-generated tests: dimensionality and information accuracy in latent trait evaluation - https://arxiv.org/abs/2510.24739 - arXiv:2510.24739v2 Announce Type: replace -Abstract: Artificial Intelligence (AI) and large language models (LLMs) are increasingly used in social and psychological research. Among potential applications, LLMs can be used to generate, customise, or adapt measurement instruments. This study presents a preliminary investigation of AI-generated questionnaires by comparing two ChatGPT-based adaptations of the Body Awareness Questionnaire (BAQ) with the validated human-developed version. The AI instruments were designed with different levels of explicitness in content and instructions on construct facets, and their psychometric properties were assessed using a Bayesian Graded Response Model. Results show that although surface wording between AI and original items was similar, differences emerged in dimensionality and in the distribution of item and test information across latent traits. These findings illustrate the importance of applying statistical measures of accuracy to ensure the validity and interpretability of AI-driven tools. - oai:arXiv.org:2510.24739v2 - cs.HC - cs.IT - math.IT - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Mario Angelelli, Morena Oliva, Serena Arima, Enrico Ciavolino - - - Learning Spatial-Aware Manipulation Ordering - https://arxiv.org/abs/2510.25138 - arXiv:2510.25138v2 Announce Type: replace -Abstract: Manipulation in cluttered environments is challenging due to spatial dependencies among objects, where an improper manipulation order can cause collisions or blocked access. Existing approaches often overlook these spatial relationships, limiting their flexibility and scalability. To address these limitations, we propose OrderMind, a unified spatial-aware manipulation ordering framework that directly learns object manipulation priorities based on spatial context. Our architecture integrates a spatial context encoder with a temporal priority structuring module. We construct a spatial graph using k-Nearest Neighbors to aggregate geometric information from the local layout and encode both object-object and object-manipulator interactions to support accurate manipulation ordering in real-time. To generate physically and semantically plausible supervision signals, we introduce a spatial prior labeling method that guides a vision-language model to produce reasonable manipulation orders for distillation. We evaluate OrderMind on our Manipulation Ordering Benchmark, comprising 163,222 samples of varying difficulty. Extensive experiments in both simulation and real-world environments demonstrate that our method significantly outperforms prior approaches in effectiveness and efficiency, enabling robust manipulation in cluttered scenes. - oai:arXiv.org:2510.25138v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yuxiang Yan, Zhiyuan Zhou, Xin Gao, Guanghao Li, Shenglin Li, Jiaqi Chen, Qunyan Pu, Jian Pu - - - ATLAS: Artifact Generation Through Layered Constraints and LLM x MDE Synergy - https://arxiv.org/abs/2510.25890 - arXiv:2510.25890v2 Announce Type: replace -Abstract: ATLAS unifies Large Language Models with Model-Driven Engineering to generate regulator-ready artifacts and machine-checkable evidence for safety- and compliance-critical domains. ATLAS integrates three pillars: a Unified Meta-Model (UMM) reconciles heterogeneous schemas and regulatory text into a single semantic space; an Integrated Constraint Model (ICM) extends our prior Dual-Stage(S2D2) extraction logic to compile layered requirements into deterministic generation-time automata (Layer~1) and post-generation validators (Layer~2); and Constraint-Guided Verifiable Generation (CVG) applies these through two-layer enforcement -- Layer~1 structural constraints drive prefix-safe decoding while Layer~2 semantic/logical validation produces machine-checkable certificates. When violations occur, ATLAS performs audit-guided repair and records generation traces for compliance review. We evaluate ATLAS in automotive software engineering (AUTOSAR) and cross-border legal jurisdiction (Brussels~I~bis). ATLAS produces structurally valid, auditable artifacts that integrate with existing tooling and substantially reduce manual remediation effort, validating a graduated automation paradigm that automates routine construction while empowering experts to resolve complex semantic ambiguities through machine-checkable evidence. - oai:arXiv.org:2510.25890v2 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tong Ma, Hui Lai, Hui Wang, Zhenhu Tian, Jizhou Wang, Haichao Wu, Yongfan Gao, Chaochao Li, Fengjie Xu, Ling Fang - - - On the limitation of evaluating machine unlearning using only a single training seed - https://arxiv.org/abs/2510.26714 - arXiv:2510.26714v4 Announce Type: replace -Abstract: Machine unlearning (MU) aims to remove the influence of certain data points from a trained model without costly retraining. Most practical MU algorithms are only approximate and their performance can only be assessed empirically. Care must therefore be taken to make empirical comparisons as representative as possible. A common practice is to run the MU algorithm multiple times independently starting from the same trained model. In this work, we demonstrate that this practice can give highly non-representative results because -- even for the same architecture and same dataset -- some MU methods can be highly sensitive to the choice of random number seed used for model training. We illustrate that this is particularly relevant for MU methods that are deterministic, i.e., which always produce the same result when started from the same trained model. We therefore recommend that empirical comparisons of MU algorithms should also reflect the variability across different model training seeds. - oai:arXiv.org:2510.26714v4 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jamie Lanyon, Axel Finke, Petros Andreou, Georgina Cosma - - - Deep sequence models tend to memorize geometrically; it is unclear why - https://arxiv.org/abs/2510.26745 - arXiv:2510.26745v2 Announce Type: replace -Abstract: Deep sequence models are said to store atomic facts predominantly in the form of associative memory: a brute-force lookup of co-occurring entities. We identify a dramatically different form of storage of atomic facts that we term as geometric memory. Here, the model has synthesized embeddings encoding novel global relationships between all entities, including ones that do not co-occur in training. Such storage is powerful: for instance, we show how it transforms a hard reasoning task involving an $\ell$-fold composition into an easy-to-learn $1$-step navigation task. - From this phenomenon, we extract fundamental aspects of neural embedding geometries that are hard to explain. We argue that the rise of such a geometry, as against a lookup of local associations, cannot be straightforwardly attributed to typical supervisory, architectural, or optimizational pressures. Counterintuitively, a geometry is learned even when it is more complex than the brute-force lookup. - Then, by analyzing a connection to Node2Vec, we demonstrate how the geometry stems from a spectral bias that -- in contrast to prevailing theories -- indeed arises naturally despite the lack of various pressures. This analysis also points out to practitioners a visible headroom to make Transformer memory more strongly geometric. We hope the geometric view of parametric memory encourages revisiting the default intuitions that guide researchers in areas like knowledge acquisition, capacity, discovery, and unlearning. - oai:arXiv.org:2510.26745v2 - cs.LG - cs.AI - cs.CL - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Shahriar Noroozizadeh, Vaishnavh Nagarajan, Elan Rosenfeld, Sanjiv Kumar - - - Can machines think efficiently? - https://arxiv.org/abs/2510.26954 - arXiv:2510.26954v2 Announce Type: replace -Abstract: The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update the test. This work expands upon the original imitation game by accounting for an additional factor: the energy spent answering the questions. By adding the constraint of energy, the new test forces us to evaluate intelligence through the lens of efficiency, connecting the abstract problem of thinking to the concrete reality of finite resources. Further, this proposed new test ensures the evaluation of intelligence has a measurable, practical finish line that the original test lacks. This additional constraint compels society to weigh the time savings of using artificial intelligence against its total resource cost. - oai:arXiv.org:2510.26954v2 - cs.LG - cs.AI - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Adam Winchell - - - OpenSIR: Open-Ended Self-Improving Reasoner - https://arxiv.org/abs/2511.00602 - arXiv:2511.00602v2 Announce Type: replace -Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models' ability to surpass human-level performance. While self-play offers a promising alternative, existing approaches depend on external verifiers or cannot learn open-endedly. We present Open-Ended Self-Improving Reasoner (OpenSIR), a self-play framework where an LLM learns to generate and solve novel problems by alternating teacher and student roles without external supervision. To generate novel problems, OpenSIR optimises for both difficulty and diversity, rewarding problems that challenge appropriately while exploring distinct concepts, enabling open-ended mathematical discovery. Starting from a single trivial seed problem, OpenSIR substantially improves instruction models: Llama-3.2-3B-Instruct advances from 73.9 to 78.3 on GSM8K, and from 28.8 to 34.4 on College Math, while Gemma-2-2B-Instruct rises from 38.5 to 58.7 on GSM8K. Our analyses reveal that OpenSIR achieves open-ended learning through co-evolving teacher-student roles that adaptively calibrate difficulty and drive diverse exploration, progressing autonomously from basic to advanced mathematics. - oai:arXiv.org:2511.00602v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Wai-Chung Kwan, Joshua Ong Jun Leang, Pavlos Vougiouklis, Jeff Z. Pan, Marco Valentino, Pasquale Minervini - - - Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification - https://arxiv.org/abs/2511.02992 - arXiv:2511.02992v2 Announce Type: replace -Abstract: Hybrids of Convolutional Neural Network (CNN) and Vision Transformer (ViT) have outperformed pure CNN or ViT architecture. However, since these architectures require large parameters and incur large computational costs, they are unsuitable for tinyML deployment. This paper introduces a new hybrid CNN-ViT search space for Neural Architecture Search (NAS) to find efficient hybrid architectures for image classification. The search space covers hybrid CNN and ViT blocks to learn local and global information, as well as the novel Pooling block of searchable pooling layers for efficient feature map reduction. Experimental results on the CIFAR10 dataset show that our proposed search space can produce hybrid CNN-ViT architectures with superior accuracy and inference speed to ResNet-based tinyML models under tight model size constraints. - oai:arXiv.org:2511.02992v2 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-sa/4.0/ - Mikhael Djajapermana, Moritz Reiber, Daniel Mueller-Gritschneder, Ulf Schlichtmann - - - Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework - https://arxiv.org/abs/2511.03179 - arXiv:2511.03179v3 Announce Type: replace -Abstract: The engineering design process often demands expertise from multiple domains, leading to complex collaborations and iterative refinements. Traditional methods can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a multi-agent AI framework that integrates structured design and review loops. The framework introduces specialized knowledge-driven agents that collaborate to generate and refine design candidates. As an exemplar, we demonstrate its application to the aerodynamic optimization of 4-digit NACA airfoils. The framework consists of three key AI agents: a Graph Ontologist, a Design Engineer, and a Systems Engineer. The Graph Ontologist employs a Large Language Model (LLM) to construct two domain-specific knowledge graphs from airfoil design literature. The Systems Engineer, informed by a human manager, formulates technical requirements that guide design generation and evaluation. The Design Engineer leverages the design knowledge graph and computational tools to propose candidate airfoils meeting these requirements. The Systems Engineer reviews and provides feedback both qualitative and quantitative using its own knowledge graph, forming an iterative feedback loop until a design is validated by the manager. The final design is then optimized to maximize performance metrics such as the lift-to-drag ratio. Overall, this work demonstrates how collaborative AI agents equipped with structured knowledge representations can enhance efficiency, consistency, and quality in the engineering design process. - oai:arXiv.org:2511.03179v3 - cs.AI - cs.LG - cs.MA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Varun Kumar, George Em Karniadakis - - - Necessary and Sufficient Conditions for Capacity-Achieving Private Information Retrieval with Adversarial Servers - https://arxiv.org/abs/2511.06003 - arXiv:2511.06003v3 Announce Type: replace -Abstract: Private information retrieval (PIR) is a mechanism for efficiently downloading messages while keeping the index of the desired message secret from the servers. PIR schemes have been extended to various scenarios with adversarial servers: PIR schemes where some servers are unresponsive or return noisy responses are called robust PIR and Byzantine PIR, respectively; PIR schemes where some servers collude to reveal the index are called colluding PIR. The information-theoretic upper bound on the download efficiency of these PIR schemes has been proved in previous studies. However, systematic ways to construct PIR schemes that achieve the upper bound are not known. In order to construct a capacity-achieving PIR schemes systematically, it is necessary to clarify the conditions that the queries should satisfy. This paper proves the necessary and sufficient conditions for capacity-achieving PIR schemes. - oai:arXiv.org:2511.06003v3 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Atsushi Miki, Toshiyasu Matsushima - - - Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice - https://arxiv.org/abs/2511.07017 - arXiv:2511.07017v2 Announce Type: replace -Abstract: Code review is a cornerstone of software quality assurance, and recent advances in Large Language Models (LLMs) have shown promise in its automation. However, existing benchmarks for LLM-based code review face three major limitations. Lack of semantic context: most benchmarks provide only code diffs without textual information such as issue descriptions, which are crucial for understanding developer intent. Data quality issues: without rigorous validation, many samples are noisy-e.g., reviews on outdated or irrelevant code-reducing evaluation reliability. Coarse granularity: most benchmarks operate at the file or commit level, overlooking the fine-grained, line-level reasoning essential for precise review. We introduce ContextCRBench, a high-quality, context-rich benchmark for fine-grained LLM evaluation in code review. Our construction pipeline comprises: Raw Data Crawling, collecting 153.7K issues and pull requests from top-tier repositories; Comprehensive Context Extraction, linking issue-PR pairs for textual context and extracting the full surrounding function or class for code context; and Multi-stage Data Filtering, combining rule-based and LLM-based validation to remove outdated, malformed, or low-value samples, resulting in 67,910 context-enriched entries. ContextCRBench supports three evaluation scenarios aligned with the review workflow: hunk-level quality assessment, line-level defect localization, and line-level comment generation. Evaluating eight leading LLMs (four closed-source and four open-source) reveals that textual context yields greater performance gains than code context alone, while current LLMs remain far from human-level review ability. Deployed at ByteDance, ContextCRBench drives a self-evolving code review system, improving performance by 61.98% and demonstrating its robustness and industrial utility. https://github.com/kinesiatricssxilm14/ContextCRBench. - oai:arXiv.org:2511.07017v2 - cs.SE - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Ruida Hu, Xinchen Wang, Xin-Cheng Wen, Zhao Zhang, Bo Jiang, Pengfei Gao, Chao Peng, Cuiyun Gao - - - Can ensembles improve evidence recall? A case study - https://arxiv.org/abs/2511.07055 - arXiv:2511.07055v2 Announce Type: replace -Abstract: Feature attribution methods typically provide minimal sufficient evidence justifying a model decision. However, in many applications, such as compliance and cataloging, the full set of contributing features must be identified: complete evidence. We present a case study using existing language models and a medical dataset which contains human-annotated complete evidence. Our findings show that an ensemble approach, aggregating evidence from several models, improves evidence recall over individual models. We examine different ensemble sizes, the effect of evidence-guided training, and provide qualitative insights. - oai:arXiv.org:2511.07055v2 - cs.CL - cs.IR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Katharina Beckh, Sven Heuser, Stefan R\"uping - - - Feedback Descent: Open-Ended Text Optimization via Pairwise Comparison - https://arxiv.org/abs/2511.07919 - arXiv:2511.07919v2 Announce Type: replace -Abstract: We introduce \textit{Feedback Descent}, a framework that optimizes text artifacts -- prompts, code, and molecules -- through structured textual feedback, rather than relying solely on scalar rewards. By preserving detailed critiques instead of compressing them to binary preferences, Feedback Descent widens the information bottleneck in preference learning, enabling directed optimization in text space rather than weight space. We show that in-context learning can transform structured feedback into gradient-like directional information, enabling targeted edits. Unlike prior approaches that collapse judgments into single bits, our evaluators pair each comparison with textual feedback, which functions as high-bandwidth supervision. The iteration loop is done purely at inference time, without modifying any model weights, and is task-agnostic. We evaluate Feedback Descent on three diverse domains and find that it outperforms state-of-the-art prompt optimization (GEPA), reinforcement learning methods (GRPO, REINVENT), and even specialized graph-based molecular optimizers. In the DOCKSTRING molecule discovery benchmark, Feedback Descent identifies novel drug-like molecules surpassing the $99.9$th percentile of a database with more than $260{,}000$ compounds across six protein targets. - oai:arXiv.org:2511.07919v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yoonho Lee, Joseph Boen, Chelsea Finn - - - Training Language Models to Explain Their Own Computations - https://arxiv.org/abs/2511.08579 - arXiv:2511.08579v2 Announce Type: replace -Abstract: Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs' privileged access to their own internals can be leveraged to produce new techniques for explaining their behavior. Using existing interpretability techniques as a source of ground truth, we fine-tune LMs to generate natural language descriptions of (1) the information encoded by LM features, (2) the causal structure of LMs' internal activations, and (3) the influence of specific input tokens on LM outputs. When trained with only tens of thousands of example explanations, explainer models exhibit non-trivial generalization to new queries. This generalization appears partly attributable to explainer models' privileged access to their own internals: using a model to explain its own computations generally works better than using a *different* model to explain its computations (even if the other model is significantly more capable). Our results suggest not only that LMs can learn to reliably explain their internal computations, but that such explanations offer a scalable complement to existing interpretability methods. Code and data at https://github.com/TransluceAI/introspective-interp - oai:arXiv.org:2511.08579v2 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Belinda Z. Li, Zifan Carl Guo, Vincent Huang, Jacob Steinhardt, Jacob Andreas - - - Do Language Models Associate Sound with Meaning? A Multimodal Study of Sound Symbolism - https://arxiv.org/abs/2511.10045 - arXiv:2511.10045v4 Announce Type: replace -Abstract: Sound symbolism is a linguistic concept that refers to non-arbitrary associations between phonetic forms and their meanings. We suggest that this can be a compelling probe into how Multimodal Large Language Models (MLLMs) interpret auditory information in human languages. We investigate MLLMs' performance on phonetic iconicity across textual (orthographic and IPA) and auditory forms of inputs with up to 25 semantic dimensions (e.g., sharp vs. round), observing models' layer-wise information processing by measuring phoneme-level attention fraction scores. To this end, we present LEX-ICON, an extensive mimetic word dataset consisting of 8,052 words from four natural languages (English, French, Japanese, and Korean) and 2,930 systematically constructed pseudo-words, annotated with semantic features applied across both text and audio modalities. Our key findings demonstrate (1) MLLMs' phonetic intuitions that align with existing linguistic research across multiple semantic dimensions and (2) phonosemantic attention patterns that highlight models' focus on iconic phonemes. These results bridge domains of artificial intelligence and cognitive linguistics, providing the first large-scale, quantitative analyses of phonetic iconicity in terms of MLLMs' interpretability. - oai:arXiv.org:2511.10045v4 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jinhong Jeong, Sunghyun Lee, Jaeyoung Lee, Seonah Han, Youngjae Yu - - - SpiderGen: Towards Procedure Generation For Carbon Life Cycle Assessments with Generative AI - https://arxiv.org/abs/2511.10684 - arXiv:2511.10684v3 Announce Type: replace -Abstract: Investigating the effects of climate change and global warming caused by GHG emissions have been a key concern worldwide. These emissions are largely contributed to by the production, use and disposal of consumer products. Thus, it is important to build tools to estimate the environmental impact of consumer goods, an essential part of which is conducting Life Cycle Assessments (LCAs). LCAs specify and account for the appropriate processes involved with the production, use, and disposal of the products. We present SpiderGen, an LLM-based workflow which integrates the taxonomy and methodology of traditional LCA with the reasoning capabilities and world knowledge of LLMs to generate graphical representations of the key procedural information used for LCA, known as Product Category Rules Process Flow Graphs (PCR PFGs). We additionally evaluate the output of SpiderGen by comparing it with 65 real-world LCA documents. We find that SpiderGen provides accurate LCA process information that is either fully correct or has minor errors, achieving an F1-Score of 65% across 10 sample data points, as compared to 53% using a one-shot prompting method. We observe that the remaining errors occur primarily due to differences in detail between LCA documents, as well as differences in the "scope" of which auxiliary processes must also be included. We also demonstrate that SpiderGen performs better than several baselines techniques, such as chain-of-thought prompting and one-shot prompting. Finally, we highlight SpiderGen's potential to reduce the human effort and costs for estimating carbon impact, as it is able to produce LCA process information for less than \$1 USD in under 10 minutes as compared to the status quo LCA, which can cost over \$25000 USD and take up to 21-person days. - oai:arXiv.org:2511.10684v3 - cs.CL - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Anupama Sitaraman, Bharathan Balaji, Yuvraj Agarwal - - - Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities - https://arxiv.org/abs/2511.11512 - arXiv:2511.11512v3 Announce Type: replace -Abstract: Tactile sensing offers rich and complementary information to vision and language, enabling robots to perceive fine-grained object properties. However, existing tactile sensors lack standardization, leading to redundant features that hinder cross-sensor generalization. Moreover, existing methods fail to fully integrate the intermediate communication among tactile, language, and vision modalities. To address this, we propose TLV-CoRe, a CLIP-based Tactile-Language-Vision Collaborative Representation learning method. TLV-CoRe introduces a Sensor-Aware Modulator to unify tactile features across different sensors and employs tactile-irrelevant decoupled learning to disentangle irrelevant tactile features. Additionally, a Unified Bridging Adapter is introduced to enhance tri-modal interaction within the shared representation space. To fairly evaluate the effectiveness of tactile models, we further propose the RSS evaluation framework, focusing on Robustness, Synergy, and Stability across different methods. Experimental results demonstrate that TLV-CoRe significantly improves sensor-agnostic representation learning and cross-modal alignment, offering a new direction for multimodal tactile representation. - oai:arXiv.org:2511.11512v3 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yiyun Zhou, Mingjing Xu, Jingwei Shi, Quanjiang Li, Jingyuan Chen - - - FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI - https://arxiv.org/abs/2511.12899 - arXiv:2511.12899v2 Announce Type: replace -Abstract: Due to the diversity of brain anatomy and the scarcity of annotated data, supervised anomaly detection for brain MRI remains challenging, driving the development of unsupervised anomaly detection (UAD) approaches. Current UAD methods typically utilize artificially generated noise perturbations on healthy MRIs to train generative models for normal anatomy reconstruction, enabling anomaly detection via residual maps. However, such simulated anomalies lack the biophysical fidelity and morphological complexity characteristic of true clinical lesions. To advance UAD in brain MRI, we conduct the first systematic frequency-domain analysis of pathological signatures, revealing two key properties: (1) anomalies exhibit unique frequency patterns distinguishable from normal anatomy, and (2) low-frequency signals maintain consistent representations across healthy scans. These insights motivate our Frequency-Decomposition Preprocessing (FDP) framework, the first UAD method to leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation. FDP can integrate seamlessly with existing anomaly simulation techniques, consistently enhancing detection performance across diverse architectures while maintaining diagnostic fidelity. Experimental results demonstrate that FDP consistently improves anomaly detection performance when integrated with existing methods. Notably, FDP achieves a 17.63% increase in DICE score with LDM while maintaining robust improvements across multiple baselines. The code is available at https://github.com/ls1rius/MRI_FDP. - oai:arXiv.org:2511.12899v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hao Li, Zhenfeng Zhuang, Jingyu Lin, Yu Liu, Yifei Chen, Qiong Peng, Lequan Yu, Liansheng Wang - - - Scaling Spatial Intelligence with Multimodal Foundation Models - https://arxiv.org/abs/2511.13719 - arXiv:2511.13719v3 Announce Type: replace -Abstract: Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the SenseNova-SI family, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks: 68.7% on VSI-Bench, 43.3% on MMSI, 85.6% on MindCube, 54.6% on ViewSpatial, and 50.1% on SITE, while maintaining strong general multimodal understanding (e.g., 84.9% on MMBench-En). More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. SenseNova-SI is an ongoing project, and this report will be updated continuously. All newly trained multimodal foundation models are publicly released to facilitate further research in this direction. - oai:arXiv.org:2511.13719v3 - cs.CV - cs.AI - cs.LG - cs.MM - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhongang Cai, Ruisi Wang, Chenyang Gu, Fanyi Pu, Junxiang Xu, Yubo Wang, Wanqi Yin, Zhitao Yang, Chen Wei, Qingping Sun, Tongxi Zhou, Jiaqi Li, Hui En Pang, Oscar Qian, Yukun Wei, Zhiqian Lin, Xuanke Shi, Kewang Deng, Xiaoyang Han, Zukai Chen, Xiangyu Fan, Hanming Deng, Lewei Lu, Liang Pan, Bo Li, Ziwei Liu, Quan Wang, Dahua Lin, Lei Yang - - - Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges - https://arxiv.org/abs/2511.14478 - arXiv:2511.14478v3 Announce Type: replace -Abstract: Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this gap by providing a comprehensive review that establishes a precise definition and taxonomy for "agentic AI," with the aim of distinguishing it from previous AI paradigms. The concepts are gradually introduced, starting with a highlight of its diverse applications across the broader field of engineering. The paper then presents four detailed, state-of-the-art use case applications specifically within electrical engineering. These case studies demonstrate practical impact, ranging from an advanced agentic framework for streamlining complex power system studies and benchmarking to a novel system developed for survival analysis of dynamic pricing strategies in battery swapping stations. Finally, to ensure robust deployment, the paper provides detailed failure mode investigations. From these findings, we derive actionable recommendations for the design and implementation of safe, reliable, and accountable agentic AI systems, offering a critical resource for researchers and practitioners. - oai:arXiv.org:2511.14478v3 - eess.SY - cs.AI - cs.ET - cs.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Soham Ghosh, Gaurav Mittal - - - ForensicFlow: A Tri-Modal Adaptive Network for Robust Deepfake Detection - https://arxiv.org/abs/2511.14554 - arXiv:2511.14554v2 Announce Type: replace -Abstract: Modern deepfakes evade detection by leaving subtle, domain-speci c artifacts that single branch networks miss. ForensicFlow addresses this by fusing evidence across three forensic dimensions: global visual inconsistencies (via ConvNeXt-tiny), ne-grained texture anomalies (via Swin Transformer-tiny), and spectral noise patterns (via CNN with channel attention). Our attention-based temporal pooling dynamically prioritizes high-evidence frames, while adaptive fusion weights each branch according to forgery type. Trained on CelebDF(v2) with Focal Loss, the model achieves AUC 0.9752, F1 0.9408, and accuracy 0.9208 out performing single-stream detectors. Ablation studies con rm branch synergy, and Grad-CAM visualizations validate focus on genuine manipulation regions (e.g., facial boundaries). This multi-domain fusion strategy establishes robustness against increasingly sophisticated forgeries. - oai:arXiv.org:2511.14554v2 - cs.CV - cs.CR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Mohammad Romani - - - Complex variational autoencoders admit K\"ahler structure - https://arxiv.org/abs/2511.15172 - arXiv:2511.15172v4 Announce Type: replace -Abstract: It has been discovered that latent-Euclidean variational autoencoders (VAEs) admit, in various capacities, Riemannian structure. We adapt these arguments but for complex VAEs with a complex latent stage. We show that complex VAEs reveal to some level K\"ahler geometric structure. Our methods will be tailored for decoder geometry. We derive the Fisher information metric in the complex case under a latent complex Gaussian with trivial relation matrix. It is well known from statistical information theory that the Fisher information coincides with the Hessian of the Kullback-Leibler (KL) divergence. Thus, the metric K\"ahler potential relation is exactly achieved under relative entropy. We propose a K\"ahler potential derivative of complex Gaussian mixtures that acts as a rough proxy to the Fisher information metric while still being faithful to the underlying K\"ahler geometry. Computation of the metric via this potential is efficient, and through our potential, valid as a plurisubharmonic (PSH) function, large scale computational burden of automatic differentiation is displaced to small scale. Our methods leverage the law of total covariance to bridge behavior between our potential and the Fisher metric. We show that we can regularize the latent space with decoder geometry, and that we can sample in accordance with a weighted complex volume element. We demonstrate these strategies, at the exchange of sample variation, yield consistently smoother representations and fewer semantic outliers. - oai:arXiv.org:2511.15172v4 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Andrew Gracyk - - - How Robot Dogs See the Unseeable: Improving Visual Interpretability via Peering for Exploratory Robots - https://arxiv.org/abs/2511.16262 - arXiv:2511.16262v3 Announce Type: replace -Abstract: In vegetated environments, such as forests, exploratory robots play a vital role in navigating complex, cluttered environments where human access is limited and traditional equipment struggles. Visual occlusion from obstacles, such as foliage, can severely obstruct a robot's sensors, impairing scene understanding. We show that "peering", a characteristic side-to-side movement used by insects to overcome their visual limitations, can also allow robots to markedly improve visual reasoning under partial occlusion. This is accomplished by applying core signal processing principles, specifically optical synthetic aperture sensing, together with the vision reasoning capabilities of modern large multimodal models. Peering enables real-time, high-resolution, and wavelength-independent perception, which is crucial for vision-based scene understanding across a wide range of applications. The approach is low-cost and immediately deployable on any camera-equipped robot. We investigated different peering motions and occlusion masking strategies, demonstrating that, unlike peering, state-of-the-art multi-view 3D vision techniques fail in these conditions due to their high susceptibility to occlusion. Our experiments were carried out on an industrial-grade quadrupedal robot. However, the ability to peer is not limited to such platforms, but potentially also applicable to bipedal, hexapod, wheeled, or crawling platforms. Robots that can effectively see through partial occlusion will gain superior perception abilities - including enhanced scene understanding, situational awareness, camouflage breaking, and advanced navigation in complex environments. - oai:arXiv.org:2511.16262v3 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Oliver Bimber, Karl Dietrich von Ellenrieder, Michael Haller, Rakesh John Amala Arokia Nathan, Gianni Lunardi, Mohamed Youssef, Marco Camurri, Santos Miguel Orozco Soto, Jeremy E. Niven - - - BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction - https://arxiv.org/abs/2511.17597 - arXiv:2511.17597v2 Announce Type: replace -Abstract: Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing interest in data-driven approaches, publicly available benchmark datasets that support long-term temporal modeling, large-scale spatial coverage, and multimodal drivers remain scarce. To address this gap, we present a 25-year, daily-resolution wildfire dataset covering 240 million hectares across British Columbia and surrounding regions. The dataset includes 38 covariates, encompassing active fire detections, weather variables, fuel conditions, terrain features, and anthropogenic factors. Using this benchmark, we evaluate a diverse set of time-series forecasting models, including CNN-based, linear-based, Transformer-based, and Mamba-based architectures. We also investigate effectiveness of position embedding and the relative importance of different fire-driving factors. The dataset and the corresponding code can be found at https://github.com/SynUW/mmFire - oai:arXiv.org:2511.17597v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Zhengsen Xu, Sibo Cheng, Lanying Wang, Hongjie He, Wentao Sun, Jonathan Li, Lincoln Linlin Xu - - - Hear: Hierarchically Enhanced Aesthetic Representations For Multidimensional Music Evaluation - https://arxiv.org/abs/2511.18869 - arXiv:2511.18869v2 Announce Type: replace -Abstract: Evaluating song aesthetics is challenging due to the multidimensional nature of musical perception and the scarcity of labeled data. We propose HEAR, a robust music aesthetic evaluation framework that combines: (1) a multi-source multi-scale representations module to obtain complementary segment- and track-level features, (2) a hierarchical augmentation strategy to mitigate overfitting, and (3) a hybrid training objective that integrates regression and ranking losses for accurate scoring and reliable top-tier song identification. Experiments demonstrate that HEAR consistently outperforms the baseline across all metrics on both tracks of the ICASSP 2026 SongEval benchmark. The code and trained model weights are available at https://github.com/Eps-Acoustic-Revolution-Lab/EAR_HEAR. - oai:arXiv.org:2511.18869v2 - cs.SD - cs.AI - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Shuyang Liu, Yuan Jin, Rui Lin, Shizhe Chen, Junyu Dai, Tao Jiang - - - Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning - https://arxiv.org/abs/2511.19026 - arXiv:2511.19026v3 Announce Type: replace -Abstract: Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermittent connectivity. However, routing performance suffers due to dynamic topology changes, unpredictable contact patterns, and resource constraints including limited energy and buffer capacity. These challenges compromise delivery reliability, increase latency, and reduce node longevity in highly dynamic environments. This paper proposes Cluster-based Routing using Deep Reinforcement Learning (CR-DRL), an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function. CR-DRL enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency. Simulation results demonstrate significant improvements over state-of-the-art baselines. CR-DRL extends node lifetimes by up to 21%, overall energy use is reduced by 17%, and nodes remain active for 15% longer. Communication performance also improves, with up to 10% higher delivery ratio, 28.5% lower delay, 7% higher throughput, and data requiring 30% fewer transmission steps across the network. - oai:arXiv.org:2511.19026v3 - cs.NI - stat.ME - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Meisam Sahrifi Sani, Saeid Iranmanesh, Raad Raad, Faisel Tubbal - - - Information Physics of Intelligence: Unifying Logical Depth and Entropy under Thermodynamic Constraints - https://arxiv.org/abs/2511.19156 - arXiv:2511.19156v4 Announce Type: replace -Abstract: The rapid scaling of artificial intelligence models has revealed a fundamental tension between model capacity (storage) and inference efficiency (computation). While classical information theory focuses on transmission and storage limits, it lacks a unified physical framework to quantify the thermodynamic costs of generating information from compressed laws versus retrieving it from memory. In this paper, we propose a theoretical framework that treats information processing as an enabling mapping from ontological states to carrier states. We introduce a novel metric, Derivation Entropy, which quantifies the effective work required to compute a target state from a given logical depth. By analyzing the interplay between Shannon entropy (storage) and computational complexity (time/energy), we demonstrate the existence of a critical phase transition point. Below this threshold, memory retrieval is thermodynamically favorable; above it, generative computation becomes the optimal strategy. This "Energy-Time-Space" conservation law provides a physical explanation for the efficiency of generative models and offers a rigorous mathematical bound for designing next-generation, energy-efficient AI architectures. Our findings suggest that the minimization of Derivation Entropy is a governing principle for the evolution of both biological and artificial intelligence. - oai:arXiv.org:2511.19156v4 - cs.IT - cs.AI - cs.LO - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jianfeng Xu, Zeyan Li - - - SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Gird-based Discontinuous NER Models - https://arxiv.org/abs/2511.20143 - arXiv:2511.20143v2 Announce Type: replace -Abstract: Named Entity Recognition (NER) is a critical task in natural language processing, yet it remains particularly challenging for discontinuous entities. The primary difficulty lies in text segmentation, as traditional methods often missegment or entirely miss cross-sentence discontinuous entities, significantly affecting recognition accuracy. Therefore, we aim to address the segmentation and omission issues associated with such entities. Recent studies have shown that grid-tagging methods are effective for information extraction due to their flexible tagging schemes and robust architectures. Building on this, we integrate image data augmentation techniques, such as cropping, scaling, and padding, into grid-based models to enhance their ability to recognize discontinuous entities and handle segmentation challenges. Experimental results demonstrate that traditional segmentation methods often fail to capture cross-sentence discontinuous entities, leading to decreased performance. In contrast, our augmented grid models achieve notable improvements. Evaluations on the CADEC, ShARe13, and ShARe14 datasets show F1 score gains of 1-2.5% overall and 3.7-8.4% for discontinuous entities, confirming the effectiveness of our approach. - oai:arXiv.org:2511.20143v2 - cs.CL - cs.AI - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Wen-Fang Su, Hsiao-Wei Chou, Wen-Yang Lin - - - APT-CGLP: Advanced Persistent Threat Hunting via Contrastive Graph-Language Pre-Training - https://arxiv.org/abs/2511.20290 - arXiv:2511.20290v2 Announce Type: replace -Abstract: Provenance-based threat hunting identifies Advanced Persistent Threats (APTs) on endpoints by correlating attack patterns described in Cyber Threat Intelligence (CTI) with provenance graphs derived from system audit logs. A fundamental challenge in this paradigm lies in the modality gap -- the structural and semantic disconnect between provenance graphs and CTI reports. Prior work addresses this by framing threat hunting as a graph matching task: 1) extracting attack graphs from CTI reports, and 2) aligning them with provenance graphs. However, this pipeline incurs severe \textit{information loss} during graph extraction and demands intensive manual curation, undermining scalability and effectiveness. - In this paper, we present APT-CGLP, a novel cross-modal APT hunting system via Contrastive Graph-Language Pre-training, facilitating end-to-end semantic matching between provenance graphs and CTI reports without human intervention. First, empowered by the Large Language Model (LLM), APT-CGLP mitigates data scarcity by synthesizing high-fidelity provenance graph-CTI report pairs, while simultaneously distilling actionable insights from noisy web-sourced CTIs to improve their operational utility. Second, APT-CGLP incorporates a tailored multi-objective training algorithm that synergizes contrastive learning with inter-modal masked modeling, promoting cross-modal attack semantic alignment at both coarse- and fine-grained levels. Extensive experiments on four real-world APT datasets demonstrate that APT-CGLP consistently outperforms state-of-the-art threat hunting baselines in terms of accuracy and efficiency. - oai:arXiv.org:2511.20290v2 - cs.CR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Xuebo Qiu, Mingqi Lv, Yimei Zhang, Tieming Chen, Tiantian Zhu, Qijie Song, Shouling Ji - - - Infinity-RoPE: Action-Controllable Infinite Video Generation Emerges From Autoregressive Self-Rollout - https://arxiv.org/abs/2511.20649 - arXiv:2511.20649v2 Announce Type: replace -Abstract: Current autoregressive video diffusion models are constrained by three core bottlenecks: (i) the finite temporal horizon imposed by the base model's 3D Rotary Positional Embedding (3D-RoPE), (ii) slow prompt responsiveness in maintaining fine-grained action control during long-form rollouts, and (iii) the inability to realize discontinuous cinematic transitions within a single generation stream. We introduce $\infty$-RoPE, a unified inference-time framework that addresses all three limitations through three interconnected components: Block-Relativistic RoPE, KV Flush, and RoPE Cut. Block-Relativistic RoPE reformulates temporal encoding as a moving local reference frame, where each newly generated latent block is rotated relative to the base model's maximum frame horizon while earlier blocks are rotated backward to preserve relative temporal geometry. This relativistic formulation eliminates fixed temporal positions, enabling continuous video generation far beyond the base positional limits. To obtain fine-grained action control without re-encoding, KV Flush renews the KV cache by retaining only two latent frames, the global sink and the last generated latent frame, thereby ensuring immediate prompt responsiveness. Finally, RoPE Cut introduces controlled discontinuities in temporal RoPE coordinates, enabling multi-cut scene transitions within a single continuous rollout. Together, these components establish $\infty$-RoPE as a training-free foundation for infinite-horizon, controllable, and cinematic video diffusion. Comprehensive experiments show that $\infty$-RoPE consistently surpasses previous autoregressive models in overall VBench scores. - oai:arXiv.org:2511.20649v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Hidir Yesiltepe, Tuna Han Salih Meral, Adil Kaan Akan, Kaan Oktay, Pinar Yanardag - - - Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities - https://arxiv.org/abs/2511.21382 - arXiv:2511.21382v2 Announce Type: replace -Abstract: Automated unit test generation is critical for software quality but traditional structure-driven methods often lack the semantic understanding required to produce realistic inputs and oracles. Large language models (LLMs) address this limitation by leveraging their extensive data-driven knowledge of code semantics and programming patterns. To analyze the state of the art in this domain, we conducted a systematic literature review of 115 publications published between May 2021 and August 2025. We propose a taxonomy based on the unit test generation lifecycle that divides the process into a generative phase for creating test artifacts and a quality assurance phase for refining them. Our analysis reveals that prompt engineering has emerged as the dominant utilization approach and accounts for 89% of the studies due to its flexibility. We find that iterative validation and repair loops have become the standard mechanism to ensure robust usability by significantly improving compilation and execution pass rates. However, critical challenges remain regarding the weak fault detection capabilities and the lack of standardized benchmarks. We conclude with a roadmap for future research that emphasizes the progression toward autonomous testing agents and hybrid systems combining LLMs with traditional software engineering tools. - oai:arXiv.org:2511.21382v2 - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Bei Chu, Yang Feng, Kui Liu, Zhaoqiang Guo, Yichi Zhang, Hange Shi, Zifan Nan, Baowen Xu - - - TIM-PRM: Verifying multimodal reasoning with Tool-Integrated PRM - https://arxiv.org/abs/2511.22998 - arXiv:2511.22998v2 Announce Type: replace -Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning, yet they remain vulnerable to visual hallucinations and logical inconsistencies that standard outcome-based supervision fails to mitigate. While Process Reward Models (PRMs) promise step-by-step verification, current approaches typically operate as scalar scorers or generative critics that suffer from sycophancy, blindly validating the flawed hypotheses rather than grounding them in visual reality. To bridge this gap, we introduce TIM-PRM (Tool-Integrated Multimodal PRM), a novel agentic framework that transforms verification from a passive classification task into an active, tool-augmented investigation. TIM-PRM is trained to explicitly plan verification strategies and utilizes a mechanism of Independent Question Asking to query evidence via external tools, effectively decoupling verification from the reasoning context to eliminate confirmation bias. We instantiate this method by curating a high-quality dataset of tool-integrated verification trajectories. Extensive experiments on VisualProcessBench demonstrate that our 8B parameter model surpasses existing open-source multimodal PRMs, significantly outperforming much larger models like Qwen2.5-72B and InternVL-78B, while offering interpretable insights into the verification process. - oai:arXiv.org:2511.22998v2 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Peng Kuang, Xiangxiang Wang, Wentao Liu, Jian Dong, Kaidi Xu - - - Multi-Objective Agentic Rewrites for Unstructured Data Processing - https://arxiv.org/abs/2512.02289 - arXiv:2512.02289v2 Announce Type: replace -Abstract: One year ago, we open-sourced DocETL, a declarative system for LLM-powered data processing that, as of November 2025, has 3.2K GitHub stars and users across domains (e.g., journalism, law, medicine, policy, finance, and urban planning). In DocETL, users build pipelines by composing operators described in natural language, also known as semantic operators, with an LLM executing each operator's logic. However, due to complexity in the operator or the data it operates on, LLMs often give inaccurate results. To address this challenge, DocETL introduced rewrite directives, or abstract rules that guide LLM agents in rewriting pipelines by decomposing operators or data. For example, decomposing a single filter("is this email sent from an executive and discussing fraud?") into the conjunction of two separate semantic filters may improve accuracy. However, DocETL only optimizes for accuracy, not cost. How do we optimize for both? - We present MOAR (Multi-Objective Agentic Rewrites), a new optimizer for DocETL. To target cost optimization, we introduce two new categories of directives and extend all three existing categories with new ones, bringing the total to over 30 directives -- more than doubling what DocETL originally had. Moreover, since operators can interact with each other unpredictably due to LLM behavior, optimizing operators or sub-pipelines individually can yield suboptimal overall plans. Recognizing this, we design a new global search algorithm that explores rewrites in the context of entire pipelines. Since the space of rewrites is infinite -- pipelines can be rewritten in many ways, and each rewritten pipeline can itself be rewritten -- our algorithm adapts a multi-armed bandit framework to prioritize which pipelines to rewrite. Across six workloads, MOAR achieves 27% higher accuracy than ABACUS, the next-best optimizer, while matching its best accuracy at 55% of its cost. - oai:arXiv.org:2512.02289v2 - cs.DB - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Lindsey Linxi Wei, Shreya Shankar, Sepanta Zeighami, Yeounoh Chung, Fatma Ozcan, Aditya G. Parameswaran - - - TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction - https://arxiv.org/abs/2512.02341 - arXiv:2512.02341v2 Announce Type: replace -Abstract: 3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass. However, when deployed in online settings such as driving scenarios, predictions are made over temporal windows, making it non-trivial to maintain consistency across time. Recent strategies align consecutive predictions by solving global transformation, yet our analysis reveals their fundamental limitations in assumption validity, local alignment scope, and robustness under noisy geometry. In this work, we propose a higher-DOF and long-term alignment framework based on Thin Plate Spline, leveraging globally propagated control points to correct spatially varying inconsistencies. In addition, we adopt a point-agnostic submap registration design that is inherently robust to noisy geometry predictions. The proposed framework is fully plug-and-play, compatible with diverse 3D foundation models and camera configurations (e.g., monocular or surround-view). Extensive experiments demonstrate that our method consistently yields more coherent geometry and lower trajectory errors across multiple datasets, backbone models, and camera setups, highlighting its robustness and generality. Codes are publicly available at https://github.com/Xian-Bei/TALO. - oai:arXiv.org:2512.02341v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Fengyi Zhang, Tianjun Zhang, Kasra Khosoussi, Zheng Zhang, Zi Huang, Yadan Luo - - - Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates - https://arxiv.org/abs/2512.03402 - arXiv:2512.03402v3 Announce Type: replace -Abstract: Low-rank adaptation (LoRA) is one of the most popular methods among parameter-efficient fine-tuning (PEFT) methods to adapt pre-trained large language models (LLMs) to specific downstream tasks. However, the model trained based on LoRA often has an unsatisfactory performance due to its low-rank assumption. In this paper, we propose a novel method called Dual LoRA to improve the performance by incorporating an inductive bias into the original LoRA. Specifically, we separate low-rank matrices into two groups: the magnitude group to control whether or not and how far we should update a parameter and the direction group to decide whether this parameter should move forward or backward, to better simulate the parameter updating process of the full fine-tuning based on gradient-based optimization algorithms. We show that this can be simply achieved by adding a ReLU function to the magnitude group and a sign function to the direction group. We conduct several experiments over a wide range of NLP tasks, including natural language understanding (NLU) and commonsense reasoning datasets on RoBERTa, DeBERTa, and LLaMA-1/2/3 as baseline models. The results show that we consistently outperform LoRA and its state-of-the-art variants with the same number of trainable parameters. - oai:arXiv.org:2512.03402v3 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-sa/4.0/ - Yixing Xu, Chao Li, Xuanwu Yin, Spandan Tiwari, Dong Li, Ashish Sirasao, Emad Barsoum - - - Cross-embodied Co-design for Dexterous Hands - https://arxiv.org/abs/2512.03743 - arXiv:2512.03743v2 Announce Type: replace -Abstract: Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework will be open-sourced and available on our website: https://an-axolotl.github.io/co-design-for-dexterity.github.io/ - oai:arXiv.org:2512.03743v2 - cs.RO - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Kehlani Fay, Darin Anthony Djapri, Anya Zorin, James Clinton, Ali El Lahib, Hao Su, Michael T. Tolley, Sha Yi, Xiaolong Wang - - - GRASP: GRouped Activation Shared Parameterization for Parameter-Efficient Fine-Tuning and Robust Inference of Transformers - https://arxiv.org/abs/2512.04296 - arXiv:2512.04296v2 Announce Type: replace -Abstract: Parameter-efficient fine-tuning (PEFT) provides a scalable alternative to full-model adaptation by updating only a small subset of parameters in large pre-trained models. We introduce GRASP - GRouped Activation Shared Parameterization - a lightweight PEFT framework that partitions the D-dimensional token representations of selected layers into K << D groups and learns a shared scaling and shifting vector for each group. This grouped modulation reduces the number of trainable parameters significantly while preserving the ability of the model to learn task-specific features. Building on this formulation, we further propose StochGRASP, which learns Gaussian distributions as perturbations to the pre-trained weights rather than deterministic values. This probabilistic parameterization along with a noise-aware loss function formulation enables modelling hardware-level variability in programmed weights and significantly improves robustness under non-ideal inference conditions-an important requirement for deployment on edge-based emerging AI hardware. Across GLUE (RoBERTa-base & RoBERTa-large) and E2E NLG (GPT-2 Medium), GRASP matches or exceeds the performance of established PEFT methods while achieving an order of magnitude reduction in trainable parameters compared to LoRA and BitFit. Under varying levels of noise, StochGRASP consistently outperforms deterministic variants, demonstrating its suitability for energy-efficient and noise-prone hardware platforms. - oai:arXiv.org:2512.04296v2 - cs.LG - cs.NE - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Malyaban Bal, Abhronil Sengupta - - - Minimization-based embedded boundary methods as polynomial corrections: a stability study of discontinuous Galerkin for hyperbolic equations - https://arxiv.org/abs/2512.05278 - arXiv:2512.05278v2 Announce Type: replace -Abstract: This work establishes a novel, unified theoretical framework for a class of high order embedded boundary methods, revealing that the Reconstruction for Off-site Data (ROD) treatment shares a fundamental structure with the recently developed shifted boundary polynomial correction [Ciallella, M., et al. (2023)]. By proving that the ROD minimization problem admits an equivalent direct polynomial correction formulation, we unlock two major advances. First, we derive a significant algorithmic simplification, replacing the solution of the minimization problem with a straightforward polynomial evaluation, thereby enhancing computational efficiency. Second, and most critically, this reformulation enables the first stability result for the ROD method when applied to the linear advection equation with discontinuous Galerkin discretization. Our analysis, supported by a comprehensive eigenspectrum study for polynomial degrees up to six, characterizes the stability region of the new ROD formulation. The theoretical findings, which demonstrate the stability constraints, are validated through targeted numerical experiments. - oai:arXiv.org:2512.05278v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mirco Ciallella - - - Randomized Algorithms for Low-Rank Matrix and Tensor Decompositions - https://arxiv.org/abs/2512.05286 - arXiv:2512.05286v2 Announce Type: replace -Abstract: This paper surveys randomized algorithms in numerical linear algebra for low-rank decompositions of matrices and tensors. The survey begins with a review of classical matrix algorithms that can be accelerated by randomized dimensionality reduction, such as the singular value decomposition (SVD) or interpolative (ID) and CUR decompositions. Recent advances in randomized dimensionality reduction are discussed, including new methods of fast matrix sketching and sampling techniques, which are incorporated into classical matrix algorithms for fast low-rank matrix approximations. The extension of randomized matrix algorithms to tensors is then explored for several low-rank tensor decompositions in the CP and Tucker formats, including the higher-order SVD, ID, and CUR decomposition. - oai:arXiv.org:2512.05286v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Katherine J. Pearce, Per-Gunnar Martinsson - - - On measuring grounding and generalizing grounding problems - https://arxiv.org/abs/2512.06205 - arXiv:2512.06205v2 Announce Type: replace -Abstract: The symbol grounding problem asks how tokens like cat can be about cats, as opposed to mere shapes manipulated in a calculus. We recast grounding from a binary judgment into an audit across desiderata, each indexed by an evaluation tuple (context, meaning type, threat model, reference distribution): authenticity (mechanisms reside inside the agent and, for strong claims, were acquired through learning or evolution); preservation (atomic meanings remain intact); faithfulness, both correlational (realized meanings match intended ones) and etiological (internal mechanisms causally contribute to success); robustness (graceful degradation under declared perturbations); compositionality (the whole is built systematically from the parts). We apply this framework to four grounding modes (symbolic; referential; vectorial; relational) and three case studies: model-theoretic semantics achieves exact composition but lacks etiological warrant; large language models show correlational fit and local robustness for linguistic tasks, yet lack selection-for-success on world tasks without grounded interaction; human language meets the desiderata under strong authenticity through evolutionary and developmental acquisition. By operationalizing a philosophical inquiry about representation, we equip philosophers of science, computer scientists, linguists, and mathematicians with a common language and technical framework for systematic investigation of grounding and meaning. - oai:arXiv.org:2512.06205v2 - cs.AI - cs.CL - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Daniel Quigley, Eric Maynard - - - DaGRPO: Rectifying Gradient Conflict in Reasoning via Distinctiveness-Aware Group Relative Policy Optimization - https://arxiv.org/abs/2512.06337 - arXiv:2512.06337v2 Announce Type: replace -Abstract: The evolution of Large Language Models (LLMs) has catalyzed a paradigm shift from superficial instruction following to rigorous long-horizon reasoning. While Group Relative Policy Optimization (GRPO) has emerged as a pivotal mechanism for eliciting such post-training reasoning capabilities due to its exceptional performance, it remains plagued by significant training instability and poor sample efficiency. We theoretically identify the root cause of these issues as the lack of distinctiveness within on-policy rollouts: for routine queries, highly homogeneous samples induce destructive gradient conflicts; whereas for hard queries, the scarcity of valid positive samples results in ineffective optimization. To bridge this gap, we propose Distinctiveness-aware Group Relative Policy Optimization (DaGRPO). DaGRPO incorporates two core mechanisms: (1) Sequence-level Gradient Rectification, which utilizes fine-grained scoring to dynamically mask sample pairs with low distinctiveness, thereby eradicating gradient conflicts at the source; and (2) Off-policy Data Augmentation, which introduces high-quality anchors to recover training signals for challenging tasks. Extensive experiments across 9 mathematical reasoning and out-of-distribution (OOD) generalization benchmarks demonstrate that DaGRPO significantly surpasses existing SFT, GRPO, and hybrid baselines, achieving new state-of-the-art performance (e.g., a +4.7% average accuracy gain on math benchmarks). Furthermore, in-depth analysis confirms that DaGRPO effectively mitigates gradient explosion and accelerates the emergence of long-chain reasoning capabilities. - oai:arXiv.org:2512.06337v2 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xuan Xie, Xuan Wang, Wenjie Wang, Shuai Chen, Wei Lin - - - A strong two-stage explicit/implicit approach combined with mixed finite element methods for a three-dimensional nonlinear radiation-conduction model in anisotropic media - https://arxiv.org/abs/2512.06456 - arXiv:2512.06456v2 Announce Type: replace -Abstract: This paper develops a strong computational approach to simulate a three-dimensional nonlinear radiation-conduction model in optically thick media, subject to suitable initial and boundary conditions. The space derivatives are approximated by the mixed finite element method ($\mathcal{P}_{p}/\mathcal{P}_{p-1}$), while the interpolation technique is employed in two stages to approximate the time derivative. The proposed strategy is so-called, a strong two-stage explicit/implicit computational technique combined with mixed finite element method. Specifically, the new algorithm should be observed as a predictor-corrector numerical scheme. Additionally, it efficiently treats the time derivative term and provides a necessary requirement on time step for stability. Under this time step limitation, the stability is deeply analyzed whereas the convergence order is numerically computed in the $L^{2}$-norm. The theoretical results suggest that the developed approach is stable and temporal second-order accurate. Some numerical experiments are performed to confirm the theory, to establish that the constructed method is spatial fourth-order convergent and to demonstrate the practical applicability and computational efficiency of the numerical scheme. - oai:arXiv.org:2512.06456v2 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-sa/4.0/ - Eric Ngondiep - - - Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translation Evaluation - https://arxiv.org/abs/2512.07540 - arXiv:2512.07540v3 Announce Type: replace -Abstract: Error Span Detection (ESD) extends automatic machine translation (MT) evaluation by localizing translation errors and labeling their severity. Current generative ESD methods typically use Maximum a Posteriori (MAP) decoding, assuming that the model-estimated probabilities are perfectly correlated with similarity to the human annotation, but we often observe higher likelihood assigned to an incorrect annotation than to the human one. We instead apply Minimum Bayes Risk (MBR) decoding to generative ESD. We use a sentence- or span-level similarity function for MBR decoding, which selects candidate hypotheses based on their approximate similarity to the human annotation. Experimental results on the WMT24 Metrics Shared Task show that MBR decoding significantly improves span-level performance and generally matches or outperforms MAP at the system and sentence levels. To reduce the computational cost of MBR decoding, we further distill its decisions into a model decoded via greedy search, removing the inference-time latency bottleneck. - oai:arXiv.org:2512.07540v3 - cs.CL - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Boxuan Lyu, Haiyue Song, Hidetaka Kamigaito, Chenchen Ding, Hideki Tanaka, Masao Utiyama, Kotaro Funakoshi, Manabu Okumura - - - Group Representational Position Encoding - https://arxiv.org/abs/2512.07805 - arXiv:2512.07805v2 Announce Type: replace -Abstract: We present GRAPE (Group RepresentAtional Position Encoding), a unified framework for positional encoding based on group actions. GRAPE brings together two families of mechanisms: (i) multiplicative rotations (Multiplicative GRAPE) in $\mathrm{SO}(d)$ and (ii) additive logit biases (Additive GRAPE) arising from unipotent actions in the general linear group $\mathrm{GL}$. In Multiplicative GRAPE, a position $n \in \mathbb{Z}$ (or $t \in \mathbb{R}$) acts as $\mathbf{G}(n)=\exp(n\,\omega\,\mathbf{L})$ with a rank-2 skew generator $\mathbf{L} \in \mathbb{R}^{d \times d}$, yielding a relative, compositional, norm-preserving map with a closed-form matrix exponential. RoPE is recovered exactly when the $d/2$ planes are the canonical coordinate pairs with log-uniform spectrum. Learned commuting subspaces and compact non-commuting mixtures strictly extend this geometry to capture cross-subspace feature coupling at $O(d)$ and $O(r d)$ cost per head, respectively. In Additive GRAPE, additive logits arise as rank-1 (or low-rank) unipotent actions, recovering ALiBi and the Forgetting Transformer (FoX) as exact special cases while preserving an exact relative law and streaming cacheability. Altogether, GRAPE supplies a principled design space for positional geometry in long-context models, subsuming RoPE and ALiBi as special cases. Project Page: https://github.com/model-architectures/GRAPE. - oai:arXiv.org:2512.07805v2 - cs.LG - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yifan Zhang, Zixiang Chen, Yifeng Liu, Zhen Qin, Huizhuo Yuan, Kangping Xu, Yang Yuan, Quanquan Gu, Andrew Chi-Chih Yao - - - Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise in Infinite, Real-Time Terrain Generation - https://arxiv.org/abs/2512.08309 - arXiv:2512.08309v2 Announce Type: replace -Abstract: For decades, procedural worlds have been built on procedural noise functions such as Perlin noise, which are fast and infinite, yet fundamentally limited in realism and large-scale coherence. We introduce Terrain Diffusion, a generative framework that bridges the fidelity of diffusion models with the properties that made procedural noise indispensable: seamless infinite extent, seed-consistency, and constant-time random access. At its core is InfiniteDiffusion, a novel algorithm for infinite generation that reformulates standard diffusion sampling for unbounded domains. While noise functions remain near-instant, our framework outpaces orbital velocity by 9 times on a consumer GPU, enabling realistic terrain generation at interactive rates. We integrate a hierarchical stack of diffusion models to couple planetary context with local detail, a compact Laplacian encoding to stabilize outputs across Earth-scale dynamic ranges, and an open-source infinite-tensor framework for constant-memory manipulation of unbounded tensors. Together, these components position diffusion models as a practical, scalable foundation for the next generation of infinite virtual worlds. - oai:arXiv.org:2512.08309v2 - cs.CV - cs.AI - cs.GR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Alexander Goslin - - - Super-Resolution of Elliptic PDE Solutions Using Least Squares Support Vector Regression - https://arxiv.org/abs/2512.09967 - arXiv:2512.09967v2 Announce Type: replace -Abstract: A hybrid computational approach that integrates the finite element method (FEM) with least squares support vector regression (LSSVR) is introduced to solve partial differential equations. The method combines FEM's ability to provide the nodal solutions and LSSVR with higher-order Legendre polynomial kernels to deliver a closed-form analytical solution for interpolation between the nodes. The hybrid approach implements element-wise enhancement (super-resolution) of a given numerical solution, resulting in high resolution accuracy, while maintaining consistency with FEM nodal values at element boundaries. It can adapt any low-order FEM code to obtain high-order resolution by leveraging localized kernel refinement and parallel computation without additional implementation overhead. Therefore, effective inference/post-processing of the obtained super-resolved solution is possible. Evaluation results show that the hybrid FEM-LSSVR approach can achieve significantly higher accuracy compared to the base FEM solution. Comparable accuracy is a achieved when comparing the hybrid solution with a standalone FEM result with the same polynomial basis function order. The convergence studies were conducted for four elliptic boundary value problems to demonstrate the method's ability, accuracy, and reliability. Finally, the algorithm can be directly used as a plug-and-play method for super-resolving low-order numerical solvers and for super-resolution of expensive/under-resolved experimental data. - oai:arXiv.org:2512.09967v2 - math.NA - cs.NA - physics.comp-ph - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/publicdomain/zero/1.0/ - Maryam Babaei, Peter Rucz, Manfred Kaltenbacher, Stefan Schoder - - - HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition - https://arxiv.org/abs/2512.10807 - arXiv:2512.10807v3 Announce Type: replace -Abstract: Sensor-based human activity recognition (HAR) mines activity patterns from the time-series sensory data. In realistic scenarios, variations across individuals, devices, environments, and time introduce significant distributional shifts for the same activities. Recent efforts attempt to solve this challenge by applying or adapting existing out-of-distribution (OOD) algorithms, but only in certain distribution shift scenarios (e.g., cross-device or cross-position), lacking comprehensive insights on the effectiveness of these algorithms. For instance, is OOD necessary to HAR? Which OOD algorithm performs the best? In this paper, we fill this gap by proposing HAROOD, a comprehensive benchmark for HAR in OOD settings. We define 4 OOD scenarios: cross-person, cross-position, cross-dataset, and cross-time, and build a testbed covering 6 datasets, 16 comparative methods (implemented with CNN-based and Transformer-based architectures), and two model selection protocols. Then, we conduct extensive experiments and present several findings for future research, e.g., no single method consistently outperforms others, highlighting substantial opportunity for advancement. Our codebase is highly modular and easy to extend for new datasets, algorithms, comparisons, and analysis, with the hope to facilitate the research in OOD-based HAR. Our implementation is released and can be found at https://github.com/AIFrontierLab/HAROOD. - oai:arXiv.org:2512.10807v3 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wang Lu, Yao Zhu, Jindong Wang - - - The Complexity of One or Many Faces in the Overlay of Many Arrangements - https://arxiv.org/abs/2512.11445 - arXiv:2512.11445v2 Announce Type: replace -Abstract: We present an extension of the Combination Lemma of [GSS89] that expresses the complexity of one or several faces in the overlay of many arrangements, as a function of the number of arrangements, the number of faces, and the complexities of these faces in the separate arrangements. Several applications of the new Combination Lemma are presented: We first show that the complexity of a single face in an arrangement of $k$ simple polygons with a total of $n$ sides is $\Theta(n \alpha(k) )$, where $\alpha(\cdot)$ is the inverse of Ackermann's function. We also give a new and simpler proof of the bound $O \left( \sqrt{m} \lambda_{s+2}( n ) \right)$ on the total number of edges of $m$ faces in an arrangement of $n$ Jordan arcs, each pair of which intersect in at most $s$ points, where $\lambda_{s}(n)$ is the maximum length of a Davenport-Schinzel sequence of order $s$ with $n$ symbols. We extend this result, showing that the total number of edges of $m$ faces in a sparse arrangement of $n$ Jordan arcs is $O \left( (n + \sqrt{m}\sqrt{w}) \frac{\lambda_{s+2}(n)}{n} \right)$, where $w$ is the total complexity of the arrangement. Several other applications and variants of the Combination Lemma are also presented. - oai:arXiv.org:2512.11445v2 - cs.CG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1016/S0925-7721(98)00042-X - Sariel Har-Peled - - - Spiking Manifesto - https://arxiv.org/abs/2512.11843 - arXiv:2512.11843v2 Announce Type: replace -Abstract: Practically everything computers do is better, faster, and more power-efficient than the brain. For example, a calculator performs numerical computations more energy-efficiently than any human. Yet modern AI models are a thousand times less efficient than the brain. These models rely on larger and larger artificial neural networks (ANNs) to boost their encoding capacity, requiring GPUs to perform large-scale matrix multiplications. In contrast, the brain's spiking neural networks (SNNs) exhibit factorially explosive encoding capacity and compute through the polychronization of spikes rather than explicit matrix-vector products, resulting in lower energy requirements. This manifesto proposes a paradigm for framing popular AI models in terms of spiking networks and polychronization, and for interpreting spiking activity as nature's way of implementing look-up tables. This suggests a path toward converting AI models into a novel class of architectures with much smaller size yet combinatorially large encoding capacity, offering the promise of a thousandfold improvement in performance. Code is available at https://github.com/izhikevich/SNN - oai:arXiv.org:2512.11843v2 - cs.NE - cs.AI - cs.AR - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Eugene Izhikevich - - - A Geometric Theory of Cognition - https://arxiv.org/abs/2512.12225 - arXiv:2512.12225v2 Announce Type: replace -Abstract: Human cognition spans perception, memory, intuitive judgment, deliberative reasoning, action selection, and social inference, yet these capacities are often explained through distinct computational theories. Here we present a unified mathematical framework in which diverse cognitive processes emerge from a single geometric principle. We represent the cognitive state as a point on a differentiable manifold endowed with a learned Riemannian metric that encodes representational constraints, computational costs, and structural relations among cognitive variables. A scalar cognitive potential combines predictive accuracy, structural parsimony, task utility, and normative or logical requirements. Cognition unfolds as the Riemannian gradient flow of this potential, providing a universal dynamical law from which a broad range of psychological phenomena arise. Classical dual-process effects--rapid intuitive responses and slower deliberative reasoning--emerge naturally from metric-induced anisotropies that generate intrinsic time-scale separations and geometric phase transitions, without invoking modular or hybrid architectures. We derive analytical conditions for these regimes and demonstrate their behavioural signatures through simulations of canonical cognitive tasks. Together, these results establish a geometric foundation for cognition and suggest guiding principles for the development of more general and human-like artificial intelligence systems. - oai:arXiv.org:2512.12225v2 - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Laha Ale - - - Reveal Hidden Pitfalls and Navigate Next Generation of Vector Similarity Search from Task-Centric Views - https://arxiv.org/abs/2512.12980 - arXiv:2512.12980v2 Announce Type: replace -Abstract: Vector Similarity Search (VSS) in high-dimensional spaces is rapidly emerging as core functionality in next-generation database systems for numerous data-intensive services -- from embedding lookups in large language models (LLMs), to semantic information retrieval and recommendation engines. Current benchmarks, however, evaluate VSS primarily on the recall-latency trade-off against a ground truth defined solely by distance metrics, neglecting how retrieval quality ultimately impacts downstream tasks. This disconnect can mislead both academic research and industrial practice. - We present Iceberg, a holistic benchmark suite for end-to-end evaluation of VSS methods in realistic application contexts. From a task-centric view, Iceberg uncovers the Information Loss Funnel, which identifies three principal sources of end-to-end performance degradation: (1) Embedding Loss during feature extraction; (2) Metric Misuse, where distances poorly reflect task relevance; (3) Data Distribution Sensitivity, highlighting index robustness across skews and modalities. For a more comprehensive assessment, Iceberg spans eight diverse datasets across key domains such as image classification, face recognition, text retrieval, and recommendation systems. Each dataset, ranging from 1M to 100M vectors, includes rich, task-specific labels and evaluation metrics, enabling assessment of retrieval algorithms within the full application pipeline rather than in isolation. Iceberg benchmarks 13 state-of-the-art VSS methods and re-ranks them based on application-level metrics, revealing substantial deviations from traditional rankings derived purely from recall-latency evaluations. Building on these insights, we define a set of task-centric meta-features and derive an interpretable decision tree to guide practitioners in selecting and tuning VSS methods for their specific workloads. - oai:arXiv.org:2512.12980v2 - cs.IR - cs.DB - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Tingyang Chen, Cong Fu, Jiahua Wu, Haotian Wu, Hua Fan, Xiangyu Ke, Yunjun Gao, Yabo Ni, Anxiang Zeng - - - DiRe: Diversity-promoting Regularization for Dataset Condensation - https://arxiv.org/abs/2512.13083 - arXiv:2512.13083v2 Announce Type: replace -Abstract: In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datasets with significant redundancy, so there is a dire need to reduce redundancy and improve the diversity of the synthesized datasets. To tackle this, we propose an intuitive Diversity Regularizer (DiRe) composed of cosine similarity and Euclidean distance, which can be applied off-the-shelf to various state-of-the-art condensation methods. Through extensive experiments, we demonstrate that the addition of our regularizer improves state-of-the-art condensation methods on various benchmark datasets from CIFAR-10 to ImageNet-1K with respect to generalization and diversity metrics. - oai:arXiv.org:2512.13083v2 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Saumyaranjan Mohanty, Aravind Reddy, Konda Reddy Mopuri - - - A homogeneous geometry of low-rank tensors - https://arxiv.org/abs/2512.13594 - arXiv:2512.13594v3 Announce Type: replace -Abstract: We consider sets of fixed CP, multilinear, and TT rank tensors, and derive conditions for when (the smooth parts of) these sets are smooth homogeneous manifolds. For CP and TT ranks, the conditions are essentially that the rank is sufficiently low. These homogeneous structures are then used to derive Riemannian metrics whose geodesics are both complete and efficient to compute. - oai:arXiv.org:2512.13594v3 - math.NA - cs.NA - math.DG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Simon Jacobsson - - - OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction - https://arxiv.org/abs/2512.13886 - arXiv:2512.13886v2 Announce Type: replace -Abstract: Post-training model pruning is a promising solution, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principled joint optimization methods recover accuracy but are computationally infeasible at modern scale. One-shot methods such as SparseGPT offer a practical trade-off in optimality by applying efficient, approximate heuristic weight updates. To close this gap, we introduce OPTIMA, a practical one-shot post-training pruning method that balances accuracy and scalability. OPTIMA casts layer-wise weight reconstruction after mask selection as independent, row-wise Quadratic Programs (QPs) that share a common layer Hessian. Solving these QPs yields the per-row globally optimal update with respect to the reconstruction objective given the estimated Hessian. The shared-Hessian structure makes the problem highly amenable to batching on accelerators. We implement an accelerator-friendly QP solver that accumulates one Hessian per layer and solves many small QPs in parallel, enabling one-shot post-training pruning at scale on a single accelerator without fine-tuning. OPTIMA integrates with existing mask selectors and consistently improves zero-shot performance across multiple LLM families and sparsity regimes, yielding up to 3.97% absolute accuracy improvement. On an NVIDIA H100, OPTIMA prunes a 8B-parameter transformer end-to-end in 40 hours with 60GB peak memory. Together, these results set a new state-of-the-art accuracy-efficiency trade-offs for one-shot post-training pruning. - oai:arXiv.org:2512.13886v2 - cs.LG - cs.AI - cs.PF - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Mohammad Mozaffari, Samuel Kushnir, Maryam Mehri Dehnavi, Amir Yazdanbakhsh - - - Probabilistic Inclusion Depth for Fuzzy Contour Ensemble Visualization - https://arxiv.org/abs/2512.15187 - arXiv:2512.15187v2 Announce Type: replace -Abstract: We propose Probabilistic Inclusion Depth (PID) for the ensemble visualization of scalar fields. By introducing a probabilistic inclusion operator $\subset_{\!p}$, our method is a general data depth model supporting ensembles of fuzzy contours, such as soft masks from modern segmentation methods, and conventional ensembles of binary contours. We also advocate to extend contour extraction in scalar field ensembles to become a fuzzy decision by considering the probabilistic distribution of an isovalue to encode the sensitivity information. To reduce the complexity of the data depth computation, an efficient approximation using the mean probabilistic contour is devised. Furthermore, an order of magnitude reduction in computational time is achieved with an efficient parallel algorithm on the GPU. Our new method enables the computation of contour boxplots for ensembles of probabilistic masks, ensembles defined on various types of grids, and large 3D ensembles that are not studied by existing methods. The effectiveness of our method is evaluated with numerical comparisons to existing techniques on synthetic datasets, through examples of real-world ensemble datasets, and expert feedback. - oai:arXiv.org:2512.15187v2 - cs.GR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Cenyang Wu, Daniel Kl\"otzl, Qinhan Yu, Shudan Guo, Runhao Lin, Daniel Weiskopf, Liang Zhou - - - Geometric Disentanglement of Text Embeddings for Subject-Consistent Text-to-Image Generation using A Single Prompt - https://arxiv.org/abs/2512.16443 - arXiv:2512.16443v3 Announce Type: replace -Abstract: Text-to-image diffusion models excel at generating high-quality images from natural language descriptions but often fail to preserve subject consistency across multiple outputs, limiting their use in visual storytelling. Existing approaches rely on model fine-tuning or image conditioning, which are computationally expensive and require per-subject optimization. 1Prompt1Story, a training-free approach, concatenates all scene descriptions into a single prompt and rescales token embeddings, but it suffers from semantic leakage, where embeddings across frames become entangled, causing text misalignment. In this paper, we propose a simple yet effective training-free approach that addresses semantic entanglement from a geometric perspective by refining text embeddings to suppress unwanted semantics. Extensive experiments prove that our approach significantly improves both subject consistency and text alignment over existing baselines. - oai:arXiv.org:2512.16443v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Shangxun Li, Youngjung Uh - - - PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies - https://arxiv.org/abs/2512.16881 - arXiv:2512.16881v2 Announce Type: replace -Abstract: A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically challenging due to the stochasticity, reproducibility, and time-consuming nature of real-world rollouts. This challenge is exacerbated for recent generalist policies, which has to be evaluated across a wide variety of scenes and tasks. Evaluation in simulation offers a scalable complement to real world evaluations, but the visual and physical domain gap between existing simulation benchmarks and the real world has made them an unreliable signal for policy improvement. Furthermore, building realistic and diverse simulated environments has traditionally required significant human effort and expertise. To bridge the gap, we introduce Policy Evaluation and Environment Reconstruction in Simulation (PolaRiS), a scalable real-to-sim framework for high-fidelity simulated robot evaluation. PolaRiS utilizes neural reconstruction methods to turn short video scans of real-world scenes into interactive simulation environments. Additionally, we develop a simple simulation data co-training recipe that bridges remaining real-to-sim gaps and enables zero-shot evaluation in unseen simulation environments. Through extensive paired evaluations between simulation and the real world, we demonstrate that PolaRiS evaluations provide a much stronger correlation to real world generalist policy performance than existing simulated benchmarks. Its simplicity also enables rapid creation of diverse simulated environments. As such, this work takes a step towards distributed and democratized evaluation for the next generation of robotic foundation models. - oai:arXiv.org:2512.16881v2 - cs.RO - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/publicdomain/zero/1.0/ - Arhan Jain, Mingtong Zhang, Kanav Arora, William Chen, Marcel Torne, Muhammad Zubair Irshad, Sergey Zakharov, Yue Wang, Sergey Levine, Chelsea Finn, Wei-Chiu Ma, Dhruv Shah, Abhishek Gupta, Karl Pertsch - - - Flowing from Reasoning to Motion: Learning 3D Hand Trajectory Prediction from Egocentric Human Interaction Videos - https://arxiv.org/abs/2512.16907 - arXiv:2512.16907v2 Announce Type: replace -Abstract: Prior works on 3D hand trajectory prediction are constrained by datasets that decouple motion from semantic supervision and by models that weakly link reasoning and action. To address these, we first present the EgoMAN dataset, a large-scale egocentric dataset for interaction stage-aware 3D hand trajectory prediction with 219K 6DoF trajectories and 3M structured QA pairs for semantic, spatial, and motion reasoning. We then introduce the EgoMAN model, a reasoning-to-motion framework that links vision-language reasoning and motion generation via a trajectory-token interface. Trained progressively to align reasoning with motion dynamics, our approach yields accurate and stage-aware trajectories with generalization across real-world scenes. - oai:arXiv.org:2512.16907v2 - cs.CV - cs.AI - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Mingfei Chen, Yifan Wang, Zhengqin Li, Homanga Bharadhwaj, Yujin Chen, Chuan Qin, Ziyi Kou, Yuan Tian, Eric Whitmire, Rajinder Sodhi, Hrvoje Benko, Eli Shlizerman, Yue Liu - - - When F1 Fails: Granularity-Aware Evaluation for Dialogue Topic Segmentation - https://arxiv.org/abs/2512.17083 - arXiv:2512.17083v3 Announce Type: replace -Abstract: Dialogue topic segmentation supports summarization, retrieval, memory management, and conversational continuity. Despite decades of work, evaluation practice remains dominated by strict boundary matching and F1-based metrics. Modern large language model (LLM) based conversational systems increasingly rely on segmentation to manage conversation history beyond fixed context windows. In such systems, unstructured context accumulation degrades efficiency and coherence. - This paper introduces an evaluation framework that reports boundary density and segment alignment diagnostics (purity and coverage) alongside window-tolerant F1 (W-F1). By separating boundary scoring from boundary selection, we evaluate segmentation quality across density regimes rather than at a single operating point. Cross-dataset evaluation shows that reported performance differences often reflect annotation granularity mismatch rather than boundary placement quality alone. - We evaluate structurally distinct segmentation strategies across eight dialogue datasets spanning task-oriented, open-domain, meeting-style, and synthetic interactions. Boundary-based metrics are strongly coupled to boundary density: threshold sweeps produce larger W-F1 changes than switching between methods. These findings support viewing topic segmentation as a granularity selection problem rather than prediction of a single correct boundary set. This motivates separating boundary scoring from boundary selection for analyzing and tuning segmentation under varying annotation granularities. - oai:arXiv.org:2512.17083v3 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Michael H. Coen - - - DAVE: A VLM Vision Encoder for Document Understanding and Web Agents - https://arxiv.org/abs/2512.17221 - arXiv:2512.17221v3 Announce Type: replace -Abstract: While Vision-language models (VLMs) have demonstrated remarkable performance across multi-modal tasks, their choice of vision encoders presents a fundamental weakness: their low-level features lack the robust structural and spatial information essential for document understanding and web agents. To bridge this gap, we introduce DAVE, a vision encoder purpose-built for VLMs and tailored for these tasks. Our training pipeline is designed to leverage abundant unlabeled data to bypass the need for costly large-scale annotations for document and web images. We begin with a self-supervised pretraining stage on unlabeled images, followed by a supervised autoregressive pretraining stage, where the model learns tasks like parsing and localization from limited, high-quality data. Within the supervised stage, we adopt two strategies to improve our encoder's alignment with both general visual knowledge and diverse document and web agentic tasks: (i) We introduce a novel model-merging scheme, combining encoders trained with different text decoders to ensure broad compatibility with different web agentic architectures. (ii) We use ensemble training to fuse features from pretrained generalist encoders (e.g., SigLIP2) with our own document and web-specific representations. Extensive experiments on classic document tasks, VQAs, web localization, and agent-based benchmarks validate the effectiveness of our approach, establishing DAVE as a strong vision encoder for document and web applications. - oai:arXiv.org:2512.17221v3 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Brandon Huang, Hang Hua, Zhuoran Yu, Trevor Darrell, Rogerio Feris, Roei Herzig - - - ProCache: Constraint-Aware Feature Caching with Selective Computation for Diffusion Transformer Acceleration - https://arxiv.org/abs/2512.17298 - arXiv:2512.17298v2 Announce Type: replace -Abstract: Diffusion Transformers (DiTs) have achieved state-of-the-art performance in generative modeling, yet their high computational cost hinders real-time deployment. While feature caching offers a promising training-free acceleration solution by exploiting temporal redundancy, existing methods suffer from two key limitations: (1) uniform caching intervals fail to align with the non-uniform temporal dynamics of DiT, and (2) naive feature reuse with excessively large caching intervals can lead to severe error accumulation. In this work, we analyze the evolution of DiT features during denoising and reveal that both feature changes and error propagation are highly time- and depth-varying. Motivated by this, we propose ProCache, a training-free dynamic feature caching framework that addresses these issues via two core components: (i) a constraint-aware caching pattern search module that generates non-uniform activation schedules through offline constrained sampling, tailored to the model's temporal characteristics; and (ii) a selective computation module that selectively computes within deep blocks and high-importance tokens for cached segments to mitigate error accumulation with minimal overhead. Extensive experiments on PixArt-alpha and DiT demonstrate that ProCache achieves up to 1.96x and 2.90x acceleration with negligible quality degradation, significantly outperforming prior caching-based methods. - oai:arXiv.org:2512.17298v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Fanpu Cao, Yaofo Chen, Zeng You, Wei Luo, Cen Chen - - - Statistical laws and linguistics inform meaning in naturalistic and fictional conversation - https://arxiv.org/abs/2512.18072 - arXiv:2512.18072v2 Announce Type: replace -Abstract: Conversation is a cornerstone of social connection and is linked to well-being outcomes. Conversations vary widely in type with some portion generating complex, dynamic stories. One approach to studying how conversations unfold in time is through statistical patterns such as Heaps' law, which holds that vocabulary size scales with document length. Little work on Heaps' law has looked at conversation and considered how language features impact scaling. We measure Heaps' law for conversations recorded in two distinct mediums: 1. Strangers brought together on video chat and 2. Fictional characters in movies. We find that scaling of vocabulary size differs by parts of speech. We discuss these findings through behavioral and linguistic frameworks. - oai:arXiv.org:2512.18072v2 - cs.CL - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ashley M. A. Fehr, Calla G. Beauregard, Julia Witte Zimmerman, Katie Ekstr\"om, Pablo Rosillo-Rodes, Christopher M. Danforth, Peter Sheridan Dodds - - - Wireless Copilot: An AI-Powered Partner for Navigating Next-Generation Wireless Complexity - https://arxiv.org/abs/2512.18582 - arXiv:2512.18582v2 Announce Type: replace -Abstract: The sixth-generation (6G) of wireless networks introduces a level of operational complexity that exceeds the limits of traditional automation and manual oversight. This paper introduces the "Wireless Copilot", an AI-powered technical assistant designed to function as a collaborative partner for human network designers, engineers, and operators. We posit that by integrating Large Language Models (LLMs) with a robust cognitive framework. It will surpass the existing AI tools and interact with wireless devices, transmitting the user's intentions into the actual network execution process. Then, Wireless Copilot can translate high-level human intent into precise, optimized, and verifiable network actions. This framework bridges the gap between human expertise and machine-scale complexity, enabling more efficient, intelligent, and trustworthy management of 6G systems. Wireless Copilot will be a novel layer between the wireless infrastructure and the network operators. Moreover, we explore Wireless Copilot's methodology and analyze its application in Low-Altitude Wireless Networks (LAWNets) assisting 6G networking, including network design, configuration, evaluation, and optimization. Additionally, we present a case study on intent-based LAWNets resource allocation, demonstrating its superior adaptability compared to others. Finally, we outline future research directions toward creating a comprehensive human-AI collaborative ecosystem for the 6G era. - oai:arXiv.org:2512.18582v2 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/publicdomain/zero/1.0/ - Haoxiang Luo, Ruichen Zhang, Yinqiu Liu, Gang Sun, Hongfang Yu, Dusit Niyato, Dong In Kim - - - GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting - https://arxiv.org/abs/2512.19108 - arXiv:2512.19108v2 Announce Type: replace -Abstract: Implicit neural representations (INRs) have achieved remarkable success in image representation and compression, but they require substantial training time and memory. Meanwhile, recent 2D Gaussian Splatting (GS) methods (\textit{e.g.}, GaussianImage) offer promising alternatives through efficient primitive-based rendering. However, these methods require excessive Gaussian primitives to maintain high visual fidelity. To exploit the potential of GS-based approaches, we present GaussianImage++, which utilizes limited Gaussian primitives to achieve impressive representation and compression performance. Firstly, we introduce a distortion-driven densification mechanism. It progressively allocates Gaussian primitives according to signal intensity. Secondly, we employ context-aware Gaussian filters for each primitive, which assist in the densification to optimize Gaussian primitives based on varying image content. Thirdly, we integrate attribute-separated learnable scalar quantizers and quantization-aware training, enabling efficient compression of primitive attributes. Experimental results demonstrate the effectiveness of our method. In particular, GaussianImage++ outperforms GaussianImage and INRs-based COIN in representation and compression performance while maintaining real-time decoding and low memory usage. - oai:arXiv.org:2512.19108v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tiantian Li, Xinjie Zhang, Xingtong Ge, Tongda Xu, Dailan He, Jun Zhang, Yan Wang - - - Enhancing PLS of Indoor IRS-VLC Systems for Colluding and Non-Colluding Eavesdroppers - https://arxiv.org/abs/2512.19339 - arXiv:2512.19339v2 Announce Type: replace -Abstract: Most intelligent reflecting surface (IRS)-aided indoor visible light communication (VLC) studies ignore the time delays introduced by reflected paths, even though these delays are inherent in practical wideband systems. In this work, we adopt a realistic assumption of IRS-induced time delay for physical layer security (PLS) enhancement. We consider an indoor VLC system where an IRS is used to shape the channel so that the reflected signals add constructively at the legitimate user and create intersymbol interference at eavesdroppers located inside the coverage area. The resulting secrecy capacity maximisation over the IRS element allocation is formulated as a complex combinatorial optimisation problem and is solved using deep reinforcement learning with proximal policy optimisation (PPO). The approach is evaluated for both colluding eavesdroppers, which combine their received signals, and non-colluding eavesdroppers, which act independently. Simulation results are shown for various simulation setups, which demonstrate significant secrecy capacity gains. In a worst-case scenario, where the eavesdroppers have stronger channels than the legitimate user, the proposed PPO-based IRS allocation improves secrecy capacity by 107\% and 235\% in the colluding and non-colluding cases, respectively, compared with allocating all IRS elements to the legitimate user. These results demonstrate that time-delay-based IRS control can provide a strong secrecy advantage in practical indoor VLC scenarios. - oai:arXiv.org:2512.19339v2 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rashid Iqbal, Ahmed Zoha, Salama Ikki, Muhammad Ali Imran, Hanaa Abumarshoud - - - MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments - https://arxiv.org/abs/2512.19432 - arXiv:2512.19432v3 Announce Type: replace -Abstract: Among existing online mobile-use benchmarks, AndroidWorld has emerged as the dominant benchmark due to its reproducible environment and deterministic evaluation; however, recent agents achieving over 90% success rates indicate its saturation and motivate the need for a more challenging benchmark. In addition, its environment lacks key application categories, such as e-commerce and enterprise communication, and does not reflect realistic mobile-use scenarios characterized by vague user instructions and hybrid tool usage. We introduce MobileWorld, a substantially more challenging benchmark designed to reflect real-world usage through 201 tasks across 20 applications. MobileWorld derives its difficulty from an emphasis on long-horizon, cross-application workflows, requiring nearly twice as many completion steps on average (27.8 vs. 14.3) and featuring a significantly higher proportion of multi-app tasks (62.2% vs. 9.5%) than AndroidWorld. To overcome the limitations of existing environments, MobileWorld achieves a balance between production-grade utility and reproducible evaluation by utilizing open-source alternatives to industry standards (e.g., Mattermost for Slack). This approach enables a fully observable and controlled environment through source code modification and direct backend database access for precise verification. MobileWorld also introduces novel task categories, including agent-user interaction and Model Context Protocol (MCP)-augmented tasks, for evaluating agents in user-aware, hybrid-tool scenarios. To facilitate evaluation, we develop a planner-executor agentic framework with extended action spaces to support user interactions and MCP calls. Our results reveal a sharp performance drop compared to AndroidWorld, with the best agentic framework and end-to-end model achieving 51.7% and 20.9% success rates, respectively, highlighting ample headroom for future research. - oai:arXiv.org:2512.19432v3 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Quyu Kong, Xu Zhang, Zhenyu Yang, Nolan Gao, Chen Liu, Panrong Tong, Chenglin Cai, Hanzhang Zhou, Jianan Zhang, Liangyu Chen, Zhidan Liu, Steven Hoi, Yue Wang - - - Risk-Aware GPU-Assisted Cardinality Estimation for Cost-Based Query Optimizers - https://arxiv.org/abs/2512.19750 - arXiv:2512.19750v2 Announce Type: replace -Abstract: Cardinality estimation is a cornerstone of cost-based optimizers (CBOs), yet real-world workloads often violate the assumptions behind static statistics, degrading decision stability and increasing plan flip rates. We empirically characterize failures caused by stale statistics, skew, join correlations, hidden distributions in bind variables, and sampling bias, and quantify the overhead and break-even points of hardware-accelerated measurement. - We propose GACE (GPU-Assisted Cardinality Estimation), a hybrid auxiliary architecture that augments rather than replaces the optimizer. GACE selectively invokes GPU-based measurement only in risky intervals via a Risky Gate that detects estimation uncertainty, and a GPU Measurement Engine that performs high-speed probing with explicit cost accounting for the measurement itself. This design preserves low overhead in stable regions while improving plan stability and reducing tail latency (P99) in problematic scenarios. - oai:arXiv.org:2512.19750v2 - cs.DB - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ilsun Chang - - - Few-Shot-Based Modular Image-to-Video Adapter for Diffusion Models - https://arxiv.org/abs/2512.20000 - arXiv:2512.20000v2 Announce Type: replace -Abstract: Diffusion models (DMs) have recently achieved impressive photorealism in image and video generation. However, their application to image animation remains limited, even when trained on large-scale datasets. Two primary challenges contribute to this: the high dimensionality of video signals leads to a scarcity of training data, causing DMs to favor memorization over prompt compliance when generating motion; moreover, DMs struggle to generalize to novel motion patterns not present in the training set, and fine-tuning them to learn such patterns, especially using limited training data, is still under-explored. To address these limitations, we propose Modular Image-to-Video Adapter (MIVA), a lightweight sub-network attachable to a pre-trained DM, each designed to capture a single motion pattern and scalable via parallelization. MIVAs can be efficiently trained on approximately ten samples using a single consumer-grade GPU. At inference time, users can specify motion by selecting one or multiple MIVAs, eliminating the need for prompt engineering. Extensive experiments demonstrate that MIVA enables more precise motion control while maintaining, or even surpassing, the generation quality of models trained on significantly larger datasets. - oai:arXiv.org:2512.20000v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Zhenhao Li, Shaohan Yi, Zheng Liu, Leonartinus Gao, Minh Ngoc Le, Ambrose Ling, Zhuoran Wang, Md Amirul Islam, Zhixiang Chi, Yuanhao Yu - - - Fun-Audio-Chat Technical Report - https://arxiv.org/abs/2512.20156 - arXiv:2512.20156v3 Announce Type: replace -Abstract: Recent advancements in joint speech-text models show great potential for seamless voice interactions. However, existing models face critical challenges: temporal resolution mismatch between speech tokens (25Hz) and text tokens (~3Hz) dilutes semantic information, incurs high computational costs, and causes catastrophic forgetting of text LLM knowledge. We introduce Fun-Audio-Chat, a Large Audio Language Model addressing these limitations via two innovations from our previous work DrVoice. First, Dual-Resolution Speech Representations (DRSR): the Shared LLM processes audio at efficient 5Hz (via token grouping), while the Speech Refined Head generates high-quality tokens at 25Hz, balancing efficiency (~50% GPU reduction) and quality. Second, Core-Cocktail Training, a two-stage fine-tuning with intermediate merging that mitigates catastrophic forgetting. We then apply Multi-Task DPO Training to enhance robustness, audio understanding, instruction-following and voice empathy. This multi-stage post-training enables Fun-Audio-Chat to retain text LLM knowledge while gaining powerful audio understanding, reasoning, and generation. Unlike recent LALMs requiring large-scale audio-text pre-training, Fun-Audio-Chat leverages pre-trained models and extensive post-training. Fun-Audio-Chat 8B and MoE 30B-A3B achieve competitive performance on Speech-to-Text and Speech-to-Speech tasks, ranking top among similar-scale models on Spoken QA benchmarks. They also achieve competitive to superior performance on Audio Understanding, Speech Function Calling, Instruction-Following and Voice Empathy. We develop Fun-Audio-Chat-Duplex, a full-duplex variant with strong performance on Spoken QA and full-duplex interactions. We open-source Fun-Audio-Chat-8B with training and inference code, and provide an interactive demo, at https://github.com/FunAudioLLM/Fun-Audio-Chat . - oai:arXiv.org:2512.20156v3 - cs.CL - cs.AI - cs.SD - eess.AS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tongyi Fun Team, Qian Chen, Luyao Cheng, Chong Deng, Xiangang Li, Jiaqing Liu, Chao-Hong Tan, Wen Wang, Junhao Xu, Jieping Ye, Qinglin Zhang, Qiquan Zhang, Jingren Zhou - - - AUDRON: A Deep Learning Framework with Fused Acoustic Signatures for Drone Type Recognition - https://arxiv.org/abs/2512.20407 - arXiv:2512.20407v2 Announce Type: replace -Abstract: Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense. While these systems provide numerous benefits, their misuse raises safety and security concerns, making effective detection mechanisms essential. Acoustic sensing offers a low-cost and non-intrusive alternative to vision or radar-based detection, as drone propellers generate distinctive sound patterns. This study introduces AUDRON (AUdio-based Drone Recognition Network), a hybrid deep learning framework for drone sound detection, employing a combination of Mel-Frequency Cepstral Coefficients (MFCC), Short-Time Fourier Transform (STFT) spectrograms processed with convolutional neural networks (CNNs), recurrent layers for temporal modeling, and autoencoder-based representations. Feature-level fusion integrates complementary information before classification. Experimental evaluation demonstrates that AUDRON effectively differentiates drone acoustic signatures from background noise, achieving high accuracy while maintaining generalizability across varying conditions. AUDRON achieves 98.51 percent and 97.11 percent accuracy in binary and multiclass classification. The results highlight the advantage of combining multiple feature representations with deep learning for reliable acoustic drone detection, suggesting the framework's potential for deployment in security and surveillance applications where visual or radar sensing may be limited. - oai:arXiv.org:2512.20407v2 - cs.SD - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rajdeep Chatterjee, Sudip Chakrabarty, Trishaani Acharjee, Deepanjali Mishra - - - Leveraging High-Fidelity Digital Models and Reinforcement Learning for Mission Engineering: A Case Study of Aerial Firefighting Under Perfect Information - https://arxiv.org/abs/2512.20589 - arXiv:2512.20589v2 Announce Type: replace -Abstract: As systems engineering (SE) objectives evolve from design and operation of monolithic systems to complex System of Systems (SoS), the discipline of Mission Engineering (ME) has emerged which is increasingly being accepted as a new line of thinking for the SE community. Moreover, mission environments are uncertain, dynamic, and mission outcomes are a direct function of how the mission assets will interact with this environment. This proves static architectures brittle and calls for analytically rigorous approaches for ME. To that end, this paper proposes an intelligent mission coordination methodology that integrates digital mission models with Reinforcement Learning (RL), that specifically addresses the need for adaptive task allocation and reconfiguration. More specifically, we are leveraging a Digital Engineering (DE) based infrastructure that is composed of a high-fidelity digital mission model and agent-based simulation; and then we formulate the mission tactics management problem as a Markov Decision Process (MDP), and employ an RL agent trained via Proximal Policy Optimization. By leveraging the simulation as a sandbox, we map the system states to actions, refining the policy based on realized mission outcomes. The utility of the RL-based intelligent mission coordinator is demonstrated through an aerial firefighting case study. Our findings indicate that the RL-based intelligent mission coordinator not only surpasses baseline performance but also significantly reduces the variability in mission performance. Thus, this study serves as a proof of concept demonstrating that DE-enabled mission simulations combined with advanced analytical tools offer a mission-agnostic framework for improving ME practice; which can be extended to more complicated fleet design and selection problems in the future from a mission-first perspective. - oai:arXiv.org:2512.20589v2 - cs.CY - cs.AI - cs.SY - eess.SY - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - \.Ibrahim O\u{g}uz \c{C}etinkaya, Sajad Khodadadian, Taylan G. Topcu - - - Mixed Precision General Alternating-Direction Implicit Method for Solving Large Sparse Linear Systems - https://arxiv.org/abs/2512.21164 - arXiv:2512.21164v3 Announce Type: replace -Abstract: In this article, we introduce a three-precision formulation of the General Alternating-Direction Implicit method (GADI) designed to accelerate the solution of large-scale sparse linear systems $Ax=b$. GADI is a framework that can represent many existing Alternating-Direction Implicit (ADI) methods. These methods are a class of linear solvers based on a splitting of $A$ such that the solution of the original linear system can be decomposed into the successive computation of easy-to-solve structured subsystems. Our proposed mixed precision scheme for GADI solves these subsystems in low precision to reduce the overall execution time while computing the residual and solution update in high precision to enable the solution to converge to high accuracy. We develop a rounding error analysis of mixed precision GADI that establishes the rates of convergence of the forward and backward errors to certain limiting accuracies. Our analysis also highlights the conditions on the splitting matrices under which mixed precision GADI is guaranteed to converge for a given set of precisions. We then discuss a systematic and robust strategy for selecting the GADI regularization parameter $\alpha$, whose adjustment is critical for performance. Specifically, our proposed strategy makes use of a Gaussian Process Regression (GPR) model trained on a dataset of low-dimensional problems to initialize $\alpha$. Finally, we proceed to a performance analysis of mixed precision GADI on an NVIDIA A100 GPU to validate our approach. Using low precision (Bfloat16 or FP32) to solve the subsystems, we obtain speedups of $2.6\times$, $1.7\times$, and $3.1\times$ over a full double precision GADI implementation on large-scale 2D, 3D convection-diffusion and complex reaction-diffusion problems (up to $1.3\times 10^{8}$ unknowns), respectively. - oai:arXiv.org:2512.21164v3 - math.NA - cs.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jifeng Ge, Bastien Vieubl\'e, Juan Zhang - - - Heaven-Sent or Hell-Bent? Benchmarking the Intelligence and Defectiveness of LLM Hallucinations - https://arxiv.org/abs/2512.21635 - arXiv:2512.21635v2 Announce Type: replace -Abstract: Hallucinations in large language models (LLMs) are commonly regarded as errors to be minimized. However, recent perspectives suggest that some hallucinations may encode creative or epistemically valuable content, a dimension that remains underquantified in current literature. Existing hallucination detection methods primarily focus on factual consistency, struggling to handle heterogeneous scientific tasks and balance creativity with accuracy. To address these challenges, we propose HIC-Bench, a novel evaluation framework that categorizes hallucinations into Intelligent Hallucinations (IH) and Defective Hallucinations (DH), enabling systematic investigation of their interplay in LLM creativity. HIC-Bench features three core characteristics: (1) Structured IH/DH Assessment. using a multi-dimensional metric matrix integrating Torrance Tests of Creative Thinking (TTCT) metrics (Originality, Feasibility, Value) with hallucination-specific dimensions (scientific plausibility, factual deviation); (2) Cross-Domain Applicability. spanning ten scientific domains with open-ended innovation tasks; and (3) Dynamic Prompt Optimization. leveraging the Dynamic Hallucination Prompt (DHP) to guide models toward creative and reliable outputs. The evaluation process employs multiple LLM judges, averaging scores to mitigate bias, with human annotators verifying IH/DH classifications. Experimental results reveal a nonlinear relationship between IH and DH, demonstrating that creativity and correctness can be jointly optimized. These insights position IH as a catalyst for creativity and reveal the ability of LLM hallucinations to drive scientific innovation.Additionally, the HIC-Bench offers a valuable platform for advancing research into the creative intelligence of LLM hallucinations. - oai:arXiv.org:2512.21635v2 - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Chengxu Yang, Jingling Yuan, Siqi Cai, Jiawei Jiang, Chuang Hu - - - SlideChain: Semantic Provenance for Lecture Understanding via Blockchain Registration - https://arxiv.org/abs/2512.21684 - arXiv:2512.21684v2 Announce Type: replace -Abstract: Modern vision--language models (VLMs) are increasingly used to interpret and generate educational content, yet their semantic outputs remain challenging to verify, reproduce, and audit over time. Inconsistencies across model families, inference settings, and computing environments undermine the reliability of AI-generated instructional material, particularly in high-stakes and quantitative STEM domains. This work introduces SlideChain, a blockchain-backed provenance framework designed to provide verifiable integrity for multimodal semantic extraction at scale. Using the SlideChain Slides Dataset-a curated corpus of 1,117 medical imaging lecture slides from a university course-we extract concepts and relational triples from four state-of-the-art VLMs and construct structured provenance records for every slide. SlideChain anchors cryptographic hashes of these records on a local EVM (Ethereum Virtual Machine)-compatible blockchain, providing tamper-evident auditability and persistent semantic baselines. Through the first systematic analysis of semantic disagreement, cross-model similarity, and lecture-level variability in multimodal educational content, we reveal pronounced cross-model discrepancies, including low concept overlap and near-zero agreement in relational triples on many slides. We further evaluate gas usage, throughput, and scalability under simulated deployment conditions, and demonstrate perfect tamper detection along with deterministic reproducibility across independent extraction runs. Together, these results show that SlideChain provides a practical and scalable step toward trustworthy, verifiable multimodal educational pipelines, supporting long-term auditability, reproducibility, and integrity for AI-assisted instructional systems. - oai:arXiv.org:2512.21684v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Md Motaleb Hossen Manik, Md Zabirul Islam, Ge Wang - - - SyncAnyone: Implicit Disentanglement via Progressive Self-Correction for Lip-Syncing in the wild - https://arxiv.org/abs/2512.21736 - arXiv:2512.21736v2 Announce Type: replace -Abstract: High-quality AI-powered video dubbing demands precise audio-lip synchronization, high-fidelity visual generation, and faithful preservation of identity and background. Most existing methods rely on a mask-based training strategy, where the mouth region is masked in talking-head videos, and the model learns to synthesize lip movements from corrupted inputs and target audios. While this facilitates lip-sync accuracy, it disrupts spatiotemporal context, impairing performance on dynamic facial motions and causing instability in facial structure and background consistency. To overcome this limitation, we propose SyncAnyone, a novel two-stage learning framework that achieves accurate motion modeling and high visual fidelity simultaneously. In Stage 1, we train a diffusion-based video transformer for masked mouth inpainting, leveraging its strong spatiotemporal modeling to generate accurate, audio-driven lip movements. However, due to input corruption, minor artifacts may arise in the surrounding facial regions and the background. In Stage 2, we develop a mask-free tuning pipeline to address mask-induced artifacts. Specifically, on the basis of the Stage 1 model, we develop a data generation pipeline that creates pseudo-paired training samples by synthesizing lip-synced videos from the source video and random sampled audio. We further tune the stage 2 model on this synthetic data, achieving precise lip editing and better background consistency. Extensive experiments show that our method achieves state-of-the-art results in visual quality, temporal coherence, and identity preservation under in-the wild lip-syncing scenarios. - oai:arXiv.org:2512.21736v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xindi Zhang, Dechao Meng, Steven Xiao, Qi Wang, Peng Zhang, Bang Zhang - - - Inference-based GAN Video Generation - https://arxiv.org/abs/2512.21776 - arXiv:2512.21776v2 Announce Type: replace -Abstract: Video generation has seen remarkable progress thanks to advancements in generative deep learning. However, generating long sequences remains a significant challenge. Generated videos should not only display coherent and continuous movement but also meaningful movement in successions of scenes. Models such as GANs, VAEs, and Diffusion Networks have been used for generating short video sequences, typically up to 16 frames. In this paper, we first propose a new type of video generator by enabling adversarial-based unconditional video generators with a variational encoder, akin to a VAE-GAN hybrid structure. The proposed model, as in other video deep learning-based processing frameworks, incorporates two processing branches, one for content and another for movement. However, existing models struggle with the temporal scaling of the generated videos. Classical approaches often result in degraded video quality when attempting to increase the generated video length, especially for significantly long sequences. To overcome this limitation, our research study extends the initially proposed VAE-GAN video generation model by employing a novel, memory-efficient approach to generate long videos composed of hundreds or thousands of frames ensuring their temporal continuity, consistency and dynamics. Our approach leverages a Markov chain framework with a recall mechanism, where each state represents a short-length VAE-GAN video generator. This setup enables the sequential connection of generated video sub-sequences, maintaining temporal dependencies and resulting in meaningful long video sequences. - oai:arXiv.org:2512.21776v2 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Jingbo Yang, Adrian G. Bors - - - KG20C & KG20C-QA: Scholarly Knowledge Graph Benchmarks for Link Prediction and Question Answering - https://arxiv.org/abs/2512.21799 - arXiv:2512.21799v2 Announce Type: replace -Abstract: In this paper, we present KG20C and KG20C-QA, two curated datasets for advancing question answering (QA) research on scholarly data. KG20C is a high-quality scholarly knowledge graph constructed from the Microsoft Academic Graph through targeted selection of venues, quality-based filtering, and schema definition. Although KG20C has been available online in non-peer-reviewed sources such as GitHub repository, this paper provides the first formal, peer-reviewed description of the dataset, including clear documentation of its construction and specifications. KG20C-QA is built upon KG20C to support QA tasks on scholarly data. We define a set of QA templates that convert graph triples into natural language question--answer pairs, producing a benchmark that can be used both with graph-based models such as knowledge graph embeddings and with text-based models such as large language models. We benchmark standard knowledge graph embedding methods on KG20C-QA, analyze performance across relation types, and provide reproducible evaluation protocols. By officially releasing these datasets with thorough documentation, we aim to contribute a reusable, extensible resource for the research community, enabling future work in QA, reasoning, and knowledge-driven applications in the scholarly domain. The full datasets will be released at https://github.com/tranhungnghiep/KG20C/ upon paper publication. - oai:arXiv.org:2512.21799v2 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Hung-Nghiep Tran, Atsuhiro Takasu - - - Interpretable Perturbation Modeling Through Biomedical Knowledge Graphs - https://arxiv.org/abs/2512.22251 - arXiv:2512.22251v2 Announce Type: replace -Abstract: Understanding how small molecules perturb gene expression is essential for uncovering drug mechanisms, predicting off-target effects, and identifying repurposing opportunities. While prior deep learning frameworks have integrated multimodal embeddings into biomedical knowledge graphs (BKGs) and further improved these representations through graph neural network message-passing paradigms, these models have been applied to tasks such as link prediction and binary drug-disease association, rather than the task of gene perturbation, which may unveil more about mechanistic transcriptomic effects. To address this gap, we construct a merged biomedical graph that integrates (i) PrimeKG++, an augmentation of PrimeKG containing semantically rich embeddings for nodes with (ii) LINCS L1000 drug and cell line nodes, initialized with multimodal embeddings from foundation models such as MolFormerXL and BioBERT. Using this heterogeneous graph, we train a graph attention network (GAT) with a downstream prediction head that learns the delta expression profile of over 978 landmark genes for a given drug-cell pair. Our results show that our framework outperforms MLP baselines for differentially expressed genes (DEG) -- which predict the delta expression given a concatenated embedding of drug features, target features, and baseline cell expression -- under the scaffold and random splits. Ablation experiments with edge shuffling and node feature randomization further demonstrate that the edges provided by biomedical KGs enhance perturbation-level prediction. More broadly, our framework provides a path toward mechanistic drug modeling: moving beyond binary drug-disease association tasks to granular transcriptional effects of therapeutic intervention. - oai:arXiv.org:2512.22251v2 - cs.LG - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Pascal Passigan, Kevin Zhu, Angelina Ning - - - SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence - https://arxiv.org/abs/2512.22334 - arXiv:2512.22334v2 Announce Type: replace -Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science. - oai:arXiv.org:2512.22334v2 - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yiheng Wang, Yixin Chen, Shuo Li, Yifan Zhou, Bo Liu, Hengjian Gao, Jiakang Yuan, Jia Bu, Wanghan Xu, Yuhao Zhou, Xiangyu Zhao, Zhiwang Zhou, Fengxiang Wang, Haodong Duan, Songyang Zhang, Jun Yao, Han Deng, Yizhou Wang, Jiabei Xiao, Jiaqi Liu, Encheng Su, Yujie Liu, Weida Wang, Junchi Yao, Shenghe Zheng, Haoran Sun, Runmin Ma, Xiangchao Yan, Bo Zhang, Dongzhan Zhou, Shufei Zhang, Peng Ye, Xiaosong Wang, Shixiang Tang, Wenlong Zhang, Lei Bai - - - VL-LN Bench: Towards Long-horizon Goal-oriented Navigation with Active Dialogs - https://arxiv.org/abs/2512.22342 - arXiv:2512.22342v2 Announce Type: replace -Abstract: In most existing embodied navigation tasks, instructions are well-defined and unambiguous, such as instruction following and object searching. Under this idealized setting, agents are required solely to produce effective navigation outputs conditioned on vision and language inputs. However, real-world navigation instructions are often vague and ambiguous, requiring the agent to resolve uncertainty and infer user intent through active dialog. To address this gap, we propose Interactive Instance Object Navigation (IION), a task that requires agents not only to generate navigation actions but also to produce language outputs via active dialog, thereby aligning more closely with practical settings. IION extends Instance Object Navigation (ION) by allowing agents to freely consult an oracle in natural language while navigating. Building on this task, we present the Vision Language-Language Navigation (VL-LN) benchmark, which provides a large-scale, automatically generated dataset and a comprehensive evaluation protocol for training and assessing dialog-enabled navigation models. VL-LN comprises over 41k long-horizon dialog-augmented trajectories for training and an automatic evaluation protocol with an oracle capable of responding to agent queries. Using this benchmark, we train a navigation model equipped with dialog capabilities and show that it achieves significant improvements over the baselines. Extensive experiments and analyses further demonstrate the effectiveness and reliability of VL-LN for advancing research on dialog-enabled embodied navigation. Code and dataset: https://0309hws.github.io/VL-LN.github.io/ - oai:arXiv.org:2512.22342v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Wensi Huang, Shaohao Zhu, Meng Wei, Jinming Xu, Xihui Liu, Hanqing Wang, Tai Wang, Feng Zhao, Jiangmiao Pang - - - OxygenREC: An Instruction-Following Generative Framework for E-commerce Recommendation - https://arxiv.org/abs/2512.22386 - arXiv:2512.22386v2 Announce Type: replace -Abstract: Traditional recommendation systems suffer from inconsistency in multi-stage optimization objectives. Generative Recommendation (GR) mitigates them through an end-to-end framework; however, existing methods still rely on matching mechanisms based on inductive patterns. Although responsive, they lack the ability to uncover complex user intents that require deductive reasoning based on world knowledge. Meanwhile, LLMs show strong deep reasoning capabilities, but their latency and computational costs remain challenging for industrial applications. More critically, there are performance bottlenecks in multi-scenario scalability: as shown in Figure 1, existing solutions require independent training and deployment for each scenario, leading to low resource utilization and high maintenance costs-a challenge unaddressed in GR literature. To address these, we present OxygenREC, an industrial recommendation system that leverages Fast-Slow Thinking to deliver deep reasoning with strict latency and multi-scenario requirements of real-world environments. First, we adopt a Fast-Slow Thinking architecture. Slow thinking uses a near-line LLM pipeline to synthesize Contextual Reasoning Instructions, while fast thinking employs a high-efficiency encoder-decoder backbone for real-time generation. Second, to ensure reasoning instructions effectively enhance recommendation generation, we introduce a semantic alignment mechanism with Instruction-Guided Retrieval (IGR) to filter intent-relevant historical behaviors and use a Query-to-Item (Q2I) loss for instruction-item consistency. Finally, to resolve multi-scenario scalability, we transform scenario information into controllable instructions, using unified reward mapping and Soft Adaptive Group Clip Policy Optimization (SA-GCPO) to align policies with diverse business objectives, realizing a train-once-deploy-everywhere paradigm. - oai:arXiv.org:2512.22386v2 - cs.IR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xuegang Hao, Ming Zhang, Alex Li, Xiangyu Qian, Zhi Ma, Yanlong Zang, Shijie Yang, Zhongxuan Han, Xiaolong Ma, Jinguang Liu, Zhen Li, Zhida Jiang, Shusheng Wang, Ning Tang, Yanchen Qiao, Chenxiang Yang, Chen Sun, Jincheng Yuan, Chunhua Peng, Heng Hu, Peijun Yang, Baopeng Yuan, Caiyun Qiu, Zhaolong Xing, Haofei Yuan, Haipeng Zhang, Yuzhang Guo, Weijie Ding, Jiahua Gao, Hao Huang, Zhen Chen, Tongxuan Liu, Pinghua Gong - - - Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding - https://arxiv.org/abs/2512.22418 - arXiv:2512.22418v2 Announce Type: replace -Abstract: Large language models (LLMs) are reshaping software engineering by enabling "vibe coding," in which developers build software primarily through prompts rather than writing code. Although widely publicized as a productivity breakthrough, little is known about how practitioners actually define and engage in these practices. To shed light on this emerging phenomenon, we conducted a grounded theory study of 20 vibe-coding videos, including 7 live-streamed coding sessions (about 16 hours, 254 prompts) and 13 opinion videos (about 5 hours), supported by additional analysis of activity durations and prompt intents. Our findings reveal a spectrum of behaviors: some vibe coders rely almost entirely on AI without inspecting code, while others examine and adapt generated outputs. Across approaches, all must contend with the stochastic nature of generation, with debugging and refinement often described as "rolling the dice." Further, divergent mental models, shaped by vibe coders' expertise and reliance on AI, influence prompting strategies, evaluation practices, and levels of trust. These findings open new directions for research on the future of software engineering and point to practical opportunities for tool design and education. - oai:arXiv.org:2512.22418v2 - cs.SE - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Yi-Hung Chou, Boyuan Jiang, Yi Wen Chen, Mingyue Weng, Victoria Jackson, Thomas Zimmermann, James A. Jones - - - SuperiorGAT: Graph Attention Networks for Sparse LiDAR Point Cloud Reconstruction in Autonomous Systems - https://arxiv.org/abs/2512.22439 - arXiv:2512.22439v2 Announce Type: replace -Abstract: LiDAR-based perception in autonomous systems is constrained by fixed vertical beam resolution and further compromised by beam dropout resulting from environmental occlusions. This paper introduces SuperiorGAT, a graph attention-based framework designed to reconstruct missing elevation information in sparse LiDAR point clouds. By modeling LiDAR scans as beam-aware graphs and incorporating gated residual fusion with feed-forward refinement, SuperiorGAT enables accurate reconstruction without increasing network depth. To evaluate performance, structured beam dropout is simulated by removing every fourth vertical scanning beam. Extensive experiments across diverse KITTI environments, including Person, Road, Campus, and City sequences, demonstrate that SuperiorGAT consistently achieves lower reconstruction error and improved geometric consistency compared to PointNet-based models and deeper GAT baselines. Qualitative X-Z projections further confirm the model's ability to preserve structural integrity with minimal vertical distortion. These results suggest that architectural refinement offers a computationally efficient method for improving LiDAR resolution without requiring additional sensor hardware. - oai:arXiv.org:2512.22439v2 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Khalfalla Awedat, Mohamed Abidalrekab, Gurcan Comert, Mustafa Ayad - - - Tracking by Predicting 3-D Gaussians Over Time - https://arxiv.org/abs/2512.22489 - arXiv:2512.22489v2 Announce Type: replace -Abstract: We propose Video Gaussian Masked Autoencoders (Video-GMAE), a self-supervised approach for representation learning that encodes a sequence of images into a set of Gaussian splats moving over time. Representing a video as a set of Gaussians enforces a reasonable inductive bias: that 2-D videos are often consistent projections of a dynamic 3-D scene. We find that tracking emerges when pretraining a network with this architecture. Mapping the trajectory of the learnt Gaussians onto the image plane gives zero-shot tracking performance comparable to state-of-the-art. With small-scale finetuning, our models achieve 34.6% improvement on Kinetics, and 13.1% on Kubric datasets, surpassing existing self-supervised video approaches. The project page and code are publicly available at https://videogmae.org/ and https://github.com/tekotan/video-gmae. - oai:arXiv.org:2512.22489v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-sa/4.0/ - Tanish Baranwal, Himanshu Gaurav Singh, Jathushan Rajasegaran, Jitendra Malik - - - A Lightweight Coordinate-Conditioned Diffusion Approach for 6G C-V2X Radio Environment Maps - https://arxiv.org/abs/2512.22535 - arXiv:2512.22535v2 Announce Type: replace -Abstract: Transmitter vehicles that broadcast 6G Cellular Vehicle-to-Everything (C-V2X)-based messages, e.g., Basic Safety Messages (BSMs), are prone to be impacted by PHY issues due to the lack of dynamic high-fidelity Radio Environment Map (REM) with dynamic location variation. This paper explores a lightweight diffusion-based generative approach, the Coordinate-Conditioned Denoising Diffusion Probabilistic Model (CCDDPM), that leverages the signal intensity-based 6G V2X Radio Environment Map (REM) from limited historical transmitter vehicles in a specific region, to predict the REMs for a transmitter vehicle with arbitrary coordinates across the same region. The transmitter vehicle coordinate is encoded as a smooth Gaussian prior and fused with the Gaussian noise through a lightweight two-channel conditional U-Net architecture. We demonstrate that the predicted REM closely matches the statistics and structure of ground-truth REM while exhibiting the improved stability and over other widely applied generative AI approaches. The resulting predictor enables rapid and scenario-consistent REM with arbitrary transmitter coordinates, which thereby supports more efficient 6G C-V2X communications where transmitter vehicles are less likely to suffer from the PHY issues. - oai:arXiv.org:2512.22535v2 - cs.NI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Liu Cao, Zhaoyu Liu, Dongyu Wei, Yuan Yang, Yukun Pan, Lyutianyang Zhang - - - CritiFusion: Semantic Critique and Spectral Alignment for Faithful Text-to-Image Generation - https://arxiv.org/abs/2512.22681 - arXiv:2512.22681v2 Announce Type: replace -Abstract: Recent text-to-image diffusion models have achieved remarkable visual fidelity but often struggle with semantic alignment to complex prompts. We introduce CritiFusion, a novel inference-time framework that integrates a multimodal semantic critique mechanism with frequency-domain refinement to improve text-to-image consistency and detail. The proposed CritiCore module leverages a vision-language model and multiple large language models to enrich the prompt context and produce high-level semantic feedback, guiding the diffusion process to better align generated content with the prompt's intent. Additionally, SpecFusion merges intermediate generation states in the spectral domain, injecting coarse structural information while preserving high-frequency details. No additional model training is required. CritiFusion serves as a plug-in refinement stage compatible with existing diffusion backbones. Experiments on standard benchmarks show that our method notably improves human-aligned metrics of text-to-image correspondence and visual quality. CritiFusion consistently boosts performance on human preference scores and aesthetic evaluations, achieving results on par with state-of-the-art reward optimization approaches. Qualitative results further demonstrate superior detail, realism, and prompt fidelity, indicating the effectiveness of our semantic critique and spectral alignment strategy. - oai:arXiv.org:2512.22681v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - ZhenQi Chen, TsaiChing Ni, YuanFu Yang - - - Protonic Nickelate Device Networks for Spatiotemporal Neuromorphic Computing - https://arxiv.org/abs/2512.22722 - arXiv:2512.22722v2 Announce Type: replace -Abstract: Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. In this work, we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanoseconds scale operation with an energy cost of 0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken-digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware. - oai:arXiv.org:2512.22722v2 - cs.ET - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yue Zhou, Shaan Shah, Tamal Dey, Yucheng Zhou, Ashwani Kumar, Sashank Sriram, Siyou Guo, Siddharth Kumar, Ranjan Kumar Patel, Eva Y. Andrei, Ertugrul Cubukcu, Shriram Ramanathan, Duygu Kuzum - - - TrimTokenator-LC: Towards Adaptive Visual Token Pruning for Large Multimodal Models with Long Contexts - https://arxiv.org/abs/2512.22748 - arXiv:2512.22748v2 Announce Type: replace -Abstract: Large Multimodal Models (LMMs) have proven effective on various tasks. They typically encode visual inputs into Original Model sequences of tokens, which are then concatenated with textual tokens and jointly processed by the language model. However, the growing number of visual tokens greatly increases inference cost. Visual token pruning has emerged as a promising solution. However, existing methods often overlook scenarios involving long context inputs with multiple images. In this paper, we analyze the challenges of visual token pruning in long context, multi-image settings and introduce an adaptive pruning method tailored for such scenarios. We decompose redundancy into intra-image and inter-image components and quantify them through intra-image diversity and inter-image variation, which jointly guide dynamic budget allocation. Our approach consists of two stages. The intra-image stage allocates each image a content-aware token budget and greedily selects its most representative tokens. The inter-image stage performs global diversity filtering to form a candidate pool and then applies a Pareto selection procedure that balances diversity with text alignment. Extensive experiments show that our approach can reduce up to 80% of visual tokens while maintaining performance in long context settings. - oai:arXiv.org:2512.22748v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hao Zhang, Mengsi Lyu, Bo Huang, Yulong Ao, Yonghua Lin - - - Federated Multi-Task Clustering - https://arxiv.org/abs/2512.22897 - arXiv:2512.22897v2 Announce Type: replace -Abstract: Spectral clustering has emerged as one of the most effective clustering algorithms due to its superior performance. However, most existing models are designed for centralized settings, rendering them inapplicable in modern decentralized environments. Moreover, current federated learning approaches often suffer from poor generalization performance due to reliance on unreliable pseudo-labels, and fail to capture the latent correlations amongst heterogeneous clients. To tackle these limitations, this paper proposes a novel framework named Federated Multi-Task Clustering (i.e.,FMTC), which intends to learn personalized clustering models for heterogeneous clients while collaboratively leveraging their shared underlying structure in a privacy-preserving manner. More specifically, the FMTC framework is composed of two main components: client-side personalized clustering module, which learns a parameterized mapping model to support robust out-of-sample inference, bypassing the need for unreliable pseudo-labels; and server-side tensorial correlation module, which explicitly captures the shared knowledge across all clients. This is achieved by organizing all client models into a unified tensor and applying a low-rank regularization to discover their common subspace. To solve this joint optimization problem, we derive an efficient, privacy-preserving distributed algorithm based on the Alternating Direction Method of Multipliers, which decomposes the global problem into parallel local updates on clients and an aggregation step on the server. To the end, several extensive experiments on multiple real-world datasets demonstrate that our proposed FMTC framework significantly outperforms various baseline and state-of-the-art federated clustering algorithms. - oai:arXiv.org:2512.22897v2 - cs.LG - cs.MM - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Suyan Dai, Gan Sun, Fazeng Li, Xu Tang, Qianqian Wang, Yang Cong - - - Multimodal Fact-Checking: An Agent-based Approach - https://arxiv.org/abs/2512.22933 - arXiv:2512.22933v2 Announce Type: replace -Abstract: The rapid spread of multimodal misinformation poses a growing challenge for automated fact-checking systems. Existing approaches, including large vision language models (LVLMs) and deep multimodal fusion methods, often fall short due to limited reasoning and shallow evidence utilization. A key bottleneck is the lack of dedicated datasets that provide complete real-world multimodal misinformation instances accompanied by annotated reasoning processes and verifiable evidence. To address this limitation, we introduce RW-Post, a high-quality and explainable dataset for real-world multimodal fact-checking. RW-Post aligns real-world multimodal claims with their original social media posts, preserving the rich contextual information in which the claims are made. In addition, the dataset includes detailed reasoning and explicitly linked evidence, which are derived from human written fact-checking articles via a large language model assisted extraction pipeline, enabling comprehensive verification and explanation. Building upon RW-Post, we propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow. AgentFact consists of five specialized agents that collaboratively handle key fact-checking subtasks, including strategy planning, high-quality evidence retrieval, visual analysis, reasoning, and explanation generation. These agents are orchestrated through an iterative workflow that alternates between evidence searching and task-aware evidence filtering and reasoning, facilitating strategic decision-making and systematic evidence analysis. Extensive experimental results demonstrate that the synergy between RW-Post and AgentFact substantially improves both the accuracy and interpretability of multimodal fact-checking. - oai:arXiv.org:2512.22933v2 - cs.AI - cs.CL - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Danni Xu, Shaojing Fan, Harry Cheng, Mohan Kankanhalli - - - ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving - https://arxiv.org/abs/2512.22939 - arXiv:2512.22939v2 Announce Type: replace -Abstract: Autonomous driving requires generating safe and reliable trajectories from complex multimodal inputs. Traditional modular pipelines separate perception, prediction, and planning, while recent end-to-end (E2E) systems learn them jointly. Vision-language models (VLMs) further enrich this paradigm by introducing cross-modal priors and commonsense reasoning, yet current VLM-based planners face three key challenges: (i) a mismatch between discrete text reasoning and continuous control, (ii) high latency from autoregressive chain-of-thought decoding, and (iii) inefficient or non-causal planners that limit real-time deployment. We propose ColaVLA, a unified vision-language-action framework that transfers reasoning from text to a unified latent space and couples it with a hierarchical, parallel trajectory decoder. The Cognitive Latent Reasoner compresses scene understanding into compact, decision-oriented meta-action embeddings through ego-adaptive selection and only two VLM forward passes. The Hierarchical Parallel Planner then generates multi-scale, causality-consistent trajectories in a single forward pass. Together, these components preserve the generalization and interpretability of VLMs while enabling efficient, accurate and safe trajectory generation. Experiments on the nuScenes benchmark show that ColaVLA achieves state-of-the-art performance in both open-loop and closed-loop settings with favorable efficiency and robustness. - oai:arXiv.org:2512.22939v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qihang Peng, Xuesong Chen, Chenye Yang, Shaoshuai Shi, Hongsheng Li - - - A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging - https://arxiv.org/abs/2512.22976 - arXiv:2512.22976v2 Announce Type: replace -Abstract: Automatic sleep staging is a critical task in healthcare due to the global prevalence of sleep disorders. This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging. Existing approaches face challenges such as class imbalance, limited receptive-field modeling, and insufficient interpretability. This work proposes a context-aware and interpretable framework for single-channel EEG sleep staging, with particular emphasis on improving detection of the N1 stage. Many prior models operate as black boxes with stacked layers, lacking clearly defined and interpretable feature extraction roles.The proposed model combines compact multi-scale feature extraction with temporal modeling to capture both local and long-range dependencies. To address data imbalance, especially in the N1 stage, classweighted loss functions and data augmentation are applied. EEG signals are segmented into sub-epoch chunks, and final predictions are obtained by averaging softmax probabilities across chunks, enhancing contextual representation and robustness.The proposed framework achieves an overall accuracy of 89.72% and a macro-average F1-score of 85.46%. Notably, it attains an F1- score of 61.7% for the challenging N1 stage, demonstrating a substantial improvement over previous methods on the SleepEDF datasets. These results indicate that the proposed approach effectively improves sleep staging performance while maintaining interpretability and suitability for real-world clinical applications. - oai:arXiv.org:2512.22976v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Amirali Vakili, Salar Jahanshiri, Armin Salimi-Badr - - - PoseStreamer: A Multi-modal Framework for 6DoF Pose Estimation of Unseen Moving Objects - https://arxiv.org/abs/2512.22979 - arXiv:2512.22979v2 Announce Type: replace -Abstract: Six degree of freedom (6DoF) pose estimation for novel objects is a critical task in computer vision, yet it faces significant challenges in high-speed and low-light scenarios where standard RGB cameras suffer from motion blur. While event cameras offer a promising solution due to their high temporal resolution, current 6DoF pose estimation methods typically yield suboptimal performance in high-speed object moving scenarios. To address this gap, we propose PoseStreamer, a robust multi-modal 6DoF pose estimation framework designed specifically on high-speed moving scenarios. Our approach integrates three core components: an Adaptive Pose Memory Queue that utilizes historical orientation cues for temporal consistency, an Object-centric 2D Tracker that provides strong 2D priors to boost 3D center recall, and a Ray Pose Filter for geometric refinement along camera rays. Furthermore, we introduce MoCapCube6D, a novel multi-modal dataset constructed to benchmark performance under rapid motion. Extensive experiments demonstrate that PoseStreamer not only achieves superior accuracy in high-speed moving scenarios, but also exhibits strong generalizability as a template-free framework for unseen moving objects. - oai:arXiv.org:2512.22979v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Huiming Yang, Linglin Liao, Fei Ding, Sibo Wang, Zijian Zeng - - - OpenGround: Active Cognition-based Reasoning for Open-World 3D Visual Grounding - https://arxiv.org/abs/2512.23020 - arXiv:2512.23020v2 Announce Type: replace -Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes. Existing methods rely on a pre-defined Object Lookup Table (OLT) to query Visual Language Models (VLMs) for reasoning about object locations, which limits the applications in scenarios with undefined or unforeseen targets. To address this problem, we present OpenGround, a novel zero-shot framework for open-world 3D visual grounding. Central to OpenGround is the Active Cognition-based Reasoning (ACR) module, which is designed to overcome the fundamental limitation of pre-defined OLTs by progressively augmenting the cognitive scope of VLMs. The ACR module performs human-like perception of the target via a cognitive task chain and actively reasons about contextually relevant objects, thereby extending VLM cognition through a dynamically updated OLT. This allows OpenGround to function with both pre-defined and open-world categories. We also propose a new dataset named OpenTarget, which contains over 7000 object-description pairs to evaluate our method in open-world scenarios. Extensive experiments demonstrate that OpenGround achieves competitive performance on Nr3D, state-of-the-art on ScanRefer, and delivers a substantial 17.6% improvement on OpenTarget. Project Page at https://why-102.github.io/openground.io/. - oai:arXiv.org:2512.23020v2 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Wenyuan Huang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang, Zhenyu Zhang - - - Differentiable Physics-Driven Human Representation for Millimeter-Wave Based Pose Estimation - https://arxiv.org/abs/2512.23054 - arXiv:2512.23054v2 Announce Type: replace -Abstract: While millimeter-wave (mmWave) presents advantages for Human Pose Estimation (HPE) through its non-intrusive sensing capabilities, current mmWave-based HPE methods face limitations in two predominant input paradigms: Heatmap and Point Cloud (PC). Heatmap represents dense multi-dimensional features derived from mmWave, but is significantly affected by multipath propagation and hardware modulation noise. PC, a set of 3D points, is obtained by applying the Constant False Alarm Rate algorithm to the Heatmap, which suppresses noise but results in sparse human-related features. To address these limitations, we study the feasibility of providing an alternative input paradigm: Differentiable Physics-driven Human Representation (DIPR), which represents humans as an ensemble of Gaussian distributions with kinematic and electromagnetic parameters. Inspired by Gaussian Splatting, DIPR leverages human kinematic priors and mmWave propagation physics to enhance human features while mitigating non-human noise through two strategies: 1) We incorporate prior kinematic knowledge to initialize DIPR based on the Heatmap and establish multi-faceted optimization objectives, ensuring biomechanical validity and enhancing motion features. 2) We simulate complete mmWave processing pipelines, re-render a new Heatmap from DIPR, and compare it with the original Heatmap, avoiding spurious noise generation due to kinematic constraints overfitting. Experimental results on three datasets with four methods demonstrate that existing mmWave-based HPE methods can easily integrate DIPR and achieve superior performance. - oai:arXiv.org:2512.23054v2 - cs.HC - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Shuntian Zheng, Guangming Wang, Jiaqi Li, Minzhe Ni, Yu Guan - - - Artificial Intelligence and Employment Exposure: A Territorial and Gender Perspective - https://arxiv.org/abs/2512.23059 - arXiv:2512.23059v2 Announce Type: replace -Abstract: The diffusion of artificial intelligence, particularly generative models, is expected to transform labor markets in uneven ways across sectors, territories, and social groups. This paper proposes a methodological framework to estimate the potential exposure of employment to AI using sector based data, addressing the limitations of occupation centered approaches in the Spanish context. By constructing an AI CNAE incidence matrix and applying it to provincial employment data for the period 2021 to 2023, we provide a territorial and gender disaggregated assessment of AI exposure across Spain. The results reveal stable structural patterns, with higher exposure in metropolitan and service oriented regions and a consistent gender gap, as female employment exhibits higher exposure in all territories. Rather than predicting job displacement, the framework offers a structural perspective on where AI is most likely to reshape work and skill demands, supporting evidence based policy and strategic planning. - oai:arXiv.org:2512.23059v2 - cs.CY - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Antoni Mestre, Xavier Naya, Manoli Albert, Vicente Pelechano - - - APOLLO Blender: A Robotics Library for Visualization and Animation in Blender - https://arxiv.org/abs/2512.23103 - arXiv:2512.23103v2 Announce Type: replace -Abstract: High-quality visualizations are an essential part of robotics research, enabling clear communication of results through figures, animations, and demonstration videos. While Blender is a powerful and freely available 3D graphics platform, its steep learning curve and lack of robotics-focused integrations make it difficult and time-consuming for researchers to use effectively. In this work, we introduce a lightweight software library that bridges this gap by providing simple scripting interfaces for common robotics visualization tasks. The library offers three primary capabilities: (1) importing robots and environments directly from standardized descriptions such as URDF; (2) Python-based scripting tools for keyframing robot states and visual attributes; and (3) convenient generation of primitive 3D shapes for schematic figures and animations. Together, these features allow robotics researchers to rapidly create publication-ready images, animations, and explanatory schematics without needing extensive Blender expertise. We demonstrate the library through a series of proof-of-concept examples and conclude with a discussion of current limitations and opportunities for future extensions. - oai:arXiv.org:2512.23103v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Peter Messina, Daniel Rakita - - - InSPO: Unlocking Intrinsic Self-Reflection for LLM Preference Optimization - https://arxiv.org/abs/2512.23126 - arXiv:2512.23126v2 Announce Type: replace -Abstract: Direct Preference Optimization (DPO) and its variants have become standard for aligning Large Language Models due to their simplicity and offline stability. However, we identify two fundamental limitations. First, the optimal policy depends on arbitrary modeling choices (scalarization function, reference policy), yielding behavior reflecting parameterization artifacts rather than true preferences. Second, treating response generation in isolation fails to leverage comparative information in pairwise data, leaving the model's capacity for intrinsic self-reflection untapped. To address it, we propose Intrinsic Self-reflective Preference Optimization (InSPO), deriving a globally optimal policy conditioning on both context and alternative responses. We prove this formulation superior to DPO/RLHF while guaranteeing invariance to scalarization and reference choices. InSPO serves as a plug-and-play enhancement without architectural changes or inference overhead. Experiments demonstrate consistent improvements in win rates and length-controlled metrics, validating that unlocking self-reflection yields more robust, human-aligned LLMs. - oai:arXiv.org:2512.23126v2 - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Yu Li, Tian Lan, Zhengling Qi - - - Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models - https://arxiv.org/abs/2512.23135 - arXiv:2512.23135v2 Announce Type: replace -Abstract: Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work. - oai:arXiv.org:2512.23135v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Roshan Klein-Seetharaman, Daniel Rakita - - - A New Software Tool for Generating and Visualizing Robot Self-Collision Matrices - https://arxiv.org/abs/2512.23140 - arXiv:2512.23140v2 Announce Type: replace -Abstract: In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes attached to different robot links. However, many of these shape pairs can be excluded in advance, as their configurations are known to always or never result in contact. This information is typically encoded in a self-collision matrix, where each entry (i, j) indicates whether a check should be performed between shape i and shape j. While the MoveIt Setup Assistant is widely used to generate such matrices, current tools are limited by static visualization, lack of proximity support, rigid single-geometry assumptions, and tedious refinement workflows, hindering flexibility and reuse in downstream robotics applications. In this work, we introduce an interactive tool that overcomes these limitations by generating and visualizing self-collision matrices across multiple shape representations, enabling dynamic inspection, filtering, and refinement of shape pairs. Outputs are provided in both JSON and YAML for easy integration. The system is implemented in Rust and uses the Bevy game engine to deliver high-quality visualizations. We demonstrate its effectiveness on multiple robot platforms, showing that matrices generated using diverse shape types yield faster and more accurate self-collision and self-proximity queries. - oai:arXiv.org:2512.23140v2 - cs.RO - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Roshan Klein-Seetharama, Daniel Rakita - - - SurgWorld: Learning Surgical Robot Policies from Videos via World Modeling - https://arxiv.org/abs/2512.23162 - arXiv:2512.23162v2 Announce Type: replace -Abstract: Data scarcity remains a fundamental barrier to achieving fully autonomous surgical robots. While large scale vision language action (VLA) models have shown impressive generalization in household and industrial manipulation by leveraging paired video action data from diverse domains, surgical robotics suffers from the paucity of datasets that include both visual observations and accurate robot kinematics. In contrast, vast corpora of surgical videos exist, but they lack corresponding action labels, preventing direct application of imitation learning or VLA training. In this work, we aim to alleviate this problem by learning policy models from SurgWorld, a world model designed for surgical physical AI. We curated the Surgical Action Text Alignment (SATA) dataset with detailed action description specifically for surgical robots. Then we built SurgeWorld based on the most advanced physical AI world model and SATA. It's able to generate diverse, generalizable and realistic surgery videos. We are also the first to use an inverse dynamics model to infer pseudokinematics from synthetic surgical videos, producing synthetic paired video action data. We demonstrate that a surgical VLA policy trained with these augmented data significantly outperforms models trained only on real demonstrations on a real surgical robot platform. Our approach offers a scalable path toward autonomous surgical skill acquisition by leveraging the abundance of unlabeled surgical video and generative world modeling, thus opening the door to generalizable and data efficient surgical robot policies. - oai:arXiv.org:2512.23162v2 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yufan He, Pengfei Guo, Mengya Xu, Zhaoshuo Li, Andriy Myronenko, Dillan Imans, Bingjie Liu, Dongren Yang, Mingxue Gu, Yongnan Ji, Yueming Jin, Ren Zhao, Baiyong Shen, Daguang Xu - - - Evaluating Parameter Efficient Methods for RLVR - https://arxiv.org/abs/2512.23165 - arXiv:2512.23165v2 Announce Type: replace -Abstract: We systematically evaluate Parameter-Efficient Fine-Tuning (PEFT) methods under the paradigm of Reinforcement Learning with Verifiable Rewards (RLVR). RLVR incentivizes language models to enhance their reasoning capabilities through verifiable feedback; however, while methods like LoRA are commonly used, the optimal PEFT architecture for RLVR remains unidentified. In this work, we conduct the first comprehensive evaluation of over 12 PEFT methodologies across the DeepSeek-R1-Distill families on mathematical reasoning benchmarks. Our empirical results challenge the default adoption of standard LoRA with three main findings. First, we demonstrate that structural variants, such as DoRA, AdaLoRA, and MiSS, consistently outperform LoRA. Second, we uncover a spectral collapse phenomenon in SVD-informed initialization strategies (\textit{e.g.,} PiSSA, MiLoRA), attributing their failure to a fundamental misalignment between principal-component updates and RL optimization. Furthermore, our ablations reveal that extreme parameter reduction (\textit{e.g.,} VeRA, Rank-1) severely bottlenecks reasoning capacity. We further conduct ablation studies and scaling experiments to validate our findings. This work provides a definitive guide for advocating for more exploration for parameter-efficient RL methods. - oai:arXiv.org:2512.23165v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Qingyu Yin, Yulun Wu, Zhennan Shen, Sunbowen Li, Zhilin Wang, Yanshu Li, Chak Tou Leong, Jiale Kang, Jinjin Gu - - - A New Family of Binary Sequences via Elliptic Function Fields over Finite Fields of Odd Characteristics - https://arxiv.org/abs/2512.23194 - arXiv:2512.23194v2 Announce Type: replace -Abstract: Motivated by the constructions of binary sequences by utilizing the cyclic elliptic function fields over the finite field $\mathbb{F}_{2^{n}}$ by Jin \textit{et al.} in [IEEE Trans. Inf. Theory 71(8), 2025], we extend the construction to the cyclic elliptic function fields with odd characteristic by using the quadratic residue map $\eta$ instead of the trace map used therein. For any cyclic elliptic function field with $q+1+t$ rational points and any positive integer $d$ with $\gcd(d, q+1+t)=1$, we construct a new family of binary sequences of length $q+1+t$, size $q^{d-1}-1$, balance upper bounded by $(d+1)\cdot\lfloor2\sqrt{q}\rfloor+|t|+d,$ the correlation upper bounded by $(2d+1)\cdot\lfloor2\sqrt{q}\rfloor+|t|+2d$ and the linear complexity lower bounded by $\frac{q+1+2t-d-(d+1)\cdot\lfloor2\sqrt{q}\rfloor}{d+d\cdot\lfloor2\sqrt{q}\rfloor}$ where $\lfloor x\rfloor$ stands for the integer part of $x\in\mathbb{R}$. - oai:arXiv.org:2512.23194v2 - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xiaofeng Liu, Jun Zhang, Fang-Wei Fu - - - KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta - https://arxiv.org/abs/2512.23236 - arXiv:2512.23236v2 Announce Type: replace -Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture diversity, kernel primitive diversity, and hardware generation and architecture heterogeneity. This paper presents KernelEvolve-an agentic kernel coding framework-to tackle heterogeneity at-scale for DLRM. KernelEvolve is designed to take kernel specifications as input and automate the process of kernel generation and optimization for recommendation model across heterogeneous hardware architectures. KernelEvolve does so by operating at multiple programming abstractions, from Triton and CuTe DSL to low-level hardware agnostic languages, spanning the full hardware-software optimization stack. The kernel optimization process is described as graph-based search with selection policy, universal operator, fitness function, and termination rule, dynamically adapts to runtime execution context through retrieval-augmented prompt synthesis. We designed, implemented, and deployed KernelEvolve to optimize a wide variety of production recommendation models across generations of NVIDIA and AMD GPUs, as well as Meta's AI accelerators. We validate KernelEvolve on the publicly-available KernelBench suite, achieving 100% pass rate on all 250 problems across three difficulty levels, and 160 PyTorch ATen operators across three heterogeneous hardware platforms, demonstrating 100% correctness. KernelEvolve reduces development time from weeks to hours and achieves substantial performance improvements over PyTorch baselines across diverse production use cases and for heterogeneous AI systems at-scale. Beyond performance efficiency improvements, KernelEvolve significantly mitigates the programmability barrier for new AI hardware by enabling automated kernel generation for in-house developed AI hardware. - oai:arXiv.org:2512.23236v2 - cs.LG - cs.AI - cs.AR - cs.MA - cs.PF - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Dianshi Li, Uladzimir Pashkevich, Varna Puvvada, Feng Shi, Matt Steiner, Ruichao Xiao, Nathan Yan, Xiayu Yu, Zhou Fang, Abdul Zainul-Abedin, Ketan Singh, Hongtao Yu, Wenyuan Chi, Barney Huang, Sean Zhang, Noah Weller, Zach Marine, Wyatt Cook, Carole-Jean Wu, Gaoxiang Liu - - - YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection - https://arxiv.org/abs/2512.23273 - arXiv:2512.23273v2 Announce Type: replace -Abstract: Existing Real-Time Object Detection (RTOD) methods commonly adopt YOLO-like architectures for their favorable trade-off between accuracy and speed. However, these models rely on static dense computation that applies uniform processing to all inputs, misallocating representational capacity and computational resources such as over-allocating on trivial scenes while under-serving complex ones. This mismatch results in both computational redundancy and suboptimal detection performance. To overcome this limitation, we propose YOLO-Master, a novel YOLO-like framework that introduces instance-conditional adaptive computation for RTOD. This is achieved through a Efficient Sparse Mixture-of-Experts (ES-MoE) block that dynamically allocates computational resources to each input according to its scene complexity. At its core, a lightweight dynamic routing network guides expert specialization during training through a diversity enhancing objective, encouraging complementary expertise among experts. Additionally, the routing network adaptively learns to activate only the most relevant experts, thereby improving detection performance while minimizing computational overhead during inference. Comprehensive experiments on five large-scale benchmarks demonstrate the superiority of YOLO-Master. On MS COCO, our model achieves 42.4% AP with 1.62ms latency, outperforming YOLOv13-N by +0.8% mAP and 17.8% faster inference. Notably, the gains are most pronounced on challenging dense scenes, while the model preserves efficiency on typical inputs and maintains real-time inference speed. Code will be available. - oai:arXiv.org:2512.23273v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Xu Lin, Jinlong Peng, Zhenye Gan, Jiawen Zhu, Jun Liu - - - CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations - https://arxiv.org/abs/2512.23328 - arXiv:2512.23328v2 Announce Type: replace -Abstract: Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robust spatial mental model. We identify three core cognitive challenges hindering this transition: spatial reasoning, long-horizon state tracking via mental simulation, and active exploration under partial observation. To isolate and evaluate these faculties, we introduce CubeBench, a novel generative benchmark centered on the Rubik's Cube. CubeBench uses a three-tiered diagnostic framework that progressively assesses agent capabilities, from foundational state tracking with full symbolic information to active exploration with only partial visual data. Our experiments on leading LLMs reveal critical limitations, including a uniform 0.00% pass rate on all long-horizon tasks, exposing a fundamental failure in long-term planning. We also propose a diagnostic framework to isolate these cognitive bottlenecks by providing external solver tools. By analyzing the failure modes, we provide key insights to guide the development of more physically-grounded intelligent agents. - oai:arXiv.org:2512.23328v2 - cs.AI - cs.CL - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Huan-ang Gao, Zikang Zhang, Tianwei Luo, Kaisen Yang, Xinzhe Juan, Jiahao Qiu, Tianxing Chen, Bingxiang He, Hao Zhao, Hao Zhou, Shilong Liu, Mengdi Wang - - - Visual Language Hypothesis - https://arxiv.org/abs/2512.23335 - arXiv:2512.23335v2 Announce Type: replace -Abstract: We study visual representation learning from a structural and topological perspective. We begin from a single hypothesis: that visual understanding presupposes a semantic language for vision, in which many perceptual observations correspond to a small number of discrete semantic states. Together with widely assumed premises on transferability and abstraction in representation learning, this hypothesis implies that the visual observation space must be organized in a fiber bundle like structure, where nuisance variation populates fibers and semantics correspond to a quotient base space. From this structure we derive two theoretical consequences. First, the semantic quotient X/G is not a submanifold of X and cannot be obtained through smooth deformation alone, semantic invariance requires a non homeomorphic, discriminative target for example, supervision via labels, cross-instance identification, or multimodal alignment that supplies explicit semantic equivalence. Second, we show that approximating the quotient also places structural demands on the model architecture. Semantic abstraction requires not only an external semantic target, but a representation mechanism capable of supporting topology change: an expand and snap process in which the manifold is first geometrically expanded to separate structure and then collapsed to form discrete semantic regions. We emphasize that these results are interpretive rather than prescriptive: the framework provides a topological lens that aligns with empirical regularities observed in large-scale discriminative and multimodal models, and with classical principles in statistical learning theory. - oai:arXiv.org:2512.23335v2 - cs.CV - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Xiu Li - - - ISOPO: Proximal policy gradients without pi-old - https://arxiv.org/abs/2512.23353 - arXiv:2512.23353v2 Announce Type: replace -Abstract: This note introduces Isometric Policy Optimization (ISOPO), an efficient method to approximate the natural policy gradient in a single gradient step. In comparison, existing proximal policy methods such as GRPO or CISPO use multiple gradient steps with variants of importance ratio clipping to approximate a natural gradient step relative to a reference policy. In its simplest form, ISOPO normalizes the log-probability gradient of each sequence in the Fisher metric before contracting with the advantages. Another variant of ISOPO transforms the microbatch advantages based on the neural tangent kernel in each layer. ISOPO applies this transformation layer-wise in a single backward pass and can be implemented with negligible computational overhead compared to vanilla REINFORCE. - oai:arXiv.org:2512.23353v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Nilin Abrahamsen - - - SoulX-LiveTalk: Real-Time Infinite Streaming of Audio-Driven Avatars via Self-Correcting Bidirectional Distillation - https://arxiv.org/abs/2512.23379 - arXiv:2512.23379v2 Announce Type: replace -Abstract: Deploying massive diffusion models for real-time, infinite-duration, audio-driven avatar generation presents a significant engineering challenge, primarily due to the conflict between computational load and strict latency constraints. Existing approaches often compromise visual fidelity by enforcing strictly unidirectional attention mechanisms or reducing model capacity. To address this problem, we introduce \textbf{SoulX-LiveTalk}, a 14B-parameter framework optimized for high-fidelity real-time streaming. Diverging from conventional unidirectional paradigms, we use a \textbf{Self-correcting Bidirectional Distillation} strategy that retains bidirectional attention within video chunks. This design preserves critical spatiotemporal correlations, significantly enhancing motion coherence and visual detail. To ensure stability during infinite generation, we incorporate a \textbf{Multi-step Retrospective Self-Correction Mechanism}, enabling the model to autonomously recover from accumulated errors and preventing collapse. Furthermore, we engineered a full-stack inference acceleration suite incorporating hybrid sequence parallelism, Parallel VAE, and kernel-level optimizations. Extensive evaluations confirm that SoulX-LiveTalk is the first 14B-scale system to achieve a \textbf{sub-second start-up latency (0.87s)} while reaching a real-time throughput of \textbf{32 FPS}, setting a new standard for high-fidelity interactive digital human synthesis. - oai:arXiv.org:2512.23379v2 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Le Shen, Qiao Qian, Tan Yu, Ke Zhou, Tianhang Yu, Yu Zhan, Zhenjie Wang, Ming Tao, Shunshun Yin, Siyuan Liu - - - DriveLaW:Unifying Planning and Video Generation in a Latent Driving World - https://arxiv.org/abs/2512.23421 - arXiv:2512.23421v2 Announce Type: replace -Abstract: World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approaches relegate world models to limited roles: they operate within ostensibly unified architectures that still keep world prediction and motion planning as decoupled processes. To bridge this gap, we propose DriveLaW, a novel paradigm that unifies video generation and motion planning. By directly injecting the latent representation from its video generator into the planner, DriveLaW ensures inherent consistency between high-fidelity future generation and reliable trajectory planning. Specifically, DriveLaW consists of two core components: DriveLaW-Video, our powerful world model that generates high-fidelity forecasting with expressive latent representations, and DriveLaW-Act, a diffusion planner that generates consistent and reliable trajectories from the latent of DriveLaW-Video, with both components optimized by a three-stage progressive training strategy. The power of our unified paradigm is demonstrated by new state-of-the-art results across both tasks. DriveLaW not only advances video prediction significantly, surpassing best-performing work by 33.3% in FID and 1.8% in FVD, but also achieves a new record on the NAVSIM planning benchmark. - oai:arXiv.org:2512.23421v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Tianze Xia, Yongkang Li, Lijun Zhou, Jingfeng Yao, Kaixin Xiong, Haiyang Sun, Bing Wang, Kun Ma, Guang Chen, Hangjun Ye, Wenyu Liu, Xinggang Wang - - - Distilled HuBERT for Mobile Speech Emotion Recognition: A Cross-Corpus Validation Study - https://arxiv.org/abs/2512.23435 - arXiv:2512.23435v2 Announce Type: replace -Abstract: Speech Emotion Recognition (SER) has significant potential for mobile applications, yet deployment remains constrained by the computational demands of state-of-the-art transformer architectures. This paper presents a mobile-efficient SER system based on DistilHuBERT, a distilled and 8-bit quantized transformer that achieves approximately 92% parameter reduction compared to full-scale Wav2Vec 2.0 models while maintaining competitive accuracy. We conduct a rigorous 5-fold Leave-One-Session-Out (LOSO) cross-validation on the IEMOCAP dataset to ensure speaker independence, augmented with cross-corpus training on CREMA-D to enhance generalization. Cross-corpus training with CREMA-D yields a 1.2% improvement in Weighted Accuracy, a 1.4% gain in Macro F1-score, and a 32% reduction in cross-fold variance, with the Neutral class showing the most substantial benefit at 5.4% F1-score improvement. Our approach achieves an Unweighted Accuracy of 61.4% with a quantized model footprint of only 23 MB, representing approximately 91% of the Unweighted Accuracy of a full-scale baseline. Cross-corpus evaluation on RAVDESS reveals that the theatrical nature of acted emotions causes predictions to cluster by arousal level rather than by specific emotion categories - happiness predictions systematically bleed into anger predictions, and sadness predictions bleed into neutral predictions, due to acoustic saturation when actors prioritize clarity over subtlety. Despite this theatricality effect reducing overall RAVDESS accuracy to 46.64%, the model maintains robust arousal detection with 99% recall for anger, 55% recall for neutral, and 27% recall for sadness. These findings demonstrate a Pareto-optimal tradeoff between model size and accuracy, enabling practical affect recognition on resource-constrained mobile devices. - oai:arXiv.org:2512.23435v2 - cs.SD - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Saifelden M. Ismail - - - Theory of Mind for Explainable Human-Robot Interaction - https://arxiv.org/abs/2512.23482 - arXiv:2512.23482v2 Announce Type: replace -Abstract: Within the context of human-robot interaction (HRI), Theory of Mind (ToM) is intended to serve as a user-friendly backend to the interface of robotic systems, enabling robots to infer and respond to human mental states. When integrated into robots, ToM allows them to adapt their internal models to users' behaviors, enhancing the interpretability and predictability of their actions. Similarly, Explainable Artificial Intelligence (XAI) aims to make AI systems transparent and interpretable, allowing humans to understand and interact with them effectively. Since ToM in HRI serves related purposes, we propose to consider ToM as a form of XAI and evaluate it through the eValuation XAI (VXAI) framework and its seven desiderata. This paper identifies a critical gap in the application of ToM within HRI, as existing methods rarely assess the extent to which explanations correspond to the robot's actual internal reasoning. To address this limitation, we propose to integrate ToM within XAI frameworks. By embedding ToM principles inside XAI, we argue for a shift in perspective, as current XAI research focuses predominantly on the AI system itself and often lacks user-centered explanations. Incorporating ToM would enable a change in focus, prioritizing the user's informational needs and perspective. - oai:arXiv.org:2512.23482v2 - cs.RO - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Marie S. Bauer, Julia Gachot, Matthias Kerzel, Cornelius Weber, Stefan Wermter - - - Circle graphs can be recognized in linear time - https://arxiv.org/abs/2512.23492 - arXiv:2512.23492v2 Announce Type: replace -Abstract: To date, the best circle graph recognition algorithm runs in almost linear time as it relies on a split decomposition algorithm that uses the union-find data-structure. We show that in the case of circle graphs, the PC-tree data-structure allows one to avoid the union-find data-structure to compute the split decomposition in linear time. As a consequence, we obtain the first linear-time recognition algorithm for circle graphs. - oai:arXiv.org:2512.23492v2 - cs.DS - cs.DM - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Christophe Paul, Ignaz Rutter - - - UniHetero: Could Generation Enhance Understanding for Vision-Language-Model at Large Data Scale? - https://arxiv.org/abs/2512.23512 - arXiv:2512.23512v2 Announce Type: replace -Abstract: Vision-language large models are moving toward the unification of visual understanding and visual generation tasks. However, whether generation can enhance understanding is still under-explored on large data scale. In this work, we analysis the unified structure with a concise model, UniHetero, under large-scale pretraining (>200M samples). Our key observations are: (1) Generation can improve understanding, but Only if you generate Semantics, Not Pixels. A common assumption in unified vision-language models is that adding generation will naturally strengthen understanding. However, this is not always true at scale. At 200M+ pretraining samples, generation helps understanding only when it operates at the semantic level, i.e. when the model learns to autoregress high-level visual representations inside the LLM. Once pixel-level objectives (e.g., diffusion losses) directly interfere with the LLM, understanding performance often degrades. (2) Generation reveals a superior Data Scaling trend and higher Data Utilization. Unified generation-understanding demonstrates a superior scaling trend compared to understanding alone, revealing a more effective way to learn vision-only knowledge directive from vision modality rather than captioning to text. (3) Autoregression on Input Embedding is effective to capture visual details. Compared to the commonly-used vision encoder, make visual autoregression on input embedding shows less cumulative error and is modality independent, which can be extend to all modalities. The learned semantic representations capture visual information such as objects, locations, shapes, and colors; further enable pixel-level image generation. - oai:arXiv.org:2512.23512v2 - cs.CL - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Fengjiao Chen, Minhao Jing, Weitao Lu, Yan Feng, Xiaoyu Li, Xuezhi Cao - - - RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature - https://arxiv.org/abs/2512.23565 - arXiv:2512.23565v2 Announce Type: replace -Abstract: The integration of Multimodal Large Language Models (MLLMs) into chemistry promises to revolutionize scientific discovery, yet their ability to comprehend the dense, graphical language of reactions within authentic literature remains underexplored. Here, we introduce RxnBench, a multi-tiered benchmark designed to rigorously evaluate MLLMs on chemical reaction understanding from scientific PDFs. RxnBench comprises two tasks: Single-Figure QA (SF-QA), which tests fine-grained visual perception and mechanistic reasoning using 1,525 questions derived from 305 curated reaction schemes, and Full-Document QA (FD-QA), which challenges models to synthesize information from 108 articles, requiring cross-modal integration of text, schemes, and tables. Our evaluation of MLLMs reveals a critical capability gap: while models excel at extracting explicit text, they struggle with deep chemical logic and precise structural recognition. Notably, models with inference-time reasoning significantly outperform standard architectures, yet none achieve 50\% accuracy on FD-QA. These findings underscore the urgent need for domain-specific visual encoders and stronger reasoning engines to advance autonomous AI chemists. - oai:arXiv.org:2512.23565v2 - cs.CV - cs.AI - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Hanzheng Li, Xi Fang, Yixuan Li, Chaozheng Huang, Junjie Wang, Xi Wang, Hongzhe Bai, Bojun Hao, Shenyu Lin, Huiqi Liang, Linfeng Zhang, Guolin Ke - - - Algorithms for Distance Sensitivity Oracles and other Graph Problems on the PRAM - https://arxiv.org/abs/2512.23604 - arXiv:2512.23604v2 Announce Type: replace -Abstract: The distance sensitivity oracle (DSO) problem asks us to preprocess a given graph $G=(V,E)$ in order to answer queries of the form $d(x,y,e)$, which denotes the shortest path distance in $G$ from vertex $x$ to vertex $y$ when edge $e$ is removed. This is an important problem for network communication, and it has been extensively studied in the sequential settingand recently in the distributed CONGEST model. However, no prior DSO results tailored to the parallel setting were known. - We present the first PRAM algorithms to construct DSOs in directed weighted graphs, that can answer a query in $O(1)$ time with a single processor after preprocessing. We also present the first work-optimal PRAM algorithms for other graph problems that belong to the sequential $\tilde{O}(mn)$ fine-grained complexity class: Replacement Paths, Second Simple Shortest Path, All Pairs Second Simple Shortest Paths and Minimum Weight Cycle. - oai:arXiv.org:2512.23604v2 - cs.DS - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Vignesh Manoharan, Vijaya Ramachandran - - - Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE - https://arxiv.org/abs/2512.23624 - arXiv:2512.23624v2 Announce Type: replace -Abstract: We present NeuroSPICE, a physics-informed neural network (PINN) framework for device and circuit simulation. Unlike conventional SPICE, which relies on time-discretized numerical solvers, NeuroSPICE leverages PINNs to solve circuit differential-algebraic equations (DAEs) by minimizing the residual of the equations through backpropagation. It models device and circuit waveforms using analytical equations in time domain with exact temporal derivatives. While PINNs do not outperform SPICE in speed or accuracy during training, they offer unique advantages such as surrogate models for design optimization and inverse problems. NeuroSPICE's flexibility enables the simulation of emerging devices, including highly nonlinear systems such as ferroelectric memories. - oai:arXiv.org:2512.23624v2 - cs.AI - physics.app-ph - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Chien-Ting Tung, Chenming Hu - - - RoboMirror: Understand Before You Imitate for Video to Humanoid Locomotion - https://arxiv.org/abs/2512.23649 - arXiv:2512.23649v2 Announce Type: replace -Abstract: Humans learn locomotion through visual observation, interpreting visual content first before imitating actions. However, state-of-the-art humanoid locomotion systems rely on either curated motion capture trajectories or sparse text commands, leaving a critical gap between visual understanding and control. Text-to-motion methods suffer from semantic sparsity and staged pipeline errors, while video-based approaches only perform mechanical pose mimicry without genuine visual understanding. We propose RoboMirror, the first retargeting-free video-to-locomotion framework embodying "understand before you imitate". Leveraging VLMs, it distills raw egocentric/third-person videos into visual motion intents, which directly condition a diffusion-based policy to generate physically plausible, semantically aligned locomotion without explicit pose reconstruction or retargeting. Extensive experiments validate the effectiveness of RoboMirror, it enables telepresence via egocentric videos, drastically reduces third-person control latency by 80%, and achieves a 3.7% higher task success rate than baselines. By reframing humanoid control around video understanding, we bridge the visual understanding and action gap. - oai:arXiv.org:2512.23649v2 - cs.RO - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Zhe Li, Cheng Chi, Boan Zhu, Yangyang Wei, Shuanghao Bai, Yuheng Ji, Yibo Peng, Tao Huang, Pengwei Wang, Zhongyuan Wang, S. -H. Gary Chan, Chang Xu, Shanghang Zhang - - - IDT: A Physically Grounded Transformer for Feed-Forward Multi-View Intrinsic Decomposition - https://arxiv.org/abs/2512.23667 - arXiv:2512.23667v2 Announce Type: replace -Abstract: Intrinsic image decomposition is fundamental for visual understanding, as RGB images entangle material properties, illumination, and view-dependent effects. Recent diffusion-based methods have achieved strong results for single-view intrinsic decomposition; however, extending these approaches to multi-view settings remains challenging, often leading to severe view inconsistency. We propose \textbf{Intrinsic Decomposition Transformer (IDT)}, a feed-forward framework for multi-view intrinsic image decomposition. By leveraging transformer-based attention to jointly reason over multiple input images, IDT produces view-consistent intrinsic factors in a single forward pass, without iterative generative sampling. IDT adopts a physically grounded image formation model that explicitly decomposes images into diffuse reflectance, diffuse shading, and specular shading. This structured factorization separates Lambertian and non-Lambertian light transport, enabling interpretable and controllable decomposition of material and illumination effects across views. Experiments on both synthetic and real-world datasets demonstrate that IDT achieves cleaner diffuse reflectance, more coherent diffuse shading, and better-isolated specular components, while substantially improving multi-view consistency compared to prior intrinsic decomposition methods. - oai:arXiv.org:2512.23667v2 - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Kang Du, Yirui Guan, Zeyu Wang - - - End-to-End Test-Time Training for Long Context - https://arxiv.org/abs/2512.23675 - arXiv:2512.23675v2 Announce Type: replace -Abstract: We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Transformer with sliding-window attention. However, our model continues learning at test time via next-token prediction on the given context, compressing the context it reads into its weights. In addition, we improve the model's initialization for learning at test time via meta-learning at training time. Overall, our method, a form of Test-Time Training (TTT), is End-to-End (E2E) both at test time (via next-token prediction) and training time (via meta-learning), in contrast to previous forms. We conduct extensive experiments with a focus on scaling properties. In particular, for 3B models trained with 164B tokens, our method (TTT-E2E) scales with context length in the same way as Transformer with full attention, while others, such as Mamba 2 and Gated DeltaNet, do not. However, similar to RNNs, TTT-E2E has constant inference latency regardless of context length, making it 2.7 times faster than full attention for 128K context. Our code is publicly available. - oai:arXiv.org:2512.23675v2 - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace - http://creativecommons.org/licenses/by/4.0/ - Arnuv Tandon, Karan Dalal, Xinhao Li, Daniel Koceja, Marcel R{\o}d, Sam Buchanan, Xiaolong Wang, Jure Leskovec, Sanmi Koyejo, Tatsunori Hashimoto, Carlos Guestrin, Jed McCaleb, Yejin Choi, Yu Sun - - - The Simultaneous Triple Product Property and Group-theoretic Results for the Exponent of Matrix Multiplication - https://arxiv.org/abs/cs/0703145 - arXiv:cs/0703145v5 Announce Type: replace -Abstract: We describe certain special consequences of certain elementary methods from group theory for studying the algebraic complexity of matrix multiplication, as developed by H. Cohn, C. Umans et. al. in 2003 and 2005. The measure of complexity here is the exponent of matrix multiplication, a real parameter between 2 and 3, which has been conjectured to be 2. More specifically, a finite group may simultaneously "realize" several independent matrix multiplications via its regular algebra if it has a family of triples of "index" subsets which satisfy the so-called simultaneous triple product property (STPP), in which case the complexity of these several multiplications does not exceed the rank (complexity) of the algebra. This leads to bounds for the exponent in terms of the size of the group and the sizes of its STPP triples, as well as the dimensions of its distinct irreducible representations. Wreath products of Abelian with symmetric groups appear especially important, in this regard, and we give an example of such a group which shows that the exponent is less than 2.84, and could be possibly be as small as 2.02 depending on the number of simultaneous matrix multiplications it realizes. - oai:arXiv.org:cs/0703145v5 - cs.DS - cs.CC - math.GR - Thu, 01 Jan 2026 00:00:00 -0500 - replace - Sandeep Murthy - - - Efficient Active Learning with Abstention - https://arxiv.org/abs/2204.00043 - arXiv:2204.00043v3 Announce Type: replace-cross -Abstract: The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in terms of label complexity have been proved in very special cases, but fundamental lower bounds show that such improvements are impossible in general. This suggests a need to explore alternative goals for active learning. Learning with abstention is one such alternative. In this setting, the active learning algorithm may abstain from prediction and incur an error that is marginally smaller than random guessing. We develop the first computationally efficient active learning algorithm with abstention. Our algorithm provably achieves $\mathsf{polylog}(\frac{1}{\varepsilon})$ label complexity, without any low noise conditions. Such performance guarantee reduces the label complexity by an exponential factor, relative to passive learning and active learning that is not allowed to abstain. Furthermore, our algorithm is guaranteed to only abstain on hard examples (where the true label distribution is close to a fair coin), a novel property we term proper abstention that also leads to a host of other desirable characteristics (e.g., recovering minimax guarantees in the standard setting, and avoiding the undesirable "noise-seeking" behavior often seen in active learning). We also provide novel extensions of our algorithm that achieve constant label complexity and deal with model misspecification. - oai:arXiv.org:2204.00043v3 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Yinglun Zhu, Robert Nowak - - - Hedonic Prices and Quality Adjusted Price Indices Powered by AI - https://arxiv.org/abs/2305.00044 - arXiv:2305.00044v3 Announce Type: replace-cross -Abstract: We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices. - oai:arXiv.org:2305.00044v3 - econ.GN - cs.LG - q-fin.EC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - 10.1016/j.jeconom.2025.106052 - Journal of Econometrics, Volume 251, 2025Journal of Econometrics, Volume 251, Journal of Econometrics, Volume 251, 2025 - Patrick Bajari, Zhihao Cen, Victor Chernozhukov, Manoj Manukonda, Suhas Vijaykumar, Jin Wang, Ramon Huerta, Junbo Li, Ling Leng, George Monokroussos, Shan Wang - - - Machine learning for option pricing: an empirical investigation of network architectures - https://arxiv.org/abs/2307.07657 - arXiv:2307.07657v2 Announce Type: replace-cross -Abstract: We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. In this article, motivated by methods in image classification and recent advances in machine learning methods for PDEs, we investigate empirically whether and how the choice of network architecture affects the accuracy and training time of a machine learning algorithm. We find that the generalized highway network architecture achieves the best performance, when considering the mean squared error and the training time as criteria, within the considered parameter budgets for the Black-Scholes and Heston option pricing problems. Considering the transformed implied volatility problem, a simplified DGM variant achieves the lowest error among the tested architectures. We also carry out a capacity-normalised comparison for completeness, where all architectures are evaluated with an equal number of parameters. Finally, for the implied volatility problem, we additionally include experiments using real market data. - oai:arXiv.org:2307.07657v2 - q-fin.CP - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Serena Della Corte, Laurens Van Mieghem, Antonis Papapantoleon, Jonas Papazoglou-Hennig - - - Generative Modelling of L\'evy Area for High Order SDE Simulation - https://arxiv.org/abs/2308.02452 - arXiv:2308.02452v2 Announce Type: replace-cross -Abstract: It is well understood that, when numerically simulating SDEs with general noise, achieving a strong convergence rate better than $O(\sqrt{h})$ (where h is the step size) requires the use of certain iterated integrals of Brownian motion, commonly referred to as its "L\'evy areas". However, these stochastic integrals are difficult to simulate due to their non-Gaussian nature and for a $d$-dimensional Brownian motion with $d > 2$, no fast almost-exact sampling algorithm is known. - In this paper, we propose L\'evyGAN, a deep-learning-based model for generating approximate samples of L\'evy area conditional on a Brownian increment. Due to our "Bridge-flipping" operation, the output samples match all joint and conditional odd moments exactly. Our generator employs a tailored GNN-inspired architecture, which enforces the correct dependency structure between the output distribution and the conditioning variable. Furthermore, we incorporate a mathematically principled characteristic-function based discriminator. Lastly, we introduce a novel training mechanism termed "Chen-training", which circumvents the need for expensive-to-generate training data-sets. This new training procedure is underpinned by our two main theoretical results. - For 4-dimensional Brownian motion, we show that L\'evyGAN exhibits state-of-the-art performance across several metrics which measure both the joint and marginal distributions. We conclude with a numerical experiment on the log-Heston model, a popular SDE in mathematical finance, demonstrating that high-quality synthetic L\'evy area can lead to high order weak convergence and variance reduction when using multilevel Monte Carlo (MLMC). - oai:arXiv.org:2308.02452v2 - stat.ML - cs.LG - cs.NA - math.NA - math.PR - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1137/23M161077X - SIAM Journal on Mathematics of Data Science, vol. 7, no. 4, pp. 1541-1567, 2025 - Andra\v{z} Jelin\v{c}i\v{c}, Jiajie Tao, William F. Turner, Thomas Cass, James Foster, Hao Ni - - - Are Ensembles Getting Better all the Time? - https://arxiv.org/abs/2311.17885 - arXiv:2311.17885v3 Announce Type: replace-cross -Abstract: Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on the kind of ensemble considered, as well as the predictive metric chosen. We focus on situations where all members of the ensemble are a priori expected to perform equally well, which is the case of several popular methods such as random forests or deep ensembles. In this setting, we show that ensembles are getting better all the time if, and only if, the considered loss function is convex. More precisely, in that case, the loss of the ensemble is a decreasing function of the number of models. When the loss function is nonconvex, we show a series of results that can be summarised as: ensembles of good models keep getting better, and ensembles of bad models keep getting worse. To this end, we prove a new result on the monotonicity of tail probabilities that may be of independent interest. We illustrate our results on a medical problem (diagnosing melanomas using neural nets) and a "wisdom of crowds" experiment (guessing the ratings of upcoming movies). - oai:arXiv.org:2311.17885v3 - stat.ML - cs.LG - math.ST - stat.ME - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Journal of Machine Learning Research, vol. 26 (201), 1-46, 2025 - Pierre-Alexandre Mattei, Damien Garreau - - - Distribution-Dependent Rates for Multi-Distribution Learning - https://arxiv.org/abs/2312.13130 - arXiv:2312.13130v2 Announce Type: replace-cross -Abstract: To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks. The recent multi-distribution learning (MDL) framework tackles this objective in a dynamic interaction with the environment, where the learner has sampling access to each target distribution. Drawing inspiration from the field of pure-exploration multi-armed bandits, we provide distribution-dependent guarantees in the MDL regime, that scale with suboptimality gaps and result in superior dependence on the sample size when compared to the existing distribution-independent analyses. We investigate two non-adaptive strategies, uniform and non-uniform exploration, and present non-asymptotic regret bounds using novel tools from empirical process theory. Furthermore, we devise an adaptive optimistic algorithm, LCB-DR, that showcases enhanced dependence on the gaps, mirroring the contrast between uniform and optimistic allocation in the multi-armed bandit literature. We also conduct a small synthetic experiment illustrating the comparative strengths of each strategy. - oai:arXiv.org:2312.13130v2 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Rafael Hanashiro, Patrick Jaillet - - - Stochastic Gradient Descent for Nonparametric Additive Regression - https://arxiv.org/abs/2401.00691 - arXiv:2401.00691v5 Announce Type: replace-cross -Abstract: This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be viewed as the functional counterpart of stochastic gradient descent, applied to the coefficients of a truncated basis expansion of the component functions. We show that the resulting estimator satisfies an oracle inequality that allows for model mis-specification. In the well-specified setting, by choosing the learning rate carefully across three distinct stages of training, we demonstrate that its risk is minimax optimal in terms of the dependence on both the dimensionality of the data and the size of the training sample. Unlike past work, we also provide polynomial convergence rates even when the covariates do not have full support on their domain. - oai:arXiv.org:2401.00691v5 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Xin Chen, Jason M. Klusowski - - - Conditions for eigenvalue configurations of two real symmetric matrices (signature approach) - https://arxiv.org/abs/2401.00866 - arXiv:2401.00866v4 Announce Type: replace-cross -Abstract: For two real symmetric matrices, their eigenvalue configuration is therelative arrangement of their eigenvalues on the real line. We consider the following problem: given two parametric real symmetric matrices and an eigenvalue configuration, find a simple condition on the parameters such that the two matrices have the given eigenvalue configuration. In this paper, we develop theory and give an algorithm for this problem. The output of the algorithm is a condition written in terms of the signatures of certain related symmetric matrices. - oai:arXiv.org:2401.00866v4 - math.AG - cs.SC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Hoon Hong, Daniel Profili, J. Rafael Sendra - - - Shill-Proof Auctions - https://arxiv.org/abs/2404.00475 - arXiv:2404.00475v3 Announce Type: replace-cross -Abstract: We characterize single-item auction formats that are shill-proof in the sense that a profit-maximizing seller has no incentive to submit shill bids. We distinguish between strong shill-proofness, in which a seller with full knowledge of bidders' valuations can never profit from shilling, and weak shill-proofness, which requires only that the expected equilibrium profit from shilling is non-positive. The Dutch auction (with a suitable reserve) is the unique (revenue-)optimal and strongly shill-proof auction. Any deterministic auction can satisfy only two properties in the set {static, strategy-proof, weakly shill-proof}. Our main results extend to settings with affiliated and interdependent values. - oai:arXiv.org:2404.00475v3 - econ.TH - cs.GT - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Andrew Komo, Scott Duke Kominers, Tim Roughgarden - - - Symmetric Linear Bandits with Hidden Symmetry - https://arxiv.org/abs/2405.13899 - arXiv:2405.13899v3 Announce Type: replace-cross -Abstract: High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure in the literature is sparsity. However, it may not be available in practice. Symmetry, where the reward is invariant under certain groups of transformations on the set of arms, is another important inductive bias in the high-dimensional case that covers many standard structures, including sparsity. In this work, we study high-dimensional symmetric linear bandits where the symmetry is hidden from the learner, and the correct symmetry needs to be learned in an online setting. We examine the structure of a collection of hidden symmetry and provide a method based on model selection within the collection of low-dimensional subspaces. Our algorithm achieves a regret bound of $ O(d_0^{2/3} T^{2/3} \log(d))$, where $d$ is the ambient dimension which is potentially very large, and $d_0$ is the dimension of the true low-dimensional subspace such that $d_0 \ll d$. With an extra assumption on well-separated models, we can further improve the regret to $ O(d_0\sqrt{T\log(d)} )$. - oai:arXiv.org:2405.13899v3 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Nam Phuong Tran, The Anh Ta, Debmalya Mandal, Long Tran-Thanh - - - Lattice operations for the stable set in many-to-many markets via re-equilibration dynamics - https://arxiv.org/abs/2407.21198 - arXiv:2407.21198v2 Announce Type: replace-cross -Abstract: We compute the lattice operations for the (pairwise) stable set in many-to-many matching markets where only path-independence on agents' choice functions is imposed. To do this, we construct Tarski operators defined on the lattices of worker-quasi-stable and firm-quasi-stable matchings. These operators resemble lay-off and vacancy chain dynamics, respectively. - oai:arXiv.org:2407.21198v2 - econ.TH - cs.GT - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Agustin G. Bonifacio, Noelia Juarez, Paola B. Manasero - - - The Z-Gromov-Wasserstein Distance - https://arxiv.org/abs/2408.08233 - arXiv:2408.08233v4 Announce Type: replace-cross -Abstract: The Gromov-Wasserstein (GW) distance is a powerful tool for comparing metric measure spaces which has found broad applications in data science and machine learning. Driven by the need to analyze datasets whose objects have increasingly complex structure (such as node and edge-attributed graphs), several variants of GW distance have been introduced in the recent literature. With a view toward establishing a general framework for the theory of GW-like distances, this paper considers a vast generalization of the notion of a metric measure space: for an arbitrary metric space $Z$, we define a $Z$-network to be a measure space endowed with a kernel valued in $Z$. We introduce a method for comparing $Z$-networks by defining a generalization of GW distance, which we refer to as $Z$-Gromov-Wasserstein ($Z$-GW) distance. This construction subsumes many previously known metrics and offers a unified approach to understanding their shared properties. This paper demonstrates that the $Z$-GW distance defines a metric on the space of $Z$-networks which retains desirable properties of $Z$, such as separability, completeness, and geodesicity. Many of these properties were unknown for existing variants of GW distance that fall under our framework. Our focus is on foundational theory, but our results also include computable lower bounds and approximations of the distance which will be useful for practical applications. - oai:arXiv.org:2408.08233v4 - math.MG - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/publicdomain/zero/1.0/ - Martin Bauer, Facundo M\'emoli, Tom Needham, Mao Nishino - - - Regularized autoregressive modeling and its application to audio signal reconstruction - https://arxiv.org/abs/2410.17790 - arXiv:2410.17790v3 Announce Type: replace-cross -Abstract: Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although these attempts are appealing, an encompassing and generic modeling framework is still missing. We propose such a framework and the related optimization problem and algorithm. We discuss the computational demands of the algorithm and explore the effects of various improvements on its convergence speed. In the experimental part, we demonstrate the usefulness of our approach on the audio declipping and dequantization problems. We compare its performance against state-of-the-art methods and demonstrate the competitiveness of the proposed method in declipping musical signals, and its superiority in declipping speech. The evaluation includes a heuristic algorithm of generalized linear prediction (GLP), a strong competitor which has only been presented as a patent and is new in the scientific community. - oai:arXiv.org:2410.17790v3 - eess.AS - cs.SD - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Ond\v{r}ej Mokr\'y, Pavel Rajmic - - - A Particle Algorithm for Mean-Field Variational Inference - https://arxiv.org/abs/2412.20385 - arXiv:2412.20385v4 Announce Type: replace-cross -Abstract: Variational inference is a fast and scalable alternative to Markov chain Monte Carlo and has been widely applied to posterior inference tasks in statistics and machine learning. A traditional approach for implementing mean-field variational inference (MFVI) is coordinate ascent variational inference (CAVI), which relies crucially on parametric assumptions on complete conditionals. We introduce a novel particle-based algorithm for MFVI, named PArticle VI (PAVI), for nonparametric mean-field approximation. We obtain non-asymptotic error bounds for our algorithm. To our knowledge, this is the first end-to-end guarantee for particle-based MFVI. - oai:arXiv.org:2412.20385v4 - math.ST - cs.LG - math.OC - stat.ML - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Qiang Du, Kaizheng Wang, Edith Zhang, Chenyang Zhong - - - NeuroPMD: Neural Fields for Density Estimation on Product Manifolds - https://arxiv.org/abs/2501.02994 - arXiv:2501.02994v2 Announce Type: replace-cross -Abstract: We propose a novel deep neural network methodology for density estimation on product Riemannian manifold domains. In our approach, the network directly parameterizes the unknown density function and is trained using a penalized maximum likelihood framework, with a penalty term formed using manifold differential operators. The network architecture and estimation algorithm are carefully designed to handle the challenges of high-dimensional product manifold domains, effectively mitigating the curse of dimensionality that limits traditional kernel and basis expansion estimators, as well as overcoming the convergence issues encountered by non-specialized neural network methods. Extensive simulations and a real-world application to brain structural connectivity data highlight the clear advantages of our method over the competing alternatives. - oai:arXiv.org:2501.02994v2 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - William Consagra, Zhiling Gu, Zhengwu Zhang - - - coverforest: Conformal Predictions with Random Forest in Python - https://arxiv.org/abs/2501.14570 - arXiv:2501.14570v3 Announce Type: replace-cross -Abstract: Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial computational costs due to required pairwise comparisons between training and test samples' out-of-bag scores. Observing that these methods naturally extend from ensemble models, particularly random forests, we leverage existing optimized random forest implementations to enable efficient cross-conformal predictions. - We present coverforest, a Python package that implements efficient conformal prediction methods specifically optimized for random forests. coverforest supports both regression and classification tasks through various conformal prediction methods, including split conformal, CV+, Jackknife+-after-bootstrap, and adaptive prediction sets. Our package leverages parallel computing and Cython optimizations to speed up out-of-bag calculations. Our experiments demonstrate that coverforest's predictions achieve the desired level of coverage. In addition, its training and prediction times can be faster than an existing implementation by 2--9 times. The source code for the coverforest is hosted on GitHub at https://github.com/donlap/coverforest. - oai:arXiv.org:2501.14570v3 - stat.ML - cs.LG - stat.CO - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - 10.1016/j.neucom.2025.132362 - Neurocomputing. 668, (Mar. 2026), 132362 - Panisara Meehinkong, Donlapark Ponnoprat - - - Concentration Inequalities for Stochastic Optimization of Unbounded Objective Functions with Application to Denoising Score Matching - https://arxiv.org/abs/2502.08628 - arXiv:2502.08628v2 Announce Type: replace-cross -Abstract: We derive novel concentration inequalities that bound the statistical error for a large class of stochastic optimization problems, focusing on the case of unbounded objective functions. Our derivations utilize the following key tools: 1) A new form of McDiarmid's inequality that is based on sample-dependent one-component mean-difference bounds and which leads to a novel uniform law of large numbers result for unbounded functions. 2) A new Rademacher complexity bound for families of functions that satisfy an appropriate sample-dependent Lipschitz property, which allows for application to a large class of distributions with unbounded support. As an application of these results, we derive statistical error bounds for denoising score matching (DSM), an application that inherently requires one to consider unbounded objective functions and distributions with unbounded support, even in cases where the data distribution has bounded support. In addition, our results quantify the benefit of sample-reuse in algorithms that employ easily-sampled auxiliary random variables in addition to the training data, e.g., as in DSM, which uses auxiliary Gaussian random variables. - oai:arXiv.org:2502.08628v2 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Jeremiah Birrell - - - A mathematical model for a universal digital quantum computer with an application to the Grover-Rudolph algorithm - https://arxiv.org/abs/2503.13388 - arXiv:2503.13388v5 Announce Type: replace-cross -Abstract: In this work, we develop a novel mathematical framework for universal digital quantum computation using algebraic probability theory. We rigorously define quantum circuits as finite sequences of elementary quantum gates and establish their role in implementing unitary transformations. A key result demonstrates that every unitary matrix in \(\mathrm{U}(N)\) can be expressed as a product of elementary quantum gates, leading to the concept of a universal dictionary for quantum computation. We apply this framework to the construction of quantum circuits that encode probability distributions, focusing on the Grover-Rudolph algorithm. By leveraging controlled quantum gates and rotation matrices, we design a quantum circuit that approximates a given probability density function. Numerical simulations, conducted using Qiskit, confirm the theoretical predictions and validate the effectiveness of our approach. These results provide a rigorous foundation for quantum circuit synthesis within an algebraic probability framework and offer new insights into the encoding of probability distributions in quantum algorithms. Potential applications include quantum machine learning, circuit optimization, and experimental implementations on real quantum hardware. - oai:arXiv.org:2503.13388v5 - quant-ph - cs.NA - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Antonio Falc\'o, Daniela Falc\'o--Pomares, Hermann G. Matthies - - - Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge - https://arxiv.org/abs/2504.02382 - arXiv:2504.02382v2 Announce Type: replace-cross -Abstract: The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture segmentation by benchmarking state-of-the-art algorithms on these complex tasks. A diverse dataset of 150 CT scans was collected from multiple clinical centers, and a large set of simulated X-ray images was generated using the DeepDRR method. Final submissions from 16 teams worldwide were evaluated under a rigorous multi-metric testing scheme. The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy. However, in the X-ray task, the best algorithm achieved an IoU of 0.774, which is promising but not yet sufficient for intra-operative decision-making, reflecting the inherent challenges of fragment overlap in projection imaging. Beyond the quantitative evaluation, the challenge revealed methodological diversity in algorithm design. Variations in instance representation, such as primary-secondary classification versus boundary-core separation, led to differing segmentation strategies. Despite promising results, the challenge also exposed inherent uncertainties in fragment definition, particularly in cases of incomplete fractures. These findings suggest that interactive segmentation approaches, integrating human decision-making with task-relevant information, may be essential for improving model reliability and clinical applicability. - oai:arXiv.org:2504.02382v2 - eess.IV - cs.AI - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Yudi Sang, Yanzhen Liu, Sutuke Yibulayimu, Yunning Wang, Benjamin D. Killeen, Mingxu Liu, Ping-Cheng Ku, Ole Johannsen, Karol Gotkowski, Maximilian Zenk, Klaus Maier-Hein, Fabian Isensee, Peiyan Yue, Yi Wang, Haidong Yu, Zhaohong Pan, Yutong He, Xiaokun Liang, Daiqi Liu, Fuxin Fan, Artur Jurgas, Andrzej Skalski, Yuxi Ma, Jing Yang, Szymon P{\l}otka, Rafa{\l} Litka, Gang Zhu, Yingchun Song, Mathias Unberath, Mehran Armand, Dan Ruan, S. Kevin Zhou, Qiyong Cao, Chunpeng Zhao, Xinbao Wu, Yu Wang - - - Discovery and inference beyond linearity by integrating Bayesian regression, tree ensembles and Shapley values - https://arxiv.org/abs/2505.00571 - arXiv:2505.00571v2 Announce Type: replace-cross -Abstract: Machine Learning (ML) is gaining popularity for hypothesis-free discovery of risk and protective factors in healthcare studies. ML is strong at discovering nonlinearities and interactions, but this power is compromised by a lack of reliable inference. Although Shapley values provide local measures of features' effects, valid uncertainty quantification for these effects is typically lacking, thus precluding statistical inference. We propose RuleSHAP, a framework that addresses this limitation by combining a dedicated Bayesian sparse regression model with a new tree-based rule generator and Shapley value attribution. RuleSHAP provides detection of nonlinear and interaction effects with uncertainty quantification at the individual level. We derive an efficient formula for computing marginal Shapley values within this framework. We demonstrate the validity of our framework on simulated data. Finally, we apply RuleSHAP to data from an epidemiological cohort to detect and infer several effects for high cholesterol and blood pressure, such as nonlinear interaction effects between features like age, sex, ethnicity, BMI and glucose level. - oai:arXiv.org:2505.00571v2 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Giorgio Spadaccini, Marjolein Fokkema, Mark A. van de Wiel - - - Decomposing graphs into stable and ordered parts - https://arxiv.org/abs/2505.00594 - arXiv:2505.00594v2 Announce Type: replace-cross -Abstract: Connections between structural graph theory and finite model theory recently gained a lot of attention. In this setting, many interesting questions remain on the properties of dependent (NIP) hereditary classes of graphs, in particular related to first-order transductions. In this paper, we study modelizations (which are strong forms of transduction pairings) of classes of graphs by classes of structures. In particular, we consider models obtained by coupling a partial order and a colored graph (thus forming a partially ordered colored graph). Motivated by Simon's decomposition theorem of dependent types into a stable part and a distal (order-like) part, we conjecture that every dependent hereditary class of graphs admits a modelization in a monadically dependent coupling of a class of posets with bounded treewidth cover graphs and a monadically stable class of colored graphs. In this paper, we consider the first non-trivial case (classes with bounded linear cliquewidth) and prove that the conjecture holds in a strong form, the model class being a monadically dependent coupling of a class of disjoint unions of chains and a class of colored graphs with bounded pathwidth. We extend our study to classes that admit bounded-size bounded linear cliquewidth decompositions and prove that they have a modelization in a monadically dependent coupling of a class of disjoint unions of chains and a class of colored graphs with bounded expansion, the model class also admitting bounded-size bounded linear cliquewidth decompositions. - oai:arXiv.org:2505.00594v2 - math.CO - cs.LO - math.LO - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Hector Buffi\`ere, Patrice Ossona de Mendez - - - Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate - https://arxiv.org/abs/2505.01985 - arXiv:2505.01985v2 Announce Type: replace-cross -Abstract: In constraint learning, we use a neural network as a surrogate for part of the constraints or of the objective function of an optimization model. However, the tractability of the resulting model is heavily influenced by the size of the neural network used as a surrogate. One way to obtain a more tractable surrogate is by pruning the neural network first. In this work, we consider how to approach the setting in which the neural network is actually a given: how can we solve an optimization model embedding a large and predetermined neural network? We propose surrogating the neural network itself by pruning it, which leads to a sparse and more tractable optimization model, for which we hope to still obtain good solutions with respect to the original neural network. For network verification and function maximization models, that indeed leads to better solutions within a time limit, especially -- and surprisingly -- if we skip the standard retraining step known as finetuning. Hence, a pruned network with worse inference for lack of finetuning can be a better surrogate. - oai:arXiv.org:2505.01985v2 - math.OC - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Hung Pham, Aiden Ren, Ibrahim Tahir, Jiatai Tong, Thiago Serra - - - New affine invariant ensemble samplers and their dimensional scaling - https://arxiv.org/abs/2505.02987 - arXiv:2505.02987v3 Announce Type: replace-cross -Abstract: We introduce new affine invariant ensemble Markov chain Monte Carlo (MCMC) samplers that are easy to construct and improve upon existing methods, especially for high-dimensional problems. We first propose a simple derivative-free side move sampler that improves upon popular samplers in the \texttt{emcee} package by generating more effective proposal directions. We then develop a class of derivative-based affine invariant ensemble Hamiltonian Monte Carlo (HMC) samplers based on antisymmetric preconditioning using complementary ensembles, which outperform standard, non-affine-invariant HMC when sampling highly anisotropic distributions. We provide asymptotic scaling analysis for high-dimensional Gaussian targets to further elucidate the properties of these affine invariant ensemble samplers. In particular, with derivative information, the affine invariant ensemble HMC can scale much better with dimension compared to derivative-free ensemble samplers. - oai:arXiv.org:2505.02987v3 - stat.CO - cs.LG - math.ST - stat.ML - stat.TH - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yifan Chen - - - Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI - https://arxiv.org/abs/2505.05703 - arXiv:2505.05703v2 Announce Type: replace-cross -Abstract: Deep learning has demonstrated strong potential for MRI reconstruction. However, conventional supervised learning requires high-quality, high-SNR references for network training, which are often difficult or impossible to obtain in different scenarios, particularly in low-field MRI. Self-supervised learning provides an alternative by removing the need for training references, but its reconstruction performance can degrade when the baseline SNR is low. To address these limitations, we propose hybrid learning, a two-stage training framework that integrates self-supervised and supervised learning for joint MRI reconstruction and denoising when only low-SNR training references are available. Hybrid learning is implemented in two sequential stages. In the first stage, self-supervised learning is applied to fully sampled low-SNR data to generate higher-quality pseudo-references. In the second stage, these pseudo-references are used as targets for supervised learning to reconstruct and denoise undersampled noisy data. The proposed technique was evaluated in multiple experiments involving simulated and real low-field MRI in the lung and brain at different field strengths. Hybrid learning consistently improved image quality over both standard self-supervised learning and supervised learning with noisy training references at different acceleration rates, noise levels, and field strengths, achieving higher SSIM and lower NMSE. The hybrid learning approach is effective for both Cartesian and non-Cartesian acquisitions. Hybrid learning provides an effective solution for training deep MRI reconstruction models in the absence of high-SNR references. By improving image quality in low-SNR settings, particularly for low-field MRI, it holds promise for broader clinical adoption of deep learning-based reconstruction methods. - oai:arXiv.org:2505.05703v2 - eess.IV - cs.CV - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Haoyang Pei, Nikola Janjuvsevic, Renqing Luo, Ding Xia, Xiang Xu, William Moore, Yao Wang, Hersh Chandarana, Li Feng - - - Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures - https://arxiv.org/abs/2505.07427 - arXiv:2505.07427v2 Announce Type: replace-cross -Abstract: Recent advances in Structural Health Monitoring (SHM) have attracted industry interest, yet real-world applications, such as in ship structures remain scarce. Despite SHM's potential to optimise maintenance, its adoption in ships is limited due to the lack of clearly quantifiable benefits for hull maintenance. This study employs a Bayesian pre-posterior decision analysis to quantify the value of information (VoI) from SHM systems monitoring corrosion-induced thickness loss (CITL) in ship hulls, in a first-of-its-kind analysis for ship structures. We define decision-making consequence cost functions based on exceedance probabilities relative to a target CITL threshold, which can be set by the decision-maker. This introduces a practical aspect to our framework, that enables implicitly modelling the decision-maker's risk perception. We apply this framework to a large-scale, high-fidelity numerical model of a commercial vessel and examine the relative benefits of different CITL monitoring strategies, including strain-based SHM and traditional on-site inspections. - oai:arXiv.org:2505.07427v2 - stat.AP - cs.CE - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Nicholas E. Silionis, Konstantinos N. Anyfantis - - - Algorithms for Nonlinear Mixed-Integer Location Estimation - https://arxiv.org/abs/2505.12980 - arXiv:2505.12980v2 Announce Type: replace-cross -Abstract: For three decades, carrier-phase observations have been used to obtain the most accurate location estimates using global navigation satellite systems (GNSS). These estimates are computed by minimizing a nonlinear mixed-integer least-squares problem. Existing algorithms linearize the problem, orthogonally project it to eliminate real variables, and then solve the integer least-square problem. There is now considerable interest in developing similar localization techniques for terrestrial and indoor settings. We show that algorithms that linearize first fail in these settings and we propose several algorithms for computing the estimates. Some of our algorithms are elimination algorithms that start by eliminating the non-linear terms in the constraints; others construct a geometric arrangement that allows us to efficiently enumerate integer solutions (in polynomial time). We focus on simplified localization problems in which the measurements are range (distance) measurements and carrier phase range measurements, with no nuisance parameters. The simplified problem allows us to focus on the core question of untangling the nonlinearity and the integer nature of some parameters. We show using simulations that the new algorithms are effective at close ranges at which the linearize-first approach fails. - oai:arXiv.org:2505.12980v2 - eess.SP - cs.MS - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ophir Uziel, Efi Fogel, Dan Halperin, Sivan Toledo - - - Adversarial quantum channel discrimination - https://arxiv.org/abs/2506.03060 - arXiv:2506.03060v2 Announce Type: replace-cross -Abstract: We introduce a new framework for quantum channel discrimination in an adversarial setting, where the tester plays against an adversary. We show that in asymmetric hypothesis testing, the optimal type-II error exponent is precisely characterized by a new notion of quantum channel divergence (termed the minimum output channel divergence). This serves as a direct analog of the quantum Stein's lemma in this new framework, and complements previous studies on ``best-case'' channel discrimination, thereby providing a complete understanding of the ultimate limits of quantum channel discrimination. Notably, the optimal error exponent can be achieved by simple non-adaptive adversarial strategies, and despite the need for regularization, it remains efficiently computable and satisfies the strong converse property in general. Furthermore, we show that entropy accumulation, a powerful tool in quantum cryptography, can be reframed as an adversarial channel discrimination problem, establishing a new connection between quantum information theory and quantum cryptography. - oai:arXiv.org:2506.03060v2 - quant-ph - cs.IT - math.IT - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - 10.1103/mtbj-grbk - Phys. Rev. Lett. 135, 260201 (2025) - Kun Fang, Hamza Fawzi, Omar Fawzi - - - Some remarks on stochastic converse Lyapunov theorems - https://arxiv.org/abs/2506.06053 - arXiv:2506.06053v2 Announce Type: replace-cross -Abstract: In this brief note, we investigate some constructions of Lyapunov functions for stochastic discrete-time stabilizable dynamical systems, in other words, controlled Markov chains. The main question here is whether a Lyapunov function in some statistical sense exists if the respective controlled Markov chain admits a stabilizing policy. We demonstrate some constructions extending on the classical results for deterministic systems. Some limitations of the constructed Lyapunov functions for stabilization are discussed, particularly for stabilization in mean. Although results for deterministic systems are well known, the stochastic case was addressed in less detail, which the current paper remarks on. A distinguishable feature of this work is the study of stabilizers that possess computationally tractable convergence certificates. - oai:arXiv.org:2506.06053v2 - math.DS - cs.SY - eess.SY - math.OC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Pavel Osinenko, Grigory Yaremenko - - - Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws - https://arxiv.org/abs/2508.03688 - arXiv:2508.03688v3 Announce Type: replace-cross -Abstract: We study the optimization and sample complexity of gradient-based training of a two-layer neural network with quadratic activation function in the high-dimensional regime, where the data is generated as $f_*(\boldsymbol{x}) \propto \sum_{j=1}^{r}\lambda_j \sigma\left(\langle \boldsymbol{\theta_j}, \boldsymbol{x}\rangle\right), \boldsymbol{x} \sim N(0,\boldsymbol{I}_d)$, $\sigma$ is the 2nd Hermite polynomial, and $\lbrace\boldsymbol{\theta}_j \rbrace_{j=1}^{r} \subset \mathbb{R}^d$ are orthonormal signal directions. We consider the extensive-width regime $r \asymp d^\beta$ for $\beta \in [0, 1)$, and assume a power-law decay on the (non-negative) second-layer coefficients $\lambda_j\asymp j^{-\alpha}$ for $\alpha \geq 0$. We present a sharp analysis of the SGD dynamics in the feature learning regime, for both the population limit and the finite-sample (online) discretization, and derive scaling laws for the prediction risk that highlight the power-law dependencies on the optimization time, sample size, and model width. Our analysis combines a precise characterization of the associated matrix Riccati differential equation with novel matrix monotonicity arguments to establish convergence guarantees for the infinite-dimensional effective dynamics. - oai:arXiv.org:2508.03688v3 - stat.ML - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - G\'erard Ben Arous, Murat A. Erdogdu, Nuri Mert Vural, Denny Wu - - - Rapid Variable Resolution Particle Initialization for Complex Geometries - https://arxiv.org/abs/2508.12835 - arXiv:2508.12835v2 Announce Type: replace-cross -Abstract: The accuracy of meshless methods like Smoothed Particle Hydrodynamics (SPH) is highly dependent on the quality of the particle distribution. Existing particle initialization techniques often struggle to simultaneously achieve adaptive resolution, handle intricate boundaries, and efficiently generate well-packed distributions inside and outside a boundary. This work presents a fast and robust particle initialization method that achieves these goals using standard SPH building blocks. Our approach enables simultaneous initialization of fluid and solid regions, supports arbitrary geometries, and achieves high-quality, quasi-uniform particle arrangements without complex procedures like surface bonding. Extensive results in both 2D and 3D demonstrate that the obtained particle distributions exhibit good boundary conformity, low spatial disorder, and minimal density variation, all with significantly reduced computational cost compared to existing approaches. This work paves the way for automated particle initialization to accurately model flow in and around bodies with meshless methods, particularly with SPH. - oai:arXiv.org:2508.12835v2 - physics.comp-ph - cs.MS - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - 10.1016/j.cpc.2025.109992 - Computer Physics Communications 320 (2026) 109992 - Navaneet Villodi, Prabhu Ramachandran - - - Reconstructing graphs and their connectivity using graphlets - https://arxiv.org/abs/2508.19189 - arXiv:2508.19189v2 Announce Type: replace-cross -Abstract: Graphlets are subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence (gds), gives a topological description of the surrounding of the analyzed vertex. Graphlet degree distribution (gdd) of a graph is a matrix containing graphlet degree sequence for all vertices in the given graph. A long standing open problem called reconstruction conjecture (RC) asks whether the structure of a graph is uniquely determined by the multiset of its vertex-deleted subgraphs. Graphlet degree distribution up to size (n - 1), (<= n - 1)-gdd, gives more information to reconstruct the graph and we use it to reconstruct any graph having a unique almost-asymmetric vertex-deleted subgraph, where almost-asymmetric means that at most one automorphism orbit has size larger than one. Moreover, we prove that any graph containing a vertex-cut of size 1 or any graph of order n having a vertex with degree at most 2 or at least n-2 is reconstructible from its (<= n - 1)-gdd, which expands results shown in the standard RC. We also discuss the relation between gdd and graph connectivity and the conditions on (<= 3)-gdd, whose breaking means that no graph with such gdd exists. - oai:arXiv.org:2508.19189v2 - math.CO - cs.DM - cs.SI - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - David Hartman, Aneta Pokorn\'a, Daniel Trlifaj, Llu\'is Vena - - - Lipschitz-Guided Design of Interpolation Schedules in Generative Models - https://arxiv.org/abs/2509.01629 - arXiv:2509.01629v2 Announce Type: replace-cross -Abstract: We study the design of interpolation schedules in the stochastic interpolants framework for flow and diffusion-based generative models. We show that while all scalar interpolation schedules achieve identical statistical efficiency under Kullback-Leibler divergence in path space after optimal diffusion coefficient tuning, their numerical efficiency can differ substantially. This motivates focusing on numerical properties of the resulting drift fields rather than purely statistical criteria for schedule design. We propose averaged squared Lipschitzness minimization as a principled criterion for numerical optimization, providing an alternative to kinetic energy minimization used in optimal transport approaches. A transfer formula is derived that enables conversion between different schedules at inference time without retraining neural networks. For Gaussian distributions, the optimized schedules achieve exponential improvements in Lipschitz constants over standard linear schedules, while for Gaussian mixtures, they reduce mode collapse in few-step sampling. We also validate our approach on high-dimensional invariant distributions from stochastic Allen-Cahn equations and Navier-Stokes equations, demonstrating robust performance improvements across resolutions. - oai:arXiv.org:2509.01629v2 - stat.ML - cs.LG - cs.NA - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yifan Chen, Eric Vanden-Eijnden, Jiawei Xu - - - Dynamical Learning in Deep Asymmetric Recurrent Neural Networks - https://arxiv.org/abs/2509.05041 - arXiv:2509.05041v2 Announce Type: replace-cross -Abstract: We investigate recurrent neural networks with asymmetric interactions and demonstrate that the inclusion of self-couplings or sparse excitatory inter-module connections leads to the emergence of a densely connected manifold of dynamically accessible stable configurations. This representation manifold is exponentially large in system size and is reachable through simple local dynamics, despite constituting a subdominant subset of the global configuration space. We further show that learning can be implemented directly on this structure via a fully local, gradient-free mechanism that selectively stabilizes a single task-relevant network configuration. Unlike error-driven or contrastive learning schemes, this approach does not require explicit comparisons between network states obtained with and without output supervision. Instead, transient supervisory signals bias the dynamics toward the representation manifold, after which local plasticity consolidates the attained configuration, effectively shaping the latent representation space. Numerical evaluations on standard image classification benchmarks indicate performance comparable to that of multilayer perceptrons trained using backpropagation. More generally, these results suggest that the dynamical accessibility of fixed points and the stabilization of internal network dynamics constitute viable alternative principles for learning in recurrent systems, with conceptual links to statistical physics and potential implications for biologically motivated and neuromorphic computing architectures. - oai:arXiv.org:2509.05041v2 - cond-mat.dis-nn - cs.LG - q-bio.NC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-nc-nd/4.0/ - Davide Badalotti, Carlo Baldassi, Marc M\'ezard, Mattia Scardecchia, Riccardo Zecchina - - - Cubic Incompleteness: Hilbert's Tenth Problem at Degree Three - https://arxiv.org/abs/2510.00759 - arXiv:2510.00759v4 Announce Type: replace-cross -Abstract: We analyze the cubic fragment $\mathcal D_3$ over $\mathbb N$ by isolating the uniform closure principle any total correct cubic solver would have to realize. In $\mathsf{HA}$ we give a fully constructive, additive and degree-controlled encoding of bounded verification: for each externally fixed bound, we effectively produce a finite system of degree-3 Diophantine equations whose solutions represent the existence of the corresponding finite proof or computation trace. The encoding is purely syntactic, using "gadgets" and "Carryless Pairing". In a classical metatheory (e.g. $\mathsf{PA}$) we show that the global solver hypothesis implies a uniform operator eliminating the bound inside $\mathcal D_3$, which is incompatible with standard non-uniformity/realizability constraints. Hence no uniform cubic can exist clasically. - oai:arXiv.org:2510.00759v4 - math.LO - cs.CC - cs.LO - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Milan Rosko - - - Enhancing Diffusion-Based Sampling with Molecular Collective Variables - https://arxiv.org/abs/2510.11923 - arXiv:2510.11923v2 Announce Type: replace-cross -Abstract: Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, information-rich, low-dimensional projections of atomic coordinates known as collective variables (CVs). We introduce a repulsive potential centered on the CVs from recent samples, which pushes future samples towards novel CV regions and effectively increases the temperature in the projected space. Our resulting method improves efficiency, mode discovery, enables the estimation of free energy differences, and retains independent sampling from the approximate Boltzmann distribution via reweighting by the bias. On standard peptide conformational sampling benchmarks, the method recovers diverse conformational states and accurate free energy profiles. We are the first to demonstrate reactive sampling using a diffusion-based sampler, capturing bond breaking and formation with universal interatomic potentials at near-first-principles accuracy. The approach resolves reactive energy landscapes at a fraction of the wall-clock time of standard sampling methods, advancing diffusion-based sampling towards practical use in molecular sciences. - oai:arXiv.org:2510.11923v2 - physics.chem-ph - cs.LG - stat.ML - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Juno Nam, B\'alint M\'at\'e, Artur P. Toshev, Manasa Kaniselvan, Rafael G\'omez-Bombarelli, Ricky T. Q. Chen, Brandon Wood, Guan-Horng Liu, Benjamin Kurt Miller - - - Fast, Differentiable, GPU-Accelerated Ray Tracing for Multiple Diffraction and Reflection Paths - https://arxiv.org/abs/2510.16172 - arXiv:2510.16172v2 Announce Type: replace-cross -Abstract: We present a fast, differentiable, GPU-accelerated optimization method for ray path tracing in environments containing planar reflectors and straight diffraction edges. Based on Fermat's principle, our approach reformulates the path-finding problem as the minimization of total path length, enabling efficient parallel execution on modern GPU architectures. Unlike existing methods that require separate algorithms for reflections and diffractions, our unified formulation maintains consistent problem dimensions across all interaction sequences, making it particularly suitable for vectorized computation. Through implicit differentiation, we achieve efficient gradient computation without differentiating through solver iterations, significantly outperforming traditional automatic differentiation approaches. Numerical simulations demonstrate convergence rates comparable to specialized Newton methods while providing superior scalability for large-scale applications. The method integrates seamlessly with differentiable programming libraries such as JAX and DrJIT, enabling new possibilities in inverse design and optimization for wireless propagation modeling. The source code is openly available at https://github.com/jeertmans/fpt-jax. - oai:arXiv.org:2510.16172v2 - eess.SP - cs.MS - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - J\'erome Eertmans, Sophie Lequeu, Beno\^it Legat, Laurent Jacques, Claude Oestges - - - Value of Multi-pursuer Single-evader Pursuit-evasion Game with Terminal Cost of Evader's Position: Relaxation of Convexity Condition - https://arxiv.org/abs/2510.27271 - arXiv:2510.27271v2 Announce Type: replace-cross -Abstract: In this study, we consider a multi-pursuer single-evader quantitative pursuit-evasion game with payoff function that includes only the terminal cost. The terminal cost is a function related only to the terminal position of the evader. This problem has been extensively studied in target defense games. Here, we prove that a candidate for the value function generated by geometric method is the viscosity solution of the corresponding Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) Dirichlet problem. Therefore, the value function of the game at each point can be computed by a mathematical program. In our work, the convexity of the terminal cost or the target is not required. The terminal cost only needs to be locally Lipschitz continuous. The cases in which the terminal costs or the targets are not convex are covered. Therefore, our result is more universal than those of previous studies, and the complexity of the proof is improved. We also discuss the optimal strategies in this game and present an intuitive explanation of this value function. - oai:arXiv.org:2510.27271v2 - math.OC - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Weiwen Huang, Li Liang, Ningsheng Xu, Fang Deng - - - Improving Directions in Mixed Integer Bilevel Linear Optimization - https://arxiv.org/abs/2511.03566 - arXiv:2511.03566v3 Announce Type: replace-cross -Abstract: We consider the central role of improving directions in solution methods for mixed integer bilevel linear optimization problems (MIBLPs). Current state-of-the-art methods for solving MIBLPs employ the branch-and-cut framework originally developed for solving mixed integer linear optimization problems. This approach relies on oracles for two kinds of subproblems: those for checking whether a candidate pair of leader's and follower's decisions is bilevel feasible, and those required for generating valid inequalities. Typically, these two types of oracles are managed separately, but in this work, we explore their close connection and propose a solution framework based on solving a single type of subproblem: determining whether there exists a so-called improving feasible direction for the follower's problem. Solution of this subproblem yields information that can be used both to check feasibility and to generate strong valid inequalities. Building on prior works, we expose the foundational role of improving directions in enforcing the follower's optimality condition and extend a previously known hierarchy of optimality-based relaxations to the mixed-integer setting, showing that the associated relaxed feasible regions coincide exactly with the closure associated with intersection cuts derived from improving directions. Numerical results with an implementation using a modified version of the open source solver MibS show that this approach can yield practical improvements. - oai:arXiv.org:2511.03566v3 - math.OC - cs.MS - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Federico Battista, Ted K. Ralphs - - - Orchestrating Rewards in the Era of Intelligence-Driven Commerce - https://arxiv.org/abs/2512.00738 - arXiv:2512.00738v2 Announce Type: replace-cross -Abstract: Despite their evolution from early copper-token schemes to sophisticated digital solutions, loyalty programs remain predominantly closed ecosystems, with brands retaining full control over all components. Coalition loyalty programs emerged to enable cross-brand interoperability, but approximately 60\% fail within 10 years in spite of theoretical advantages rooted in network economics. This paper demonstrates that coalition failures stem from fundamental architectural limitations in centralized operator models rather than operational deficiencies, and argues further that neither closed nor coalition systems can scale in intelligence-driven paradigms where AI agents mediate commerce and demand trustless, protocol-based coordination that existing architectures cannot provide. We propose a hybrid framework where brands maintain sovereign control over their programs while enabling cross-brand interoperability through trustless exchange mechanisms. Our framework preserves closed system advantages while enabling open system benefits without the structural problems that doom traditional coalitions. We derive a mathematical pricing model accounting for empirically-validated market factors while enabling fair value exchange across interoperable reward systems. - oai:arXiv.org:2512.00738v2 - econ.GN - cs.AI - cs.CY - q-fin.EC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Paul Osemudiame Oamen, Robert Wesley, Pius Onobhayedo - - - Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming - https://arxiv.org/abs/2512.15735 - arXiv:2512.15735v4 Announce Type: replace-cross -Abstract: This work proposes a unified control architecture that couples a Reinforcement Learning (RL)-driven controller with a disturbance-rejection Extended State Observer (ESO), complemented by an Event-Triggered Mechanism (ETM) to limit unnecessary computations. The ESO is utilized to estimate the system states and the lumped disturbance in real time, forming the foundation for effective disturbance compensation. To obtain near-optimal behavior without an accurate system description, a value-iteration-based Adaptive Dynamic Programming (ADP) method is adopted for policy approximation. The inclusion of the ETM ensures that parameter updates of the learning module are executed only when the state deviation surpasses a predefined bound, thereby preventing excessive learning activity and substantially reducing computational load. A Lyapunov-oriented analysis is used to characterize the stability properties of the resulting closed-loop system. Numerical experiments further confirm that the developed approach maintains strong control performance and disturbance tolerance, while achieving a significant reduction in sampling and processing effort compared with standard time-triggered ADP schemes. - oai:arXiv.org:2512.15735v4 - math.OC - cs.AI - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Ningwei Bai, Chi Pui Chan, Qichen Yin, Tengyang Gong, Yunda Yan, Zezhi Tang - - - Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model - https://arxiv.org/abs/2512.16251 - arXiv:2512.16251v3 Announce Type: replace-cross -Abstract: We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), a framework that reconciles the predictive power of deep learning with the structural transparency of traditional finance. By embedding aggregate analyst consensus as a structural "bottleneck", the model treats professional beliefs as a sufficient statistic for the market's high-dimensional information set. We document a striking "interpretability-accuracy amplification effect" for annual horizons, the structural constraint acts as an endogenous regularizer that significantly improves out-of-sample R2 over unconstrained benchmarks. Portfolios sorted on CB-APM forecasts exhibit a strong monotonic return gradient, delivering an annualized Sharpe ratio of 1.44 and robust performance across macroeconomic regimes. Furthermore, pricing diagnostics reveal that the learned consensus captures priced variation only partially spanned by canonical factor models, identifying structured risk heterogeneity that standard linear models systematically miss. Our results suggest that anchoring machine intelligence to human-expert belief formation is not merely a tool for transparency, but a catalyst for uncovering new dimensions of belief-driven risk premiums. - oai:arXiv.org:2512.16251v3 - q-fin.PR - cs.AI - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/publicdomain/zero/1.0/ - 10.2139/ssrn.5165817 - Bong-Gyu Jang, Younwoo Jeong, Changeun Kim - - - On The Hidden Biases of Flow Matching Samplers - https://arxiv.org/abs/2512.16768 - arXiv:2512.16768v2 Announce Type: replace-cross -Abstract: We study the implicit bias of flow matching (FM) samplers via the lens of empirical flow matching. Although population FM may produce gradient-field velocities resembling optimal transport (OT), we show that the empirical FM minimizer is generally not a gradient field, even when each conditional flow is. Consequently, empirical FM is intrinsically not OT-optimal in the Benamou-Brenier sense. In view of this, we analyze the kinetic energy of generated samples. With Gaussian sources, both instantaneous and integrated kinetic energies exhibit exponential concentration, while heavy-tailed sources lead to polynomial tails. These behaviors are governed primarily by the choice of source distribution rather than the data. Overall, these notes provide a concise mathematical account of the structural and energetic biases arising in empirical FM. - oai:arXiv.org:2512.16768v2 - stat.ML - cs.LG - math.PR - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Soon Hoe Lim - - - On the Complexity of Bipartite Degree Realizability - https://arxiv.org/abs/2512.17709 - arXiv:2512.17709v2 Announce Type: replace-cross -Abstract: We study the \emph{Bipartite Degree Realization} (BDR) problem: given a graphic degree sequence $D$, decide whether it admits a realization as a bipartite graph. While bipartite realizability for a fixed vertex partition can be decided in polynomial time via the Gale--Ryser theorem, the computational complexity of BDR without a prescribed partition remains unresolved. We address this question through a parameterized analysis. - For constants $0 \le c_1 \le c_2 \le 1$, we define $\mathrm{BDR}_{c_1,c_2}$ as the restriction of BDR to degree sequences of length $n$ whose degrees lie in the interval $[c_1 n, c_2 n]$. Our main result shows that $\mathrm{BDR}_{c_1,c_2}$ is solvable in polynomial time whenever $0 \le c_1 \le c_2 \le \frac{\sqrt{c_1(c_1+4)}-c_1}{2}$, as well as for all $c_1 > \tfrac12$. The proof relies on a reduction to extremal \emph{least balanced degree sequences} and a detailed verification of the critical Gale--Ryser inequalities, combined with a bounded subset-sum formulation. - We further show that, assuming the NP-completeness of unrestricted BDR, the problem $\mathrm{BDR}_{c_1,c_2}$ remains NP-complete for all $0 < c_2 < \frac{1}{2}$ and $c_1 < 1 - c_2 - \sqrt{1-2c_2}$. % This establishes a sharp conditional boundary between tractable and intractable parameter regimes. Our results clarify the algorithmic landscape of bipartite degree realization and contribute to the broader study of potentially bipartite graphic degree sequences. - oai:arXiv.org:2512.17709v2 - math.CO - cs.CC - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Istv\'an Mikl\'os - - - GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics - https://arxiv.org/abs/2512.19936 - arXiv:2512.19936v2 Announce Type: replace-cross -Abstract: We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are measured, our approach infers unknown closure terms in the governing equations as neural networks. The target to be discovered is the constitutive relations only, while the temporal derivative, convective transport terms, and pressure-gradient term in the governing equations are prescribed. The formulation is rooted in a variational principle from non-equilibrium thermodynamics, where the dynamics is defined by a free-energy functional and a dissipation functional. The unknown constitutive terms arise as functional derivatives of these functionals with respect to the state variables. To enable a flexible and structured model discovery, the free-energy and dissipation functionals are parameterized using neural networks, while their functional derivatives are obtained via automatic differentiation. This construction enforces thermodynamic consistency by design, guaranteeing monotonic decay of the total free energy and non-negative entropy production. The resulting method, termed GIMLET (Generalizable and Interpretable Model Learning through Embedded Thermodynamics), avoids reliance on a predefined library of candidate functions, unlike sparse regression or symbolic identification approaches. The learned models are generalizable in that functionals identified from one dataset can be transferred to distinct datasets governed by the same underlying equations. Moreover, the inferred free-energy and dissipation functions provide direct physical interpretability of the learned dynamics. The framework is demonstrated on several benchmark systems, including the viscous Burgers equation, the Kuramoto--Sivashinsky equation, and the incompressible Navier--Stokes equations for both Newtonian and non-Newtonian fluids. - oai:arXiv.org:2512.19936v2 - physics.flu-dyn - cs.LG - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-nc-sa/4.0/ - Suguru Shiratori, Elham Kiyani, Khemraj Shukla, George Em Karniadakis - - - Contingency Model-based Control (CMC) for Communicationless Cooperative Collision Avoidance in Robot Swarms - https://arxiv.org/abs/2512.20391 - arXiv:2512.20391v3 Announce Type: replace-cross -Abstract: Cooperative collision avoidance between robots, or `agents,' in swarm operations remains an open challenge. Assuming a decentralized architecture, each agent is responsible for making its own decisions and choosing its control actions. Most existing approaches rely on a (wireless) communication network between (some of) the agents. In reality, however, communication is brittle. It may be affected by latency, further delays and packet losses, and transmission faults. Moreover, it is subject to adversarial attacks, such as jamming or spoofing. This paper proposes Contingency Model-based Control (CMC), a decentralized cooperative approach that does not rely on communication. Instead, the control algorithm is based on consensual rules that are designed for all agents offline, similar to traffic rules. For CMC, this includes the definition of a contingency trajectory for each robot, and perpendicular bisecting planes as collision avoidance constraints. The setup permits a full guarantee of recursive feasibility and collision avoidance between all swarm members in closed-loop operation. CMC naturally satisfies the plug & play paradigm, i.e., new robots may enter the swarm dynamically. The effectiveness of the CMC regime is demonstrated in two numerical examples, showing that the collision avoidance guarantee is intact and the robot swarm operates smoothly in a constrained environment. - oai:arXiv.org:2512.20391v3 - math.OC - cs.RO - cs.SY - eess.SY - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by/4.0/ - Georg Schildbach - - - Poincar\'e Duality and Multiplicative Structures on Quantum Codes - https://arxiv.org/abs/2512.21922 - arXiv:2512.21922v2 Announce Type: replace-cross -Abstract: Quantum LDPC codes have attracted intense interest due to their advantageous properties for realizing efficient fault-tolerant quantum computing. In particular, sheaf codes represent a novel framework that encompasses all well-known good qLDPC codes with profound underlying mathematics. In this work, we generalize Poincar\'e duality from manifolds to both classical and quantum codes defined via sheaf theory on $t$-dimensional cell complexes. Viewing important code properties including the encoding rate, code distance, local testability soundness, and efficient decoders as parameters of the underlying (co)chain complexes, we rigorously prove a duality relationship between the $i$-th chain and the $(t-i)$-th cochain of sheaf codes. - We further build multiplicative structures such as cup and cap products on sheaved chain complexes, inspired by the standard notions of multiplicative structures and Poincar\'e duality on manifolds. This immediately leads to an explicit isomorphism between (co)homology groups of sheaf codes via a cap product. As an application, we obtain transversal disjoint logical $\mathrm{C}Z$ gates with $k_{\mathrm{C}Z}=\Theta(n)$ on families of good qLDPC and almost-good quantum locally testable codes. Moreover, we provide multiple new methods to construct transversal circuits composed of $\mathrm{C}\mathrm{C}Z$ gates as well as for higher order controlled-$Z$ that are provably logical operations on the code space. We conjecture that they generate nontrivial logical actions, pointing towards fault-tolerant non-Clifford gates on nearly optimal qLDPC sheaf codes. Mathematically, our results are built on establishing the equivalence between sheaf cohomology in the derived-functor sense, \v{C}ech cohomology, and the cohomology of sheaf codes, thereby introducing new mathematical tools into quantum coding theory. - oai:arXiv.org:2512.21922v2 - quant-ph - cs.CC - cs.IT - math-ph - math.IT - math.MP - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Yiming Li, Zimu Li, Zi-Wen Liu, Quynh T. Nguyen - - - LogosQ: A High-Performance and Type-Safe Quantum Computing Library in Rust - https://arxiv.org/abs/2512.23183 - arXiv:2512.23183v2 Announce Type: replace-cross -Abstract: Developing robust and high performance quantum software is challenging due to the dynamic nature of existing Python-based frameworks, which often suffer from runtime errors and scalability bottlenecks. In this work, we present LogosQ, a high performance backend agnostic quantum computing library implemented in Rust that enforces correctness through compile time type safety. Unlike existing tools, LogosQ leverages Rust static analysis to eliminate entire classes of runtime errors, particularly in parameter-shift rule gradient computations for variational algorithms. We introduce novel optimization techniques, including direct state-vector manipulation, adaptive parallel processing, and an FFT optimized Quantum Fourier Transform, which collectively deliver speedups of up to 900 times for state preparation (QFT) and 2 to 5 times for variational workloads over Python frameworks (PennyLane, Qiskit), 6 to 22 times over Julia implementations (Yao), and competitive performance with Q sharp. Beyond performance, we validate numerical stability through variational quantum eigensolver (VQE) experiments on molecular hydrogen and XYZ Heisenberg models, achieving chemical accuracy even in edge cases where other libraries fail. By combining the safety of systems programming with advanced circuit optimization, LogosQ establishes a new standard for reliable and efficient quantum simulation. - oai:arXiv.org:2512.23183v2 - quant-ph - cs.SE - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://creativecommons.org/licenses/by-sa/4.0/ - Shiwen An, Jiayi Wang, Konstantinos Slavakis - - - A space-time extension of a conservative two-fluid cut-cell method for moving diffusion problems - https://arxiv.org/abs/2512.23358 - arXiv:2512.23358v2 Announce Type: replace-cross -Abstract: We present a space-time extension of a conservative Cartesian cut-cell finite-volume method for two-phase diffusion problems with prescribed interface motion. The formulation follows a two-fluid approach: one scalar field is solved in each phase with discontinuous material properties, coupled by sharp interface conditions enforcing flux continuity and jump laws. To handle moving boundaries on a fixed Cartesian grid, the discrete balance is written over phase-restricted space-time control volumes, whose geometric moments (swept volumes and apertures) are used as weights in the finite-volume operators. This construction naturally accounts for the creation and destruction of cut cells (fresh/dead-cell events) and yields strict discrete conservation. The resulting scheme retains the algebraic structure of the static cut-cell formulation while incorporating motion through local geometric weights and interface coupling operators. A series of verification and validation tests in two and three dimensions demonstrate super-linear accuracy in space, robust behavior under repeated topology changes and conservation across strong coefficient jumps and moving interfaces. The proposed space-time cut-cell framework provides a conservative building block for multiphase transport in evolving geometries and a foundation for future free-boundary extensions such as Stefan-type phase change. - oai:arXiv.org:2512.23358v2 - physics.comp-ph - cs.NA - math.NA - Thu, 01 Jan 2026 00:00:00 -0500 - replace-cross - http://arxiv.org/licenses/nonexclusive-distrib/1.0/ - Louis Libat, Can Sel\c{c}uk, Eric Ch\'enier, Vincent Le Chenadec -