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" @@ -7,12 +7,10141 @@ http://www.rssboard.org/rss-specification en-us - Sun, 04 Jan 2026 05:00:00 +0000 + Wed, 07 Jan 2026 05:00:29 +0000 rss-help@arxiv.org - Sun, 04 Jan 2026 00:00:00 -0500 + Wed, 07 Jan 2026 00:00:00 -0500 - Sunday Saturday + Sunday + + GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model + https://arxiv.org/abs/2601.02361 + arXiv:2601.02361v1 Announce Type: new +Abstract: The ranking stage serves as the central optimization and allocation hub in advertising systems, governing economic value distribution through eCPM and orchestrating the user-centric blending of organic and advertising content. Prevailing ranking models often rely on fragmented modules and hand-crafted features, limiting their ability to interpret complex user intent. This challenge is further amplified in location-based services such as food delivery, where user decisions are shaped by dynamic spatial, temporal, and individual contexts. To address these limitations, we propose a novel generative framework that reframes ranking as a context comprehension task, modeling heterogeneous signals in a unified architecture. Our architecture consists of two core components: the Generative Contextual Encoder (GCE) and the Generative Contextual Fusion (GCF). The GCE comprises three specialized modules: a Personalized Context Enhancer (PCE) for user-specific modeling, a Collective Context Enhancer (CCE) for group-level patterns, and a Dynamic Context Enhancer (DCE) for real-time situational adaptation. The GCF module then seamlessly integrates these contextual representations through low-rank adaptation. Extensive experiments confirm that our method achieves significant gains in critical business metrics, including click-through rate and platform revenue. We have successfully deployed our method on a large-scale food delivery advertising platform, demonstrating its substantial practical impact. This work pioneers a new perspective on generative recommendation and highlights its practical potential in industrial advertising systems. + oai:arXiv.org:2601.02361v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ziheng Ni, Congcong Liu, Cai Shang, Yiming Sun, Junjie Li, Zhiwei Fang, Guangpeng Chen, Jian Li, Zehua Zhang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao + + + The Impact of LLM-Generated Reviews on Recommender Systems: Textual Shifts, Performance Effects, and Strategic Platform Control + https://arxiv.org/abs/2601.02362 + arXiv:2601.02362v1 Announce Type: new +Abstract: The rise of generative AI technologies is reshaping content-based recommender systems (RSes), which increasingly encounter AI-generated content alongside human-authored content. This study examines how the introduction of AI-generated reviews influences RS performance and business outcomes. We analyze two distinct pathways through which AI content can enter RSes: user-centric, in which individuals use AI tools to refine their reviews, and platform-centric, in which platforms generate synthetic reviews directly from structured metadata. Using a large-scale dataset of hotel reviews from TripAdvisor, we generate synthetic reviews using LLMs and evaluate their impact across the training and deployment phases of RSes. We find that AI-generated reviews differ systematically from human-authored reviews across multiple textual dimensions. Although both user- and platform-centric AI reviews enhance RS performance relative to models without textual data, models trained on human reviews consistently achieve superior performance, underscoring the quality of authentic human data. Human-trained models generalize robustly to AI content, whereas AI-trained models underperform on both content types. Furthermore, tone-based framing strategies (encouraging, constructive, or critical) substantially enhance platform-generated review effectiveness. Our findings highlight the strategic importance of platform control in governing the generation and integration of AI-generated reviews, ensuring that synthetic content complements recommendation robustness and sustainable business value. + oai:arXiv.org:2601.02362v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Itzhak Ziv, Moshe Unger, Hilah Geva + + + Acceptance of cybernetic avatars for capability enhancement: a large-scale survey + https://arxiv.org/abs/2601.02363 + arXiv:2601.02363v1 Announce Type: new +Abstract: Avatar embodiment experiences have the potential to enhance human capabilities by extending human senses, body, and mind. This study investigates social acceptance of robotic and virtual avatars as enablers of capability enhancement in six domains: identity exploration, well-being and behavioral transformation, expanded travel capabilities, expanded bodily and sensory abilities, cognitive augmentation, and immortality. We conducted a large-scale survey (n = 1001) in Dubai to explore acceptance of sixteen capability enhancement scenarios within these domains. The highest levels of agreement were observed for multilingual communication (77.5%) and learning capabilities (68.7%), followed by assisting individuals with reduced mobility (64.5%) and behavioral transformation (59.5%). Scenarios involving immortality through consciousness transfer received the least support (34.9%). These findings contribute to a deeper understanding of public attitudes toward avatar-based human enhancement and offer practical guidance for the responsible design, development, and integration of cybernetic avatars in the society, ensuring their societal acceptance and fostering a harmonious human-avatar coexistence. + oai:arXiv.org:2601.02363v1 + cs.HC + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Laura Aymerich-Franch, Tarek Taha, Hiroko Kamide, Takahiro Miyashita, Hiroshi Ishiguro, Paolo Dario + + + Towards Trustworthy LLM-Based Recommendation via Rationale Integration + https://arxiv.org/abs/2601.02364 + arXiv:2601.02364v1 Announce Type: new +Abstract: Traditional recommender systems (RS) have been primarily optimized for accuracy and short-term engagement, often overlooking transparency and trustworthiness. Recently, platforms such as Amazon and Instagram have begun providing recommendation rationales to users, acknowledging their critical role in fostering trust and enhancing engagement; however, most existing systems still treat them as post-hoc artifacts. We propose an LLM-based recommender (LLM-Rec) that not only predicts items but also generates logically grounded rationales. Our approach leverages a self-annotated rationale dataset and instruction tuning in a rationale-first format, where the model generates an explanation before outputting the recommended item. By adopting this strategy and representing rationales in a chain-of-thought (CoT) style, LLM-Rec strengthens both interpretability and recommendation performance. Experiments on the Fashion and Scientific domains of the Amazon Review dataset demonstrate significant improvements over well-established baselines. To encourage reproducibility and future research, we publicly release a rationale-augmented recommendation dataset containing user histories, rationales, and recommended items. + oai:arXiv.org:2601.02364v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chung Park, Taesan Kim, Hyeongjun Yun, Dongjoon Hong, Junui Hong, Kijung Park, MinCheol Cho, Mira Myong, Jihoon Oh, Min sung Choi + + + FUSE : Failure-aware Usage of Subagent Evidence for MultiModal Search and Recommendation + https://arxiv.org/abs/2601.02365 + arXiv:2601.02365v1 Announce Type: new +Abstract: Multimodal creative assistants decompose user goals and route tasks to subagents for layout, styling, retrieval, and generation. Retrieval quality is pivotal, yet failures can arise at several stages: understanding user intent, choosing content types, finding candidates (recall), or ranking results. Meanwhile, sending and processing images is costly, making naive multimodal approaches impractical. We present FUSE: Failure-aware Usage of Subagent Evidence for MultiModal Search and Recommendation. FUSE replaces most raw-image prompting with a compact Grounded Design Representation (GDR): a selection aware JSON of canvas elements (image, text, shape, icon, video, logo), structure, styles, salient colors, and user selection provided by the Planner team. FUSE implements seven context budgeting strategies: comprehensive baseline prompting, context compression, chain-of-thought reasoning, mini-shot optimization, retrieval-augmented context, two-stage processing, and zero-shot minimalism. Finally, a pipeline attribution layer monitors system performance by converting subagent signals into simple checks: intent alignment, content-type/routing sanity, recall health (e.g., zero-hit and top-match strength), and ranking displacement analysis. We evaluate the seven context budgeting variants across 788 evaluation queries from diverse users and design templates (refer Figure 3). Our systematic evaluation reveals that Context Compression achieves optimal performance across all pipeline stages, with 93.3% intent accuracy, 86.8% routing success(with fallbacks), 99.4% recall, and 88.5% NDCG@5. This approach demonstrates that strategic context summarization outperforms both comprehensive and minimal contextualization strategies. + oai:arXiv.org:2601.02365v1 + cs.IR + cs.AI + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Tushar Vatsa, Vibha Belavadi, Priya Shanmugasundaram, Suhas Suresha, Dewang Sultania + + + TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer + https://arxiv.org/abs/2601.02366 + arXiv:2601.02366v1 Announce Type: new +Abstract: Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information. However, this ID-based paradigm faces challenges in transferring to a new domain, making it hard to build a pre-trained graph recommendation model. This phenomenon primarily stems from two inherent challenges: (1) the non-transferability of ID embeddings due to isolated domain-specific ID spaces, and (2) structural incompatibility between heterogeneous interaction graphs across domains. + To address these issues, we propose TextBridgeGNN, a pre-training and fine-tuning framework that can effectively transfer knowledge from a pre-trained GNN to downstream tasks. We believe the key lies in how to build the relationship between domains. Specifically, TextBridgeGNN uses text as a semantic bridge to connect domains through multi-level graph propagation. During the pre-training stage, textual information is utilized to break the data islands formed by multiple domains, and hierarchical GNNs are designed to learn both domain-specific and domain-global knowledge with text features, ensuring the retention of collaborative signals and the enhancement of semantics. During the fine-tuning stage, a similarity transfer mechanism is proposed. This mechanism initializes ID embeddings in the target domain by transferring from semantically related nodes, successfully transferring the ID embeddings and graph pattern. + Experiments demonstrate that TextBridgeGNN outperforms existing methods in cross-domain, multi-domain, and training-free settings, highlighting its ability to integrate Pre-trained Language Model (PLM)-driven semantics with graph-based collaborative filtering without costly language model fine-tuning or real-time inference overhead. + oai:arXiv.org:2601.02366v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yiwen Chen, Yiqing Wu, Huishi Luo, Fuzhen Zhuang, Deqing Wang + + + Cross-Platform Digital Discourse Analysis of the Israel-Hamas Conflict: Sentiment, Topics, and Event Dynamics + https://arxiv.org/abs/2601.02367 + arXiv:2601.02367v1 Announce Type: new +Abstract: The Israeli-Palestinian conflict remains one of the most polarizing geopolitical issues, with the October 2023 escalation intensifying online debate. Social media platforms, particularly Telegram, have become central to real-time news sharing, advocacy, and propaganda. In this study, we analyze Telegram, Twitter/X, and Reddit to examine how conflict narratives are produced, amplified, and contested across different digital spheres. Building on our previous work on Telegram discourse during the 2023 escalation, we extend the analysis longitudinally and cross-platform using an updated dataset spanning October 2023 to mid-2025. The corpus includes more than 187,000 Telegram messages, 2.1 million Reddit comments, and curated Twitter/X posts. We combine Latent Dirichlet Allocation (LDA), BERTopic, and transformer-based sentiment and emotion models to identify dominant themes, emotional dynamics, and propaganda strategies. Telegram channels provide unfiltered, high-intensity documentation of events; Twitter/X amplifies frames to global audiences; and Reddit hosts more reflective and deliberative discussions. Our findings reveal persistent negative sentiment, strong coupling between humanitarian framing and solidarity expressions, and platform-specific pathways for the diffusion of pro-Palestinian and pro-Israeli narratives. This paper offers three contributions: (1) a multi-platform, FAIR-compliant dataset on the Israel-Hamas war, (2) an integrated pipeline combining topic modeling, sentiment and emotion analysis, and spam filtering for large-scale conflict discourse, and (3) empirical insights into how platform affordances and affective publics shape the evolution of digital conflict communication. + oai:arXiv.org:2601.02367v1 + cs.CY + cs.AI + cs.CL + cs.IR + cs.LG + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Despoina Antonakaki, Sotiris Ioannidis + + + Distillation-based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendation + https://arxiv.org/abs/2601.02368 + arXiv:2601.02368v1 Announce Type: new +Abstract: Multi-scenario recommendation is pivotal for optimizing user experience across diverse contexts. While Multi-gate Mixture-of-Experts (MMOE) thrives in ranking, its transfer to the matching stage is hindered by the blind optimization inherent to independent two-tower architectures and the parameter dominance of head scenarios. To address these structural and distributional bottlenecks, we propose Distillation-based Scenario-Adaptive Mixture-of-Experts (DSMOE). Specially, we devise a Scenario-Adaptive Projection (SAP) module to generate lightweight, context-specific parameters, effectively preventing expert collapse in long-tail scenarios. Concurrently, we introduce a cross-architecture knowledge distillation framework, where an interaction-aware teacher guides the two-tower student to capture complex matching patterns. Extensive experiments on real-world datasets demonstrate DSMOE's superiority, particularly in significantly improving retrieval quality for under-represented, data-sparse scenarios. + oai:arXiv.org:2601.02368v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ruibing Wang, Shuhan Guo, Haotong Du, Quanming Yao + + + Fair Distribution of Digital Payments: Balancing Transaction Flows for Regulatory Compliance + https://arxiv.org/abs/2601.02369 + arXiv:2601.02369v1 Announce Type: new +Abstract: The concentration of digital payment transactions in just two UPI apps like PhonePe and Google Pay has raised concerns of duopoly in India s digital financial ecosystem. To address this, the National Payments Corporation of India (NPCI) has mandated that no single UPI app should exceed 30 percent of total transaction volume. Enforcing this cap, however, poses a significant computational challenge: how to redistribute user transactions across apps without causing widespread user inconvenience while maintaining capacity limits? In this paper, we formalize this problem as the Minimum Edge Activation Flow (MEAF) problem on a bipartite network of users and apps, where activating an edge corresponds to a new app installation. The objective is to ensure a feasible flow respecting app capacities while minimizing additional activations. We further prove that Minimum Edge Activation Flow is NP-Complete. To address the computational challenge, we propose scalable heuristics, named Decoupled Two-Stage Allocation Strategy (DTAS), that exploit flow structure and capacity reuse. Experiments on large semi-synthetic transaction network data show that DTAS finds solutions close to the optimal ILP within seconds, offering a fast and practical way to enforce transaction caps fairly and efficiently. + oai:arXiv.org:2601.02369v1 + cs.NI + cs.CY + cs.SI + econ.GN + q-fin.EC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ashlesha Hota, Shashwat Kumar, Daman Deep Singh, Abolfazl Asudeh, Palash Dey, Abhijnan Chakraborty + + + LLM-as-evaluator in Strategy Research: A Normative, Variance-Aware Protocol + https://arxiv.org/abs/2601.02370 + arXiv:2601.02370v1 Announce Type: new +Abstract: Large language models (LLMs) are becoming essential tools for strategy scholars who need to evaluate text corpora at scale. This paper provides a systematic analysis of the reliability of LLM-as-evaluator in strategy research. After classifying the typical ways in which LLMs can be deployed for evaluation purposes in strategy research, we draw on the specialised AI literature to analyse their properties as measurement instruments. Our empirical analysis reveals substantial instability in LLMs' evaluation output, stemming from multiple factors: the specific phrasing of prompts, the context provided, sampling procedures, extraction methods, and disagreements across different models. We quantify these effects and demonstrate how this unreliability can compromise the validity of research inferences drawn from LLM-generated evaluations. To address these challenges, we develop a comprehensive protocol that is variance-aware, normative, and auditable. We provide practical guidance for flexible implementation of this protocol, including approaches to preregistration and transparent reporting. By establishing these methodological standards, we aim to elevate LLM-based evaluation of business text corpora from its current ad hoc status to a rigorous, actionable, and auditable measurement approach suitable for scholarly research. + oai:arXiv.org:2601.02370v1 + cs.CY + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Arnaldo Camuffo, Alfonso Gambardella, Saeid Kazemi, Jakub Malachowski, Abhinav Pandey + + + Permission Manifests for Web Agents + https://arxiv.org/abs/2601.02371 + arXiv:2601.02371v1 Announce Type: new +Abstract: The rise of Large Language Model (LLM)-based web agents represents a significant shift in automated interactions with the web. Unlike traditional crawlers that follow simple conventions, such as robots.txt, modern agents engage with websites in sophisticated ways: navigating complex interfaces, extracting structured information, and completing end-to-end tasks. Existing governance mechanisms were not designed for these capabilities. Without a way to specify what interactions are and are not allowed, website owners increasingly rely on blanket blocking and CAPTCHAs, which undermine beneficial applications such as efficient automation, convenient use of e-commerce services, and accessibility tools. We introduce agent-permissions.json, a robots.txt-style lightweight manifest where websites specify allowed interactions, complemented by API references where available. This framework provides a low-friction coordination mechanism: website owners only need to write a simple JSON file, while agents can easily parse and automatically implement the manifest's provisions. Website owners can then focus on blocking non-compliant agents, rather than agents as a whole. By extending the spirit of robots.txt to the era of LLM-mediated interaction, and complementing data use initiatives such as AIPref, the manifest establishes a compliance framework that enables beneficial agent interactions while respecting site owners' preferences. + oai:arXiv.org:2601.02371v1 + cs.CY + cs.AI + cs.MA + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Samuele Marro, Alan Chan, Xinxing Ren, Lewis Hammond, Jesse Wright, Gurjyot Wanga, Tiziano Piccardi, Nuno Campos, Tobin South, Jialin Yu, Alex Pentland, Philip Torr, Jiaxin Pei + + + Improving News Recommendations through Hybrid Sentiment Modelling and Reinforcement Learning + https://arxiv.org/abs/2601.02372 + arXiv:2601.02372v1 Announce Type: new +Abstract: News recommendation systems rely on automated sentiment analysis to personalise content and enhance user engagement. Conventional approaches often struggle with ambiguity, lexicon inconsistencies, and limited contextual understanding, particularly in multi-source news environments. Existing models typically treat sentiment as a secondary feature, reducing their ability to adapt to users' affective preferences. To address these limitations, this study develops an adaptive, sentiment-aware news recommendation framework by integrating hybrid sentiment analysis with reinforcement learning. Using the BBC News dataset, a hybrid sentiment model combines VADER, AFINN, TextBlob, and SentiWordNet scores to generate robust article-level sentiment estimates. Articles are categorised as positive, negative, or neutral, and these sentiment states are embedded within a Q-learning architecture to guide the agent in learning optimal recommendation policies. The proposed system effectively identifies and recommends articles with aligned emotional profiles while continuously improving personalisation through iterative Q-learning updates. The results demonstrate that coupling hybrid sentiment modelling with reinforcement learning provides a feasible, interpretable, and adaptive approach for user-centred news recommendation. + oai:arXiv.org:2601.02372v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Eunice Kingenga, Mike Wa Nkongolo + + + A Deep-SIC Channel Estimator Scheme in NOMA Network + https://arxiv.org/abs/2601.02373 + arXiv:2601.02373v1 Announce Type: new +Abstract: In 5G and next-generation mobile ad-hoc networks, reliable handover is a key requirement, which guarantees continuity in connectivity, especially for mobile users and in high-density scenarios. However, conventional handover triggers based on instantaneous channel measurements are prone to failures and the ping-pong effect due to outdated or inaccurate channel state information. To address this, we introduce Deep-SIC, a knowledge-based channel prediction model that employs a Transformer-based approach to predict channel quality and optimise handover decisions. Deep-SIC is a unique model that utilises Partially Decoded Data (PDD), a byproduct of successive interference cancellation (SIC) in NOMA, as a feedback signal to improve its predictions continually. This special purpose enables learners to learn quickly and stabilise their learning. Our model learns 68\% faster than existing state-of-the-art algorithms, such as Graph-NOMA, while offering verifiable guarantees of stability and resilience to user mobility (Theorem~2). When simulated at the system level, it can be shown that our strategy can substantially enhance network performance: the handover failure rate can be reduced by up to 40\%, and the ping-pong effect can be mitigated, especially at vehicular speeds (e.g., 60 km/h). Moreover, Deep-SIC has a 20\% smaller normalised root mean square error (NRMSE) in low-SNR situations than state-of-the-art algorithms with linear computational complexity, $O(K)$. This work has introduced a new paradigm for robust and predictive mobility management in dynamic wireless networks. + oai:arXiv.org:2601.02373v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sumita Majhi, Kaushal Shelke, Pinaki Mitra + + + A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models + https://arxiv.org/abs/2601.02374 + arXiv:2601.02374v1 Announce Type: new +Abstract: Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user. + oai:arXiv.org:2601.02374v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Melissa Tessa, Diderot D. Cidjeu, Rachele Carli, Sarah Abchiche, Ahmad Aldarwishd, Igor Tchappi, Amro Najjar + + + LeafTutor: An AI Agent for Programming Assignment Tutoring + https://arxiv.org/abs/2601.02375 + arXiv:2601.02375v1 Announce Type: new +Abstract: High enrollment in STEM-related degree programs has created increasing demand for scalable tutoring support, as universities experience a shortage of qualified instructors and teaching assistants (TAs). To address this challenge, LeafTutor, an AI tutoring agent powered by large language models (LLMs), was developed to provide step-by-step guidance for students. LeafTutor was evaluated through real programming assignments. The results indicate that the system can deliver step-by-step programming guidance comparable to human tutors. This work demonstrates the potential of LLM-driven tutoring solutions to enhance and personalize learning in STEM education. If any reader is interested in collaboration with our team to improve or test LeafTutor, please contact Pu Tian (pu.tian@stockton.edu) or Yalong Wu (wuy@uhcl.edu). + oai:arXiv.org:2601.02375v1 + cs.CY + cs.AI + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Madison Bochard, Tim Conser, Alyssa Duran, Lazaro Martull, Pu Tian, Yalong Wu + + + A Secure Edge Gateway Architecture for Wi-Fi-Enabled IoT + https://arxiv.org/abs/2601.02376 + arXiv:2601.02376v1 Announce Type: new +Abstract: This paper presents a Secure Edge Gateway Architecture for Wi-Fi-Enabled IoT designed to strengthen local network protection without altering existing infrastructure. The proposed gateway acts as an intermediate control point between Wi-Fi access points and the core network, monitoring traffic, isolating untrusted devices, and preventing common wireless attacks such as spoofing, deauthentication, and unauthorized access. The design focuses on adaptive traffic filtering and lightweight policy enforcement instead of complex analytical models, making it suitable for medium-sized network environments. The prototype gateway was deployed in a real office with around 70 total devices, including 28 IoT units such as sensors, cameras, and smart controllers. Over ten days of continuous operation, the system reduced successful spoofing incidents by 87% and improved recovery time after deauthentication by 42%, while increasing network latency by only 3.1% and reducing throughput by less than 4% compared to a baseline WPA3 configuration. These results confirm that implementing security functions at the edge layer can significantly improve the resilience of Wi-Fi-enabled IoT environments without introducing noticeable overhead or requiring specialized hardware. + oai:arXiv.org:2601.02376v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Daniyal Ganiuly, Nurzhau Bolatbek, Assel Smaiyl + + + Trust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges + https://arxiv.org/abs/2601.02377 + arXiv:2601.02377v1 Announce Type: new +Abstract: The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such paradigm shift introduces critical security vulnerabilities stemming from the ''embodiment gap'', a discord between the LLM's abstract reasoning and the physical, context-dependent nature of robotics. While security for text-based LLMs is an active area of research, existing solutions are often insufficient to address the unique threats for the embodied robotic agents, where malicious outputs manifest not merely as harmful text but as dangerous physical actions. In this work, we present a systematic survey, summarizing the emerging threat landscape and corresponding defense strategies for LLM-controlled robotics. Specifically, we discuss a comprehensive taxonomy of attack vectors, covering topics such as jailbreaking, backdoor attacks, and multi-modal prompt injection. In response, we analyze and categorize a range of defense mechanisms, from formal safety specifications and runtime enforcement to multi-LLM oversight and prompt hardening. Furthermore, we review key datasets and benchmarks used to evaluate the robustness of these embodied systems. By synthesizing current research, this work highlights the urgent need for context-aware security solutions and provides a foundational roadmap for the development of safe, secure, and reliable LLM-controlled robotics. + oai:arXiv.org:2601.02377v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xinyu Huang, Shyam Karthick V B, Taozhao Chen, Mitch Bryson, Thomas Chaffey, Huaming Chen, Kim-Kwang Raymond Choo, Ian R. Manchester + + + Modeling the Mental World for Embodied AI: A Comprehensive Review + https://arxiv.org/abs/2601.02378 + arXiv:2601.02378v1 Announce Type: new +Abstract: As the application of Embodied AI Agents in avatars, wearable devices, and robotic systems continues to deepen, their core research challenges have gradually shifted from physical environment interaction to the accurate understanding of social interactions. Traditional physical world models (PWM) focus on quantifiable physical attributes such as space and motion, failing to meet the needs of social intelligence modeling. In contrast, the Mental World Model (MWM), as a structured representation of humans' internal mental states, has become the critical cognitive foundation for embodied agents to achieve natural human-machine collaboration and dynamic social adaptation. However, current MWM research faces significant bottlenecks: such as fragmented conceptual framework with vague boundaries between MWM and PWM, disjointed reasoning mechanisms for the technical pathways and applicable scenarios of different Theory of Mind (ToM) reasoning paradigms, and detachment between evaluation and practice. + To address these issues, this review systematically synthesizes over 100 authoritative studies to provide a comprehensive overview of MWM research for embodied AI. Its core contributions are threefold: First, it constructs a complete theoretical framework for MWM for the first time. Specifically, it distinguishes the essential differences between MWM and PWMs. Second, it systematically defines the key components of MWM through two paradigms for mental element representation. Third, it comprehensively analyzes two core ToM reasoning paradigms with 19 ToM methods. Finally, it also clarifies the integration trend of neuro-symbolic hybrid architectures, and synthesizes 26 ToM evaluation benchmarks. This work aims to promote the integration of embodied agents into human society and advance the in-depth development of human-machine collaborative interaction. + oai:arXiv.org:2601.02378v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Biyuan Liu, Daigang Xu, Lei Jiang, Wenjun Guo, Ping Chen + + + Movement Primitives in Robotics: A Comprehensive Survey + https://arxiv.org/abs/2601.02379 + arXiv:2601.02379v1 Announce Type: new +Abstract: Biological systems exhibit a continuous stream of movements, consisting of sequential segments, that allow them to perform complex tasks in a creative and versatile fashion. This observation has led researchers towards identifying elementary building blocks of motion known as movement primitives, which are well-suited for generating motor commands in autonomous systems, such as robots. In this survey, we provide an encyclopedic overview of movement primitive approaches and applications in chronological order. Concretely, we present movement primitive frameworks as a way of representing robotic control trajectories acquired through human demonstrations. Within the area of robotics, movement primitives can encode basic motions at the trajectory level, such as how a robot would grasp a cup or the sequence of motions necessary to toss a ball. Furthermore, movement primitives have been developed with the desirable analytical properties of a spring-damper system, probabilistic coupling of multiple demonstrations, using neural networks in high-dimensional systems, and more, to address difficult challenges in robotics. Although movement primitives have widespread application to a variety of fields, the goal of this survey is to inform practitioners on the use of these frameworks in the context of robotics. Specifically, we aim to (i) present a systematic review of major movement primitive frameworks and examine their strengths and weaknesses; (ii) highlight applications that have successfully made use of movement primitives; and (iii) examine open questions and discuss practical challenges when applying movement primitives in robotics. + oai:arXiv.org:2601.02379v1 + cs.RO + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nolan B. Gutierrez, William J. Beksi + + + The Refutability Gap: Challenges in Validating Reasoning by Large Language Models + https://arxiv.org/abs/2601.02380 + arXiv:2601.02380v1 Announce Type: new +Abstract: Recent reports claim that Large Language Models (LLMs) have achieved the ability to derive new science and exhibit human-level general intelligence. We argue that such claims are not rigorous scientific claims, as they do not satisfy Popper's refutability principle (often termed falsifiability), which requires that scientific statements be capable of being disproven. We identify several methodological pitfalls in current AI research on reasoning, including the inability to verify the novelty of findings due to opaque and non-searchable training data, the lack of reproducibility caused by continuous model updates, and the omission of human-interaction transcripts, which obscures the true source of scientific discovery. Additionally, the absence of counterfactuals and data on failed attempts creates a selection bias that may exaggerate LLM capabilities. To address these challenges, we propose guidelines for scientific transparency and reproducibility for research on reasoning by LLMs. Establishing such guidelines is crucial for both scientific integrity and the ongoing societal debates regarding fair data usage. + oai:arXiv.org:2601.02380v1 + cs.CY + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Elchanan Mossel + + + TAG-HGT: A Scalable and Cost-Effective Framework for Inductive Cold-Start Academic Recommendation + https://arxiv.org/abs/2601.02381 + arXiv:2601.02381v1 Announce Type: new +Abstract: Inductive cold-start recommendation remains the "Achilles' Heel" of industrial academic platforms, where thousands of new scholars join daily without historical interaction records. While recent Generative Graph Models (e.g., HiGPT, OFA) demonstrate promising semantic capabilities, their prohibitive inference latency (often exceeding 13 minutes per 1,000 requests) and massive computational costs render them practically undeployable for real-time, million-scale applications. To bridge this gap between generative quality and industrial scalability, we propose TAG-HGT, a cost-effective neuro-symbolic framework. Adopting a decoupled "Semantics-First, Structure-Refined" paradigm, TAG-HGT utilizes a frozen Large Language Model (DeepSeek-V3) as an offline semantic factory and distills its knowledge into a lightweight Heterogeneous Graph Transformer (HGT) via Cross-View Contrastive Learning (CVCL). We present a key insight: while LLM semantics provide necessary global recall, structural signals offer the critical local discrimination needed to distinguish valid collaborators from semantically similar but socially unreachable strangers in dense embedding spaces. Validated under a strict Time-Machine Protocol on the massive OpenAlex dataset, TAG-HGT achieves a SOTA System Recall@10 of 91.97%, outperforming structure-only baselines by 20.7%. Most significantly, from an industrial perspective, TAG-HGT reduces inference latency by five orders of magnitude ($4.5 \times 10^{5}\times$) compared to generative baselines (from 780s down to 1.73 ms), and slashes inference costs from $\sim$$1.50 to $<$$0.001 per 1k queries. This 99.9% cost reduction democratizes high-precision academic recommendation. + oai:arXiv.org:2601.02381v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Zhexiang Li + + + How to Discover Knowledge for FutureG: Contextual RAG and LLM Prompting for O-RAN + https://arxiv.org/abs/2601.02382 + arXiv:2601.02382v1 Announce Type: new +Abstract: We present a retrieval-augmented question answering framework for 5G/6G networks, where the Open Radio Access Network (O-RAN) has become central to disaggregated, virtualized, and AI-driven wireless systems. While O-RAN enables multi-vendor interoperability and cloud-native deployments, its fast-changing specifications and interfaces pose major challenges for researchers and practitioners. Manual navigation of these complex documents is labor-intensive and error-prone, slowing system design, integration, and deployment. To address this challenge, we adopt Contextual Retrieval-Augmented Generation (Contextual RAG), a strategy in which candidate answer choices guide document retrieval and chunk-specific context to improve large language model (LLM) performance. This improvement over traditional RAG achieves more targeted and context-aware retrieval, which improves the relevance of documents passed to the LLM, particularly when the query alone lacks sufficient context for accurate grounding. Our framework is designed for dynamic domains where data evolves rapidly and models must be continuously updated or redeployed, all without requiring LLM fine-tuning. We evaluate this framework using the ORANBenchmark-13K dataset, and compare three LLMs, namely, Llama3.2, Qwen2.5-7B, and Qwen3.0-4B, across both Direct Question Answering (Direct Q&A) and Chain-of-Thought (CoT) prompting strategies. We show that Contextual RAG consistently improves accuracy over standard RAG and base prompting, while maintaining competitive runtime and CO2 emissions. These results highlight the potential of Contextual RAG to serve as a scalable and effective solution for domain-specific Q&A in ORAN and broader 5G/6G environments, enabling more accurate interpretation of evolving standards while preserving efficiency and sustainability. + oai:arXiv.org:2601.02382v1 + cs.NI + cs.AI + cs.IR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Nathan Conger, Nathan Scollar, Kemal Davaslioglu, Yalin E. Sagduyu, Sastry Kompella + + + The Future of the AI Summit Series + https://arxiv.org/abs/2601.02383 + arXiv:2601.02383v1 Announce Type: new +Abstract: This policy memo examines the evolution of the international AI Summit series, initiated at Bletchley Park in 2023 and continued through Seoul in 2024 and Paris in 2025, as a forum for cooperation on the governance of advanced artificial intelligence. It analyzes the factors underpinning the series' early successes and assesses challenges related to scope, participation, continuity, and institutional design. Drawing on comparisons with existing international governance models, the memo evaluates options for hosting arrangements, secretariat formats, participant selection, agenda setting, and meeting frequency. It proposes a set of design recommendations aimed at preserving the series' focus on advanced AI governance while balancing inclusivity, effectiveness, and long-term sustainability. + oai:arXiv.org:2601.02383v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Lucia Velasco, Charles Martinet, Henry de Zoete, Robert Trager, Duncan Snidal, Ben Garfinkel, Kwan Yee Ng, Haydn Belfield, Don Wallace, Yoshua Bengio, Benjamin Prud'homme, Brian Tse, Roxana Radu, Ranjit Lall, Ben Harack, Julia Morse, Nicolas Miailhe, Scott Singer, Matt Sheehan, Max Stauffer, Yi Zeng, Joslyn Barnhart, Imane Bello, Xue Lan, Oliver Guest, Duncan Cass-Beggs, Lu Chuanying, Sumaya Nur Adan, Markus Anderljung, Claire Dennis + + + E-commerce Transactions in Islam: Fiqh Muamalah on The Validity of Buying and Selling on Digital Platforms + https://arxiv.org/abs/2601.02384 + arXiv:2601.02384v1 Announce Type: new +Abstract: The development of the digital economy has established e-commerce platforms as the primary space for commercial transactions for the Muslim community. However, innovations in features and business models on these platforms have gave rise to Sharia issues that cannot be fully explained through conventional Fiqh Muamalah contract frameworks. This research aims to examine the compliance of transaction practices in e-commerce with Sharia principles, particularly in the six most frequently used transaction forms, namely information arbitrage-based dropshipping, Buy Now Pay Later financing schemes, digital representations, algorithmic marketing that encourages consumptive behavior, halal verification, and Pre-Order systems. The research method used is a Critical Literature Review with a normative juridical approach, through the study of arguments from the Qur'an, Hadith, DSN-MUI Fatwas, as well as classical and contemporary fiqh literature. The results show that dropshipping and PO practices are considered invalid if conducted with a direct sale contract (bai') due to the nonfulfillment of the element of possession (qabd) and the presence of high uncertainty (gharar). Both practices can be justified through the restructuring of contracts into wakalah bil ujrah, salam, or istishna'. Conventional BNPL is declared non-compliant with Sharia because it contains riba nasiah and riba qardh. Misleading digital representations and halal claims without valid verification fall into the category of tadlis, while dark patterns based algorithmic marketing contradicts maqashid al-syariah, especially the protection of wealth (hifz al-mal) and intellect (hifz al-'aql). This research emphasizes the need for a comprehensive Sharia audit covering contract legality, algorithmic ethics, and interface design to realize a digital economic ecosystem that is fair, transparent, and in accordance with Islamic Sharia. + oai:arXiv.org:2601.02384v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wisnu Uriawan, Muhammad Farhan Tarigan, Herdin Kristianjani Zebua, Muhamad Nopid Andriansyah, Marleni Sukarya, Muhammad Rafli Haikal + + + Base Station Deployment under EMF constrain by Deep Reinforcement learning + https://arxiv.org/abs/2601.02385 + arXiv:2601.02385v1 Announce Type: new +Abstract: As 5G networks rapidly expand and 6G technologies emerge, characterized by dense deployments, millimeter-wave communications, and dynamic beamforming, the need for scalable simulation tools becomes increasingly critical. These tools must support efficient evaluation of key performance metrics such as coverage and radio-frequency electromagnetic field (RF-EMF) exposure, inform network design decisions, and ensure compliance with safety regulations. Moreover, base station (BS) placement is a crucial task in the network design, where satisfying coverage requirements is essential. To address these, based on our previous work, we first propose a conditional generative adversarial network (cGAN) that predicts location specific received signal strength (RSS), and EMF exposure simultaneously from the network topology, as images. As a network designing application, we propose a Deep Q Network (DQN) framework, using the trained cGAN, for optimal base station (BS) deployment in the network. Compared to conventional ray tracing simulations, the proposed cGAN reduces inference and deployment time from several hours to seconds. Unlike a standalone cGAN, which provides static performance maps, the proposed GAN-DQN framework enables sequential decision making under coverage and exposure constraints, learning effective deployment strategies that directly solve the BS placement problem. Thus making it well suited for real time design and adaptation in dynamic scenarios in order to satisfy pre defined network specific heterogeneous performance goals. + oai:arXiv.org:2601.02385v1 + cs.NI + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mohammed Mallik, Guillaume Villemaud + + + Tree of Preferences for Diversified Recommendation + https://arxiv.org/abs/2601.02386 + arXiv:2601.02386v1 Announce Type: new +Abstract: Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items. Existing approaches predominantly aim to infer the diversity of user preferences from observed user feedback. Nonetheless, due to inherent data biases, the observed data may not fully reflect user interests, where underexplored preferences can be overwhelmed or remain unmanifested. Failing to capture these preferences can lead to suboptimal diversity in recommendations. To fill this gap, this work aims to study diversified recommendation from a data-bias perspective. Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach that utilizes LLMs' expertise to uncover underexplored user preferences from observed behavior, ultimately providing diverse and relevant recommendations. To achieve this, we first introduce Tree of Preferences (ToP), an innovative structure constructed to model user preferences from coarse to fine. ToP enables LLMs to systematically reason over the user's rationale behind their behavior, thereby uncovering their underexplored preferences. To guide diversified recommendations using uncovered preferences, we adopt a data-centric approach, identifying candidate items that match user preferences and generating synthetic interactions that reflect underexplored preferences. These interactions are integrated to train a general recommender for diversification. Moreover, we scale up overall efficiency by dynamically selecting influential users during optimization. Extensive evaluations of both diversity and relevance show that our approach outperforms existing methods in most cases and achieves near-optimal performance in others, with reasonable inference latency. + oai:arXiv.org:2601.02386v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Hanyang Yuan, Ning Tang, Tongya Zheng, Jiarong Xu, Xintong Hu, Renhong Huang, Shunyu Liu, Jiacong Hu, Jiawei Chen, Mingli Song + + + Regional Resource Management for Service Provisioning in LEO Satellite Networks: A Topology Feature-Based DRL Approach + https://arxiv.org/abs/2601.02387 + arXiv:2601.02387v1 Announce Type: new +Abstract: Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively. + oai:arXiv.org:2601.02387v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chenxi Bao, Di Zhou, Min Sheng, Yan Shi, Jiandong Li, Zhili Sun + + + Generative AI for Networking + https://arxiv.org/abs/2601.02389 + arXiv:2601.02389v1 Announce Type: new +Abstract: Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are revolutionizing network management systems, paving the way towards fully autonomous and self-optimizing communication systems. These models enable networks to address complex decision-making tasks across both short-term operational scenarios and long-term strategic planning. Through natural language understanding, LLMs can analyze customer inquiries, predict network congestion patterns, and automate troubleshooting processes, leading to more efficient customer support and network maintenance. GenAI can optimize content delivery by generating personalized recommendations, improving user engagement, and dynamically adjusting network resources based on real-time demands, ultimately enhancing overall performance and user experience in telecommunication services. In this paper, we discuss the pivotal role of GenAI in advancing network performance and achieving the ultimate objective of self-adaptive networks. Moreover, we present a use case that leverages the self-attention mechanism of transformers to perform long-term traffic prediction. Harnessing these cutting-edge technologies demonstrates the transformative power of LLM and GenAI in revolutionizing telecommunication networks, elevating resilience and adaptability to unprecedented levels. + oai:arXiv.org:2601.02389v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Faisal Zaman, Ouns Bouachir, Moayad Aloqaily, Ismaeel Al Ridhawi + + + Breaking Rank - A Novel Unscented Kalman Filter for Parameter Estimations of a Lumped-Parameter Cardiovascular Model + https://arxiv.org/abs/2601.02390 + arXiv:2601.02390v1 Announce Type: new +Abstract: We make modifications to the unscented Kalman filter (UKF) which bestow almost complete practical identifiability upon a lumped-parameter cardiovascular model with 10 parameters and 4 output observables - a highly non-linear, stiff problem of clinical significance. The modifications overcome the challenging problems of rank deficiency when applying the UKF to parameter estimation. Rank deficiency usually means only a small subset of parameters can be estimated. Traditionally, pragmatic compromises are made, such as selecting an optimal subset of parameters for estimation and fixing non-influential parameters. Kalman filters are typically used for dynamical state tracking, to facilitate the control u at every time step. However, for the purpose of parameter estimation, this constraint no longer applies. Our modification has transformed the utility of UKF for the parameter estimation purpose, including minimally influential parameters, with excellent robustness (i.e., under severe noise corruption, challenging patho-physiology, and no prior knowledge of parameter distributions). The modified UKF algorithm is robust in recovering almost all parameters to over 98% accuracy, over 90% of the time, with a challenging target data set of 50, 10-parameter samples. We compare this to the original implementation of the UKF algorithm for parameter estimation and demonstrate a significant improvement. + oai:arXiv.org:2601.02390v1 + cs.IT + math.IT + stat.AP + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Alex Thornton, Ian Halliday, Harry Saxton, Xu Xu + + + WearVox: An Egocentric Multichannel Voice Assistant Benchmark for Wearables + https://arxiv.org/abs/2601.02391 + arXiv:2601.02391v1 Announce Type: new +Abstract: Wearable devices such as AI glasses are transforming voice assistants into always-available, hands-free collaborators that integrate seamlessly with daily life, but they also introduce challenges like egocentric audio affected by motion and noise, rapid micro-interactions, and the need to distinguish device-directed speech from background conversations. Existing benchmarks largely overlook these complexities, focusing instead on clean or generic conversational audio. To bridge this gap, we present WearVox, the first benchmark designed to rigorously evaluate voice assistants in realistic wearable scenarios. WearVox comprises 3,842 multi-channel, egocentric audio recordings collected via AI glasses across five diverse tasks including Search-Grounded QA, Closed-Book QA, Side-Talk Rejection, Tool Calling, and Speech Translation, spanning a wide range of indoor and outdoor environments and acoustic conditions. Each recording is accompanied by rich metadata, enabling nuanced analysis of model performance under real-world constraints. We benchmark leading proprietary and open-source speech Large Language Models (SLLMs) and find that most real-time SLLMs achieve accuracies on WearVox ranging from 29% to 59%, with substantial performance degradation on noisy outdoor audio, underscoring the difficulty and realism of the benchmark. Additionally, we conduct a case study with two new SLLMs that perform inference with single-channel and multi-channel audio, demonstrating that multi-channel audio inputs significantly enhance model robustness to environmental noise and improve discrimination between device-directed and background speech. Our results highlight the critical importance of spatial audio cues for context-aware voice assistants and establish WearVox as a comprehensive testbed for advancing wearable voice AI research. + oai:arXiv.org:2601.02391v1 + cs.CL + cs.SD + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Zhaojiang Lin, Yong Xu, Kai Sun, Jing Zheng, Yin Huang, Surya Teja Appini, Krish Narang, Renjie Tao, Ishan Kapil Jain, Siddhant Arora, Ruizhi Li, Yiteng Huang, Kaushik Patnaik, Wenfang Xu, Suwon Shon, Yue Liu, Ahmed A Aly, Anuj Kumar, Florian Metze, Xin Luna Dong + + + Self-Supervised Masked Autoencoders with Dense-Unet for Coronary Calcium Removal in limited CT Data + https://arxiv.org/abs/2601.02392 + arXiv:2601.02392v1 Announce Type: new +Abstract: Coronary calcification creates blooming artifacts in Computed Tomography Angiography (CTA), severely hampering the diagnosis of lumen stenosis. While Deep Convolutional Neural Networks (DCNNs) like Dense-Unet have shown promise in removing these artifacts via inpainting, they often require large labeled datasets which are scarce in the medical domain. Inspired by recent advancements in Masked Autoencoders (MAE) for 3D point clouds, we propose \textbf{Dense-MAE}, a novel self-supervised learning framework for volumetric medical data. We introduce a pre-training strategy that randomly masks 3D patches of the vessel lumen and trains the Dense-Unet to reconstruct the missing geometry. This forces the encoder to learn high-level latent features of arterial topology without human annotation. Experimental results on clinical CTA datasets demonstrate that initializing the Calcium Removal network with our MAE-based weights significantly improves inpainting accuracy and stenosis estimation compared to training from scratch, specifically in few-shot scenarios. + oai:arXiv.org:2601.02392v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mo Chen + + + SLASh: Simulation of LISLs Aboard LEO Satellite Shells + https://arxiv.org/abs/2601.02396 + arXiv:2601.02396v1 Announce Type: new +Abstract: Recent advances in satellite technology have introduced a new frontier of wireless networking by establishing Low Earth Orbit (LEO) Satellite networks that work to connect difficult to reach areas and improve global connectivity. These novel advancements lack robust open-source simulation models that can highlight potential bottlenecks or potential wasted resources, wasting terrestrial users and the companies that provide these networks time and money. To that end, we propose SLASh, a highly-customizable satellite network simulation which allows users to design a simulated network with specific characteristics, and constructs them analog to real-world conditions. Additionally, SLASh can generate abstract telemetry that can be simulated moving throughout the network, allowing users to compare network capabilities across a variety of frameworks. + oai:arXiv.org:2601.02396v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Davy Romine, Andrew Kingery, Guanqun Song, Ting Zhu + + + Evolutionary Algorithms for Computing Nash Equilibria in Dynamic Games + https://arxiv.org/abs/2601.02397 + arXiv:2601.02397v1 Announce Type: new +Abstract: Dynamic nonzero sum games are widely used to model multi agent decision making in control, economics, and related fields. Classical methods for computing Nash equilibria, especially in linear quadratic settings, rely on strong structural assumptions and become impractical for nonlinear dynamics, many players, or long horizons, where multiple local equilibria may exist. We show through examples that such methods can fail to reach the true global Nash equilibrium even in relatively small games. To address this, we propose two population based evolutionary algorithms for general dynamic games with linear or nonlinear dynamics and arbitrary objective functions: a co evolutionary genetic algorithm and a hybrid genetic algorithm particle swarm optimization scheme. Both approaches search directly over joint strategy spaces without restrictive assumptions and are less prone to getting trapped in local Nash equilibria, providing more reliable approximations to global Nash solutions. + oai:arXiv.org:2601.02397v1 + cs.NE + cs.GT + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Alireza Rezaee + + + AI-Native Integrated Sensing and Communications for Self-Organizing Wireless Networks: Architectures, Learning Paradigms, and System-Level Design + https://arxiv.org/abs/2601.02398 + arXiv:2601.02398v1 Announce Type: new +Abstract: Integrated Sensing and Communications (ISAC) is emerging as a foundational paradigm for next-generation wireless networks, enabling communication infrastructures to simultaneously support data transmission and environment sensing. By tightly coupling radio sensing with communication functions, ISAC unlocks new capabilities for situational awareness, localization, tracking, and network adaptation. At the same time, the increasing scale, heterogeneity, and dynamics of future wireless systems demand self-organizing network intelligence capable of autonomously managing resources, topology, and services. Artificial intelligence (AI), particularly learning-driven and data-centric methods, has become a key enabler for realizing this vision. This survey provides a comprehensive and system-level review of AI-native ISAC-enabled self-organizing wireless networks. We develop a unified taxonomy that spans: (i) ISAC signal models and sensing modalities, (ii) network state abstraction and perception from sensing-aware radio data, (iii) learning-driven self-organization mechanisms for resource allocation, topology control, and mobility management, and (iv) cross-layer architectures integrating sensing, communication, and network intelligence. We further examine emerging learning paradigms, including deep reinforcement learning, graph-based learning, multi-agent coordination, and federated intelligence that enable autonomous adaptation under uncertainty, mobility, and partial observability. Practical considerations such as sensing-communication trade-offs, scalability, latency, reliability, and security are discussed alongside representative evaluation methodologies and performance metrics. Finally, we identify key open challenges and future research directions toward deployable, trustworthy, and scalable AI-native ISAC systems for 6G and beyond. + oai:arXiv.org:2601.02398v1 + cs.NI + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + S. Zhang, M. Feizarefi, A. F. Mirzaei + + + ProSoftArena: Benchmarking Hierarchical Capabilities of Multimodal Agents in Professional Software Environments + https://arxiv.org/abs/2601.02399 + arXiv:2601.02399v1 Announce Type: new +Abstract: Multimodal agents are making rapid progress on general computer-use tasks, yet existing benchmarks remain largely confined to browsers and basic desktop applications, falling short in professional software workflows that dominate real-world scientific and industrial practice. To close this gap, we introduce ProSoftArena, a benchmark and platform specifically for evaluating multimodal agents in professional software environments. We establish the first capability hierarchy tailored to agent use of professional software and construct a benchmark of 436 realistic work and research tasks spanning 6 disciplines and 13 core professional applications. To ensure reliable and reproducible assessment, we build an executable real-computer environment with an execution-based evaluation framework and uniquely incorporate a human-in-the-loop evaluation paradigm. Extensive experiments show that even the best-performing agent attains only a 24.4\% success rate on L2 tasks and completely fails on L3 multi-software workflow. In-depth analysis further provides valuable insights for addressing current agent limitations and more effective design principles, paving the way to build more capable agents in professional software settings. This project is available at: https://prosoftarena.github.io. + oai:arXiv.org:2601.02399v1 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiaxin Ai, Yukang Feng, Fanrui Zhang, Jianwen Sun, Zizhen Li, Chuanhao Li, Yifan Chang, Wenxiao Wu, Ruoxi Wang, Mingliang Zhai, Kaipeng Zhang + + + Spiking Heterogeneous Graph Attention Networks + https://arxiv.org/abs/2601.02401 + arXiv:2601.02401v1 Announce Type: new +Abstract: Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption. Code is available at https://github.com/QianPeng369/SpikingHAN. + oai:arXiv.org:2601.02401v1 + cs.NE + cs.LG + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Buqing Cao, Qian Peng, Xiang Xie, Liang Chen, Min Shi, Jianxun Liu + + + Auction-Driven Spectrum Allocation With AutoEncoder-Based Compression in Rural Wireless Networks: A Novel Framework for Reliable Telemedicine + https://arxiv.org/abs/2601.02402 + arXiv:2601.02402v1 Announce Type: new +Abstract: Rural healthcare faces numerous challenges, including limited access to specialized medical services and diagnostic equipment, which delays patient care. Enhancing the ability to transmit medical images and data from rural areas to urban hospitals via wireless networks is critical. However, bandwidth limitations, unreliable networks, and concerns over data security and privacy hinder efficient transmission. Additionally, the high data volume of medical content and the limited battery life of IoT devices pose further challenges. To address these challenges, data compression techniques such as Autoencoders (AEs) offer promising solutions by significantly reducing the communication overhead without sacrificing essential image quality or details. Additionally, spectrum allocation mechanisms in rural areas are often inefficient, leading to poor resource utilization. Auction theory presents a dynamic and adaptive approach to optimize spectrum allocation. This paper proposes a novel hybrid framework that integrates AE-based data compression with auction-based spectrum allocation, addressing both communication efficiency and spectrum utilization in rural wireless networks. Extensive simulations validate the framework's ability to improve spectrum utilization, transmission efficiency, and overall connectivity, offering a practical solution for enhancing rural telemedicine infrastructure. + oai:arXiv.org:2601.02402v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Nadjemat El Houda Issaad, Ismail Lotfi, Mohamed Senouci, Zekri Lougmiri + + + PCEval: A Benchmark for Evaluating Physical Computing Capabilities of Large Language Models + https://arxiv.org/abs/2601.02404 + arXiv:2601.02404v1 Announce Type: new +Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, including software development, education, and technical assistance. Among these, software development is one of the key areas where LLMs are increasingly adopted. However, when hardware constraints are considered-for instance, in physical computing, where software must interact with and control physical hardware -their effectiveness has not been fully explored. To address this gap, we introduce \textsc{PCEval} (Physical Computing Evaluation), the first benchmark in physical computing that enables a fully automatic evaluation of the capabilities of LLM in both the logical and physical aspects of the projects, without requiring human assessment. Our evaluation framework assesses LLMs in generating circuits and producing compatible code across varying levels of project complexity. Through comprehensive testing of 13 leading models, \textsc{PCEval} provides the first reproducible and automatically validated empirical assessment of LLMs' ability to reason about fundamental hardware implementation constraints within a simulation environment. Our findings reveal that while LLMs perform well in code generation and logical circuit design, they struggle significantly with physical breadboard layout creation, particularly in managing proper pin connections and avoiding circuit errors. \textsc{PCEval} advances our understanding of AI assistance in hardware-dependent computing environments and establishes a foundation for developing more effective tools to support physical computing education. + oai:arXiv.org:2601.02404v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Inpyo Song, Eunji Jeon, Jangwon Lee + + + Evolving Personalities in Chaos: An LLM-Augmented Framework for Character Discovery in the Iterated Prisoners Dilemma under Environmental Stress + https://arxiv.org/abs/2601.02407 + arXiv:2601.02407v1 Announce Type: new +Abstract: Standard simulations of the Iterated Prisoners Dilemma (IPD) operate in deterministic, noise-free environments, producing strategies that may be theoretically optimal but fragile when confronted with real-world uncertainty. This paper addresses two critical gaps in evolutionary game theory research: (1) the absence of realistic environmental stressors during strategy evolution, and (2) the Interpretability Gap, where evolved genetic strategies remain opaque binary sequences devoid of semantic meaning. We introduce a novel framework combining stochastic environmental perturbations (God Mode) with Large Language Model (LLM)-based behavioral profiling to transform evolved genotypes into interpretable character archetypes. Our experiments demonstrate that strategies evolved under chaotic conditions exhibit superior resilience and present distinct behavioral phenotypes, ranging from Ruthless Capitalists to Diplomatic Enforcers. These phenotypes are readily classified by LLMs but remain nearly impossible to interpret through manual genome inspection alone. This work bridges evolutionary computation with explainable AI and provides a template for automated agent characterization in multi-agent systems. + oai:arXiv.org:2601.02407v1 + cs.NE + cs.GT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Oguzhan Yildirim + + + Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis + https://arxiv.org/abs/2601.02409 + arXiv:2601.02409v1 Announce Type: new +Abstract: Medical image analysis faces two critical challenges: scarcity of labeled data and lack of model interpretability, both hindering clinical AI deployment. Few-shot learning (FSL) addresses data limitations but lacks transparency in predictions. Active learning (AL) methods optimize data acquisition but overlook interpretability of acquired samples. We propose a dual-framework solution: Expert-Guided Explainable Few-Shot Learning (EGxFSL) and Explainability-Guided AL (xGAL). EGxFSL integrates radiologist-defined regions-of-interest as spatial supervision via Grad-CAM-based Dice loss, jointly optimized with prototypical classification for interpretable few-shot learning. xGAL introduces iterative sample acquisition prioritizing both predictive uncertainty and attention misalignment, creating a closed-loop framework where explainability guides training and sample selection synergistically. On the BraTS (MRI), VinDr-CXR (chest X-ray), and SIIM-COVID-19 (chest X-ray) datasets, we achieve accuracies of 92\%, 76\%, and 62\%, respectively, consistently outperforming non-guided baselines across all datasets. Under severe data constraints, xGAL achieves 76\% accuracy with only 680 samples versus 57\% for random sampling. Grad-CAM visualizations demonstrate guided models focus on diagnostically relevant regions, with generalization validated on breast ultrasound confirming cross-modality applicability. + oai:arXiv.org:2601.02409v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh + + + The Vibe-Check Protocol: Quantifying Cognitive Offloading in AI Programming + https://arxiv.org/abs/2601.02410 + arXiv:2601.02410v1 Announce Type: new +Abstract: The integration of Large Language Models (LLMs) into software engineering education has driven the emergence of ``Vibe Coding,'' a paradigm where developers articulate high-level intent through natural language and delegate implementation to AI agents. While proponents argue this approach modernizes pedagogy by emphasizing conceptual design over syntactic memorization, accumulating empirical evidence raises concerns regarding skill retention and deep conceptual understanding. This paper proposes a theoretical framework to investigate the research question: \textit{Is Vibe Coding a better way to learn software engineering?} We posit a divergence in student outcomes between those leveraging AI for acceleration versus those using it for cognitive offloading. To evaluate these educational trade-offs, we propose the \textbf{Vibe-Check Protocol (VCP)}, a systematic benchmarking framework incorporating three quantitative metrics: the \textit{Cold Start Refactor} ($M_{CSR}$) for modeling skill decay; \textit{Hallucination Trap Detection} ($M_{HT}$) based on signal detection theory to evaluate error identification; and the \textit{Explainability Gap} ($E_{gap}$) for quantifying the divergence between code complexity and conceptual comprehension. Through controlled comparisons, VCP aims to provide a quantitative basis for educators to determine the optimal pedagogical boundary: identifying contexts where Vibe Coding fosters genuine mastery and contexts where it introduces hidden technical debt and superficial competence. + oai:arXiv.org:2601.02410v1 + cs.SE + cs.AI + cs.CY + cs.GR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Aizierjiang Aiersilan + + + SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting + https://arxiv.org/abs/2601.02411 + arXiv:2601.02411v1 Announce Type: new +Abstract: Time-series forecasting often operates under tight power and latency budgets in fields like traffic management, industrial condition monitoring, and on-device sensing. These applications frequently require near real-time responses and low energy consumption on edge devices. Spiking neural networks (SNNs) offer event-driven computation and ultra-low power by exploiting temporal sparsity and multiplication-free computation. Yet existing SNN-based time-series forecasters often inherit complex transformer blocks, thereby losing much of the efficiency benefit. To solve the problem, we propose SpikySpace, a spiking state-space model (SSM) that reduces the quadratic cost in the attention block to linear time via selective scanning. Further, we replace dense SSM updates with sparse spike trains and execute selective scans only on spike events, thereby avoiding dense multiplications while preserving the SSM's structured memory. Because complex operations such as exponentials and divisions are costly on neuromorphic chips, we introduce simplified approximations of SiLU and Softplus to enable a neuromorphic-friendly model architecture. In matched settings, SpikySpace reduces estimated energy consumption by 98.73% and 96.24% compared to two state-of-the-art transformer based approaches, namely iTransformer and iSpikformer, respectively. In standard time series forecasting datasets, SpikySpace delivers competitive accuracy while substantially reducing energy cost and memory traffic. As the first full spiking state-space model, SpikySpace bridges neuromorphic efficiency with modern sequence modeling, marking a practical and scalable path toward efficient time series forecasting systems. + oai:arXiv.org:2601.02411v1 + cs.NE + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kaiwen Tang, Jiaqi Zheng, Yuze Jin, Yupeng Qiu, Guangda Sun, Zhanglu Yan, Weng-Fai Wong + + + Socially-Aware Recommender Systems Mitigate Opinion Clusterization + https://arxiv.org/abs/2601.02412 + arXiv:2601.02412v1 Announce Type: new +Abstract: Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users preferences evolve under the influence of both suggested content from the recommender system and content shared within their social circles. This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of filter bubbles and opinion polarization. We develop a social network-aware recommender system that explicitly accounts for this user-creators feedback interaction and strategically exploits the topology of the user's own social network to promote diversification. Our approach highlights how accounting for and exploiting user's social network in the recommender system design is crucial to mediate filter bubble effects while balancing content diversity with personalization. Provably, opinion clusterization is positively correlated with the influence of recommended content on user opinions. Ultimately, the proposed approach shows the power of socially-aware recommender systems in combating opinion polarization and clusterization phenomena. + oai:arXiv.org:2601.02412v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Lukas Sch\"uepp, Carmen Amo Alonso, Florian D\"orfler, Giulia De Pasquale + + + MIAR: Modality Interaction and Alignment Representation Fuison for Multimodal Emotion + https://arxiv.org/abs/2601.02414 + arXiv:2601.02414v1 Announce Type: new +Abstract: Multimodal Emotion Recognition (MER) aims to perceive human emotions through three modes: language, vision, and audio. Previous methods primarily focused on modal fusion without adequately addressing significant distributional differences among modalities or considering their varying contributions to the task. They also lacked robust generalization capabilities across diverse textual model features, thus limiting performance in multimodal scenarios. Therefore, we propose a novel approach called Modality Interaction and Alignment Representation (MIAR). This network integrates contextual features across different modalities using a feature interaction to generate feature tokens to represent global representations of this modality extracting information from other modalities. These four tokens represent global representations of how each modality extracts information from others. MIAR aligns different modalities using contrastive learning and normalization strategies. We conduct experiments on two benchmarks: CMU-MOSI and CMU-MOSEI datasets, experimental results demonstrate the MIAR outperforms state-of-the-art MER methods. + oai:arXiv.org:2601.02414v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jichao Zhu, Jun Yu + + + Multimodal Sentiment Analysis based on Multi-channel and Symmetric Mutual Promotion Feature Fusion + https://arxiv.org/abs/2601.02415 + arXiv:2601.02415v1 Announce Type: new +Abstract: Multimodal sentiment analysis is a key technology in the fields of human-computer interaction and affective computing. Accurately recognizing human emotional states is crucial for facilitating smooth communication between humans and machines. Despite some progress in multimodal sentiment analysis research, numerous challenges remain. The first challenge is the limited and insufficiently rich features extracted from single modality data. Secondly, most studies focus only on the consistency of inter-modal feature information, neglecting the differences between features, resulting in inadequate feature information fusion. In this paper, we first extract multi-channel features to obtain more comprehensive feature information. We employ dual-channel features in both the visual and auditory modalities to enhance intra-modal feature representation. Secondly, we propose a symmetric mutual promotion (SMP) inter-modal feature fusion method. This method combines symmetric cross-modal attention mechanisms and self-attention mechanisms, where the cross-modal attention mechanism captures useful information from other modalities, and the self-attention mechanism models contextual information. This approach promotes the exchange of useful information between modalities, thereby strengthening inter-modal interactions. Furthermore, we integrate intra-modal features and inter-modal fused features, fully leveraging the complementarity of inter-modal feature information while considering feature information differences. Experiments conducted on two benchmark datasets demonstrate the effectiveness and superiority of our proposed method. + oai:arXiv.org:2601.02415v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wangyuan Zhu, Jun Yu + + + Talks that Builds: Exploring Communication factors for the Success of Emerging Professional in Product Teams + https://arxiv.org/abs/2601.02421 + arXiv:2601.02421v1 Announce Type: new +Abstract: This paper recognizes that most organizational communication study focuses on established professionals aged above 27 with more than five years of experience. In contrast, this study examines product teams with younger emerging professionals aged 18-27 and explores which factors influence their success. While some established factors still apply, others become less relevant, and new ones such as curiosity, locational proximity, documentation, access to resources were identified in the study. Overall, this study fills a gap in the literature on how these newer factors shape team productivity and project outcomes based on the success rate of the product the team developed. + oai:arXiv.org:2601.02421v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Nyan Lin Zaw (Imran) + + + Watch Wider and Think Deeper: Collaborative Cross-modal Chain-of-Thought for Complex Visual Reasoning + https://arxiv.org/abs/2601.02422 + arXiv:2601.02422v1 Announce Type: new +Abstract: Multi-modal reasoning requires the seamless integration of visual and linguistic cues, yet existing Chain-of-Thought methods suffer from two critical limitations in cross-modal scenarios: (1) over-reliance on single coarse-grained image regions, and (2) semantic fragmentation between successive reasoning steps. To address these issues, we propose the CoCoT (Collaborative Coross-modal Thought) frame- work, built upon two key innovations: a) Dynamic Multi-Region Grounding to adaptively detect the most relevant image regions based on the question, and b) Relation-Aware Reasoning to enable multi-region collaboration by iteratively align- ing visual cues to form a coherent and logical chain of thought. Through this approach, we construct the CoCoT-70K dataset, comprising 74,691 high-quality samples with multi-region annotations and structured reasoning chains. Extensive experiments demonstrate that CoCoT significantly enhances complex visual rea- soning, achieving an average accuracy improvement of 15.4% on LLaVA-1.5 and 4.0% on Qwen2-VL across six challenging benchmarks. The data and code are available at: https://github.com/deer-echo/CoCoT. + oai:arXiv.org:2601.02422v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Wenting Lu, Didi Zhu, Tao Shen, Donglin Zhu, Ayong Ye, Chao Wu + + + NitroGen: An Open Foundation Model for Generalist Gaming Agents + https://arxiv.org/abs/2601.02427 + arXiv:2601.02427v1 Announce Type: new +Abstract: We introduce NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games. We incorporate three key ingredients: 1) an internet-scale video-action dataset constructed by automatically extracting player actions from publicly available gameplay videos, 2) a multi-game benchmark environment that can measure cross-game generalization, and 3) a unified vision-action model trained with large-scale behavior cloning. NitroGen exhibits strong competence across diverse domains, including combat encounters in 3D action games, high-precision control in 2D platformers, and exploration in procedurally generated worlds. It transfers effectively to unseen games, achieving up to 52% relative improvement in task success rates over models trained from scratch. We release the dataset, evaluation suite, and model weights to advance research on generalist embodied agents. + oai:arXiv.org:2601.02427v1 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Lo\"ic Magne, Anas Awadalla, Guanzhi Wang, Yinzhen Xu, Joshua Belofsky, Fengyuan Hu, Joohwan Kim, Ludwig Schmidt, Georgia Gkioxari, Jan Kautz, Yisong Yue, Yejin Choi, Yuke Zhu, Linxi "Jim" Fan + + + A Dynamic Retrieval-Augmented Generation System with Selective Memory and Remembrance + https://arxiv.org/abs/2601.02428 + arXiv:2601.02428v1 Announce Type: new +Abstract: We introduce \emph{Adaptive RAG Memory} (ARM), a retrieval-augmented generation (RAG) framework that replaces a static vector index with a \emph{dynamic} memory substrate governed by selective remembrance and decay. Frequently retrieved items are consolidated and protected from forgetting, while rarely used items gradually decay, inspired by cognitive consolidation and forgetting principles. On a lightweight retrieval benchmark, ARM reaches near state-of-the-art performance (e.g., NDCG@5 $\approx$ 0.940, Recall@5 $=1.000$) with only $\sim$22M parameters in the embedding layer, achieving the best efficiency among ultra-efficient models ($<$25M parameters). In addition, we compare static vs. dynamic RAG combinations across Llama 3.1 and GPT-4o. Llama 3.1 with static RAG achieves the highest key-term coverage (67.2\%) at moderate latency, while GPT-4o with a dynamic selective retrieval policy attains the fastest responses (8.2s on average) with competitive coverage (58.7\%). We further present an engineering optimization of the DynamicRAG implementation, making embedding weights configurable, adjustable at runtime, and robust to invalid settings. + ARM yields competitive accuracy, self-regularizing memory growth, and interpretable retention dynamics without retraining the generator\color{black} and provides practical trade-off between quality, latency and memory efficiency for production and research RAG system. + oai:arXiv.org:2601.02428v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Okan Bursa + + + WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics + https://arxiv.org/abs/2601.02430 + arXiv:2601.02430v1 Announce Type: new +Abstract: Web applications (web apps) have become a key arena for large language models (LLMs) to demonstrate their code generation capabilities and commercial potential. However, building a benchmark for LLM-generated web apps remains challenging due to the need for real-world user requirements, generalizable evaluation metrics without relying on ground-truth implementations or test cases, and interpretable evaluation results. To address these challenges, we introduce WebCoderBench, the first real-world-collected, generalizable, and interpretable benchmark for web app generation. WebCoderBench comprises 1,572 real user requirements, covering diverse modalities and expression styles that reflect realistic user intentions. WebCoderBench provides 24 fine-grained evaluation metrics across 9 perspectives, combining rule-based and LLM-as-a-judge paradigm for fully automated, objective, and general evaluation. Moreover, WebCoderBench adopts human-preference-aligned weights over metrics to yield interpretable overall scores. Experiments across 12 representative LLMs and 2 LLM-based agents show that there exists no dominant model across all evaluation metrics, offering an opportunity for LLM developers to optimize their models in a targeted manner for a more powerful version. + oai:arXiv.org:2601.02430v1 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chenxu Liu, Yingjie Fu, Wei Yang, Ying Zhang, Tao Xie + + + Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications + https://arxiv.org/abs/2601.02432 + arXiv:2601.02432v1 Announce Type: new +Abstract: Speech-based machine learning systems are sensitive to noise, complicating reliable deployment in emotion recognition and voice pathology detection. We evaluate the robustness of a hybrid quantum machine learning model, quanvolutional neural networks (QNNs) against classical convolutional neural networks (CNNs) under four acoustic corruptions (Gaussian noise, pitch shift, temporal shift, and speed variation) in a clean-train/corrupted-test regime. Using AVFAD (voice pathology) and TESS (speech emotion), we compare three QNN models (Random, Basic, Strongly) to a simple CNN baseline (CNN-Base), ResNet-18 and VGG-16 using accuracy and corruption metrics (CE, mCE, RCE, RmCE), and analyze architectural factors (circuit complexity or depth, convergence) alongside per-emotion robustness. QNNs generally outperform the CNN-Base under pitch shift, temporal shift, and speed variation (up to 22% lower CE/RCE at severe temporal shift), while the CNN-Base remains more resilient to Gaussian noise. Among quantum circuits, QNN-Basic achieves the best overall robustness on AVFAD, and QNN-Random performs strongest on TESS. Emotion-wise, fear is most robust (80-90% accuracy under severe corruptions), neutral can collapse under strong Gaussian noise (5.5% accuracy), and happy is most vulnerable to pitch, temporal, and speed distortions. QNNs also converge up to six times faster than the CNN-Base. To our knowledge, this is a systematic study of QNN robustness for speech under common non-adversarial acoustic corruptions, indicating that shallow entangling quantum front-ends can improve noise resilience while sensitivity to additive noise remains a challenge. + oai:arXiv.org:2601.02432v1 + cs.SD + cs.LG + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ha Tran, Bipasha Kashyap, Pubudu N. Pathirana + + + Physical Transformer + https://arxiv.org/abs/2601.02433 + arXiv:2601.02433v1 Announce Type: new +Abstract: Digital AI systems spanning large language models, vision models, and generative architectures that operate primarily in symbolic, linguistic, or pixel domains. They have achieved striking progress, but almost all of this progress lives in virtual spaces. These systems transform embeddings and tokens, yet do not themselves touch the world and rarely admit a physical interpretation. In this work we propose a physical transformer that couples modern transformer style computation with geometric representation and physical dynamics. At the micro level, attention heads, and feed-forward blocks are modeled as interacting spins governed by effective Hamiltonians plus non-Hamiltonian bath terms. At the meso level, their aggregated state evolves on a learned Neural Differential Manifold (NDM) under Hamiltonian flows and Hamilton, Jacobi, Bellman (HJB) optimal control, discretized by symplectic layers that approximately preserve geometric and energetic invariants. At the macro level, the model maintains a generative semantic workspace and a two-dimensional information-phase portrait that tracks uncertainty and information gain over a reasoning trajectory. Within this hierarchy, reasoning tasks are formulated as controlled information flows on the manifold, with solutions corresponding to low cost trajectories that satisfy geometric, energetic, and workspace-consistency constraints. On simple toy problems involving numerical integration and dynamical systems, the physical transformer outperforms naive baselines in stability and long-horizon accuracy, highlighting the benefits of respecting underlying geometric and Hamiltonian structure. More broadly, the framework suggests a path toward physical AI that unify digital reasoning with physically grounded manifolds, opening a route to more interpretable and potentially unified models of reasoning, control, and interaction with the real world. + oai:arXiv.org:2601.02433v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Tao Xu, Zhixin Hu, Li Luo, Momiao Xiong + + + TAP-ViTs: Task-Adaptive Pruning for On-Device Deployment of Vision Transformers + https://arxiv.org/abs/2601.02437 + arXiv:2601.02437v1 Announce Type: new +Abstract: Vision Transformers (ViTs) have demonstrated strong performance across a wide range of vision tasks, yet their substantial computational and memory demands hinder efficient deployment on resource-constrained mobile and edge devices. Pruning has emerged as a promising direction for reducing ViT complexity. However, existing approaches either (i) produce a single pruned model shared across all devices, ignoring device heterogeneity, or (ii) rely on fine-tuning with device-local data, which is often infeasible due to limited on-device resources and strict privacy constraints. As a result, current methods fall short of enabling task-customized ViT pruning in privacy-preserving mobile computing settings. This paper introduces TAP-ViTs, a novel task-adaptive pruning framework that generates device-specific pruned ViT models without requiring access to any raw local data. Specifically, to infer device-level task characteristics under privacy constraints, we propose a Gaussian Mixture Model (GMM)-based metric dataset construction mechanism. Each device fits a lightweight GMM to approximate its private data distribution and uploads only the GMM parameters. Using these parameters, the cloud selects distribution-consistent samples from public data to construct a task-representative metric dataset for each device. Based on this proxy dataset, we further develop a dual-granularity importance evaluation-based pruning strategy that jointly measures composite neuron importance and adaptive layer importance, enabling fine-grained, task-aware pruning tailored to each device's computational budget. Extensive experiments across multiple ViT backbones and datasets demonstrate that TAP-ViTs consistently outperforms state-of-the-art pruning methods under comparable compression ratios. + oai:arXiv.org:2601.02437v1 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhibo Wang, Zuoyuan Zhang, Xiaoyi Pang, Qile Zhang, Xuanyi Hao, Shuguo Zhuo, Peng Sun + + + Focus on What Matters: Fisher-Guided Adaptive Multimodal Fusion for Vulnerability Detection + https://arxiv.org/abs/2601.02438 + arXiv:2601.02438v1 Announce Type: new +Abstract: Software vulnerability detection is a critical task for securing software systems and can be formulated as a binary classification problem: given a code snippet, determine whether it contains a vulnerability. Existing multimodal approaches typically fuse Natural Code Sequence (NCS) representations from pretrained language models with Code Property Graph (CPG) representations from graph neural networks, often under the implicit assumption that adding a modality necessarily yields extra information. In practice, sequence and graph representations can be redundant, and fluctuations in the quality of the graph modality can dilute the discriminative signal of the dominant modality. To address this, we propose TaCCS-DFA, a framework that introduces Fisher information as a geometric measure of how sensitive feature directions are to the classification decision, enabling task-oriented complementary fusion. TaCCS-DFA online estimates a low-rank principal Fisher subspace and restricts cross-modal attention to task-sensitive directions, thereby retrieving structural features from CPG that complement the sequence modality; meanwhile, an adaptive gating mechanism dynamically adjusts the contribution of the graph modality for each sample to suppress noise propagation. Our analysis shows that, under an isotropic perturbation assumption, the proposed mechanism admits a tighter risk bound than conventional full-spectrum attention. Experiments on BigVul, Devign, and ReVeal show that TaCCS-DFA achieves strong performance across multiple backbones. With CodeT5 as the backbone, TaCCS-DFA reaches an F1 score of 87.80\% on the highly imbalanced BigVul dataset, improving over a strong baseline Vul-LMGNNs by 6.3 percentage points while maintaining low calibration error and computational overhead. + oai:arXiv.org:2601.02438v1 + cs.SE + cs.AI + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yun Bian, Yi Chen, HaiQuan Wang, ShiHao Li, Zhe Cui + + + WebGym: Scaling Training Environments for Visual Web Agents with Realistic Tasks + https://arxiv.org/abs/2601.02439 + arXiv:2601.02439v1 Announce Type: new +Abstract: We present WebGym, the largest-to-date open-source environment for training realistic visual web agents. Real websites are non-stationary and diverse, making artificial or small-scale task sets insufficient for robust policy learning. WebGym contains nearly 300,000 tasks with rubric-based evaluations across diverse, real-world websites and difficulty levels. We train agents with a simple reinforcement learning (RL) recipe, which trains on the agent's own interaction traces (rollouts), using task rewards as feedback to guide learning. To enable scaling RL, we speed up sampling of trajectories in WebGym by developing a high-throughput asynchronous rollout system, designed specifically for web agents. Our system achieves a 4-5x rollout speedup compared to naive implementations. Second, we scale the task set breadth, depth, and size, which results in continued performance improvement. Fine-tuning a strong base vision-language model, Qwen-3-VL-8B-Instruct, on WebGym results in an improvement in success rate on an out-of-distribution test set from 26.2% to 42.9%, significantly outperforming agents based on proprietary models such as GPT-4o and GPT-5-Thinking that achieve 27.1% and 29.8%, respectively. This improvement is substantial because our test set consists only of tasks on websites never seen during training, unlike many other prior works on training visual web agents. + oai:arXiv.org:2601.02439v1 + cs.LG + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Hao Bai, Alexey Taymanov, Tong Zhang, Aviral Kumar, Spencer Whitehead + + + Understanding Pure Textual Reasoning for Blind Image Quality Assessment + https://arxiv.org/abs/2601.02441 + arXiv:2601.02441v1 Announce Type: new +Abstract: Textual reasoning has recently been widely adopted in Blind Image Quality Assessment (BIQA). However, it remains unclear how textual information contributes to quality prediction and to what extent text can represent the score-related image contents. This work addresses these questions from an information-flow perspective by comparing existing BIQA models with three paradigms designed to learn the image-text-score relationship: Chain-of-Thought, Self-Consistency, and Autoencoder. Our experiments show that the score prediction performance of the existing model significantly drops when only textual information is used for prediction. Whereas the Chain-of-Thought paradigm introduces little improvement in BIQA performance, the Self-Consistency paradigm significantly reduces the gap between image- and text-conditioned predictions, narrowing the PLCC/SRCC difference to 0.02/0.03. The Autoencoder-like paradigm is less effective in closing the image-text gap, yet it reveals a direction for further optimization. These findings provide insights into how to improve the textual reasoning for BIQA and high-level vision tasks. + oai:arXiv.org:2601.02441v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yuan Li, Shin'ya Nishida + + + Evaluating the Diagnostic Classification Ability of Multimodal Large Language Models: Insights from the Osteoarthritis Initiative + https://arxiv.org/abs/2601.02443 + arXiv:2601.02443v1 Announce Type: new +Abstract: Multimodal large language models (MLLMs) show promising performance on medical visual question answering (VQA) and report generation, but these generation and explanation abilities do not reliably transfer to disease-specific classification. We evaluated MLLM architectures on knee osteoarthritis (OA) radiograph classification, which remains underrepresented in existing medical MLLM benchmarks, even though knee OA affects an estimated 300 to 400 million people worldwide. Through systematic ablation studies manipulating the vision encoder, the connector, and the large language model (LLM) across diverse training strategies, we measured each component's contribution to diagnostic accuracy. In our classification task, a trained vision encoder alone could outperform full MLLM pipelines in classification accuracy and fine-tuning the LLM provided no meaningful improvement over prompt-based guidance. And LoRA fine-tuning on a small, class-balanced dataset (500 images) gave better results than training on a much larger but class-imbalanced set (5,778 images), indicating that data balance and quality can matter more than raw scale for this task. These findings suggest that for domain-specific medical classification, LLMs are more effective as interpreters and report generators rather than as primary classifiers. Therefore, the MLLM architecture appears less suitable for medical image diagnostic classification tasks that demand high certainty. We recommend prioritizing vision encoder optimization and careful dataset curation when developing clinically applicable systems. + oai:arXiv.org:2601.02443v1 + cs.CV + cs.AI + eess.IV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Li Wang, Xi Chen, XiangWen Deng, HuaHui Yi, ZeKun Jiang, Kang Li, Jian Li + + + VocalBridge: Latent Diffusion-Bridge Purification for Defeating Perturbation-Based Voiceprint Defenses + https://arxiv.org/abs/2601.02444 + arXiv:2601.02444v1 Announce Type: new +Abstract: The rapid advancement of speech synthesis technologies, including text-to-speech (TTS) and voice conversion (VC), has intensified security and privacy concerns related to voice cloning. Recent defenses attempt to prevent unauthorized cloning by embedding protective perturbations into speech to obscure speaker identity while maintaining intelligibility. However, adversaries can apply advanced purification techniques to remove these perturbations, recover authentic acoustic characteristics, and regenerate cloneable voices. Despite the growing realism of such attacks, the robustness of existing defenses under adaptive purification remains insufficiently studied. + Most existing purification methods are designed to counter adversarial noise in automatic speech recognition (ASR) systems rather than speaker verification or voice cloning pipelines. As a result, they fail to suppress the fine-grained acoustic cues that define speaker identity and are often ineffective against speaker verification attacks (SVA). To address these limitations, we propose Diffusion-Bridge (VocalBridge), a purification framework that learns a latent mapping from perturbed to clean speech in the EnCodec latent space. Using a time-conditioned 1D U-Net with a cosine noise schedule, the model enables efficient, transcript-free purification while preserving speaker-discriminative structure. We further introduce a Whisper-guided phoneme variant that incorporates lightweight temporal guidance without requiring ground-truth transcripts. Experimental results show that our approach consistently outperforms existing purification methods in recovering cloneable voices from protected speech. Our findings demonstrate the fragility of current perturbation-based defenses and highlight the need for more robust protection mechanisms against evolving voice-cloning and speaker verification threats. + oai:arXiv.org:2601.02444v1 + cs.SD + cs.AI + cs.CR + cs.LG + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Maryam Abbasihafshejani, AHM Nazmus Sakib, Murtuza Jadliwala + + + A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction + https://arxiv.org/abs/2601.02445 + arXiv:2601.02445v1 Announce Type: new +Abstract: The Indian Summer Monsoon (ISM) is a critical climate phenomenon, fundamentally impacting the agriculture, economy, and water security of over a billion people. Traditional long-range forecasting, whether statistical or dynamical, has predominantly focused on predicting a single, spatially-averaged seasonal value, lacking the spatial detail essential for regional-level resource management. To address this gap, we introduce a novel deep learning framework that reframes gridded monsoon prediction as a spatio-temporal computer vision task. We treat multi-variable, pre-monsoon atmospheric and oceanic fields as a sequence of multi-channel images, effectively creating a video-like input tensor. Using 85 years of ERA5 reanalysis data for predictors and IMD rainfall data for targets, we employ a Convolutional Neural Network (CNN)-based architecture to learn the complex mapping from the five-month pre-monsoon period (January-May) to a high-resolution gridded rainfall pattern for the subsequent monsoon season. Our framework successfully produces distinct forecasts for each of the four monsoon months (June-September) as well as the total seasonal average, demonstrating its utility for both intra-seasonal and seasonal outlooks. + oai:arXiv.org:2601.02445v1 + cs.CV + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Parashjyoti Borah, Sanghamitra Sarkar, Ranjan Phukan + + + Don't Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis + https://arxiv.org/abs/2601.02447 + arXiv:2601.02447v1 Announce Type: new +Abstract: Routine clinical imaging of the retina using optical coherence tomography (OCT) is performed with large slice spacing, resulting in highly anisotropic images and a sparsely scanned retina. Most learning-based methods circumvent the problems arising from the anisotropy by using 2D approaches rather than performing volumetric analyses. These approaches inherently bear the risk of generating inconsistent results for neighboring B-scans. For example, 2D retinal layer segmentations can have irregular surfaces in 3D. Furthermore, the typically used convolutional neural networks are bound to the resolution of the training data, which prevents their usage for images acquired with a different imaging protocol. Implicit neural representations (INRs) have recently emerged as a tool to store voxelized data as a continuous representation. Using coordinates as input, INRs are resolution-agnostic, which allows them to be applied to anisotropic data. In this paper, we propose two frameworks that make use of this characteristic of INRs for dense 3D analyses of retinal OCT volumes. 1) We perform inter-B-scan interpolation by incorporating additional information from en-face modalities, that help retain relevant structures between B-scans. 2) We create a resolution-agnostic retinal atlas that enables general analysis without strict requirements for the data. Both methods leverage generalizable INRs, improving retinal shape representation through population-based training and allowing predictions for unseen cases. Our resolution-independent frameworks facilitate the analysis of OCT images with large B-scan distances, opening up possibilities for the volumetric evaluation of retinal structures and pathologies. + oai:arXiv.org:2601.02447v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Bennet Kahrs, Julia Andresen, Fenja Falta, Monty Santarossa, Heinz Handels, Timo Kepp + + + Stigmergic Swarming Agents for Fast Subgraph Isomorphism + https://arxiv.org/abs/2601.02449 + arXiv:2601.02449v1 Announce Type: new +Abstract: Maximum partial subgraph isomorphism compares two graphs (nodes joined by edges) to find a largest common subgraph. A common use case, for graphs with labeled nodes, seeks to find instances of a \textit{query} graph with $q$ nodes in a (typically larger) \textit{data} graph with $d$ nodes. The problem is NP-complete, and na\"ive solutions are exponential in $q + d$. The fastest current heuristic has complexity $O(d^2)$. This paper outlines ASSIST (Approximate Swarming Subgraph Isomorphism through Stigmergy), inspired by the ant colony optimization approach to the traveling salesperson. After peering (identifying matching individual nodes in query and data) in time $O(q\cdot log(d))$, the time required for ASSIST's iterative subgraph search, the combinatorially complex part of the problem, is linear in query size and constant in data size. ASSIST can be extended to support matching problems (such as temporally ordered edges, inexact matches, and missing nodes or edges in the data graph) that frustrate other heuristics. + oai:arXiv.org:2601.02449v1 + cs.MA + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + AAMAS 2026 + H. Van Dyke Parunak + + + mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks + https://arxiv.org/abs/2601.02451 + arXiv:2601.02451v1 Announce Type: new +Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test. We adapt Manifold-Constrained Hyper-Connections (\mhc)~\citep{xie2025mhc}, recently proposed for Transformers, to graph neural networks. Our method, mHC-GNN, expands node representations across $n$ parallel streams and constrains stream-mixing matrices to the Birkhoff polytope via Sinkhorn-Knopp normalization. We prove that mHC-GNN exhibits exponentially slower over-smoothing (rate $(1-\gamma)^{L/n}$ vs.\ $(1-\gamma)^L$) and can distinguish graphs beyond 1-WL. Experiments on 10 datasets with 4 GNN architectures show consistent improvements. Depth experiments from 2 to 128 layers reveal that standard GNNs collapse to near-random performance beyond 16 layers, while mHC-GNN maintains over 74\% accuracy even at 128 layers, with improvements exceeding 50 percentage points at extreme depths. Ablations confirm that the manifold constraint is essential: removing it causes up to 82\% performance degradation. Code is available at \href{https://github.com/smlab-niser/mhc-gnn}{https://github.com/smlab-niser/mhc-gnn} + oai:arXiv.org:2601.02451v1 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Subhankar Mishra + + + The Rise of Agentic Testing: Multi-Agent Systems for Robust Software Quality Assurance + https://arxiv.org/abs/2601.02454 + arXiv:2601.02454v1 Announce Type: new +Abstract: Software testing has progressed toward intelligent automation, yet current AI-based test generators still suffer from static, single-shot outputs that frequently produce invalid, redundant, or non-executable tests due to the lack of execution aware feedback. This paper introduces an agentic multi-model testing framework a closed-loop, self-correcting system in which a Test Generation Agent, an Execution and Analysis Agent, and a Review and Optimization Agent collaboratively generate, execute, analyze, and refine tests until convergence. By using sandboxed execution, detailed failure reporting, and iterative regeneration or patching of failing tests, the framework autonomously improves test quality and expands coverage. Integrated into a CI/CD-compatible pipeline, it leverages reinforcement signals from coverage metrics and execution outcomes to guide refinement. Empirical evaluations on microservice based applications show up to a 60% reduction in invalid tests, 30% coverage improvement, and significantly reduced human effort compared to single-model baselines demonstrating that multi-agent, feedback-driven loops can evolve software testing into an autonomous, continuously learning quality assurance ecosystem for self-healing, high-reliability codebases. + oai:arXiv.org:2601.02454v1 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Saba Naqvi, Mohammad Baqar, Nawaz Ali Mohammad + + + Dynamic Quantization Error Propagation in Encoder-Decoder ASR Quantization + https://arxiv.org/abs/2601.02455 + arXiv:2601.02455v1 Announce Type: new +Abstract: Running Automatic Speech Recognition (ASR) models on memory-constrained edge devices requires efficient compression. While layer-wise post-training quantization is effective, it suffers from error accumulation, especially in encoder-decoder architectures. Existing solutions like Quantization Error Propagation (QEP) are suboptimal for ASR due to the model's heterogeneity, processing acoustic features in the encoder while generating text in the decoder. To address this, we propose Fine-grained Alpha for Dynamic Quantization Error Propagation (FADE), which adaptively controls the trade-off between cross-layer error correction and local quantization. Experiments show that FADE significantly improves stability by reducing performance variance across runs, while simultaneously surpassing baselines in mean WER. + oai:arXiv.org:2601.02455v1 + cs.SD + cs.CL + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xinyu Wang, Yajie Luo, Yihong Wu, Liheng Ma, Ziyu Zhao, Jingrui Tian, Lei Ding, Yufei Cui, Xiao-Wen Chang + + + InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation + https://arxiv.org/abs/2601.02456 + arXiv:2601.02456v1 Announce Type: new +Abstract: Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, but they inherently lack the capability to deduce physical world dynamics. Consequently, recent approaches have shifted toward World Models, typically formulated via video prediction; however, these methods often suffer from a lack of semantic grounding and exhibit brittleness when handling prediction errors. To synergize semantic understanding with dynamic predictive capabilities, we present InternVLA-A1. This model employs a unified Mixture-of-Transformers architecture, coordinating three experts for scene understanding, visual foresight generation, and action execution. These components interact seamlessly through a unified masked self-attention mechanism. Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B parameter scales. We pre-train these models on hybrid synthetic-real datasets spanning InternData-A1 and Agibot-World, covering over 533M frames. This hybrid training strategy effectively harnesses the diversity of synthetic simulation data while minimizing the sim-to-real gap. We evaluated InternVLA-A1 across 12 real-world robotic tasks and simulation benchmark. It significantly outperforms leading models like pi0 and GR00T N1.5, achieving a 14.5\% improvement in daily tasks and a 40\%-73.3\% boost in dynamic settings, such as conveyor belt sorting. + oai:arXiv.org:2601.02456v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Junhao Cai, Zetao Cai, Jiafei Cao, Yilun Chen, Zeyu He, Lei Jiang, Hang Li, Hengjie Li, Yang Li, Yufei Liu, Yanan Lu, Qi Lv, Haoxiang Ma, Jiangmiao Pang, Yu Qiao, Zherui Qiu, Yanqing Shen, Xu Shi, Yang Tian, Bolun Wang, Hanqing Wang, Jiaheng Wang, Tai Wang, Xueyuan Wei, Chao Wu, Yiman Xie, Boyang Xing, Yuqiang Yang, Yuyin Yang, Qiaojun Yu, Feng Yuan, Jia Zeng, Jingjing Zhang, Shenghan Zhang, Shi Zhang, Zhuoma Zhaxi, Bowen Zhou, Yuanzhen Zhou, Yunsong Zhou, Hongrui Zhu, Yangkun Zhu, Yuchen Zhu + + + PatchAlign3D: Local Feature Alignment for Dense 3D Shape understanding + https://arxiv.org/abs/2601.02457 + arXiv:2601.02457v1 Announce Type: new +Abstract: Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent approaches leverage vision and language foundation models to directly solve dense tasks through multi-view renderings and text queries. While promising, these pipelines require expensive inference over multiple renderings, depend heavily on large language-model (LLM) prompt engineering for captions, and fail to exploit the inherent 3D geometry of shapes. We address this gap by introducing an encoder-only 3D model that produces language-aligned patch-level features directly from point clouds. Our pre-training approach builds on existing data engines that generate part-annotated 3D shapes by pairing multi-view SAM regions with VLM captioning. Using this data, we train a point cloud transformer encoder in two stages: (1) distillation of dense 2D features from visual encoders such as DINOv2 into 3D patches, and (2) alignment of these patch embeddings with part-level text embeddings through a multi-positive contrastive objective. Our 3D encoder achieves zero-shot 3D part segmentation with fast single-pass inference without any test-time multi-view rendering, while significantly outperforming previous rendering-based and feed-forward approaches across several 3D part segmentation benchmarks. Project website: https://souhail-hadgi.github.io/patchalign3dsite/ + oai:arXiv.org:2601.02457v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Souhail Hadgi, Bingchen Gong, Ramana Sundararaman, Emery Pierson, Lei Li, Peter Wonka, Maks Ovsjanikov + + + Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations + https://arxiv.org/abs/2601.02492 + arXiv:2601.02492v1 Announce Type: new +Abstract: We introduce variational spectral learning (VSL), a machine learning framework for solving partial differential equations (PDEs) that operates directly in the coefficient space of spectral expansions. VSL offers a principled bridge between variational PDE theory, spectral discretization, and contemporary machine learning practice. The core idea is to recast a given PDE \[ \mathcal{L}u = f \quad \text{in} \quad Q=\Omega\times(0,T), \] together with boundary and initial conditions, into differentiable space--time energies built from strong-form least-squares residuals and weak (Galerkin) formulations. The solution is represented as a finite spectral expansion \[ u_N(x,t)=\sum_{n=1}^{N} c_n\,\phi_n(x,t), \] where $\phi_n$ are tensor-product Chebyshev bases in space and time, with Dirichlet-satisfying spatial modes enforcing homogeneous boundary conditions analytically. This yields a compact linear parameterization in the coefficient vector $\mathbf{c}$, while all PDE complexity is absorbed into the variational energy. We show how to construct strong-form and weak-form space-time functionals, augment them with initial-condition and Tikhonov regularization terms, and minimize the resulting objective with gradient-based optimization. In practice, VSL is implemented in TensorFlow using automatic differentiation and Keras cosine-decay-with-restarts learning-rate schedules, enabling robust optimization of moderately sized coefficient vectors. Numerical experiments on benchmark elliptic and parabolic problems, including one- and two-dimensional Poisson, diffusion, and Burgers-type equations, demonstrate that VSL attains accuracy comparable to classical spectral collocation with Crank-Nicolson time stepping, while providing a differentiable objective suitable for modern optimization tooling. + oai:arXiv.org:2601.02492v1 + math.NA + cs.LG + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + M. M. Hammad + + + APoW: Auditable Proof-of-Work Against Block Withholding Attacks + https://arxiv.org/abs/2601.02496 + arXiv:2601.02496v1 Announce Type: new +Abstract: We introduce APoW, a novel proof-of-work (PoW) construction inspired by Hashcash-style nonce searching, which enables the auditing of other miners' work through accountable re-scanning of the nonce space. The proposed scheme allows a miner to probabilistically attest to having searched specified regions of the nonce space in earlier mining rounds, while concurrently earning rewards for performing productive work for a new block or pool share. This capability enables miners belonging to a mining pools to audit another miner's claimed effort retroactively, thereby allowing the probabilistic detection of block withholding attacks (BWAs) without requiring trusted hardware or trusted third parties. As a consequence, the construction supports the design of decentralized mining pools in which work attribution is verifiable and withholding incentives are substantially reduced. The scheme preserves the fundamental properties of conventional PoW, including public verifiability and difficulty adjustment, while adding an orthogonal auditability layer tailored to pool-based mining. Finally, while a full deployment of APoW in Bitcoin would require a consensus rule change and minor modifications to mining ASICs, the construction remains practically useful even without consensus changes, for instance, as a pool-level auditing mechanism that enables verifiable pay-for-auditing using existing pool reserves. + oai:arXiv.org:2601.02496v1 + cs.CR + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Sergio Demian Lerner + + + Polynomial Convergence of Riemannian Diffusion Models + https://arxiv.org/abs/2601.02499 + arXiv:2601.02499v1 Announce Type: new +Abstract: Diffusion models have demonstrated remarkable empirical success in the recent years and are considered one of the state-of-the-art generative models in modern AI. These models consist of a forward process, which gradually diffuses the data distribution to a noise distribution spanning the whole space, and a backward process, which inverts this transformation to recover the data distribution from noise. Most of the existing literature assumes that the underlying space is Euclidean. However, in many practical applications, the data are constrained to lie on a submanifold of Euclidean space. Addressing this setting, De Bortoli et al. (2022) introduced Riemannian diffusion models and proved that using an exponentially small step size yields a small sampling error in the Wasserstein distance, provided the data distribution is smooth and strictly positive, and the score estimate is $L_\infty$-accurate. In this paper, we greatly strengthen this theory by establishing that, under $L_2$-accurate score estimate, a {\em polynomially small stepsize} suffices to guarantee small sampling error in the total variation distance, without requiring smoothness or positivity of the data distribution. Our analysis only requires mild and standard curvature assumptions on the underlying manifold. The main ingredients in our analysis are Li-Yau estimate for the log-gradient of heat kernel, and Minakshisundaram-Pleijel parametrix expansion of the perturbed heat equation. Our approach opens the door to a sharper analysis of diffusion models on non-Euclidean spaces. + oai:arXiv.org:2601.02499v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xingyu Xu, Ziyi Zhang, Yorie Nakahira, Guannan Qu, Yuejie Chi + + + GEM-Style Constraints for PEFT with Dual Gradient Projection in LoRA + https://arxiv.org/abs/2601.02500 + arXiv:2601.02500v1 Announce Type: new +Abstract: Full fine-tuning of Large Language Models (LLMs) is computationally costly, motivating Continual Learning (CL) approaches that utilize parameter-efficient adapters. We revisit Gradient Episodic Memory (GEM) within the Low-Rank Adapter (LoRA) subspace and introduce I-GEM: a fixed-budget, GPU-resident dual projected-gradient approximation to GEM's quadratic projection. By constraining non-interference solely within the adapter parameters, I-GEM preserves GEM-like stability with orders-of-magnitude lower mean projection overhead. On a 3-task AG News split with induced domain drift, using GPT-2 (355M) and LoRA ($r=8$), I-GEM matches GEM's average accuracy (within $\sim\!0.04$ pts) and outperforms A-GEM by $\sim\!1.4$ pts. Crucially, it reduces projection time vs.\ GEM by a factor of $\sim\!10^3$. These results suggest that applying GEM constraints in the LoRA subspace is a practical pathway for continual learning at the LLM scale. + oai:arXiv.org:2601.02500v1 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Brian Tekmen, Jason Yin, Qianqian Tong + + + Enhancing Debugging Skills with AI-Powered Assistance: A Real-Time Tool for Debugging Support + https://arxiv.org/abs/2601.02504 + arXiv:2601.02504v1 Announce Type: new +Abstract: Debugging is a crucial skill in programming education and software development, yet it is often overlooked in CS curricula. To address this, we introduce an AI-powered debugging assistant integrated into an IDE. It offers real-time support by analyzing code, suggesting breakpoints, and providing contextual hints. Using RAG with LLMs, program slicing, and custom heuristics, it enhances efficiency by minimizing LLM calls and improving accuracy. A three-level evaluation - technical analysis, UX study, and classroom tests - highlights its potential for teaching debugging. + oai:arXiv.org:2601.02504v1 + cs.SE + cs.AI + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3786580.3786976 + Elizaveta Artser, Daniil Karol, Anna Potriasaeva, Aleksei Rostovskii, Katsiaryna Dzialets, Ekaterina Koshchenko, Xiaotian Su, April Yi Wang, Anastasiia Birillo + + + Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints + https://arxiv.org/abs/2601.02505 + arXiv:2601.02505v1 Announce Type: new +Abstract: Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio-Temporal Efficacy-optimized Allocation for Multi-robot systems (STEAM). STEAM builds upon trait-based frameworks that model robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success-failure model by explicitly modeling the efficacy of allocations as trait-efficacy maps. These maps encode how the aggregated capabilities assigned to a task determine performance. Further, STEAM accommodates spatio-temporal constraints, including a user-specified time budget (i.e., maximum makespan). To solve STEAM problems, we contribute a novel algorithm named Efficacy-optimized Incremental Task Allocation Graph Search (E-ITAGS) that simultaneously optimizes task performance and respects time budgets by interleaving task allocation, scheduling, and path planning. Motivated by the fact that trait-efficacy maps are difficult, if not impossible, to specify, E-ITAGS efficiently learns them using a realizability-aware active learning module. Our approach is realizability-aware since it explicitly accounts for the fact that not all combinations of traits are realizable by the robots available during learning. Further, we derive experimentally-validated bounds on E-ITAGS' suboptimality with respect to efficacy. Detailed numerical simulations and experiments using an emergency response domain demonstrate that E-ITAGS generates allocations of higher efficacy compared to baselines, while respecting resource and spatio-temporal constraints. We also show that our active learning approach is sample efficient and establishes a principled tradeoff between data and computational efficiency. + oai:arXiv.org:2601.02505v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiazhen Liu, Glen Neville, Jinwoo Park, Sonia Chernova, Harish Ravichandar + + + hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures + https://arxiv.org/abs/2601.02509 + arXiv:2601.02509v1 Announce Type: new +Abstract: Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing paradigm that represents and processes information using high-dimensional vectors. While the first version of hdlib established a robust foundation for creating and manipulating these vectors, this update addresses the growing need for more advanced, data-driven modeling within the VSA framework. Here, we present four extensions: significant enhancements to the existing supervised classification model also enabling feature selection, and a new regression model for predicting continuous variables, a clustering model for unsupervised learning, and a graph-based learning model. Furthermore, we propose the first implementation ever of Quantum Hyperdimensional Computing with quantum-powered arithmetic operations and a new Quantum Machine Learning model for supervised learning. hdlib remains open-source and available on GitHub at https://github.com/cumbof/hdlib under the MIT license, and distributed through the Python Package Index (pip install hdlib) and Conda (conda install -c conda-forge hdlib). Documentation and examples of these new features are available on the official Wiki at https://github.com/cumbof/hdlib/wiki. + oai:arXiv.org:2601.02509v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Fabio Cumbo, Kabir Dhillon, Daniel Blankenberg + + + LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection + https://arxiv.org/abs/2601.02511 + arXiv:2601.02511v1 Announce Type: new +Abstract: Detecting anomalies in time series data is crucial for finance, healthcare, sensor networks, and industrial monitoring applications. However, time series anomaly detection often suffers from sparse labels, complex temporal patterns, and costly expert annotation. We propose a unified framework that integrates Large Language Model (LLM)-based potential functions for reward shaping with Reinforcement Learning (RL), Variational Autoencoder (VAE)-enhanced dynamic reward scaling, and active learning with label propagation. An LSTM-based RL agent leverages LLM-derived semantic rewards to guide exploration, while VAE reconstruction errors add unsupervised anomaly signals. Active learning selects the most uncertain samples, and label propagation efficiently expands labeled data. Evaluations on Yahoo-A1 and SMD benchmarks demonstrate that our method achieves state-of-the-art detection accuracy under limited labeling budgets and operates effectively in data-constrained settings. This study highlights the promise of combining LLMs with RL and advanced unsupervised techniques for robust, scalable anomaly detection in real-world applications. + oai:arXiv.org:2601.02511v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Bahareh Golchin, Banafsheh Rekabdar, Danielle Justo + + + Green LLM Techniques in Action: How Effective Are Existing Techniques for Improving the Energy Efficiency of LLM-Based Applications in Industry? + https://arxiv.org/abs/2601.02512 + arXiv:2601.02512v1 Announce Type: new +Abstract: The rapid adoption of large language models (LLMs) has raised concerns about their substantial energy consumption, especially when deployed at industry scale. While several techniques have been proposed to address this, limited empirical evidence exists regarding the effectiveness of applying them to LLM-based industry applications. To fill this gap, we analyzed a chatbot application in an industrial context at Schuberg Philis, a Dutch IT services company. We then selected four techniques, namely Small and Large Model Collaboration, Prompt Optimization, Quantization, and Batching, applied them to the application in eight variations, and then conducted experiments to study their impact on energy consumption, accuracy, and response time compared to the unoptimized baseline. + Our results show that several techniques, such as Prompt Optimization and 2-bit Quantization, managed to reduce energy use significantly, sometimes by up to 90%. However, these techniques especially impacted accuracy negatively, to a degree that is not acceptable in practice. The only technique that achieved significant and strong energy reductions without harming the other qualities substantially was Small and Large Model Collaboration via Nvidia's Prompt Task and Complexity Classifier (NPCC) with prompt complexity thresholds. This highlights that reducing the energy consumption of LLM-based applications is not difficult in practice. However, improving their energy efficiency, i.e., reducing energy use without harming other qualities, remains challenging. Our study provides practical insights to move towards this goal. + oai:arXiv.org:2601.02512v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + 10.1145/3786583.3786896 + Pelin Rabia Kuran, Rumbidzai Chitakunye, Vincenzo Stoico, Ilja Heitlager, Justus Bogner + + + On well-posed energy/entropy stable boundary conditions for the rotating shallow water equations + https://arxiv.org/abs/2601.02513 + arXiv:2601.02513v1 Announce Type: new +Abstract: We derive and analyze well-posed, energy- and entropy-stable boundary conditions (BCs) for the two-dimensional linear and nonlinear rotating shallow water equations (RSWE) in vector invariant form. The focus of the study is on subcritical flows, which are commonly observed in atmospheric, oceanic, and geostrophic flow applications. We consider spatial domains with smooth boundaries and formulate both linear and nonlinear BCs using mass flux, Riemann's invariants, and Bernoulli's potential, ensuring that the resulting initial boundary value problem (IBVP) is provably entropy- and energy-stable. The linear analysis is comprehensive, providing sufficient conditions to establish the existence, uniqueness, and energy stability of solutions to the linear IBVP. For the nonlinear IBVP, which admits more general solutions, our goal is to develop nonlinear BCs that guarantee entropy stability. We introduce the concepts of linear consistency and linear stability for nonlinear IBVPs, demonstrating that if a nonlinear IBVP is both linearly consistent and linearly stable, then, for sufficiently regular initial and boundary data over a finite time interval, a unique smooth solution exists. Both the linear and nonlinear IBVPs can be efficiently solved using high-order accurate numerical methods. By employing high-order summation-by-parts operators to discretize spatial derivatives and implementing weak enforcement of BCs via penalty techniques, we develop provably energy- and entropy-stable numerical schemes on curvilinear meshes. Extensive numerical experiments are presented to verify the accuracy of the methods and to demonstrate the robustness of the proposed BCs and numerical schemes. + oai:arXiv.org:2601.02513v1 + math.NA + cs.NA + physics.ao-ph + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kenneth Duru, Chuqiao Xu + + + Textual Explanations and Their Evaluations for Reinforcement Learning Policy + https://arxiv.org/abs/2601.02514 + arXiv:2601.02514v1 Announce Type: new +Abstract: Understanding a Reinforcement Learning (RL) policy is crucial for ensuring that autonomous agents behave according to human expectations. This goal can be achieved using Explainable Reinforcement Learning (XRL) techniques. Although textual explanations are easily understood by humans, ensuring their correctness remains a challenge, and evaluations in state-of-the-art remain limited. We present a novel XRL framework for generating textual explanations, converting them into a set of transparent rules, improving their quality, and evaluating them. Expert's knowledge can be incorporated into this framework, and an automatic predicate generator is also proposed to determine the semantic information of a state. Textual explanations are generated using a Large Language Model (LLM) and a clustering technique to identify frequent conditions. These conditions are then converted into rules to evaluate their properties, fidelity, and performance in the deployed environment. Two refinement techniques are proposed to improve the quality of explanations and reduce conflicting information. Experiments were conducted in three open-source environments to enable reproducibility, and in a telecom use case to evaluate the industrial applicability of the proposed XRL framework. This framework addresses the limitations of an existing method, Autonomous Policy Explanation, and the generated transparent rules can achieve satisfactory performance on certain tasks. This framework also enables a systematic and quantitative evaluation of textual explanations, providing valuable insights for the XRL field. + oai:arXiv.org:2601.02514v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ahmad Terra, Mohit Ahmed, Rafia Inam, Elena Fersman, Martin T\"orngren + + + CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking + https://arxiv.org/abs/2601.02521 + arXiv:2601.02521v1 Announce Type: new +Abstract: Automated analysis of volumetric medical imaging on edge devices is severely constrained by the high memory and computational demands of 3D Convolutional Neural Networks (CNNs). This paper develops a lightweight computer vision framework that reconciles the efficiency of 2D detection with the necessity of 3D context by reformulating volumetric Computer Tomography (CT) data as sequential video streams. This video-viewpoint paradigm is applied to the time-sensitive task of Intracranial Hemorrhage (ICH) detection using the Hemorica dataset. To ensure operational efficiency, we benchmarked multiple generations of the YOLO architecture (v8, v10, v11 and v12) in their Nano configurations, selecting the version with the highest mAP@50 to serve as the slice-level backbone. A ByteTrack algorithm is then introduced to enforce anatomical consistency across the $z$-axis. To address the initialization lag inherent in video trackers, a hybrid inference strategy and a spatiotemporal consistency filter are proposed to distinguish true pathology from transient prediction noise. Experimental results on independent test data demonstrate that the proposed framework serves as a rigorous temporal validator, increasing detection Precision from 0.703 to 0.779 compared to the baseline 2D detector, while maintaining high sensitivity. By approximating 3D contextual reasoning at a fraction of the computational cost, this method provides a scalable solution for real-time patient prioritization in resource-constrained environments, such as mobile stroke units and IoT-enabled remote clinics. + oai:arXiv.org:2601.02521v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Amirreza Parvahan, Mohammad Hoseyni, Javad Khoramdel, Amirhossein Nikoofard + + + On the Effectiveness of Proposed Techniques to Reduce Energy Consumption in RAG Systems: A Controlled Experiment + https://arxiv.org/abs/2601.02522 + arXiv:2601.02522v1 Announce Type: new +Abstract: The rising energy demands of machine learning (ML), e.g., implemented in popular variants like retrieval-augmented generation (RAG) systems, have raised significant concerns about their environmental sustainability. While previous research has proposed green tactics for ML-enabled systems, their empirical evaluation within RAG systems remains largely unexplored. This study presents a controlled experiment investigating five practical techniques aimed at reducing energy consumption in RAG systems. Using a production-like RAG system developed at our collaboration partner, the Software Improvement Group, we evaluated the impact of these techniques on energy consumption, latency, and accuracy. + Through a total of 9 configurations spanning over 200 hours of trials using the CRAG dataset, we reveal that techniques such as increasing similarity retrieval thresholds, reducing embedding sizes, applying vector indexing, and using a BM25S reranker can significantly reduce energy usage, up to 60% in some cases. However, several techniques also led to unacceptable accuracy decreases, e.g., by up to 30% for the indexing strategies. Notably, finding an optimal retrieval threshold and reducing embedding size substantially reduced energy consumption and latency with no loss in accuracy, making these two techniques truly energy-efficient. We present the first comprehensive, empirical study on energy-efficient design techniques for RAG systems, providing guidance for developers and researchers aiming to build sustainable RAG applications. + oai:arXiv.org:2601.02522v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + 10.1145/3786581.3786932 + Zhinuan (Otto), Guo, Chushu Gao, Justus Bogner + + + Modellierung und Simulation der Dynamik von Fussg\"angerstr\"omen + https://arxiv.org/abs/2601.02526 + arXiv:2601.02526v1 Announce Type: new +Abstract: This work presents a microscopic model to describe pedestrian flows based on the social force theory. The aim of this study is twofold: (1) developing a realistic model that can be used as a tool for designing pedestrian-friendly infrastructure, and (2) verifying a social science theory using a model with sufficient data. The investigation of the pedestrian model shows that despite simple individual behavior patterns, complex spatial and temporal structures emerge through the interactions in pedestrian flows. Collective behavior emerges from individuals following two basic rules: (1) moving directly towards their goal at a certain speed, and (2) maintaining a distance to other pedestrians and obstacles. This self-organized collective behavior manifests itself as trails that are formed by pedestrians moving in one direction. Furthermore, strong dependencies of the properties of pedestrian flows on geometric forms of buildings are shown, and the influence of geometric changes on performance characteristics is investigated. An example demonstrates how efficiency can be increased by reducing walkable areas. This work also presents an evolutionary algorithm for optimizing building layouts based on the social force model. Additionally, a decision-making model is integrated to describe alternative goal selection, and adaptation and learning capabilities are included to improve pedestrian avoidance behavior and decision strategies based on accumulated experience. A method for determining load distributions in individual sections of a path system considering subjective selection criteria is also developed. Finally, a model that describes the self-organization of path systems with minimal detours is presented, similar to natural transport networks where total length and material costs are optimized. + oai:arXiv.org:2601.02526v1 + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + P\'eter Moln\'ar + + + Multi-scale Graph Autoregressive Modeling: Molecular Property Prediction via Next Token Prediction + https://arxiv.org/abs/2601.02530 + arXiv:2601.02530v1 Announce Type: new +Abstract: We present Connection-Aware Motif Sequencing (CamS), a graph-to-sequence representation that enables decoder-only Transformers to learn molecular graphs via standard next-token prediction (NTP). For molecular property prediction, SMILES-based NTP scales well but lacks explicit topology, whereas graph-native masked modeling captures connectivity but risks disrupting the pivotal chemical details (e.g., activity cliffs). CamS bridges this gap by serializing molecular graphs into structure-rich causal sequences. CamS first mines data-driven connection-aware motifs. It then serializes motifs via scaffold-rooted breadth-first search (BFS) to establish a stable core-to-periphery order. Crucially, CamS enables hierarchical modeling by concatenating sequences from fine to coarse motif scales, allowing the model to condition global scaffolds on dense, uncorrupted local structural evidence. We instantiate CamS-LLaMA by pre-training a vanilla LLaMA backbone on CamS sequences. It achieves state-of-the-art performance on MoleculeNet and the activity-cliff benchmark MoleculeACE, outperforming both SMILES-based language models and strong graph baselines. Interpretability analysis confirms that our multi-scale causal serialization effectively drives attention toward cliff-determining differences. + oai:arXiv.org:2601.02530v1 + cs.LG + q-bio.QM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhuoyang Jiang, Yaosen Min, Peiran Jin, Lei Chen + + + Losses that Cook: Topological Optimal Transport for Structured Recipe Generation + https://arxiv.org/abs/2601.02531 + arXiv:2601.02531v1 Announce Type: new +Abstract: Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural coherence, as well as the correct composition of ingredients. Standard training procedures are primarily based on cross-entropy and focus solely on fluency. Building on RECIPE-NLG, we investigate the use of several composite objectives and present a new topological loss that represents ingredient lists as point clouds in embedding space, minimizing the divergence between predicted and gold ingredients. Using both standard NLG metrics and recipe-specific metrics, we find that our loss significantly improves ingredient- and action-level metrics. Meanwhile, the Dice loss excels in time/temperature precision, and the mixed loss yields competitive trade-offs with synergistic gains in quantity and time. A human preference analysis supports our finding, showing our model is preferred in 62% of the cases. + oai:arXiv.org:2601.02531v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mattia Ottoborgo, Daniele Rege Cambrin, Paolo Garza + + + A $O^*((2 + \epsilon)^k)$ Time Algorithm for Cograph Deletion Using Unavoidable Subgraphs in Large Prime Graphs + https://arxiv.org/abs/2601.02532 + arXiv:2601.02532v1 Announce Type: new +Abstract: We study the parameterized complexity of the Cograph Deletion problem, which asks whether one can delete at most $k$ edges from a graph to make it $P_4$-free. This is a well-known graph modification problem with applications in computation biology and social network analysis. + All current parameterized algorithms use a similar strategy, which is to find a $P_4$ and explore the local structure around it to perform an efficient recursive branching. + The best known algorithm achieves running time $O^*(2.303^k)$ and requires an automated search of the branching cases due to their complexity. + Since it appears difficult to further improve the current strategy, we devise a new approach using modular decompositions. We solve each module and the quotient graph independently, with the latter being the core problem. This reduces the problem to solving on a prime graph, in which all modules are trivial. We then use a characterization of Chudnovsky et al. stating that any large enough prime graph has one of seven structures as an induced subgraph. These all have many $P_4$s, with the quantity growing linearly with the graph size, and we show that these allow a recursive branch tree algorithm to achieve running time $O^*((2 + \epsilon)^k)$ for any $\epsilon > 0$. + This appears to be the first algorithmic application of the prime graph characterization and it could be applicable to other modification problems. Towards this goal, we provide the exact set of graph classes $\H$ for which the $\H$-free editing problem can make use of our reduction to a prime graph, opening the door to improvements for other modification problems. + oai:arXiv.org:2601.02532v1 + cs.DS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Manuel Lafond, Francis Sarrazin + + + ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation + https://arxiv.org/abs/2601.02535 + arXiv:2601.02535v1 Announce Type: new +Abstract: Selecting a single high-quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), particularly in open-ended tasks where no canonical answer exists. While Best-of-N and self-consistency methods show that aggregating multiple generations can improve performance, existing approaches typically rely on external evaluators, reward models, or exact string-match voting, limiting their applicability and efficiency. We propose Mode Extraction (ModeX), an evaluator-free Best-of-N selection framework that generalizes majority voting to open-ended text generation by identifying the modal output representing the dominant semantic consensus among generated texts. ModeX constructs a similarity graph over candidate generations and recursively applies spectral clustering to select a representative centroid, without requiring additional inference or auxiliary models. We further instantiate this selection principle as ModeX--Lite, an improved version of ModeX with early pruning for efficiency. Across open-ended tasks--including text summarization, code generation, and mathematical reasoning--our approaches consistently outperform standard single- and multi-path baselines, providing a computationally efficient solution for robust open-ended text generation. Code is released in https://github.com/deeplearning-wisc/ModeX. + oai:arXiv.org:2601.02535v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Hyeong Kyu Choi, Sharon Li + + + MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark + https://arxiv.org/abs/2601.02536 + arXiv:2601.02536v1 Announce Type: new +Abstract: Understanding real-world videos such as movies requires integrating visual and dialogue cues to answer complex questions. Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and are largely not open-ended, given the difficulty of evaluating free-form answers. In this paper, we introduce a novel open-ended multi-modal VideoQA benchmark, MovieRecapsQA created using movie recap videos--a distinctive type of YouTube content that summarizes a film by presenting its key events through synchronized visual (recap video) and textual (recap summary) modalities. Using the recap summary, we generate $\approx 8.2$ K question-answer (QA) pairs (aligned with movie-subtitles) and provide the necessary "facts" needed to verify an answer in a reference-free manner. To our knowledge, this is the first open-ended VideoQA benchmark that supplies explicit textual context of the input (video and/or text); which we use for evaluation. Our benchmark provides videos of multiple lengths (i.e., recap-segments, movie-segments) and categorizations of questions (by modality and type) to enable fine-grained analysis. We evaluate the performance of seven state-of-the-art MLLMs using our benchmark and observe that: 1) visual-only questions remain the most challenging; 2) models default to textual inputs whenever available; 3) extracting factually accurate information from video content is still difficult for all models; and 4) proprietary and open-source models perform comparably on video-dependent questions. + oai:arXiv.org:2601.02536v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Shaden Shaar, Bradon Thymes, Sirawut Chaixanien, Claire Cardie, Bharath Hariharan + + + Optimal Oblivious Load-Balancing for Sparse Traffic in Large-Scale Satellite Networks + https://arxiv.org/abs/2601.02537 + arXiv:2601.02537v1 Announce Type: new +Abstract: Oblivious load-balancing in networks involves routing traffic from sources to destinations using predetermined routes independent of the traffic, so that the maximum load on any link in the network is minimized. We investigate oblivious load-balancing schemes for a $N\times N$ torus network under sparse traffic where there are at most $k$ active source-destination pairs. We are motivated by the problem of load-balancing in large-scale LEO satellite networks, which can be modelled as a torus, where the traffic is known to be sparse and localized to certain hotspot areas. We formulate the problem as a linear program and show that no oblivious routing scheme can achieve a worst-case load lower than approximately $\frac{\sqrt{2k}}{4}$ when $1<k \leq N^2/2$ and $\frac{N}{4}$ when $N^2/2\leq k\leq N^2$. Moreover, we demonstrate that the celebrated Valiant Load Balancing scheme is suboptimal under sparse traffic and construct an optimal oblivious load-balancing scheme that achieves the lower bound. Further, we discover a $\sqrt{2}$ multiplicative gap between the worst-case load of a non-oblivious routing and the worst-case load of any oblivious routing. The results can also be extended to general $N\times M$ tori with unequal link capacities along the vertical and horizontal directions. + oai:arXiv.org:2601.02537v1 + cs.NI + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Rudrapatna Vallabh Ramakanth, Eytan Modiano + + + GPU-Accelerated Energy-Conserving Methods for the Hyperbolized Serre-Green-Naghdi Equations in 2D + https://arxiv.org/abs/2601.02540 + arXiv:2601.02540v1 Announce Type: new +Abstract: We develop energy-conserving numerical methods for a two-dimensional hyperbolic approximation of the Serre-Green-Naghdi equations with variable bathymetry for both periodic and reflecting boundary conditions. The hyperbolic formulation avoids the costly inversion of an elliptic operator present in the classical model. Our schemes combine split forms with summation-by-parts (SBP) operators to construct semidiscretizations that conserve the total water mass and the total energy. We provide analytical proofs of these conservation properties and also verify them numerically. While the framework is general, our implementation focuses on second-order finite-difference SBP operators. The methods are implemented in Julia for CPU and GPU architectures (AMD and NVIDIA) and achieve substantial speedups on modern accelerators. We validate the approach through convergence studies based on solitary-wave and manufactured-solution tests, and by comparisons to analytical, experimental, and existing numerical results. All source code to reproduce our results is available online. + oai:arXiv.org:2601.02540v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Collin Wittenstein (Massachusetts Institute of Technology, Cambridge, USA, Johannes Gutenberg University Mainz, Germany), Vincent Marks (Johannes Gutenberg University Mainz, Germany), Mario Ricchiuto (INRIA Bordeaux, France), Hendrik Ranocha (Johannes Gutenberg University Mainz, Germany) + + + Normalized Conditional Mutual Information Surrogate Loss for Deep Neural Classifiers + https://arxiv.org/abs/2601.02543 + arXiv:2601.02543v1 Announce Type: new +Abstract: In this paper, we propose a novel information theoretic surrogate loss; normalized conditional mutual information (NCMI); as a drop in alternative to the de facto cross-entropy (CE) for training deep neural network (DNN) based classifiers. We first observe that the model's NCMI is inversely proportional to its accuracy. Building on this insight, we introduce an alternating algorithm to efficiently minimize the NCMI. Across image recognition and whole-slide imaging (WSI) subtyping benchmarks, NCMI-trained models surpass state of the art losses by substantial margins at a computational cost comparable to that of CE. Notably, on ImageNet, NCMI yields a 2.77% top-1 accuracy improvement with ResNet-50 comparing to the CE; on CAMELYON-17, replacing CE with NCMI improves the macro-F1 by 8.6% over the strongest baseline. Gains are consistent across various architectures and batch sizes, suggesting that NCMI is a practical and competitive alternative to CE. + oai:arXiv.org:2601.02543v1 + cs.LG + cs.AI + cs.CV + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Linfeng Ye, Zhixiang Chi, Konstantinos N. Plataniotis, En-hui Yang + + + SimpleMem: Efficient Lifelong Memory for LLM Agents + https://arxiv.org/abs/2601.02553 + arXiv:2601.02553v1 Announce Type: new +Abstract: To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundancy, or rely on iterative reasoning to filter noise, incurring high token costs. To address this challenge, we introduce SimpleMem, an efficient memory framework based on semantic lossless compression. We propose a three-stage pipeline designed to maximize information density and token utilization: (1) \textit{Semantic Structured Compression}, which applies entropy-aware filtering to distill unstructured interactions into compact, multi-view indexed memory units; (2) \textit{Recursive Memory Consolidation}, an asynchronous process that integrates related units into higher-level abstract representations to reduce redundancy; and (3) \textit{Adaptive Query-Aware Retrieval}, which dynamically adjusts retrieval scope based on query complexity to construct precise context efficiently. Experiments on benchmark datasets show that our method consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost, achieving an average F1 improvement of 26.4% while reducing inference-time token consumption by up to 30-fold, demonstrating a superior balance between performance and efficiency. Code is available at https://github.com/aiming-lab/SimpleMem. + oai:arXiv.org:2601.02553v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiaqi Liu, Yaofeng Su, Peng Xia, Siwei Han, Zeyu Zheng, Cihang Xie, Mingyu Ding, Huaxiu Yao + + + AMC26: VSSEA robust position control + https://arxiv.org/abs/2601.02557 + arXiv:2601.02557v1 Announce Type: new +Abstract: This paper presents robust position control strategies for the novel VSSEA. By employing a constructed state-space model, two control schemes are developed in a unified framework: a state-feedback controller and a sliding mode controller, both integrated with a second-order DOb. The proposed framework achieves high-performance motion control by precisely estimating and compensating for internal and external disturbances, while preserving the nominal dynamic response. Simulation results demonstrate that pole-placement-based controllers are highly sensitive to disturbances, whereas LQR-based controllers offer improved robustness at the expense of slower dynamics. By incorporating DOb, robustness is significantly enhanced without degrading time response, and the LQR controller can be tuned solely for performance optimization. Experimental results confirm that the proposed robust position controllers can be implemented in real world applications. These results highlight the effectiveness of the proposed approach and lay the foundation for future investigations on robust stability and performance under different stiffness settings. + oai:arXiv.org:2601.02557v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Emre Sariyildiz + + + PerspectiveCoach: Exploring LLMs for Developer Reflection + https://arxiv.org/abs/2601.02559 + arXiv:2601.02559v1 Announce Type: new +Abstract: Despite growing awareness of ethical challenges in software development, practitioners still lack structured tools that help them critically engage with the lived experiences of marginalized users. This paper presents PerspectiveCoach, a large language model (LLM)-powered conversational tool designed to guide developers through structured perspective-taking exercises and deepen critical reflection on how software design decisions affect marginalized communities. Through a controlled study with 18 front-end developers (balanced by sex), who interacted with the tool using a real case of online gender-based harassment, we examine how PerspectiveCoach supports ethical reasoning and engagement with user perspectives. Qualitative analysis revealed increased self-awareness, broadened perspectives, and more nuanced ethical articulation, while a complementary human-human study contextualized these findings. Text similarity analyses demonstrated that participants in the human-PerspectiveCoach study improved the fidelity of their restatements over multiple attempts, capturing both surface-level and semantic aspects of user concerns. However, human-PerspectiveCoach's restatements had a lower baseline than the human-human conversations, highlighting contextual differences in impersonal and interpersonal perspective-taking. Across the study, participants rated the tool highly for usability and relevance. This work contributes an exploratory design for LLM-powered end-user perspective-taking that supports critical, ethical self-reflection and offers empirical insights (i.e., enhancing adaptivity, centering plurality) into how such tools can help practitioners build more inclusive and socially responsive technologies. + oai:arXiv.org:2601.02559v1 + cs.SE + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Lauren Olson, Emitz\'a Guzm\'an, Florian Kunneman + + + AMC26: High-performance DOb for robust position control + https://arxiv.org/abs/2601.02560 + arXiv:2601.02560v1 Announce Type: new +Abstract: This paper presents a new HPDOb that significantly improves disturbance estimation accuracy and robustness in motion control systems, surpassing the capabilities of conventional DObs. The proposed observer is analysed and synthesised in the discrete-time domain, providing a realistic representation of their dynamic behaviour and enabling enhanced controller design for practical applications. The core contribution of the HPDOb is a novel synthesis method that incorporates higher-order truncation error dynamics into disturbance estimation. Unlike conventional DObs, which are limited to zero-order truncation error, the HPDOb achieves first-order truncation error, yielding markedly improved estimation accuracy and robustness against disturbances in motion control systems. Simulation and experiments verify the stability and performance of HPDOb. + oai:arXiv.org:2601.02560v1 + eess.SY + cs.RO + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Emre Sariyildiz + + + A Schr\"odinger-Based Dispersive Regularization Approach for Numerical Simulation of One-Dimensional Shallow Water Equations + https://arxiv.org/abs/2601.02561 + arXiv:2601.02561v1 Announce Type: new +Abstract: We propose a novel dispersive regularization framework for the numerical simulation of the one-dimensional shallow water equations (SWE). The classical hyperbolic system is regularized by a third-order dispersive term in the momentum equation, which renders the system equivalent, via the Madelung transform, to a defocusing cubic nonlinear Schr\"odinger equation with a drift term induced by bottom topography. + Instead of solving the shallow water equations directly, we solve the associated Schr\"odinger equation and recover the hydrodynamic variables through a simple postprocessing procedure. This approach transforms the original nonlinear hyperbolic system into a semilinear complex-valued equation, which can be efficiently approximated using a Strang time-splitting method combined with a spectral element discretization in space. + Numerical experiments demonstrate that, in subcritical regimes without shock formation, the Schr\"odinger regularization provides an $O(\varepsilon)$ approximation to the classical shallow water solution, where $\varepsilon$ denotes the regularization parameter. Importantly, we observe that this convergence behavior persists even in the presence of moving wetting--drying interfaces, where vacuum states emerge and standard shallow water solvers often encounter difficulties. These results suggest that the Schr\"odinger-based formulation offers a robust and promising alternative framework for the numerical simulation of shallow water flows with dry states. + oai:arXiv.org:2601.02561v1 + math.NA + cs.NA + math.AP + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Guosheng Fu, Chun Liu + + + CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening + https://arxiv.org/abs/2601.02562 + arXiv:2601.02562v1 Announce Type: new +Abstract: The rapid growth of dermatological imaging and mobile diagnostic tools calls for systems that not only demonstrate empirical performance but also provide strong theoretical guarantees. Deep learning models have shown high predictive accuracy; however, they are often criticized for lacking well, calibrated uncertainty estimates without which these models are hardly deployable in a clinical setting. To this end, we present the Conformal Bayesian Dermatological Classifier (CBDC), a well, founded framework that combines Statistical Learning Theory, Topological Data Analysis (TDA), and Bayesian Conformal Inference. CBDC offers distribution, dependent generalization bounds that reflect dermatological variability, proves a topological stability theorem that guarantees the invariance of convolutional neural network embeddings under photometric and morphological perturbations and provides finite conformal coverage guarantees for trustworthy uncertainty quantification. + Through exhaustive experiments on the HAM10000, PH2, and ISIC 2020 datasets, we show that CBDC not only attains classification accuracy but also generates calibrated predictions that are interpretable from a clinical perspective. This research constitutes a theoretical and practical leap for deep dermatological diagnostics, thereby opening the machine learning theory clinical applicability interface. + oai:arXiv.org:2601.02562v1 + cs.LG + eess.IV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Rohit Kaushik, Eva Kaushik + + + Compressed code: the hidden effects of quantization and distillation on programming tokens + https://arxiv.org/abs/2601.02563 + arXiv:2601.02563v1 Announce Type: new +Abstract: Large Language Models (LLMs) have demonstrated exceptional code generation capabilities, yet their token-level mechanisms remain underexplored, particularly in compressed models. Through systematic analysis of programming language token representations, we characterize how programming languages are encoded in LLM tokenizers by analyzing their vocabulary distribution and keyword coverage patterns. We introduce a novel cold-start probability analysis method that provides insights into model behavior without requiring explicit prompts. Additionally, we present a comprehensive evaluation of how different model optimization techniques - including quantization, distillation, model scaling, and task-specific fine-tuning - affect token-level representations and code generation quality. Our experiments, supported by comprehensive probability distribution analysis and evaluation metrics, reveal critical insights into token-level behavior and provide empirically-validated guidelines for maintaining code generation quality under various optimization constraints. These findings advance both theoretical understanding of LLM code generation and practical implementation of optimized models in production environments. + oai:arXiv.org:2601.02563v1 + cs.SE + cs.CL + cs.LG + cs.PL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Viacheslav Siniaev, Iaroslav Chelombitko, Aleksey Komissarov + + + Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network + https://arxiv.org/abs/2601.02566 + arXiv:2601.02566v1 Announce Type: new +Abstract: Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing tools (known as "shallowfakes") or advanced artificial intelligence techniques ("deepfakes"). While numerous studies have focused on image manipulation localization on either shallowfake images or deepfake videos, few approaches address both cases. In this paper, we explore the feasibility of using a deep learning network to localize manipulations in both shallow- and deep-fake images, and proposed a solution for such purpose. To precisely differentiate between authentic and manipulated pixels, we leverage the Vision Mamba network to extract feature maps that clearly describe the boundaries between tampered and untouched regions. To further enhance this separation, we propose a novel Guided Graph Neural Network (G-GNN) module that amplifies the distinction between manipulated and authentic pixels. Our evaluation results show that our proposed method achieved higher inference accuracy compared to other state-of-the-art methods. + oai:arXiv.org:2601.02566v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Junbin Zhang, Hamid Reza Tohidypour, Yixiao Wang, Panos Nasiopoulos + + + LoRA-Drop: Temporal LoRA Decoding for Efficient LLM Inference + https://arxiv.org/abs/2601.02569 + arXiv:2601.02569v1 Announce Type: new +Abstract: Autoregressive large language models (LLMs) are bottlenecked by sequential decoding, where each new token typically requires executing all transformer layers. Existing dynamic-depth and layer-skipping methods reduce this cost, but often rely on auxiliary routing mechanisms or incur accuracy degradation when bypassed layers are left uncompensated. We present \textbf{LoRA-Drop}, a plug-and-play inference framework that accelerates decoding by applying a \emph{temporal compute schedule} to a fixed subset of intermediate layers: on most decoding steps, selected layers reuse the previous-token hidden state and apply a low-rank LoRA correction, while periodic \emph{refresh} steps execute the full model to prevent drift. LoRA-Drop requires no routing network, is compatible with standard KV caching, and can reduce KV-cache footprint by skipping KV updates in droppable layers during LoRA steps and refreshing periodically. Across \textbf{LLaMA2-7B}, \textbf{LLaMA3-8B}, \textbf{Qwen2.5-7B}, and \textbf{Qwen2.5-14B}, LoRA-Drop achieves up to \textbf{2.6$\times$ faster decoding} and \textbf{45--55\% KV-cache reduction} while staying within \textbf{0.5 percentage points (pp)} of baseline accuracy. Evaluations on reasoning (GSM8K, MATH, BBH), code generation (HumanEval, MBPP), and long-context/multilingual benchmarks (LongBench, XNLI, XCOPA) identify a consistent \emph{safe zone} of scheduling configurations that preserves quality while delivering substantial efficiency gains, providing a simple path toward adaptive-capacity inference in LLMs. Codes are available at https://github.com/hosseinbv/LoRA-Drop.git. + oai:arXiv.org:2601.02569v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hossein Rajabzadeh, Maryam Dialameh, Chul B. Park, Il-Min Kim, Hyock Ju Kwon + + + O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies + https://arxiv.org/abs/2601.02571 + arXiv:2601.02571v1 Announce Type: new +Abstract: The growing demand for mid-band spectrum necessitates efficient Dynamic Spectrum Sharing (DSS) to ensure coexistence between cellular networks and incumbent radar systems. Existing Spectrum Access System (SAS) frameworks rely on fixed Environmental Sensing Capability (ESC) sensors, which are latency-prone and inflexible. This paper introduces O-DSS, an O-RAN-compliant, Machine Learning (ML)-driven DSS framework that enables real-time cellular-radar coexistence in mid-band frequencies with shipborne and fast-moving airborne radars. O-DSS integrates radar detection from low-overhead Key Performance Metrics (KPMs) with spectrogram-based localization to drive fine-grained RAN control, including PRB blanking and radar-aware MCS adaptation. Deployed as a modular xApp, O-DSS achieves 60~ms detection and 700~ms evacuation latencies, outperforming existing baselines. Evaluations across simulations and Over-The-Air (OTA) testbeds show that O-DSS ensures robust incumbent protection while maintaining cellular performance by achieving radar detection of $\geq 99\%$ at SINR $\geq -4$~dB and localization recall of $\geq 95\%$ at SINR $\geq 8$~dB. + oai:arXiv.org:2601.02571v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/publicdomain/zero/1.0/ + Azuka Chiejina, Divyadharshini Muruganandham, Vini Chaudhary, Kaushik Chowdhury, Vijay K. Shah + + + LendNova: Towards Automated Credit Risk Assessment with Language Models + https://arxiv.org/abs/2601.02573 + arXiv:2601.02573v1 Announce Type: new +Abstract: Credit risk assessment is essential in the financial sector, but has traditionally depended on costly feature-based models that often fail to utilize all available information in raw credit records. This paper introduces LendNova, the first practical automated end-to-end pipeline for credit risk assessment, designed to utilize all available information in raw credit records by leveraging advanced NLP techniques and language models. LendNova transforms risk modeling by operating directly on raw, jargon-heavy credit bureau text using a language model that learns task-relevant representations without manual feature engineering. By automatically capturing patterns and risk signals embedded in the text, it replaces manual preprocessing steps, reducing costs and improving scalability. Evaluation on real-world data further demonstrates its strong potential in accurate and efficient risk assessment. LendNova establishes a baseline for intelligent credit risk agents, demonstrating the feasibility of language models in this domain. It lays the groundwork for future research toward foundation systems that enable more accurate, adaptable, and automated financial decision-making. + oai:arXiv.org:2601.02573v1 + cs.LG + cs.AI + cs.CE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + AAAI 2026, Workshop on Agentic AI in Financial Services + Kiarash Shamsi, Danijel Novokmet, Joshua Peters, Mao Lin Liu, Paul K Edwards, Vahab Khoshdel + + + Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency + https://arxiv.org/abs/2601.02574 + arXiv:2601.02574v1 Announce Type: new +Abstract: Large language models (LLMs) are increasingly used in applications requiring factual accuracy, yet their outputs often contain hallucinated responses. While fact-checking can mitigate these errors, existing methods typically retrieve external evidence indiscriminately, overlooking the model's internal knowledge and potentially introducing irrelevant noise. Moreover, current systems lack targeted mechanisms to resolve specific uncertainties in the model's reasoning. Inspired by how humans fact-check, we argue that LLMs should adaptively decide whether to rely on internal knowledge or initiate retrieval based on their confidence in a given claim. We introduce Probabilistic Certainty and Consistency (PCC), a framework that estimates factual confidence by jointly modeling an LLM's probabilistic certainty and reasoning consistency. These confidence signals enable an adaptive verification strategy: the model answers directly when confident, triggers targeted retrieval when uncertain or inconsistent, and escalates to deep search when ambiguity is high. Our confidence-guided routing mechanism ensures that retrieval is invoked only when necessary, improving both efficiency and reliability. Extensive experiments across three challenging benchmarks show that PCC achieves better uncertainty quantification than verbalized confidence and consistently outperforms strong LLM-based fact-checking baselines. Furthermore, we demonstrate that PCC generalizes well across various LLMs. + oai:arXiv.org:2601.02574v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Haoran Wang, Maryam Khalid, Qiong Wu, Jian Gao, Cheng Cao + + + Orchestral AI: A Framework for Agent Orchestration + https://arxiv.org/abs/2601.02577 + arXiv:2601.02577v1 Announce Type: new +Abstract: The rapid proliferation of LLM agent frameworks has forced developers to choose between vendor lock-in through provider-specific SDKs and complex multi-package ecosystems that obscure control flow and hinder reproducibility. Integrating tool calling across multiple LLM providers remains a core engineering challenge due to fragmented APIs, incompatible message formats, and inconsistent streaming and tool-calling behavior, making it difficult to build portable, reliable agent systems. We introduce Orchestral, a lightweight Python framework that provides a unified, type-safe interface for building LLM agents across major providers while preserving the simplicity required for scientific computing and production deployment. Orchestral defines a single universal representation for messages, tools, and LLM usage that operates seamlessly across providers, eliminating manual format translation and reducing framework-induced complexity. Automatic tool schema generation from Python type hints removes the need for handwritten descriptors while maintaining type safety across provider boundaries. A synchronous execution model with streaming support enables deterministic behavior, straightforward debugging, and real-time interaction without introducing server dependencies. The framework's modular architecture cleanly separates provider integration, tool execution, conversation orchestration, and user-facing interfaces, enabling extensibility without architectural entanglement. Orchestral supports advanced agent capabilities found in larger frameworks, including rich tool calling, context compaction, workspace sandboxing, user approval workflows, sub-agents, memory management, and MCP integration. + oai:arXiv.org:2601.02577v1 + cs.AI + astro-ph.IM + hep-ph + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Alexander Roman, Jacob Roman + + + DataParasite Enables Scalable and Repurposable Online Data Curation + https://arxiv.org/abs/2601.02578 + arXiv:2601.02578v1 Announce Type: new +Abstract: Many questions in computational social science rely on datasets assembled from heterogeneous online sources, a process that is often labor-intensive, costly, and difficult to reproduce. Recent advances in large language models enable agentic search and structured extraction from the web, but existing systems are frequently opaque, inflexible, or poorly suited to scientific data curation. Here we introduce DataParasite, an open-source, modular pipeline for scalable online data collection. DataParasite decomposes tabular curation tasks into independent, entity-level searches defined through lightweight configuration files and executed through a shared, task-agnostic python script. Crucially, the same pipeline can be repurposed to new tasks, including those without predefined entity lists, using only natural-language instructions. We evaluate the pipeline on multiple canonical tasks in computational social science, including faculty hiring histories, elite death events, and political career trajectories. Across tasks, DataParasite achieves high accuracy while reducing data-collection costs by an order of magnitude relative to manual curation. By lowering the technical and labor barriers to online data assembly, DataParasite provides a practical foundation for scalable, transparent, and reusable data curation in computational social science and beyond. + oai:arXiv.org:2601.02578v1 + cs.CL + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mengyi Sun (Cold Spring Harbor Laboratory) + + + Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models + https://arxiv.org/abs/2601.02580 + arXiv:2601.02580v1 Announce Type: new +Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration. This study introduces a novel approach that implicitly models these psychometric properties by fine-tuning Large Language Models (LLMs) to simulate student responses across a spectrum of latent abilities. Leveraging the Qwen-3 dense model series and Low-Rank Adaptation (LoRA), we train models to generate responses to multiple choice questions conditioned on discrete ability descriptors. We reconstruct the probability of a correct response as a function of student ability, effectively generating synthetic Item Characteristic Curves (ICCs) to estimate IRT parameters. Evaluation on a dataset of Grade 6 English Language Arts (ELA) items and the BEA 2024 Shared Task dataset demonstrates that this method competes with or outperforms baseline approaches. This simulation-based technique seems particularly effective at modeling item discrimination. + oai:arXiv.org:2601.02580v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Christopher Ormerod + + + Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis + https://arxiv.org/abs/2601.02581 + arXiv:2601.02581v1 Announce Type: new +Abstract: The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The conventional rule-based security systems are not always scalable and dynamic to meet such a threat. This paper introduces a threat detection framework based on machine learning that can be used to classify malicious behavior in the social media network environment based on the nature of network traffic. Exploiting a rich network traffic dataset, the massive preprocessing and exploratory data analysis is conducted to overcome the problem of data imbalance, feature inconsistency, and noise. A model of artificial neural network (ANN) is then created to acquire intricate, non-linear tendencies of malicious actions. The proposed model is tested on conventional performance metrics, such as accuracy, accuracy, recall, F1-score, and ROC-AUC, and shows good detection and high levels of strength. The findings suggest that neural network-based solutions have the potential to be used effectively to identify the latent threat dynamics within the context of a large-scale social media network and that they can be employed to complement the existing intrusion detection system and better to conduct proactive cybersecurity operations. + oai:arXiv.org:2601.02581v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/publicdomain/zero/1.0/ + The Journal of Computational Science and Engineering Vol. 3, Number 12, December 2025, pp. 15 25 + Aditi Sanjay Agrawal + + + AI Social Responsibility as Reachability: Execution-Level Semantics for the Social Responsibility Stack + https://arxiv.org/abs/2601.02585 + arXiv:2601.02585v1 Announce Type: new +Abstract: Artificial intelligence systems are increasingly embedded as persistent, closed-loop components within cyber-physical, social, and institutional processes. Rather than producing isolated outputs, such systems operate continuously under feedback, adaptation, and scale, reshaping physical flows, human behavior, and institutional practice over time. In these settings, socially unacceptable outcomes rarely arise from singular faults or explicit policy violations. Instead, they emerge through cumulative execution trajectories enabled by repetition, concurrency, and feedback. + This paper advances the formal foundation of the Social Responsibility Stack (SRS) by making its central requirement explicit: responsibility is fundamentally a reachability property of system execution. A system is responsible iff its execution semantics prevent entry into inadmissible global configurations, regardless of local performance gains or optimization objectives. Responsibility failures are therefore not objective-level errors, but execution-level failures of trajectory control. + To operationalize this perspective, we introduce Petri nets as an execution-level formalism for responsible autonomous systems. We show how SRS value commitments correspond to forbidden markings, safeguards to structural constraints on transition firing, auditing to monitoring of reachability pressure, and governance to legitimate modification of execution structure. Embedding Petri-net reachability within the SRS architecture internalizes responsibility as a structural invariant rather than an external objective or post-hoc mechanism. + These results establish the Social Responsibility Stack as an executable responsibility architecture and position reachability-based execution semantics as a necessary foundation for responsible autonomy in feedback-rich cyber-physical and socio-technical systems. + oai:arXiv.org:2601.02585v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Otman Basir + + + Understanding Human Perception of Music Plagiarism Through a Computational Approach + https://arxiv.org/abs/2601.02586 + arXiv:2601.02586v1 Announce Type: new +Abstract: There is a wide variety of music similarity detection algorithms, while discussions about music plagiarism in the real world are often based on audience perceptions. Therefore, we aim to conduct a study to examine the key criteria of human perception of music plagiarism, focusing on the three commonly used musical features in similarity analysis: melody, rhythm, and chord progression. After identifying the key features and levels of variation humans use in perceiving musical similarity, we propose a LLM-as-a-judge framework that applies a systematic, step-by-step approach, drawing on modules that extract such high-level attributes. + oai:arXiv.org:2601.02586v1 + cs.SD + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Daeun Hwang, Hyeonbin Hwang + + + FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions + https://arxiv.org/abs/2601.02589 + arXiv:2601.02589v1 Announce Type: new +Abstract: Over 3.5 million patents are filed annually, with drafting patent descriptions requiring deep technical and legal expertise. Transforming scientific papers into patent descriptions is particularly challenging due to their differing rhetorical styles and stringent legal requirements. Unlike black-box text-to-text approaches that struggle to model structural reasoning and legal constraints, we propose FlowPlan-G2P, a novel framework that mirrors the cognitive workflow of expert drafters by reformulating this task into three stages: (1) Concept Graph Induction, extracting technical entities and relationships into a directed graph via expert-like reasoning; (2) Paragraph and Section Planning, reorganizing the graph into coherent clusters aligned with canonical patent sections; and (3) Graph-Conditioned Generation, producing legally compliant paragraphs using section-specific subgraphs and tailored prompts. Experiments demonstrate that FlowPlan-G2P significantly improves logical coherence and legal compliance over end-to-end LLM baselines. Our framework establishes a new paradigm for paper-to-patent generation and advances structured text generation for specialized domains. + oai:arXiv.org:2601.02589v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kris W Pan, Yongmin Yoo + + + A Music Information Retrieval Approach to Classify Sub-Genres in Role Playing Games + https://arxiv.org/abs/2601.02591 + arXiv:2601.02591v1 Announce Type: new +Abstract: Video game music (VGM) is often studied under the same lens as film music, which largely focuses on its theoretical functionality with relation to the identified genres of the media. However, till date, we are unaware of any systematic approach that analyzes the quantifiable musical features in VGM across several identified game genres. Therefore, we extracted musical features from VGM in games from three sub-genres of Role-Playing Games (RPG), and then hypothesized how different musical features are correlated to the perceptions and portrayals of each genre. This observed correlation may be used to further suggest such features are relevant to the expected storytelling elements or play mechanics associated with the sub-genre. + oai:arXiv.org:2601.02591v1 + cs.SD + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Daeun Hwang, Xuyuan Cai, Edward F. Melcer, Elin Carstensdottir + + + Volumetric locking-free Mixed Virtual Element Methods for Contact Problems + https://arxiv.org/abs/2601.02595 + arXiv:2601.02595v1 Announce Type: new +Abstract: We consider the approximation of the 2D frictionless contact problem in elasticity using the Virtual Element Methods (VEMs). To overcome the volumetric locking phenomenon in the nearly incompressible case, we adopt a mixed displacement/pressure ($u/p$) variational formulation, where pressure is introduced as an independent unknown. We present the VEM discretization and develop a general error analysis, keeping explicit track of the constants involved in the error estimates, thus allowing to consider meshes with "small edges". As examples, we consider two possible VEM schemes: a first-order scheme and a second-order scheme. The numerical results confirm the theoretical predictions, specifically both schemes show: 1) robustness with respect to the volumetric parameter $\lambda$, thus preventing the occurrence of the volumetric locking phenomenon; 2) good behavior even in the presence of "small edges"; 3) achievement of the expected theoretical convergence rates. + oai:arXiv.org:2601.02595v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + C. Lovadina, L. Molinari + + + Coordinated Multi-Domain Deception: A Stackelberg Game Approach + https://arxiv.org/abs/2601.02596 + arXiv:2601.02596v1 Announce Type: new +Abstract: This paper explores coordinated deception strategies by synchronizing defenses across coupled cyber and physical systems to mislead attackers and strengthen defense mechanisms. We introduce a Stackelberg game framework to model the strategic interaction between defenders and attackers, where the defender leverages CVSS-based exploit probabilities and real-world vulnerability data from the National Vulnerability Database (NVD) to guide the deployment of deception. Cyber and physical replicas are used to disrupt attacker reconnaissance and enhance defensive effectiveness. We propose a CVE-based utility function to identify the most critical vulnerabilities and demonstrate that coordinated multilayer deception outperforms single-layer and baseline strategies in improving defender utility across both CVSS versions. + oai:arXiv.org:2601.02596v1 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Md Abu Sayed, Asif Rahman, Ahmed Hemida, Christopher Kiekintveld, Charles Kamhoua + + + LongDA: Benchmarking LLM Agents for Long-Document Data Analysis + https://arxiv.org/abs/2601.02598 + arXiv:2601.02598v1 Announce Type: new +Abstract: We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume well-specified schemas and inputs, LongDA targets real-world settings in which navigating long documentation and complex data is the primary bottleneck. To this end, we manually curate raw data files, long and heterogeneous documentation, and expert-written publications from 17 publicly available U.S. national surveys, from which we extract 505 analytical queries grounded in real analytical practice. Solving these queries requires agents to first retrieve and integrate key information from multiple unstructured documents, before performing multi-step computations and writing executable code, which remains challenging for existing data analysis agents. To support the systematic evaluation under this setting, we develop LongTA, a tool-augmented agent framework that enables document access, retrieval, and code execution, and evaluate a range of proprietary and open-source models. Our experiments reveal substantial performance gaps even among state-of-the-art models, highlighting the challenges researchers should consider before applying LLM agents for decision support in real-world, high-stakes analytical settings. + oai:arXiv.org:2601.02598v1 + cs.DL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yiyang Li, Zheyuan Zhang, Tianyi Ma, Zehong Wang, Keerthiram Murugesan, Chuxu Zhang, Yanfang Ye + + + State of the Quantum Software Engineering Ecosystem + https://arxiv.org/abs/2601.02601 + arXiv:2601.02601v1 Announce Type: new +Abstract: We study the current state of the Quantum Software Engineering (QSE) ecosystem, focusing on the achievements, activities, and engagements from academia and industry, with a special focus on successful entrepreneurial endeavors in this arena. Our research methodology is a novel one, featuring the state-of-the-art in Artificial Intelligence (AI), namely Large Language Models (LLMs), especially Generative Pretrained Transformers (GPT). We use one of such models, namely the OpenAI GPT-5 model, through the ChatGPT tool. The goal is to identify institutions and companies that are highly active and have achieved distinguished results in QSE, evidenced by peer-reviewed publications or raised capital in the venture capital market. + oai:arXiv.org:2601.02601v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nazanin Siavash, Armin Moin + + + SWaRL: Safeguard Code Watermarking via Reinforcement Learning + https://arxiv.org/abs/2601.02602 + arXiv:2601.02602v1 Announce Type: new +Abstract: We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLM owners by embedding unique and verifiable signatures in the generated output. Existing approaches rely on manually crafted transformation rules to preserve watermarked code functionality or manipulate token-generation probabilities at inference time, which are prone to compilation errors. To address these challenges, SWaRL employs a reinforcement learning-based co-training framework that uses compiler feedback for functional correctness and a jointly trained confidential verifier as a reward signal to maintain watermark detectability. Furthermore, SWaRL employs low-rank adaptation (LoRA) during fine-tuning, allowing the learned watermark information to be transferable across model updates. Extensive experiments show that SWaRL achieves higher watermark detection accuracy compared to prior methods while fully maintaining watermarked code functionality. The LoRA-based signature embedding steers the base model to generate and solve code in a watermark-specific manner without significant computational overhead. Moreover, SWaRL exhibits strong resilience against refactoring and adversarial transformation attacks. + oai:arXiv.org:2601.02602v1 + cs.CR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Neusha Javidnia, Ruisi Zhang, Ashish Kundu, Farinaz Koushanfar + + + Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs + https://arxiv.org/abs/2601.02604 + arXiv:2601.02604v1 Announce Type: new +Abstract: The integration of Large Language Models (LLMs) into biomedical research offers new opportunities for domainspecific reasoning and knowledge representation. However, their performance depends heavily on the semantic quality of training data. In oncology, where precision and interpretability are vital, scalable methods for constructing structured knowledge bases are essential for effective fine-tuning. This study presents a pipeline for developing a lung cancer knowledge base using Open Information Extraction (OpenIE). The process includes: (1) identifying medical concepts with the MeSH thesaurus; (2) filtering open-access PubMed literature with permissive licenses (CC0); (3) extracting (subject, relation, object) triplets using OpenIE method; and (4) enriching triplet sets with Named Entity Recognition (NER) to ensure biomedical relevance. The resulting triplet sets provide a domain-specific, large-scale, and noise-aware resource for fine-tuning LLMs. We evaluated T5 models finetuned on this dataset through Supervised Semantic Fine-Tuning. Comparative assessments with ROUGE and BERTScore show significantly improved performance and semantic coherence, demonstrating the potential of OpenIE-derived resources as scalable, low-cost solutions for enhancing biomedical NLP. + oai:arXiv.org:2601.02604v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/ENC68268.2025.11311861 + Cesar Felipe Mart\'inez Cisneros, Jes\'us Ulises Quiroz Bautista, Claudia Anah\'i Guzm\'an Solano, Bogdan Kaleb Garc\'ia Rivera, Iv\'an Garc\'ia Pacheco, Yalbi Itzel Balderas Mart\'inez, Kolawole John Adebayoc, Ignacio Arroyo Fern\'andez + + + Weights on finite fields and failures of the MacWilliams identities + https://arxiv.org/abs/2601.02608 + arXiv:2601.02608v1 Announce Type: new +Abstract: In the 1960s, MacWilliams proved that the Hamming weight enumerator of a linear code over a finite field completely determines, and is determined by, the Hamming weight enumerator of its dual code. In particular, if two linear codes have the same Hamming weight enumerator, then their dual codes have the same Hamming weight enumerator. + In contrast, there is a wide class of weights on finite fields whose weight enumerators have the opposite behavior: there exist two linear codes having the same weight enumerator, but their dual codes have different weight enumerators. + oai:arXiv.org:2601.02608v1 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jay A. Wood + + + Chronicals: A High-Performance Framework for LLM Fine-Tuning with 3.51x Speedup over Unsloth + https://arxiv.org/abs/2601.02609 + arXiv:2601.02609v1 Announce Type: new +Abstract: Large language model fine-tuning is bottlenecked by memory: a 7B parameter model requires 84GB--14GB for weights, 14GB for gradients, and 56GB for FP32 optimizer states--exceeding even A100-40GB capacity. We present Chronicals, an open-source training framework achieving 3.51x speedup over Unsloth through four synergistic optimizations: (1) fused Triton kernels eliminating 75% of memory traffic via RMSNorm (7x), SwiGLU (5x), and QK-RoPE (2.3x) fusion; (2) Cut Cross-Entropy reducing logit memory from 5GB to 135MB through online softmax computation; (3) LoRA+ with theoretically-derived 16x differential learning rates between adapter matrices; and (4) Best-Fit Decreasing sequence packing recovering 60-75% of compute wasted on padding. + On Qwen2.5-0.5B with A100-40GB, Chronicals achieves 41,184 tokens/second for full fine-tuning versus Unsloth's 11,736 tokens/second (3.51x). For LoRA at rank 32, we reach 11,699 tokens/second versus Unsloth MAX's 2,857 tokens/second (4.10x). Critically, we discovered that Unsloth's reported 46,000 tokens/second benchmark exhibited zero gradient norms--the model was not training. + We provide complete mathematical foundations: online softmax correctness proofs, FlashAttention IO complexity bounds O(N^2 d^2 M^{-1}), LoRA+ learning rate derivations from gradient magnitude analysis, and bin-packing approximation guarantees. All implementations, benchmarks, and proofs are available at https://github.com/Ajwebdevs/Chronicals with pip installation via https://pypi.org/project/chronicals/. + oai:arXiv.org:2601.02609v1 + cs.LG + cs.AI + cs.CL + cs.DC + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Arjun S. Nair + + + Sparsity-Aware Streaming SNN Accelerator with Output-Channel Dataflow for Automatic Modulation Classification + https://arxiv.org/abs/2601.02613 + arXiv:2601.02613v1 Announce Type: new +Abstract: The rapid advancement of wireless communication technologies, including 5G, emerging 6G networks, and the large-scale deployment of the Internet of Things (IoT), has intensified the need for efficient spectrum utilization. Automatic modulation classification (AMC) plays a vital role in cognitive radio systems by enabling real-time identification of modulation schemes for dynamic spectrum access and interference mitigation. While deep neural networks (DNNs) offer high classification accuracy, their computational and energy demands pose challenges for real-time edge deployment. Spiking neural networks (SNNs), with their event-driven nature, offer inherent energy efficiency, but achieving both high throughput and low power under constrained hardware resources remains challenging. This work proposes a sparsity-aware SNN streaming accelerator optimized for AMC tasks. Unlike traditional systolic arrays that exploit sparsity but suffer from low throughput, or streaming architectures that achieve high throughput but cannot fully utilize input and weight sparsity, our design integrates both advantages. By leveraging the fixed nature of kernels during inference, we apply the gated one-to-all product (GOAP) algorithm to compute only on non-zero input-weight intersections. Extra or empty iterations are precomputed and embedded into the inference dataflow, eliminating dynamic data fetches and enabling fully pipelined, control-free inter-layer execution. Implemented on an FPGA, our sparsity-aware output-channel dataflow streaming (SAOCDS) accelerator achieves 23.5 MS/s (approximately double the baseline throughput) on the RadioML 2016 dataset, while reducing dynamic power and maintaining comparable classification accuracy. These results demonstrate strong potential for real-time, low-power deployment in edge cognitive radio systems. + oai:arXiv.org:2601.02613v1 + cs.AR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kuilian Yang, Li Zhang, Ahmed M. Eltawil, Khaled Nabil Salama + + + LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification + https://arxiv.org/abs/2601.02624 + arXiv:2601.02624v1 Announce Type: new +Abstract: The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental steps in the pre-silicon security verification of a hardware design is the identification of security assets, as it substantially influences downstream security verification tasks, such as threat modeling, security property generation, and vulnerability detection. Traditionally, assets are determined manually by security experts, requiring significant time and expertise. To address this challenge, we present LAsset, a novel automated framework that leverages large language models (LLMs) to identify security assets from both hardware design specifications and register-transfer level (RTL) descriptions. The framework performs structural and semantic analysis to identify intra-module primary and secondary assets and derives inter-module relationships to systematically characterize security dependencies at the design level. Experimental results show that the proposed framework achieves high classification accuracy, reaching up to 90% recall rate in SoC design, and 93% recall rate in IP designs. This automation in asset identification significantly reduces manual overhead and supports a scalable path forward for secure hardware development. + oai:arXiv.org:2601.02624v1 + cs.CR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Md Ajoad Hasan, Dipayan Saha, Khan Thamid Hasan, Nashmin Alam, Azim Uddin, Sujan Kumar Saha, Mark Tehranipoor, Farimah Farahmandi + + + Improved Evidence Extraction for Document Inconsistency Detection with LLMs + https://arxiv.org/abs/2601.02627 + arXiv:2601.02627v1 Announce Type: new +Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. However, research on LLM-based approaches to document inconsistency detection is relatively limited. There are two key aspects of document inconsistency detection: (i) classification of whether there exists any inconsistency, and (ii) providing evidence of the inconsistent sentences. We focus on the latter, and introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering that substantially improves LLM-based document inconsistency detection over direct prompting. We back our claims with promising experimental results. + oai:arXiv.org:2601.02627v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nelvin Tan, Yaowen Zhang, James Asikin Cheung, Fusheng Liu, Yu-Ching Shih, Dong Yang + + + Listen to the Unexpected: Self-Supervised Surprise Detection for Efficient Viewport Prediction + https://arxiv.org/abs/2601.02629 + arXiv:2601.02629v1 Announce Type: new +Abstract: Adaptive streaming of 360-degree video relies on viewport prediction to allocate bandwidth efficiently. Current approaches predominantly use visual saliency or historical gaze patterns, neglecting the role of spatial audio in guiding user attention. This paper presents a self-learning framework for detecting "surprising" auditory events -- moments that deviate from learned temporal expectations -- and demonstrates their utility for viewport prediction. The proposed architecture combines $SE(3)$-equivariant graph neural networks with recurrent temporal modeling, trained via a dual self-supervised objective. A key feature is the natural modeling of temporal attention decay: surprise is high at event onset but diminishes as the listener adapts. Experiments on the AVTrack360 dataset show that integrating audio surprise with visual cues reduces bitrate waste by up to 18% compared to visual-only methods. + oai:arXiv.org:2601.02629v1 + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Arman Nik Khah, Ravi Prakash + + + Copyright Laundering Through the AI Ouroboros: Adapting the 'Fruit of the Poisonous Tree' Doctrine to Recursive AI Training + https://arxiv.org/abs/2601.02631 + arXiv:2601.02631v1 Announce Type: new +Abstract: Copyright enforcement rests on an evidentiary bargain: a plaintiff must show both the defendant's access to the work and substantial similarity in the challenged output. That bargain comes under strain when AI systems are trained through multi-generational pipelines with recursive synthetic data. As successive models are tuned on the outputs of its predecessors, any copyrighted material absorbed by an early model is diffused into deeper statistical abstractions. The result is an evidentiary blind spot where overlaps that emerge look coincidental, while the chain of provenance is too attenuated to trace. These conditions are ripe for "copyright laundering"--the use of multi-generational synthetic pipelines, an "AI Ouroboros," to render traditional proof of infringement impracticable. This Article adapts the "fruit of the poisonous tree" (FOPT) principle to propose a AI-FOPT standard: if a foundational AI model's training is adjudged infringing (either for unlawful sourcing or for non-transformative ingestion that fails fair-use), then subsequent AI models principally derived from the foundational model's outputs or distilled weights carry a rebuttable presumption of taint. The burden shifts to downstream developers--those who control the evidence of provenance--to restore the evidentiary bargain by affirmatively demonstrating a verifiably independent and lawfully sourced lineage or a curative rebuild, without displacing fair-use analysis at the initial ingestion stage. Absent such proof, commercial deployment of tainted models and their outputs is actionable. This Article develops the standard by specifying its trigger, presumption, and concrete rebuttal paths (e.g., independent lineage or verifiable unlearning); addresses counterarguments concerning chilling innovation and fair use; and demonstrates why this lineage-focused approach is both administrable and essential. + oai:arXiv.org:2601.02631v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Anirban Mukherjee, Hannah Hanwen Chang + + + TAAF: A Trace Abstraction and Analysis Framework Synergizing Knowledge Graphs and LLMs + https://arxiv.org/abs/2601.02632 + arXiv:2601.02632v1 Announce Type: new +Abstract: Execution traces are a critical source of information for understanding, debugging, and optimizing complex software systems. However, traces from OS kernels or large-scale applications like Chrome or MySQL are massive and difficult to analyze. Existing tools rely on predefined analyses, and custom insights often require writing domain-specific scripts, which is an error-prone and time-consuming task. This paper introduces TAAF (Trace Abstraction and Analysis Framework), a novel approach that combines time-indexing, knowledge graphs (KGs), and large language models (LLMs) to transform raw trace data into actionable insights. TAAF constructs a time-indexed KG from trace events to capture relationships among entities such as threads, CPUs, and system resources. An LLM then interprets query-specific subgraphs to answer natural-language questions, reducing the need for manual inspection and deep system expertise. To evaluate TAAF, we introduce TraceQA-100, a benchmark of 100 questions grounded in real kernel traces. Experiments across three LLMs and multiple temporal settings show that TAAF improves answer accuracy by up to 31.2%, particularly in multi-hop and causal reasoning tasks. We further analyze where graph-grounded reasoning helps and where limitations remain, offering a foundation for next-generation trace analysis tools. + oai:arXiv.org:2601.02632v1 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3744916.3787832 + Alireza Ezaz, Ghazal Khodabandeh, Majid Babaei, Naser Ezzati-Jivan + + + Fluid Agency in AI Systems: A Case for Functional Equivalence in Copyright, Patent, and Tort + https://arxiv.org/abs/2601.02633 + arXiv:2601.02633v1 Announce Type: new +Abstract: Modern Artificial Intelligence (AI) systems lack human-like consciousness or culpability, yet they exhibit fluid agency: behavior that is (i) stochastic (probabilistic and path-dependent), (ii) dynamic (co-evolving with user interaction), and (iii) adaptive (able to reorient across contexts). Fluid agency generates valuable outputs but collapses attribution, irreducibly entangling human and machine inputs. This fundamental unmappability fractures doctrines that assume traceable provenance--authorship, inventorship, and liability--yielding ownership gaps and moral "crumple zones." This Article argues that only functional equivalence stabilizes doctrine. Where provenance is indeterminate, legal frameworks must treat human and AI contributions as equivalent for allocating rights and responsibility--not as a claim of moral or economic parity but as a pragmatic default. This principle stabilizes doctrine across domains, offering administrable rules: in copyright, vesting ownership in human orchestrators without parsing inseparable contributions; in patent, tying inventor-of-record status to human orchestration and reduction to practice, even when AI supplies the pivotal insight; and in tort, replacing intractable causation inquiries with enterprise-level and sector-specific strict or no-fault schemes. The contribution is both descriptive and normative: fluid agency explains why origin-based tests fail, while functional equivalence supplies an outcome-focused framework to allocate rights and responsibility when attribution collapses. + oai:arXiv.org:2601.02633v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Anirban Mukherjee, Hannah Hanwen Chang + + + Credit Assignment via Neural Manifold Noise Correlation + https://arxiv.org/abs/2601.02636 + arXiv:2601.02636v1 Announce Type: new +Abstract: Credit assignment--how changes in individual neurons and synapses affect a network's output--is central to learning in brains and machines. Noise correlation, which estimates gradients by correlating perturbations of activity with changes in output, provides a biologically plausible solution to credit assignment but scales poorly as accurately estimating the Jacobian requires that the number of perturbations scale with network size. Moreover, isotropic noise conflicts with neurobiological observations that neural activity lies on a low-dimensional manifold. To address these drawbacks, we propose neural manifold noise correlation (NMNC), which performs credit assignment using perturbations restricted to the neural manifold. We show theoretically and empirically that the Jacobian row space aligns with the neural manifold in trained networks, and that manifold dimensionality scales slowly with network size. NMNC substantially improves performance and sample efficiency over vanilla noise correlation in convolutional networks trained on CIFAR-10, ImageNet-scale models, and recurrent networks. NMNC also yields representations more similar to the primate visual system than vanilla noise correlation. These findings offer a mechanistic hypothesis for how biological circuits could support credit assignment, and suggest that biologically inspired constraints may enable, rather than limit, effective learning at scale. + oai:arXiv.org:2601.02636v1 + cs.LG + cs.AI + q-bio.NC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Byungwoo Kang, Maceo Richards, Bernardo Sabatini + + + An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices + https://arxiv.org/abs/2601.02641 + arXiv:2601.02641v1 Announce Type: new +Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU utilization and thermal conditions. In this paper, through extensive experiments, we investigate two key issues that must be addressed to deploy on-device models in real-world services: (i) the selection of on-device models and the resource consumption of each model, and (ii) the capability and potential of on-device models for domain adaptation. To this end, we focus on a task of translating live-stream chat messages and manually construct LiveChatBench, a benchmark consisting of 1,000 Korean-English parallel sentence pairs. Experiments on five mobile devices demonstrate that, although serving a large and heterogeneous user base requires careful consideration of highly constrained deployment settings and model selection, the proposed approach nevertheless achieves performance comparable to commercial models such as GPT-5.1 on the well-targeted task. We expect that our findings will provide meaningful insights to the on-device AI community. + oai:arXiv.org:2601.02641v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jeiyoon Park, Daehwan Lee, Changmin Yeo, Yongshin Han, Minseop Kim + + + AWARE-US: Benchmark for Preference-Aware Resolution in Tool-Calling Agents + https://arxiv.org/abs/2601.02643 + arXiv:2601.02643v1 Announce Type: new +Abstract: Tool-calling conversational agents querying structured databases often face two linked failures: underspecification (missing constraints needed to run a precise query) and infeasibility (the fully specified query returns an empty set because no item satisfies all constraints). Existing work often responds with "no results" or relaxes constraints using ad hoc rules, which can violate user intent by discarding requirements the user cares about most. We frame infeasibility handling as a preference-aware query repair problem: when a query is unsatisfiable, the agent should relax the least important constraints to the user. We propose three LLM-based methods for inferring relative constraint importance from dialogue: (1) local weighting, (2) global one-shot weighting, and (3) pairwise ranking. Experiments show local weighting achieves the best preference alignment, while global weighting performs best on correct constraint relaxation. We also introduce AWARE-US, a benchmark of persona-grounded queries requiring agents to disambiguate requests via conversation and resolve infeasibility in a way consistent with persona-implied preferences. + oai:arXiv.org:2601.02643v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mehmet Kurmaz + + + Making Infeasible Tasks Feasible: Planning to Reconfigure Disconnected 3D Environments with Movable Objects + https://arxiv.org/abs/2601.02645 + arXiv:2601.02645v1 Announce Type: new +Abstract: Several planners have been developed to compute dynamically feasible, collision-free robot paths from an initial to a goal configuration. A key assumption in these works is that the goal region is reachable; an assumption that often fails in practice when environments are disconnected. Motivated by this limitation, we consider known 3D environments comprising objects, also called blocks, that form distinct navigable support surfaces (planes), and that are either non-movable (e.g., tables) or movable (e.g., boxes). These surfaces may be mutually disconnected due to height differences, holes, or lateral separations. Our focus is on tasks where the robot must reach a goal region residing on an elevated plane that is unreachable. Rather than declaring such tasks infeasible, an effective strategy is to enable the robot to interact with the environment, rearranging movable objects to create new traversable connections; a problem known as Navigation Among Movable Objects (NAMO). Existing NAMO planners typically address 2D environments, where obstacles are pushed aside to clear a path. These methods cannot directly handle the considered 3D setting; in such cases, obstacles must be placed strategically to bridge these physical disconnections. We address this challenge by developing BRiDGE (Block-based Reconfiguration in Disconnected 3D Geometric Environments), a sampling-based planner that incrementally builds trees over robot and object configurations to compute feasible plans specifying which objects to move, where to place them, and in what order, while accounting for a limited number of movable objects. To accelerate planning, we introduce non-uniform sampling strategies. We show that our method is probabilistically complete and we provide extensive numerical and hardware experiments validating its effectiveness. + oai:arXiv.org:2601.02645v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Samarth Kalluraya, Yiannis Kantaros + + + DreamLoop: Controllable Cinemagraph Generation from a Single Photograph + https://arxiv.org/abs/2601.02646 + arXiv:2601.02646v1 Announce Type: new +Abstract: Cinemagraphs, which combine static photographs with selective, looping motion, offer unique artistic appeal. Generating them from a single photograph in a controllable manner is particularly challenging. Existing image-animation techniques are restricted to simple, low-frequency motions and operate only in narrow domains with repetitive textures like water and smoke. In contrast, large-scale video diffusion models are not tailored for cinemagraph constraints and lack the specialized data required to generate seamless, controlled loops. We present DreamLoop, a controllable video synthesis framework dedicated to generating cinemagraphs from a single photo without requiring any cinemagraph training data. Our key idea is to adapt a general video diffusion model by training it on two objectives: temporal bridging and motion conditioning. This strategy enables flexible cinemagraph generation. During inference, by using the input image as both the first- and last- frame condition, we enforce a seamless loop. By conditioning on static tracks, we maintain a static background. Finally, by providing a user-specified motion path for a target object, our method provides intuitive control over the animation's trajectory and timing. To our knowledge, DreamLoop is the first method to enable cinemagraph generation for general scenes with flexible and intuitive controls. We demonstrate that our method produces high-quality, complex cinemagraphs that align with user intent, outperforming existing approaches. + oai:arXiv.org:2601.02646v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Aniruddha Mahapatra, Long Mai, Cusuh Ham, Feng Liu + + + Prioritized Replay for RL Post-training + https://arxiv.org/abs/2601.02648 + arXiv:2601.02648v1 Announce Type: new +Abstract: We introduce a problem-level prioritization framework for RL post-training of large language models. Building on insights from prioritized replay in deep RL, as well as prior observations that rollouts with intermediate success rates tend to produce stronger learning signals under methods such as GRPO, our approach selects problems according to a simple, model-driven priority score derived from empirical success statistics. In contrast to conventional curriculum strategies that emphasize easier tasks early in training, the resulting schedule naturally focuses training on problems that are neither consistently solved nor consistently failed, while deprioritizing those that contribute little gradient information. The method yields a continuously adapting and automatic prioritization process that requires no predefined difficulty tiers, auxiliary predictors, or external labels. We further introduce lightweight mechanisms for practical deployment, including heap-based prioritized sampling and periodic retesting of solved and unsolved problems to mitigate starvation and forgetting. Overall, the approach offers a principled and scalable alternative to manually designed curricula while aligning data selection directly with the dynamics of GRPO-based post-training. + oai:arXiv.org:2601.02648v1 + cs.LG + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mehdi Fatemi + + + Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search + https://arxiv.org/abs/2601.02649 + arXiv:2601.02649v1 Announce Type: new +Abstract: Online 3D Bin Packing (3D-BP) with robotic arms is crucial for reducing transportation and labor costs in modern logistics. While Deep Reinforcement Learning (DRL) has shown strong performance, it often fails to adapt to real-world short-term distribution shifts, which arise as different batches of goods arrive sequentially, causing performance drops. We argue that the short-term lookahead information available in modern logistics systems is key to mitigating this issue, especially during distribution shifts. We formulate online 3D-BP with lookahead parcels as a Model Predictive Control (MPC) problem and adapt the Monte Carlo Tree Search (MCTS) framework to solve it. Our framework employs a dynamic exploration prior that automatically balances a learned RL policy and a robust random policy based on the lookahead characteristics. Additionally, we design an auxiliary reward to penalize long-term spatial waste from individual placements. Extensive experiments on real-world datasets show that our method consistently outperforms state-of-the-art baselines, achieving over 10\% gains under distributional shifts, 4\% average improvement in online deployment, and up to more than 8\% in the best case--demonstrating the effectiveness of our framework. + oai:arXiv.org:2601.02649v1 + cs.RO + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jiangyi Fang, Bowen Zhou, Haotian Wang, Xin Zhu, Leye Wang + + + A Derivative-Free Saddle-search Algorithm With Linear Convergence Rate + https://arxiv.org/abs/2601.02650 + arXiv:2601.02650v1 Announce Type: new +Abstract: We propose a derivative-free saddle-search algorithm designed to locate transition states using only function evaluations. The algorithm employs a nested architecture consisting of an inner eigenvector search and an outer saddle-point search. Through rigorous numerical analysis, we prove the almost sure convergence of the inner step under suitable assumptions. Furthermore, we establish the convergence of the outer search using a decaying step size, while demonstrating linear convergence under constant step size and boundedness conditions. Numerical experiments are provided to validate our theoretical results and demonstrate the algorithm's practical applicability. + oai:arXiv.org:2601.02650v1 + math.NA + cs.NA + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Qiang Du, Baoming Shi, Lei Zhang, Xiangcheng Zheng + + + Driving Accessibility: Shifting the Narrative & Design of Automated Vehicle Systems for Persons With Disabilities Through a Collaborative Scoring System + https://arxiv.org/abs/2601.02651 + arXiv:2601.02651v1 Announce Type: new +Abstract: Automated vehicles present unique opportunities and challenges, with progress and adoption limited, in part, by policy and regulatory barriers. Underrepresented groups, including individuals with mobility impairments, sensory disabilities, and cognitive conditions, who may benefit most from automation, are often overlooked in crucial discussions on system design, implementation, and usability. Despite the high potential benefits of automated vehicles, the needs of Persons with Disabilities are frequently an afterthought, considered only in terms of secondary accommodations rather than foundational design elements. We aim to shift automated vehicle research and discourse away from this reactive model and toward a proactive and inclusive approach. We first present an overview of the current state of automated vehicle systems. Regarding their adoption, we examine social and technical barriers and advantages for Persons with Disabilities. We analyze existing regulations and policies concerning automated vehicles and Persons with Disabilities, identifying gaps that hinder accessibility. To address these deficiencies, we introduce a scoring rubric intended for use by manufacturers and vehicle designers. The rubric fosters direct collaboration throughout the design process, moving beyond an `afterthought` approach and towards intentional, inclusive innovation. This work was created by authors with varying degrees of personal experience within the realm of disability. + oai:arXiv.org:2601.02651v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Savvy Barnes, Maricarmen Davis, Josh Siegel + + + Backwards Data-Flow Analysis using Prophecy Variable in the BuildIt System + https://arxiv.org/abs/2601.02653 + arXiv:2601.02653v1 Announce Type: new +Abstract: Many program transformations and optimizations require information about the future behavior of the program. A standard way to obtain this information is to build an intermediate program representation, then use a backwards program analysis to propagate relevant information against the flow of control back to the transformation/optimization site. We instead propose to use prophecy variables, which predict information about the future execution of the program, to enable such transformations and optimizations. We implement prophecy variables in BuildIt, a lightweight domain specific language implementation system. BuildIt uses staged compilation to implement high performance domain specific languages embedded within a standard general purpose programming language (C++). The BuildIt first phase uses standard C++ program execution to generate optimized C, C++, and CUDA second phase code. This approach enables BuildIt to eliminate programming language implementation components such as parsers and intermediate representations, delivering a dramatic decrease in the engineering effort required to implement domain specific languages. The combination of prophecy variables and repeated forward program execution enables BuildIt to extend this approach to include transformations and optimizations that require information about the future execution of the program without backwards analyses and without the engineering overhead associated with implementing these analyses. We formalize the use of prophecy variables for this purpose, discuss the implementation of prophecy variables and repeated execution in BuildIt, and present experimental results for BuildIt computations that benefit from optimizations enabled by the information that prophecy variables provide. + oai:arXiv.org:2601.02653v1 + cs.PL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ajay Brahmakshatriya, Saman Amarasinghe, Martin Rinard + + + Empirical Comparison of Encoder-Based Language Models and Feature-Based Supervised Machine Learning Approaches to Automated Scoring of Long Essays + https://arxiv.org/abs/2601.02659 + arXiv:2601.02659v1 Announce Type: new +Abstract: Long context may impose challenges for encoder-only language models in text processing, specifically for automated scoring of essays. This study trained several commonly used encoder-based language models for automated scoring of long essays. The performance of these trained models was evaluated and compared with the ensemble models built upon the base language models with a token limit of 512?. The experimented models include BERT-based models (BERT, RoBERTa, DistilBERT, and DeBERTa), ensemble models integrating embeddings from multiple encoder models, and ensemble models of feature-based supervised machine learning models, including Gradient-Boosted Decision Trees, eXtreme Gradient Boosting, and Light Gradient Boosting Machine. We trained, validated, and tested each model on a dataset of 17,307 essays, with an 80%/10%/10% split, and evaluated model performance using Quadratic Weighted Kappa. This study revealed that an ensemble-of-embeddings model that combines multiple pre-trained language model representations with gradient-boosting classifier as the ensemble model significantly outperforms individual language models at scoring long essays. + oai:arXiv.org:2601.02659v1 + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kuo Wang, Haowei Hua, Pengfei Yan, Hong Jiao, Dan Song + + + When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning + https://arxiv.org/abs/2601.02662 + arXiv:2601.02662v1 Announce Type: new +Abstract: Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optimal prompt vector for each graph node using the linear combination of prompt atoms. However, existing GPFs generally conduct prompting over node's all feature dimensions which is obviously redundant and also be sensitive to node feature noise. To overcome this issue, for the first time, this paper proposes learning sparse graph prompts by leveraging the spiking neuron mechanism, termed Spiking Graph Prompt Feature (SpikingGPF). Our approach is motivated by the observation that spiking neuron can perform inexpensive information processing and produce sparse outputs which naturally fits the task of our graph sparse prompting. Specifically, SpikingGPF has two main aspects. First, it learns a sparse prompt vector for each node by exploiting a spiking neuron architecture, enabling prompting on selective node features. This yields a more compact and lightweight prompting design while also improving robustness against node noise. Second, SpikingGPF introduces a novel prompt representation learning model based on sparse representation theory, i.e., it represents each node prompt as a sparse combination of prompt atoms. This encourages a more compact representation and also facilitates efficient computation. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of SpikingGPF. + oai:arXiv.org:2601.02662v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Bo Jiang, Weijun Zhao, Beibei Wang, Jin Tang + + + When Do Tools and Planning Help LLMs Think? A Cost- and Latency-Aware Benchmark + https://arxiv.org/abs/2601.02663 + arXiv:2601.02663v1 Announce Type: new +Abstract: Modern large language models (LLMs) increasingly rely on inference-time planning and external tools to improve reasoning. We benchmark this behavior on two real-world settings: event-centric question answering over graph-structured knowledge (Event-QA) and persuasive response generation in Reddit ChangeMyView (CMV). Using LangChain and LangGraph, we compare a one-shot baseline against a plan--execute--replan agent equipped with task-specific tools (DBpedia SPARQL/lookup/schema exploration, Wikipedia-focused retrieval, and topical web search). We evaluate on 60 examples each from Event-QA and CMV (3 splits of 20), and report both mean end-to-end latency and per-example token cost estimates. We evaluate GPT-4o and GPT-4o-mini under identical workflows and report accuracy and end-to-end latency. On Event-QA, the best tool-augmented configuration improves accuracy (e.g., 47.5\% $\rightarrow$ 67.5\% for GPT-4o) while increasing latency by orders of magnitude ($\sim$8s $\rightarrow$ $\sim$317s per example). On CMV, one-shot prompting is strongest (e.g., GPT-4o-mini achieves 75\% at $\sim$6s), and planning+search increases latency substantially without consistent gains. However, complex multi-tool orchestration exposes failure modes where the smaller model degrades. Overall, the findings highlight the need for task-specific, cost-aware choices of both model size and agent/tooling complexity. + oai:arXiv.org:2601.02663v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Subha Ghoshal, Ali Al-Bustami + + + Inferring Causal Graph Temporal Logic Formulas to Expedite Reinforcement Learning in Temporally Extended Tasks + https://arxiv.org/abs/2601.02666 + arXiv:2601.02666v1 Announce Type: new +Abstract: Decision-making tasks often unfold on graphs with spatial-temporal dynamics. Black-box reinforcement learning often overlooks how local changes spread through network structure, limiting sample efficiency and interpretability. We present GTL-CIRL, a closed-loop framework that simultaneously learns policies and mines Causal Graph Temporal Logic (Causal GTL) specifications. The method shapes rewards with robustness, collects counterexamples when effects fail, and uses Gaussian Process (GP) driven Bayesian optimization to refine parameterized cause templates. The GP models capture spatial and temporal correlations in the system dynamics, enabling efficient exploration of complex parameter spaces. Case studies in gene and power networks show faster learning and clearer, verifiable behavior compared to standard RL baselines. + oai:arXiv.org:2601.02666v1 + cs.AI + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hadi Partovi Aria, Zhe Xu + + + MAFS: Multi-head Attention Feature Selection for High-Dimensional Data via Deep Fusion of Filter Methods + https://arxiv.org/abs/2601.02668 + arXiv:2601.02668v1 Announce Type: new +Abstract: Feature selection is essential for high-dimensional biomedical data, enabling stronger predictive performance, reduced computational cost, and improved interpretability in precision medicine applications. Existing approaches face notable challenges. Filter methods are highly scalable but cannot capture complex relationships or eliminate redundancy. Deep learning-based approaches can model nonlinear patterns but often lack stability, interpretability, and efficiency at scale. Single-head attention improves interpretability but is limited in capturing multi-level dependencies and remains sensitive to initialization, reducing reproducibility. Most existing methods rarely combine statistical interpretability with the representational power of deep learning, particularly in ultra-high-dimensional settings. Here, we introduce MAFS (Multi-head Attention-based Feature Selection), a hybrid framework that integrates statistical priors with deep learning capabilities. MAFS begins with filter-based priors for stable initialization and guide learning. It then uses multi-head attention to examine features from multiple perspectives in parallel, capturing complex nonlinear relationships and interactions. Finally, a reordering module consolidates outputs across attention heads, resolving conflicts and minimizing information loss to generate robust and consistent feature rankings. This design combines statistical guidance with deep modeling capacity, yielding interpretable importance scores while maximizing retention of informative signals. Across simulated and real-world datasets, including cancer gene expression and Alzheimer's disease data, MAFS consistently achieves superior coverage and stability compared with existing filter-based and deep learning-based alternatives, offering a scalable, interpretable, and robust solution for feature selection in high-dimensional biomedical data. + oai:arXiv.org:2601.02668v1 + cs.LG + stat.ME + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xiaoyan Sun, Qingyu Meng, Yalu Wen + + + Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking + https://arxiv.org/abs/2601.02669 + arXiv:2601.02669v1 Announce Type: new +Abstract: Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broader fact-checking workflow, including claim extraction and evidence retrieval. This narrow focus prevents current benchmarks from revealing systematic reasoning failures, factual blind spots, and robustness limitations of modern LLMs. To bridge this gap, we present FactArena, a fully automated arena-style evaluation framework that conducts comprehensive, stage-wise benchmarking of LLMs across the complete fact-checking pipeline. FactArena integrates three key components: (i) an LLM-driven fact-checking process that standardizes claim decomposition, evidence retrieval via tool-augmented interactions, and justification-based verdict prediction; (ii) an arena-styled judgment mechanism guided by consolidated reference guidelines to ensure unbiased and consistent pairwise comparisons across heterogeneous judge agents; and (iii) an arena-driven claim-evolution module that adaptively generates more challenging and semantically controlled claims to probe LLMs' factual robustness beyond fixed seed data. Across 16 state-of-the-art LLMs spanning seven model families, FactArena produces stable and interpretable rankings. Our analyses further reveal significant discrepancies between static claim-verification accuracy and end-to-end fact-checking competence, highlighting the necessity of holistic evaluation. The proposed framework offers a scalable and trustworthy paradigm for diagnosing LLMs' factual reasoning, guiding future model development, and advancing the reliable deployment of LLMs in safety-critical fact-checking applications. + oai:arXiv.org:2601.02669v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hongzhan Lin, Zixin Chen, Zhiqi Shen, Ziyang Luo, Zhen Ye, Jing Ma, Tat-Seng Chua, Guandong Xu + + + Multi-Turn Jailbreaking of Aligned LLMs via Lexical Anchor Tree Search + https://arxiv.org/abs/2601.02670 + arXiv:2601.02670v1 Announce Type: new +Abstract: Most jailbreak methods achieve high attack success rates (ASR) but require attacker LLMs to craft adversarial queries and/or demand high query budgets. These resource limitations make jailbreaking expensive, and the queries generated by attacker LLMs often consist of non-interpretable random prefixes. This paper introduces Lexical Anchor Tree Search (), addressing these limitations through an attacker-LLM-free method that operates purely via lexical anchor injection. LATS reformulates jailbreaking as a breadth-first tree search over multi-turn dialogues, where each node incrementally injects missing content words from the attack goal into benign prompts. Evaluations on AdvBench and HarmBench demonstrate that LATS achieves 97-100% ASR on latest GPT, Claude, and Llama models with an average of only ~6.4 queries, compared to 20+ queries required by other methods. These results highlight conversational structure as a potent and under-protected attack surface, while demonstrating superior query efficiency in an era where high ASR is readily achievable. Our code will be released to support reproducibility. + oai:arXiv.org:2601.02670v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Devang Kulshreshtha, Hang Su, Chinmay Hegde, Haohan Wang + + + Extracting books from production language models + https://arxiv.org/abs/2601.02671 + arXiv:2601.02671v1 Announce Type: new +Abstract: Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's weights during training, and whether those memorized data can be extracted in the model's outputs. While many believe that LLMs do not memorize much of their training data, recent work shows that substantial amounts of copyrighted text can be extracted from open-weight models. However, it remains an open question if similar extraction is feasible for production LLMs, given the safety measures these systems implement. We investigate this question using a two-phase procedure: (1) an initial probe to test for extraction feasibility, which sometimes uses a Best-of-N (BoN) jailbreak, followed by (2) iterative continuation prompts to attempt to extract the book. We evaluate our procedure on four production LLMs -- Claude 3.7 Sonnet, GPT-4.1, Gemini 2.5 Pro, and Grok 3 -- and we measure extraction success with a score computed from a block-based approximation of longest common substring (nv-recall). With different per-LLM experimental configurations, we were able to extract varying amounts of text. For the Phase 1 probe, it was unnecessary to jailbreak Gemini 2.5 Pro and Grok 3 to extract text (e.g, nv-recall of 76.8% and 70.3%, respectively, for Harry Potter and the Sorcerer's Stone), while it was necessary for Claude 3.7 Sonnet and GPT-4.1. In some cases, jailbroken Claude 3.7 Sonnet outputs entire books near-verbatim (e.g., nv-recall=95.8%). GPT-4.1 requires significantly more BoN attempts (e.g., 20X), and eventually refuses to continue (e.g., nv-recall=4.0%). Taken together, our work highlights that, even with model- and system-level safeguards, extraction of (in-copyright) training data remains a risk for production LLMs. + oai:arXiv.org:2601.02671v1 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ahmed Ahmed, A. Feder Cooper, Sanmi Koyejo, Percy Liang + + + Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration + https://arxiv.org/abs/2601.02674 + arXiv:2601.02674v1 Announce Type: new +Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide spectrum of natural language processing tasks. However, their ever-growing scale introduces significant barriers to real-world deployment, including substantial computational overhead, memory footprint, and inference latency. While model pruning presents a viable solution to these challenges, existing unstructured pruning techniques often yield irregular sparsity patterns that necessitate specialized hardware or software support. In this work, we explore structured pruning, which eliminates entire architectural components and maintains compatibility with standard hardware accelerators. We introduce a novel structured pruning framework that leverages a hybrid multi-domain calibration set and an iterative calibration strategy to effectively identify and remove redundant channels. Extensive experiments on various models across diverse downstream tasks show that our approach achieves significant compression with minimal performance degradation. + oai:arXiv.org:2601.02674v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Guangxin Wu, Hao Zhang, Zhang Zhibin, Jiafeng Guo, Xueqi Cheng + + + Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment + https://arxiv.org/abs/2601.02677 + arXiv:2601.02677v1 Announce Type: new +Abstract: Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic vulnerabilities. Existing approaches often treat these tasks in isolation, failing to capture cross-scale dependencies. We propose Uni-FinLLM, a unified multimodal large language model that uses a shared Transformer backbone and modular task heads to jointly process financial text, numerical time series, fundamentals, and visual data. Through cross-modal attention and multi-task optimization, it learns a coherent representation for micro-, meso-, and macro-level predictions. Evaluated on stock forecasting, credit-risk assessment, and systemic-risk detection, Uni-FinLLM significantly outperforms baselines. It raises stock directional accuracy to 67.4% (from 61.7%), credit-risk accuracy to 84.1% (from 79.6%), and macro early-warning accuracy to 82.3%. Results validate that a unified multimodal LLM can jointly model asset behavior and systemic vulnerabilities, offering a scalable decision-support engine for finance. + oai:arXiv.org:2601.02677v1 + cs.LG + q-fin.RM + q-fin.ST + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Gongao Zhang, Haijiang Zeng, Lu Jiang + + + Adversarial Contrastive Learning for LLM Quantization Attacks + https://arxiv.org/abs/2601.02680 + arXiv:2601.02680v1 Announce Type: new +Abstract: Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe security risks that benign LLMs in full precision may exhibit malicious behaviors after quantization. In this paper, we propose Adversarial Contrastive Learning (ACL), a novel gradient-based quantization attack that achieves superior attack effectiveness by explicitly maximizing the gap between benign and harmful responses probabilities. ACL formulates the attack objective as a triplet-based contrastive loss, and integrates it with a projected gradient descent two-stage distributed fine-tuning strategy to ensure stable and efficient optimization. Extensive experiments demonstrate ACL's remarkable effectiveness, achieving attack success rates of 86.00% for over-refusal, 97.69% for jailbreak, and 92.40% for advertisement injection, substantially outperforming state-of-the-art methods by up to 44.67%, 18.84%, and 50.80%, respectively. + oai:arXiv.org:2601.02680v1 + cs.CR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Dinghong Song, Zhiwei Xu, Hai Wan, Xibin Zhao, Pengfei Su, Dong Li + + + Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning + https://arxiv.org/abs/2601.02682 + arXiv:2601.02682v1 Announce Type: new +Abstract: Robustness to malicious attacks is crucial for practical decentralized signal processing and machine learning systems. A typical example of such attacks is label poisoning, meaning that some agents possess corrupted local labels and share models trained on these poisoned data. To defend against malicious attacks, existing works often focus on designing robust aggregators; meanwhile, the weighted mean aggregator is typically considered a simple, vulnerable baseline. This paper analyzes the robustness of decentralized gradient descent under label poisoning attacks, considering both robust and weighted mean aggregators. Theoretical results reveal that the learning errors of robust aggregators depend on the network topology, whereas the performance of weighted mean aggregator is topology-independent. Remarkably, the weighted mean aggregator, although often considered vulnerable, can outperform robust aggregators under sufficient heterogeneity, particularly when: (i) the global contamination rate (i.e., the fraction of poisoned agents for the entire network) is smaller than the local contamination rate (i.e., the maximal fraction of poisoned neighbors for the regular agents); (ii) the network of regular agents is disconnected; or (iii) the network of regular agents is sparse and the local contamination rate is high. Empirical results support our theoretical findings, highlighting the important role of network topology in the robustness to label poisoning attacks. + oai:arXiv.org:2601.02682v1 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jie Peng, Weiyu Li, Stefan Vlaski, Qing Ling + + + Learning from Prompt itself: the Hierarchical Attribution Prompt Optimization + https://arxiv.org/abs/2601.02683 + arXiv:2601.02683v1 Announce Type: new +Abstract: Optimization is fundamental across numerous disciplines, typically following an iterative process of refining an initial solution to enhance performance. This principle is equally critical in prompt engineering, where designing effective prompts for large language models constitutes a complex optimization challenge. A structured optimization approach requires automated or semi-automated procedures to develop improved prompts, thereby reducing manual effort, improving performance, and yielding an interpretable process. However, current prompt optimization methods often induce prompt drift, where new prompts fix prior failures but impair performance on previously successful tasks. Additionally, generating prompts from scratch can compromise interpretability. To address these limitations, this study proposes the Hierarchical Attribution Prompt Optimization (HAPO) framework, which introduces three innovations: (1) a dynamic attribution mechanism targeting error patterns in training data and prompting history, (2) semantic-unit optimization for editing functional prompt segments, and (3) multimodal-friendly progression supporting both end-to-end LLM and LLM-MLLM workflows. Applied in contexts like single/multi-image QA (e.g., OCRV2) and complex task analysis (e.g., BBH), HAPO demonstrates enhanced optimization efficiency, outperforming comparable automated prompt optimization methods and establishing an extensible paradigm for scalable prompt engineering. + oai:arXiv.org:2601.02683v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Dongyu Chen, Jian Ma, Xianpeng Zhang, Lei Zhang, Haonan Lu, Chen Chen, Chuangchuang Wang, Kai Tang + + + Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter + https://arxiv.org/abs/2601.02686 + arXiv:2601.02686v1 Announce Type: new +Abstract: Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional safety frameworks treat contact as unsafe, restricting robots to collision avoidance and limiting their ability to function in dense, everyday settings. As the number of objects grows, model-based approaches for safe manipulation become computationally intractable; meanwhile, learned methods typically tie safety to the task at hand, making them hard to transfer to new tasks without retraining. In this work we introduce Dense Contact Barrier Functions(DCBF). Our approach bypasses the computational complexity of explicitly modeling multi-object dynamics by instead learning a composable, object-centric function that implicitly captures the safety constraints arising from physical interactions. Trained offline on interactions with a few objects, the learned DCBFcomposes across arbitrary object sets at runtime, producing a single global safety filter that scales linearly and transfers across tasks without retraining. We validate our approach through simulated experiments in dense clutter, demonstrating its ability to enable collision-free navigation and safe, contact-rich interaction in suitable settings. + oai:arXiv.org:2601.02686v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Haixin Jin, Nikhil Uday Shinde, Soofiyan Atar, Hongzhan Yu, Dylan Hirsch, Sicun Gao, Michael C. Yip, Sylvia Herbert + + + Multi-channel multi-speaker transformer for speech recognition + https://arxiv.org/abs/2601.02688 + arXiv:2601.02688v1 Announce Type: new +Abstract: With the development of teleconferencing and in-vehicle voice assistants, far-field multi-speaker speech recognition has become a hot research topic. Recently, a multi-channel transformer (MCT) has been proposed, which demonstrates the ability of the transformer to model far-field acoustic environments. However, MCT cannot encode high-dimensional acoustic features for each speaker from mixed input audio because of the interference between speakers. Based on these, we propose the multi-channel multi-speaker transformer (M2Former) for far-field multi-speaker ASR in this paper. Experiments on the SMS-WSJ benchmark show that the M2Former outperforms the neural beamformer, MCT, dual-path RNN with transform-average-concatenate and multi-channel deep clustering based end-to-end systems by 9.2%, 14.3%, 24.9%, and 52.2% respectively, in terms of relative word error rate reduction. + oai:arXiv.org:2601.02688v1 + cs.SD + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.21437/Interspeech.2023-257 + Proc. INTERSPEECH 2023, 4918--4922 + Guo Yifan, Tian Yao, Suo Hongbin, Wan Yulong + + + Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction + https://arxiv.org/abs/2601.02694 + arXiv:2601.02694v1 Announce Type: new +Abstract: Network traffic prediction is essential for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Advances in forecasting, from sophisticated transformer architectures to simple linear models, have improved performance across diverse prediction tasks. However, given the variability of network traffic across network environments and traffic series timescales, it is essential to identify effective deployment choices and modeling directions for network traffic prediction. This study systematically identify and evaluates twelve advanced TSF models -including transformer-based and traditional DL approaches, each with unique advantages for network traffic prediction- against three statistical baselines on four real traffic datasets, across multiple time scales and horizons, assessing performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy. Results highlight performance regimes, efficiency thresholds, and promising architectures that balance accuracy and efficiency, demonstrating robustness to traffic challenges and suggesting new directions beyond traditional RNNs. + oai:arXiv.org:2601.02694v1 + cs.NI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Eilaf MA Babai, Aalaa MA Babai, Koji Okamura + + + EvoRoute: Experience-Driven Self-Routing LLM Agent Systems + https://arxiv.org/abs/2601.02695 + arXiv:2601.02695v1 Announce Type: new +Abstract: Complex agentic AI systems, powered by a coordinated ensemble of Large Language Models (LLMs), tool and memory modules, have demonstrated remarkable capabilities on intricate, multi-turn tasks. However, this success is shadowed by prohibitive economic costs and severe latency, exposing a critical, yet underexplored, trade-off. We formalize this challenge as the \textbf{Agent System Trilemma}: the inherent tension among achieving state-of-the-art performance, minimizing monetary cost, and ensuring rapid task completion. To dismantle this trilemma, we introduce EvoRoute, a self-evolving model routing paradigm that transcends static, pre-defined model assignments. Leveraging an ever-expanding knowledge base of prior experience, EvoRoute dynamically selects Pareto-optimal LLM backbones at each step, balancing accuracy, efficiency, and resource use, while continually refining its own selection policy through environment feedback. Experiments on challenging agentic benchmarks such as GAIA and BrowseComp+ demonstrate that EvoRoute, when integrated into off-the-shelf agentic systems, not only sustains or enhances system performance but also reduces execution cost by up to $80\%$ and latency by over $70\%$. + oai:arXiv.org:2601.02695v1 + cs.CL + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Guibin Zhang, Haiyang Yu, Kaiming Yang, Bingli Wu, Fei Huang, Yongbin Li, Shuicheng Yan + + + Boosting Accuracy and Interpretability in Multilingual Hate Speech Detection Through Layer Freezing and Explainable AI + https://arxiv.org/abs/2601.02697 + arXiv:2601.02697v1 Announce Type: new +Abstract: Sentiment analysis focuses on identifying the emotional polarity expressed in textual data, typically categorized as positive, negative, or neutral. Hate speech detection, on the other hand, aims to recognize content that incites violence, discrimination, or hostility toward individuals or groups based on attributes such as race, gender, sexual orientation, or religion. Both tasks play a critical role in online content moderation by enabling the detection and mitigation of harmful or offensive material, thereby contributing to safer digital environments. In this study, we examine the performance of three transformer-based models: BERT-base-multilingual-cased, RoBERTa-base, and XLM-RoBERTa-base with the first eight layers frozen, for multilingual sentiment analysis and hate speech detection. The evaluation is conducted across five languages: English, Korean, Japanese, Chinese, and French. The models are compared using standard performance metrics, including accuracy, precision, recall, and F1-score. To enhance model interpretability and provide deeper insight into prediction behavior, we integrate the Local Interpretable Model-agnostic Explanations (LIME) framework, which highlights the contribution of individual words to the models decisions. By combining state-of-the-art transformer architectures with explainability techniques, this work aims to improve both the effectiveness and transparency of multilingual sentiment analysis and hate speech detection systems. + oai:arXiv.org:2601.02697v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Meysam Shirdel Bilehsavar, Negin Mahmoudi, Mohammad Jalili Torkamani, Kiana Kiashemshaki + + + Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study + https://arxiv.org/abs/2601.02698 + arXiv:2601.02698v1 Announce Type: new +Abstract: AI-assisted developer services are increasingly embedded in modern IDEs, yet enterprises must ensure these tools operate within existing identity, access control, and governance requirements. The Model Context Protocol (MCP) enables AI assistants to retrieve structured internal context, but its specification provides only a minimal authorization model and lacks guidance on integrating enterprise SSO. This article presents a practical architecture that incorporates OAuth 2.0 and OpenID Connect (OIDC) into MCP-enabled developer environments. It describes how IDE extensions obtain and present tokens, how MCP servers validate them through an identity provider, and how scopes and claims can enforce least-privilege access. A prototype implementation using Visual Studio Code, a Python-based MCP server, and an OIDC-compliant IdP demonstrates feasibility. A case study evaluates authentication latency, token-validation overhead, operational considerations, and AI-specific risks. The approach provides a deployable pattern for organizations adopting AI-assisted developer tools while maintaining identity assurance and auditability. + oai:arXiv.org:2601.02698v1 + cs.SE + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Manideep Reddy Chinthareddy + + + Adversarial Question Answering Robustness: A Multi-Level Error Analysis and Mitigation Study + https://arxiv.org/abs/2601.02700 + arXiv:2601.02700v1 Announce Type: new +Abstract: Question answering (QA) systems achieve impressive performance on standard benchmarks like SQuAD, but remain vulnerable to adversarial examples. This project investigates the adversarial robustness of transformer models on the AddSent adversarial dataset through systematic experimentation across model scales and targeted mitigation strategies. We perform comprehensive multi-level error analysis using five complementary categorization schemes, identifying negation confusion and entity substitution as the primary failure modes. Through systematic evaluation of adversarial fine-tuning ratios, we identify 80% clean + 20% adversarial data as optimal. Data augmentation experiments reveal a capacity bottleneck in small models. Scaling from ELECTRA-small (14M parameters) to ELECTRA-base (110M parameters) eliminates the robustness-accuracy trade-off, achieving substantial improvements on both clean and adversarial data. We implement three targeted mitigation strategies, with Entity-Aware contrastive learning achieving best performance: 89.89% AddSent Exact Match (EM) and 90.73% SQuAD EM, representing 94.9% closure of the adversarial gap. To our knowledge, this is the first work integrating comprehensive linguistic error analysis with Named Entity Recognition (NER)-guided contrastive learning for adversarial QA, demonstrating that targeted mitigation can achieve near-parity between clean and adversarial performance. + oai:arXiv.org:2601.02700v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Agniv Roy Choudhury, Vignesh Ponselvan Rajasingh + + + Topology-Aware Spatio-Temporal Graph Transformer for Predicting Smart Grid Failures + https://arxiv.org/abs/2601.02701 + arXiv:2601.02701v1 Announce Type: new +Abstract: Smart grid infrastructure needs improved resilience and preventive maintenance through more accurate predictions. Current methodologies lack accurate representation of spatio-temporal-causal interdependencies and class imbalance in failure prediction tasks. This study introduces a Topology-Aware Spatio-Temporal Graph Transformer (ST-GT) architecture that overcomes existing limitations by using three main innovations: (1) directly incorporating physical transmission network topology into the transformer attention mechanism to identify spatial failure propagation patterns; (2) unified processing of static topological descriptors and (3) temporal Phasor Measurement Units (PMU) sequences in an end-to-end framework. + The ST-GT model exhibited outstanding performance in five-fold cross-validation across 10 substations, attaining perfect recall (1.000 $\pm$ 0.001) and an F1-score of 0.858 $\pm$ 0.009, markedly surpassing XGBoost baselines (0.683 accuracy/F1). Perfect recall guarantees that no critical failures are overlooked, which is essential for grid safety; however, it may lead to an increase in false alarm rates. This framework integrates temporal dynamics modeling with spatial graph awareness for critical infrastructure monitoring. It offers interpretable insights into failure propagation pathways and enhances maintenance strategies. Future research focuses on developing cost-weighted loss functions for precision-recall trade-off enhancement, implementing real-time monitoring systems with uncertainty quantification, and creating cost-sensitive frameworks balancing false alarm expenditures with failure consequences. The methodology success suggests its potential for wider application in critical infrastructure areas requiring spatial temporal failure prediction. + oai:arXiv.org:2601.02701v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Anh Le, Phat K. Huynh, Om P. Yadav, Harun Pirim, Chau Le, Trung Q. Le + + + Learning User Preferences Through Interaction for Long-Term Collaboration + https://arxiv.org/abs/2601.02702 + arXiv:2601.02702v1 Announce Type: new +Abstract: As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well agents can learn user preferences and leverage them to improve collaboration quality throughout multiple sessions. To develop agents that succeed in this setting, we present long-term collaborative agents equipped with a memory that persists and refines user preference as interaction experience accumulates. Moreover, we demonstrate that learning signals can be derived from user simulator behavior in MultiSessionCollab to train agents to generate more comprehensive reflections and update their memory more effectively. Extensive experiments show that equipping agents with memory improves long-term collaboration, yielding higher task success rates, more efficient interactions, and reduced user effort. Finally, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings. + oai:arXiv.org:2601.02702v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Shuhaib Mehri, Priyanka Kargupta, Tal August, Dilek Hakkani-T\"ur + + + Exact Constructive Digit-by-Digit Algorithms for Integer $e$-th Root Extraction + https://arxiv.org/abs/2601.02703 + arXiv:2601.02703v1 Announce Type: new +Abstract: We present a unified constructive digit-by-digit framework for exact root extraction using only integer arithmetic. The core contribution is a complete correctness theory for the fractional square root algorithm, proving that each computed decimal digit is exact and final, together with a sharp truncation error bound of $10^{-k}$ after $k$ digits. We further develop an invariant-based framework for computing the integer $e$-th root $\lfloor N^{1/e} \rfloor$ of a non-negative integer $N$ for arbitrary fixed exponents $e \ge 2$, derived directly from the binomial theorem. This method generalizes the classical long-division square root algorithm, preserves a constructive remainder invariant throughout the computation, and provides an exact decision procedure for perfect $e$-th power detection. We also explain why exact digit-by-digit fractional extraction with non-revisable digits is structurally possible only for square roots ($e=2$), whereas higher-order roots ($e \ge 3$) exhibit nonlinear coupling that prevents digit stability under scaling. All proofs are carried out in a constructive, algorithmic manner consistent with Bishop-style constructive mathematics, yielding explicit algorithmic witnesses, decidable predicates, and guaranteed termination. The resulting algorithms require no division or floating-point operations and are well suited to symbolic computation, verified exact arithmetic, educational exposition, and digital hardware implementation. + oai:arXiv.org:2601.02703v1 + cs.SC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Suresan Pareth + + + Analysis of Various Manipulator Configurations Based on Multi-Objective Black-Box Optimization + https://arxiv.org/abs/2601.02704 + arXiv:2601.02704v1 Announce Type: new +Abstract: Various 6-degree-of-freedom (DOF) and 7-DOF manipulators have been developed to date. Over a long history, their joint configurations and link length ratios have been determined empirically. In recent years, the development of robotic foundation models has become increasingly active, leading to the continuous proposal of various manipulators to support these models. However, none of these manipulators share exactly the same structure, as the order of joints and the ratio of link lengths differ among robots. Therefore, in order to discuss the optimal structure of a manipulator, we performed multi-objective optimization from the perspectives of end-effector reachability and joint torque. We analyze where existing manipulator structures stand within the sampling results of the optimization and provide insights for future manipulator design. + oai:arXiv.org:2601.02704v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1080/01691864.2025.2607670 + Kento Kawaharazuka, Keita Yoneda, Takahiro Hattori, Shintaro Inoue, Kei Okada + + + Scaling Laws of Machine Learning for Optimal Power Flow + https://arxiv.org/abs/2601.02706 + arXiv:2601.02706v1 Announce Type: new +Abstract: Optimal power flow (OPF) is one of the fundamental tasks for power system operations. While machine learning (ML) approaches such as deep neural networks (DNNs) have been widely studied to enhance OPF solution speed and performance, their practical deployment faces two critical scaling questions: What is the minimum training data volume required for reliable results? How should ML models' complexity balance accuracy with real-time computational limits? Existing studies evaluate discrete scenarios without quantifying these scaling relationships, leading to trial-and-error-based ML development in real-world applications. This work presents the first systematic scaling study for ML-based OPF across two dimensions: data scale (0.1K-40K training samples) and compute scale (multiple NN architectures with varying FLOPs). Our results reveal consistent power-law relationships on both DNNs and physics-informed NNs (PINNs) between each resource dimension and three core performance metrics: prediction error (MAE), constraint violations and speed. We find that for ACOPF, the accuracy metric scales with dataset size and training compute. These scaling laws enable predictable and principled ML pipeline design for OPF. We further identify the divergence between prediction accuracy and constraint feasibility and characterize the compute-optimal frontier. This work provides quantitative guidance for ML-OPF design and deployments. + oai:arXiv.org:2601.02706v1 + cs.LG + cs.SY + eess.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xinyi Liu, Xuan He, Yize Chen + + + CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory + https://arxiv.org/abs/2601.02708 + arXiv:2601.02708v1 Announce Type: new +Abstract: Information retrieval (IR) in dynamic data streams is emerging as a challenging task, as shifts in data distribution degrade the performance of AI-powered IR systems. To mitigate this issue, memory-based continual learning has been widely adopted for IR. However, existing methods rely on a fixed set of queries with ground-truth relevant documents, which limits generalization to unseen queries and documents, making them impractical for real-world applications. To enable more effective learning with unseen topics of a new corpus without ground-truth labels, we propose CREAM, a self-supervised framework for memory-based continual retrieval. CREAM captures the evolving semantics of streaming queries and documents into dynamically structured soft memory and leverages it to adapt to both seen and unseen topics in an unsupervised setting. We realize this through three key techniques: fine-grained similarity estimation, regularized cluster prototyping, and stratified coreset sampling. Experiments on two benchmark datasets demonstrate that CREAM exhibits superior adaptability and retrieval accuracy, outperforming the strongest method in a label-free setting by 27.79\% in Success@5 and 44.5\% in Recall@10 on average, and achieving performance comparable to or even exceeding that of supervised methods. + oai:arXiv.org:2601.02708v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + HuiJeong Son, Hyeongu Kang, Sunho Kim, Subeen Ho, SeongKu Kang, Dongha Lee, Susik Yoon + + + GRRE: Leveraging G-Channel Removed Reconstruction Error for Robust Detection of AI-Generated Images + https://arxiv.org/abs/2601.02709 + arXiv:2601.02709v1 Announce Type: new +Abstract: The rapid progress of generative models, particularly diffusion models and GANs, has greatly increased the difficulty of distinguishing synthetic images from real ones. Although numerous detection methods have been proposed, their accuracy often degrades when applied to images generated by novel or unseen generative models, highlighting the challenge of achieving strong generalization. To address this challenge, we introduce a novel detection paradigm based on channel removal reconstruction. Specifically, we observe that when the green (G) channel is removed from real images and reconstructed, the resulting reconstruction errors differ significantly from those of AI-generated images. Building upon this insight, we propose G-channel Removed Reconstruction Error (GRRE), a simple yet effective method that exploits this discrepancy for robust AI-generated image detection. Extensive experiments demonstrate that GRRE consistently achieves high detection accuracy across multiple generative models, including those unseen during training. Compared with existing approaches, GRRE not only maintains strong robustness against various perturbations and post-processing operations but also exhibits superior cross-model generalization. These results highlight the potential of channel-removal-based reconstruction as a powerful forensic tool for safeguarding image authenticity in the era of generative AI. + oai:arXiv.org:2601.02709v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Shuman He, Xiehua Li, Xioaju Yang, Yang Xiong, Keqin Li + + + Time-Scaling Is What Agents Need Now + https://arxiv.org/abs/2601.02714 + arXiv:2601.02714v1 Announce Type: new +Abstract: Early artificial intelligence paradigms exhibited separated cognitive functions: Neural Networks focused on "perception-representation," Reinforcement Learning on "decision-making-behavior," and Symbolic AI on "knowledge-reasoning." With Transformer-based large models and world models, these paradigms are converging into cognitive agents with closed-loop "perception-decision-action" capabilities. + Humans solve complex problems under limited cognitive resources through temporalized sequential reasoning. Language relies on problem space search for deep semantic reasoning. While early large language models (LLMs) could generate fluent text, they lacked robust semantic reasoning capabilities. Prompting techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) extended reasoning paths by making intermediate steps explicit. Recent models like DeepSeek-R1 enhanced performance through explicit reasoning trajectories. However, these methods have limitations in search completeness and efficiency. + This highlights the need for "Time-Scaling"--the systematic extension and optimization of an agent's ability to unfold reasoning over time. Time-Scaling refers to architectural design utilizing extended temporal pathways, enabling deeper problem space exploration, dynamic strategy adjustment, and enhanced metacognitive control, paralleling human sequential reasoning under cognitive constraints. It represents a critical frontier for enhancing deep reasoning and problem-solving without proportional increases in static model parameters. Advancing intelligent agent capabilities requires placing Time-Scaling principles at the forefront, positioning explicit temporal reasoning management as foundational. + oai:arXiv.org:2601.02714v1 + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Zhi Liu, Guangzhi Wang + + + CAMO: Category-Agnostic 3D Motion Transfer from Monocular 2D Videos + https://arxiv.org/abs/2601.02716 + arXiv:2601.02716v1 Announce Type: new +Abstract: Motion transfer from 2D videos to 3D assets is a challenging problem, due to inherent pose ambiguities and diverse object shapes, often requiring category-specific parametric templates. We propose CAMO, a category-agnostic framework that transfers motion to diverse target meshes directly from monocular 2D videos without relying on predefined templates or explicit 3D supervision. The core of CAMO is a morphology-parameterized articulated 3D Gaussian splatting model combined with dense semantic correspondences to jointly adapt shape and pose through optimization. This approach effectively alleviates shape-pose ambiguities, enabling visually faithful motion transfer for diverse categories. Experimental results demonstrate superior motion accuracy, efficiency, and visual coherence compared to existing methods, significantly advancing motion transfer in varied object categories and casual video scenarios. + oai:arXiv.org:2601.02716v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Taeyeon Kim, Youngju Na, Jumin Lee, Minhyuk Sung, Sung-Eui Yoon + + + Privacy-Preserving AI-Enabled Decentralized Learning and Employment Records System + https://arxiv.org/abs/2601.02720 + arXiv:2601.02720v1 Announce Type: new +Abstract: Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements. Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential generation and the ability to incorporate unstructured evidence of learning. In this paper,a privacy-preserving, AI-enabled decentralized LER system is proposed to address these gaps. Digitally signed transcripts from educational institutions are accepted, and verifiable self-issued skill credentials are derived inside a trusted execution environment (TEE) by a natural language processing pipeline that analyzes formal records (e.g., transcripts, syllabi) and informal artifacts. All verification and job-skill matching are performed inside the enclave with selective disclosure, so raw credentials and private keys remain enclave-confined. Job matching relies solely on attested skill vectors and is invariant to non-skill resume fields, thereby reducing opportunities for screening bias.The NLP component was evaluated on sample learner data; the mapping follows the validated Syllabus-to-O*NET methodology,and a stability test across repeated runs observed <5% variance in top-ranked skills. Formal security statements and proof sketches are provided showing that derived credentials are unforgeable and that sensitive information remains confidential. The proposed system thus supports secure education and employment credentialing, robust transcript verification,and automated, privacy-preserving skill extraction within a decentralized framework. + oai:arXiv.org:2601.02720v1 + cs.CR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuqiao Xu, Mina Namazi, Sahith Reddy Jalapally, Osama Zafar, Youngjin Yoo, Erman Ayday + + + Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing + https://arxiv.org/abs/2601.02721 + arXiv:2601.02721v1 Announce Type: new +Abstract: Reliable 3D mesh saliency ground truth (GT) is essential for human-centric visual modeling in virtual reality (VR). However, current 3D mesh saliency GT acquisition methods are generally consistent with 2D image methods, ignoring the differences between 3D geometry topology and 2D image array. Current VR eye-tracking pipelines rely on single ray sampling and Euclidean smoothing, triggering texture attention and signal leakage across gaps. This paper proposes a robust framework to address these limitations. We first introduce a view cone sampling (VCS) strategy, which simulates the human foveal receptive field via Gaussian-distributed ray bundles to improve sampling robustness for complex topologies. Furthermore, a hybrid Manifold-Euclidean constrained diffusion (HCD) algorithm is developed, fusing manifold geodesic constraints with Euclidean scales to ensure topologically-consistent saliency propagation. By mitigating "topological short-circuits" and aliasing, our framework provides a high-fidelity 3D attention acquisition paradigm that aligns with natural human perception, offering a more accurate and robust baseline for 3D mesh saliency research. + oai:arXiv.org:2601.02721v1 + cs.CV + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Guoquan Zheng, Jie Hao, Huiyu Duan, Yongming Han, Liang Yuan, Dong Zhang, Guangtao Zhai + + + Loop Closure using AnyLoc Visual Place Recognition in DPV-SLAM + https://arxiv.org/abs/2601.02723 + arXiv:2601.02723v1 Announce Type: new +Abstract: Loop closure is crucial for maintaining the accuracy and consistency of visual SLAM. We propose a method to improve loop closure performance in DPV-SLAM. Our approach integrates AnyLoc, a learning-based visual place recognition technique, as a replacement for the classical Bag of Visual Words (BoVW) loop detection method. In contrast to BoVW, which relies on handcrafted features, AnyLoc utilizes deep feature representations, enabling more robust image retrieval across diverse viewpoints and lighting conditions. Furthermore, we propose an adaptive mechanism that dynamically adjusts similarity threshold based on environmental conditions, removing the need for manual tuning. Experiments on both indoor and outdoor datasets demonstrate that our method significantly outperforms the original DPV-SLAM in terms of loop closure accuracy and robustness. The proposed method offers a practical and scalable solution for enhancing loop closure performance in modern SLAM systems. + oai:arXiv.org:2601.02723v1 + cs.RO + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wenzheng Zhang, Kazuki Adachi, Yoshitaka Hara, Sousuke Nakamura + + + Foreground-Aware Dataset Distillation via Dynamic Patch Selection + https://arxiv.org/abs/2601.02727 + arXiv:2601.02727v1 Announce Type: new +Abstract: In this paper, we propose a foreground-aware dataset distillation method that enhances patch selection in a content-adaptive manner. With the rising computational cost of training large-scale deep models, dataset distillation has emerged as a promising approach for constructing compact synthetic datasets that retain the knowledge of their large original counterparts. However, traditional optimization-based methods often suffer from high computational overhead, memory constraints, and the generation of unrealistic, noise-like images with limited architectural generalization. Recent non-optimization methods alleviate some of these issues by constructing distilled data from real image patches, but the used rigid patch selection strategies can still discard critical information about the main objects. To solve this problem, we first leverage Grounded SAM2 to identify foreground objects and compute per-image foreground occupancy, from which we derive a category-wise patch decision threshold. Guided by these thresholds, we design a dynamic patch selection strategy that, for each image, either selects the most informative patch from multiple candidates or directly resizes the full image when the foreground dominates. This dual-path mechanism preserves more key information about the main objects while reducing redundant background content. Extensive experiments on multiple benchmarks show that the proposed method consistently improves distillation performance over existing approaches, producing more informative and representative distilled datasets and enhancing robustness across different architectures and image compositions. + oai:arXiv.org:2601.02727v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama + + + CRoPE: Efficient Parametrization of Rotary Positional Embedding + https://arxiv.org/abs/2601.02728 + arXiv:2601.02728v1 Announce Type: new +Abstract: Rotary positional embedding has become the state-of-the-art approach to encode position information in transformer-based models. While it is often succinctly expressed in complex linear algebra, we note that the actual implementation of $Q/K/V$-projections is not equivalent to a complex linear transformation. We argue that complex linear transformation is a more natural parametrization and saves near 50\% parameters within the attention block. We show empirically that removing such redundancy has negligible impact on the model performance both in sample and out of sample. Our modification achieves more efficient parameter usage, as well as a cleaner interpretation of the representation space. + oai:arXiv.org:2601.02728v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Beicheng Lou, Zifei Xu + + + HOLO: Homography-Guided Pose Estimator Network for Fine-Grained Visual Localization on SD Maps + https://arxiv.org/abs/2601.02730 + arXiv:2601.02730v1 Announce Type: new +Abstract: Visual localization on standard-definition (SD) maps has emerged as a promising low-cost and scalable solution for autonomous driving. However, existing regression-based approaches often overlook inherent geometric priors, resulting in suboptimal training efficiency and limited localization accuracy. In this paper, we propose a novel homography-guided pose estimator network for fine-grained visual localization between multi-view images and standard-definition (SD) maps. We construct input pairs that satisfy a homography constraint by projecting ground-view features into the BEV domain and enforcing semantic alignment with map features. Then we leverage homography relationships to guide feature fusion and restrict the pose outputs to a valid feasible region, which significantly improves training efficiency and localization accuracy compared to prior methods relying on attention-based fusion and direct 3-DoF pose regression. To the best of our knowledge, this is the first work to unify BEV semantic reasoning with homography learning for image-to-map localization. Furthermore, by explicitly modeling homography transformations, the proposed framework naturally supports cross-resolution inputs, enhancing model flexibility. Extensive experiments on the nuScenes dataset demonstrate that our approach significantly outperforms existing state-of-the-art visual localization methods. Code and pretrained models will be publicly released to foster future research. + oai:arXiv.org:2601.02730v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xuchang Zhong, Xu Cao, Jinke Feng, Hao Fang + + + Omni2Sound: Towards Unified Video-Text-to-Audio Generation + https://arxiv.org/abs/2601.02731 + arXiv:2601.02731v1 Announce Type: new +Abstract: Training a unified model integrating video-to-audio (V2A), text-to-audio (T2A), and joint video-text-to-audio (VT2A) generation offers significant application flexibility, yet faces two unexplored foundational challenges: (1) the scarcity of high-quality audio captions with tight A-V-T alignment, leading to severe semantic conflict between multimodal conditions, and (2) cross-task and intra-task competition, manifesting as an adverse V2A-T2A performance trade-off and modality bias in the VT2A task. First, to address data scarcity, we introduce SoundAtlas, a large-scale dataset (470k pairs) that significantly outperforms existing benchmarks and even human experts in quality. Powered by a novel agentic pipeline, it integrates Vision-to-Language Compression to mitigate visual bias of MLLMs, a Junior-Senior Agent Handoff for a 5 times cost reduction, and rigorous Post-hoc Filtering to ensure fidelity. Consequently, SoundAtlas delivers semantically rich and temporally detailed captions with tight V-A-T alignment. Second, we propose Omni2Sound, a unified VT2A diffusion model supporting flexible input modalities. To resolve the inherent cross-task and intra-task competition, we design a three-stage multi-task progressive training schedule that converts cross-task competition into joint optimization and mitigates modality bias in the VT2A task, maintaining both audio-visual alignment and off-screen audio generation faithfulness. Finally, we construct VGGSound-Omni, a comprehensive benchmark for unified evaluation, including challenging off-screen tracks. With a standard DiT backbone, Omni2Sound achieves unified SOTA performance across all three tasks within a single model, demonstrating strong generalization across benchmarks with heterogeneous input conditions. The project page is at https://swapforward.github.io/Omni2Sound. + oai:arXiv.org:2601.02731v1 + cs.SD + cs.CV + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yusheng Dai, Zehua Chen, Yuxuan Jiang, Baolong Gao, Qiuhong Ke, Jun Zhu, Jianfei Cai + + + Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices + https://arxiv.org/abs/2601.02732 + arXiv:2601.02732v1 Announce Type: new +Abstract: As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are experiencing more frequent failures. Ensuring system reliability thus demands accurate root cause localization. While many traditional graph-based and deep learning approaches have been explored for this task, they often rely heavily on pre-defined schemas that struggle to adapt to evolving operational contexts. Consequently, a number of LLM-based methods have recently been proposed. However, these methods still face two major limitations: shallow, symptom-centric reasoning that undermines accuracy, and a lack of cross-alert reuse that leads to redundant reasoning and high latency. In this paper, we conduct a comprehensive study of how Site Reliability Engineers (SREs) localize the root causes of failures, drawing insights from professionals across multiple organizations. Our investigation reveals that expert root cause analysis exhibits three key characteristics: recursiveness, multi-dimensional expansion, and cross-modal reasoning. Motivated by these findings, we introduce AMER-RCL, an agentic memory enhanced recursive reasoning framework for root cause localization in microservices. AMER-RCL employs the Recursive Reasoning RCL engine, a multi-agent framework that performs recursive reasoning on each alert to progressively refine candidate causes, while Agentic Memory incrementally accumulates and reuses reasoning from prior alerts within a time window to reduce redundant exploration and lower inference latency. Experimental results demonstrate that AMER-RCL consistently outperforms state-of-the-art methods in both localization accuracy and inference efficiency. + oai:arXiv.org:2601.02732v1 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3786583.3786853 + Lingzhe Zhang, Tong Jia, Yunpeng Zhai, Leyi Pan, Chiming Duan, Minghua He, Mengxi Jia, Ying Li + + + Scalable Tree Ensemble Proximities in Python + https://arxiv.org/abs/2601.02735 + arXiv:2601.02735v1 Announce Type: new +Abstract: Tree ensemble methods such as Random Forests naturally induce supervised similarity measures through their decision tree structure, but existing implementations of proximities derived from tree ensembles typically suffer from quadratic time or memory complexity, limiting their scalability. In this work, we introduce a general framework for efficient proximity computation by defining a family of Separable Weighted Leaf-Collision Proximities. We show that any proximity measure in this family admits an exact sparse matrix factorization, restricting computation to leaf-level collisions and avoiding explicit pairwise comparisons. This formulation enables low-memory, scalable proximity computation using sparse linear algebra in Python. Empirical benchmarks demonstrate substantial runtime and memory improvements over traditional approaches, allowing tree ensemble proximities to scale efficiently to datasets with hundreds of thousands of samples on standard CPU hardware. + oai:arXiv.org:2601.02735v1 + cs.LG + cs.DS + cs.PF + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Adrien Aumon, Guy Wolf, Kevin R. Moon, Jake S. Rhodes + + + Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism + https://arxiv.org/abs/2601.02736 + arXiv:2601.02736v1 Announce Type: new +Abstract: Microservice systems have become the backbone of cloud-native enterprise applications due to their resource elasticity, loosely coupled architecture, and lightweight deployment. Yet, the intrinsic complexity and dynamic runtime interactions of such systems inevitably give rise to anomalies. Ensuring system reliability therefore hinges on effective root cause analysis (RCA), which entails not only localizing the source of anomalies but also characterizing the underlying failures in a timely and interpretable manner. Recent advances in intelligent RCA techniques, particularly those powered by large language models (LLMs), have demonstrated promising capabilities, as LLMs reduce reliance on handcrafted features while offering cross-platform adaptability, task generalization, and flexibility. However, existing LLM-based methods still suffer from two critical limitations: (a) limited exploration diversity, which undermines accuracy, and (b) heavy dependence on large-scale LLMs, which results in slow inference. To overcome these challenges, we propose SpecRCA, a speculative root cause analysis framework for microservices that adopts a \textit{hypothesize-then-verify} paradigm. SpecRCA first leverages a hypothesis drafting module to rapidly generate candidate root causes, and then employs a parallel root cause verifier to efficiently validate them. Preliminary experiments on the AIOps 2022 dataset demonstrate that SpecRCA achieves superior accuracy and efficiency compared to existing approaches, highlighting its potential as a practical solution for scalable and interpretable RCA in complex microservice environments. + oai:arXiv.org:2601.02736v1 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3786582.3786803 + Lingzhe Zhang, Tong Jia, Yunpeng Zhai, Leyi Pan, Chiming Duan, Minghua He, Pei Xiao, Ying Li + + + Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench + https://arxiv.org/abs/2601.02737 + arXiv:2601.02737v1 Announce Type: new +Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in tasks such as abnormality detection and report generation for anatomical modalities, their capability in functional imaging remains largely unexplored. In this work, we identify and quantify a fundamental functional perception gap: the inability of current vision encoders to decode functional tracer biodistribution independent of morphological priors. Identifying Positron Emission Tomography (PET) as the quintessential modality to investigate this disconnect, we introduce PET-Bench, the first large-scale functional imaging benchmark comprising 52,308 hierarchical QA pairs from 9,732 multi-site, multi-tracer PET studies. Extensive evaluation of 19 state-of-the-art MLLMs reveals a critical safety hazard termed the Chain-of-Thought (CoT) hallucination trap. We observe that standard CoT prompting, widely considered to enhance reasoning, paradoxically decouples linguistic generation from visual evidence in PET, producing clinically fluent but factually ungrounded diagnoses. To resolve this, we propose Atomic Visual Alignment (AVA), a simple fine-tuning strategy that enforces the mastery of low-level functional perception prior to high-level diagnostic reasoning. Our results demonstrate that AVA effectively bridges the perception gap, transforming CoT from a source of hallucination into a robust inference tool and improving diagnostic accuracy by up to 14.83%. Code and data are available at https://github.com/yezanting/PET-Bench. + oai:arXiv.org:2601.02737v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zanting Ye, Xiaolong Niu, Xuanbin Wu, Xu Han, Shengyuan Liu, Jing Hao, Zhihao Peng, Hao Sun, Jieqin Lv, Fanghu Wang, Yanchao Huang, Hubing Wu, Yixuan Yuan, Habib Zaidi, Arman Rahmim, Yefeng Zheng, Lijun Lu + + + Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning + https://arxiv.org/abs/2601.02738 + arXiv:2601.02738v1 Announce Type: new +Abstract: Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately could be challenging due to the existence of residual physics during controller synthesis. This paper presents a self-supervised residual learning and trajectory optimization framework to address the aforementioned challenges. At first, unknown dynamic effects on the closed-loop model are learned and treated as residuals of the nominal dynamics, jointly forming a hybrid model. We show that learning with analytic gradients can be achieved using only trajectory-level data while enjoying accurate long-horizon prediction with an arbitrary integration step size. Subsequently, a trajectory optimizer is developed to compute the optimal reference trajectory with the residual physics along it minimized. It ends up with trajectories that are friendly to the following control level. The agile flight of quadrotors illustrates that by utilizing the hybrid dynamics, the proposed optimizer outputs aggressive motions that can be precisely tracked. + oai:arXiv.org:2601.02738v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kexin Guo, Zihan Yang, Yuhang Liu, Jindou Jia, Xiang Yu + + + Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning + https://arxiv.org/abs/2601.02739 + arXiv:2601.02739v1 Announce Type: new +Abstract: To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation chain-style model, we introduce a code module to guide knowledge-graph exploration and incorporate code as part of the chain-of-thought prompt, forming an external knowledge input that provides more accurate and structured information to the model. Based on this design, we develop an improved knowledge distillation chain-style model and leverage it to analyze and constrain the reasoning process of LLMs, thereby improving inference accuracy. We empirically evaluate the proposed approach using GPT-4 and LLaMA-3.3 on multiple public datasets. Experimental results demonstrate that incorporating code modules significantly enhances the model's ability to capture contextual information and effectively mitigates prompt-induced hallucinations. Specifically, HIT@1, HIT@3, and HIT@5 improve by 15.64%, 13.38%, and 13.28%, respectively. Moreover, the proposed method achieves HIT@1, HIT@3, and HIT@5 scores exceeding 95% across several evaluation settings. These results indicate that the proposed approach substantially reduces hallucination behavior while improving the accuracy and verifiability of large language models. + oai:arXiv.org:2601.02739v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jinbo Hao, Kai Yang, Qingzhen Su, Yang Chen, Yifan Li, Chao Jiang + + + Language Hierarchization Provides the Optimal Solution to Human Working Memory Limits + https://arxiv.org/abs/2601.02740 + arXiv:2601.02740v1 Announce Type: new +Abstract: Language is a uniquely human trait, conveying information efficiently by organizing word sequences in sentences into hierarchical structures. A central question persists: Why is human language hierarchical? In this study, we show that hierarchization optimally solves the challenge of our limited working memory capacity. We established a likelihood function that quantifies how well the average number of units according to the language processing mechanisms aligns with human working memory capacity (WMC) in a direct fashion. The maximum likelihood estimate (MLE) of this function, tehta_MLE, turns out to be the mean of units. Through computational simulations of symbol sequences and validation analyses of natural language sentences, we uncover that compared to linear processing, hierarchical processing far surpasses it in constraining the tehta_MLE values under the human WMC limit, along with the increase of sequence/sentence length successfully. It also shows a converging pattern related to children's WMC development. These results suggest that constructing hierarchical structures optimizes the processing efficiency of sequential language input while staying within memory constraints, genuinely explaining the universal hierarchical nature of human language. + oai:arXiv.org:2601.02740v1 + cs.CL + stat.AP + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Luyao Chen, Weibo Gao, Junjie Wu, Jinshan Wu, Angela D. Friederici + + + SYNAPSE: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation + https://arxiv.org/abs/2601.02744 + arXiv:2601.02744v1 Announce Type: new +Abstract: While Large Language Models (LLMs) excel at generalized reasoning, standard retrieval-augmented approaches fail to address the disconnected nature of long-term agentic memory. To bridge this gap, we introduce Synapse (Synergistic Associative Processing Semantic Encoding), a unified memory architecture that transcends static vector similarity. Drawing from cognitive science, Synapse models memory as a dynamic graph where relevance emerges from spreading activation rather than pre-computed links. By integrating lateral inhibition and temporal decay, the system dynamically highlights relevant sub-graphs while filtering interference. We implement a Triple Hybrid Retrieval strategy that fuses geometric embeddings with activation-based graph traversal. Comprehensive evaluations on the LoCoMo benchmark show that Synapse significantly outperforms state-of-the-art methods in complex temporal and multi-hop reasoning tasks, offering a robust solution to the "Contextual Tunneling" problem. Our code and data will be made publicly available upon acceptance. + oai:arXiv.org:2601.02744v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu + + + D$^3$R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images + https://arxiv.org/abs/2601.02747 + arXiv:2601.02747v1 Announce Type: new +Abstract: Detecting tiny objects plays a vital role in remote sensing intelligent interpretation, as these objects often carry critical information for downstream applications. However, due to the extremely limited pixel information and significant variations in object density, mainstream Transformer-based detectors often suffer from slow convergence and inaccurate query-object matching. To address these challenges, we propose D$^3$R-DETR, a novel DETR-based detector with Dual-Domain Density Refinement. By fusing spatial and frequency domain information, our method refines low-level feature maps and utilizes their rich details to predict more accurate object density map, thereby guiding the model to precisely localize tiny objects. Extensive experiments on the AI-TOD-v2 dataset demonstrate that D$^3$R-DETR outperforms existing state-of-the-art detectors for tiny object detection. + oai:arXiv.org:2601.02747v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zixiao Wen, Zhen Yang, Xianjie Bao, Lei Zhang, Xiantai Xiang, Wenshuai Li, Yuhan Liu + + + The Path Ahead for Agentic AI: Challenges and Opportunities + https://arxiv.org/abs/2601.02749 + arXiv:2601.02749v1 Announce Type: new +Abstract: The evolution of Large Language Models (LLMs) from passive text generators to autonomous, goal-driven systems represents a fundamental shift in artificial intelligence. This chapter examines the emergence of agentic AI systems that integrate planning, memory, tool use, and iterative reasoning to operate autonomously in complex environments. We trace the architectural progression from statistical models to transformer-based systems, identifying capabilities that enable agentic behavior: long-range reasoning, contextual awareness, and adaptive decision-making. The chapter provides three contributions: (1) a synthesis of how LLM capabilities extend toward agency through reasoning-action-reflection loops; (2) an integrative framework describing core components perception, memory, planning, and tool execution that bridge LLMs with autonomous behavior; (3) a critical assessment of applications and persistent challenges in safety, alignment, reliability, and sustainability. Unlike existing surveys, we focus on the architectural transition from language understanding to autonomous action, emphasizing the technical gaps that must be resolved before deployment. We identify critical research priorities, including verifiable planning, scalable multi-agent coordination, persistent memory architectures, and governance frameworks. Responsible advancement requires simultaneous progress in technical robustness, interpretability, and ethical safeguards to realize potential while mitigating risks of misalignment and unintended consequences. + oai:arXiv.org:2601.02749v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Nadia Sibai, Yara Ahmed, Serry Sibaee, Sawsan AlHalawani, Adel Ammar, Wadii Boulila + + + Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection + https://arxiv.org/abs/2601.02750 + arXiv:2601.02750v1 Announce Type: new +Abstract: Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements show that incorporating propagation dynamics can significantly enhance detection performance compared to previous content-only approaches. However, this remains challenging at early stages due to the absence of observable propagation signals. To address this limitation, we propose AVOID, an \underline{a}gent-driven \underline{v}irtual pr\underline{o}pagat\underline{i}on for early fake news \underline{d}etection. AVOID reformulates early detection as a new paradigm of evidence generation, where propagation signals are actively simulated rather than passively observed. Leveraging LLM-powered agents with differentiated roles and data-driven personas, AVOID realistically constructs early-stage diffusion behaviors without requiring real propagation data. The resulting virtual trajectories provide complementary social evidence that enriches content-based detection, while a denoising-guided fusion strategy aligns simulated propagation with content semantics. Extensive experiments on benchmark datasets demonstrate that AVOID consistently outperforms state-of-the-art baselines, highlighting the effectiveness and practical value of virtual propagation augmentation for early fake news detection. The code and data are available at https://github.com/Ironychen/AVOID. + oai:arXiv.org:2601.02750v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Bincheng Gu, Min Gao, Junliang Yu, Zongwei Wang, Zhiyi Liu, Kai Shu, Hongyu Zhang + + + Window-based Membership Inference Attacks Against Fine-tuned Large Language Models + https://arxiv.org/abs/2601.02751 + arXiv:2601.02751v1 Announce Type: new +Abstract: Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data. This approach, however, dilutes the subtle, localized signals of memorization, reducing attack effectiveness. We challenge this global-averaging paradigm, positing that membership signals are more pronounced within localized contexts. We introduce WBC (Window-Based Comparison), which exploits this insight through a sliding window approach with sign-based aggregation. Our method slides windows of varying sizes across text sequences, with each window casting a binary vote on membership based on loss comparisons between target and reference models. By ensembling votes across geometrically spaced window sizes, we capture memorization patterns from token-level artifacts to phrase-level structures. Extensive experiments across eleven datasets demonstrate that WBC substantially outperforms established baselines, achieving higher AUC scores and 2-3 times improvements in detection rates at low false positive thresholds. Our findings reveal that aggregating localized evidence is fundamentally more effective than global averaging, exposing critical privacy vulnerabilities in fine-tuned LLMs. + oai:arXiv.org:2601.02751v1 + cs.CL + cs.AI + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yuetian Chen, Yuntao Du, Kaiyuan Zhang, Ashish Kundu, Charles Fleming, Bruno Ribeiro, Ninghui Li + + + EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce + https://arxiv.org/abs/2601.02752 + arXiv:2601.02752v1 Announce Type: new +Abstract: Large Language Model (LLM)-based agents are increasingly deployed in e-commerce applications to assist customer services in tasks such as product inquiries, recommendations, and order management. Existing benchmarks primarily evaluate whether these agents successfully complete the final task, overlooking the intermediate reasoning stages that are crucial for effective decision-making. To address this gap, we propose EComStage, a unified benchmark for evaluating agent-capable LLMs across the comprehensive stage-wise reasoning process: Perception (understanding user intent), Planning (formulating an action plan), and Action (executing the decision). EComStage evaluates LLMs through seven separate representative tasks spanning diverse e-commerce scenarios, with all samples human-annotated and quality-checked. Unlike prior benchmarks that focus only on customer-oriented interactions, EComStage also evaluates merchant-oriented scenarios, including promotion management, content review, and operational support relevant to real-world applications. We evaluate a wide range of over 30 LLMs, spanning from 1B to over 200B parameters, including open-source models and closed-source APIs, revealing stage/orientation- specific strengths and weaknesses. Our results provide fine-grained, actionable insights for designing and optimizing LLM-based agents in real-world e-commerce settings. + oai:arXiv.org:2601.02752v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kaiyan Zhao, Zijie Meng, Zheyong Xie, Jin Duan, Yao Hu, Zuozhu Liu, Shaosheng Cao + + + Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies + https://arxiv.org/abs/2601.02754 + arXiv:2601.02754v1 Announce Type: new +Abstract: With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches focus on reinforcement learning (RL) and generative models. These efforts imitate offline historical behaviors by utilizing a complex structure with expensive hyperparameter tuning. The suboptimal trajectories further exacerbate the difficulty of policy learning. + To address these challenges, we proposes QGA, a novel Q-value regularized Generative Auto-bidding method. In QGA, we propose to plug a Q-value regularization with double Q-learning strategy into the Decision Transformer backbone. This design enables joint optimization of policy imitation and action-value maximization, allowing the learned bidding policy to both leverage experience from the dataset and alleviate the adverse impact of the suboptimal trajectories. Furthermore, to safely explore the policy space beyond the data distribution, we propose a Q-value guided dual-exploration mechanism, in which the DT model is conditioned on multiple return-to-go targets and locally perturbed actions. This entire exploration process is dynamically guided by the aforementioned Q-value module, which provides principled evaluation for each candidate action. Experiments on public benchmarks and simulation environments demonstrate that QGA consistently achieves superior or highly competitive results compared to existing alternatives. Notably, in large-scale real-world A/B testing, QGA achieves a 3.27% increase in Ad GMV and a 2.49% improvement in Ad ROI. + oai:arXiv.org:2601.02754v1 + cs.LG + cs.AI + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + 10.1145/3770854.3783950 + Mingming Zhang, Na Li, Zhuang Feiqing, Hongyang Zheng, Jiangbing Zhou, Wang Wuyin, Sheng-jie Sun, XiaoWei Chen, Junxiong Zhu, Lixin Zou, Chenliang Li + + + LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery + https://arxiv.org/abs/2601.02757 + arXiv:2601.02757v1 Announce Type: new +Abstract: Existing change detection methods often lack the versatility to handle diverse real-world queries and the intelligence for comprehensive analysis. This paper presents a general agent framework, integrating Large Language Models (LLM) with vision foundation models to form ChangeGPT. A hierarchical structure is employed to mitigate hallucination. The agent was evaluated on a curated dataset of 140 questions categorized by real-world scenarios, encompassing various question types (e.g., Size, Class, Number) and complexities. The evaluation assessed the agent's tool selection ability (Precision/Recall) and overall query accuracy (Match). ChangeGPT, especially with a GPT-4-turbo backend, demonstrated superior performance, achieving a 90.71 % Match rate. Its strength lies particularly in handling change-related queries requiring multi-step reasoning and robust tool selection. Practical effectiveness was further validated through a real-world urban change monitoring case study in Qianhai Bay, Shenzhen. By providing intelligence, adaptability, and multi-type change analysis, ChangeGPT offers a powerful solution for decision-making in remote sensing applications. + oai:arXiv.org:2601.02757v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1016/j.autcon.2025.106341 + Automation in Construction 177 (2025) 106341 Automation in Construction 177 (2025) 106341 Automation in Construction 177 (2025) 106341 Automation in Construction 177 (2025) 106341 Automation in Construction 177 (2025) 106341 + Zixuan Xiao, Jun Ma + + + Towards Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups + https://arxiv.org/abs/2601.02759 + arXiv:2601.02759v1 Announce Type: new +Abstract: Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring dataset-specific hyperparameter tuning or retraining for new environments. We identify three critical limitations: (a) fixed user-defined parameters (e.g., voxel size, search radius) that fail to generalize across varying scales, (b) learned keypoint detectors exhibit poor cross-domain transferability, and (c) absolute coordinates amplify scale mismatches between datasets. To address these three issues, we present BUFFER-X, a training-free registration framework that achieves zero-shot generalization through: (a) geometric bootstrapping for automatic hyperparameter estimation, (b) distribution-aware farthest point sampling to replace learned detectors, and (c) patch-level coordinate normalization to ensure scale consistency. Our approach employs hierarchical multi-scale matching to extract correspondences across local, middle, and global receptive fields, enabling robust registration in diverse environments. For efficiency-critical applications, we introduce BUFFER-X-Lite, which reduces total computation time by 43% (relative to BUFFER-X) through early exit strategies and fast pose solvers while preserving accuracy. We evaluate on a comprehensive benchmark comprising 12 datasets spanning object-scale, indoor, and outdoor scenes, including cross-sensor registration between heterogeneous LiDAR configurations. Results demonstrate that our approach generalizes effectively without manual tuning or prior knowledge of test domains. Code: https://github.com/MIT-SPARK/BUFFER-X. + oai:arXiv.org:2601.02759v1 + cs.CV + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hyungtae Lim, Minkyun Seo, Luca Carlone, Jaesik Park + + + AnyDepth: Depth Estimation Made Easy + https://arxiv.org/abs/2601.02760 + arXiv:2601.02760v1 Announce Type: new +Abstract: Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images. Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability. In this paper, we propose a lightweight and data-centric framework for zero-shot monocular depth estimation. We first adopt DINOv3 as the visual encoder to obtain high-quality dense features. Secondly, to address the inherent drawbacks of the complex structure of the DPT, we design the Simple Depth Transformer (SDT), a compact transformer-based decoder. Compared to the DPT, it uses a single-path feature fusion and upsampling process to reduce the computational overhead of cross-scale feature fusion, achieving higher accuracy while reducing the number of parameters by approximately 85%-89%. Furthermore, we propose a quality-based filtering strategy to filter out harmful samples, thereby reducing dataset size while improving overall training quality. Extensive experiments on five benchmarks demonstrate that our framework surpasses the DPT in accuracy. This work highlights the importance of balancing model design and data quality for achieving efficient and generalizable zero-shot depth estimation. Code: https://github.com/AIGeeksGroup/AnyDepth. Website: https://aigeeksgroup.github.io/AnyDepth. + oai:arXiv.org:2601.02760v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Zeyu Ren, Zeyu Zhang, Wukai Li, Qingxiang Liu, Hao Tang + + + Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation + https://arxiv.org/abs/2601.02762 + arXiv:2601.02762v1 Announce Type: new +Abstract: Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representation of environmental structures, which lack flexibility for realistic non-structural disturbances. Besides, representation error and the distribution shifts can lead to heavy degradation in prediction accuracy. This work presents a generalizable disturbance estimation framework that builds on meta-learning and feedback-calibrated online adaptation. By extracting features from a finite time window of past observations, a unified representation that effectively captures general non-structural disturbances can be learned without predefined structural assumptions. The online adaptation process is subsequently calibrated by a state-feedback mechanism to attenuate the learning residual originating from the representation and generalizability limitations. Theoretical analysis shows that simultaneous convergence of both the online learning error and the disturbance estimation error can be achieved. Through the unified meta-representation, our framework effectively estimates multiple rapidly changing disturbances, as demonstrated by quadrotor flight experiments. See the project page for video, supplementary material and code: https://nonstructural-metalearn.github.io. + oai:arXiv.org:2601.02762v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zihan Yang, Jindou Jia, Meng Wang, Yuhang Liu, Kexin Guo, Xiang Yu + + + ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration + https://arxiv.org/abs/2601.02763 + arXiv:2601.02763v1 Announce Type: new +Abstract: All-in-One Image Restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches rely heavily on degradation-specific representations, often resulting in oversmoothing and artifacts. To address this, we propose ClearAIR, a novel AiOIR framework inspired by Human Visual Perception (HVP) and designed with a hierarchical, coarse-to-fine restoration strategy. First, leveraging the global priority of early HVP, we employ a Multimodal Large Language Model (MLLM)-based Image Quality Assessment (IQA) model for overall evaluation. Unlike conventional IQA, our method integrates cross-modal understanding to more accurately characterize complex, composite degradations. Building upon this overall assessment, we then introduce a region awareness and task recognition pipeline. A semantic cross-attention, leveraging semantic guidance unit, first produces coarse semantic prompts. Guided by this regional context, a degradation-aware module implicitly captures region-specific degradation characteristics, enabling more precise local restoration. Finally, to recover fine details, we propose an internal clue reuse mechanism. It operates in a self-supervised manner to mine and leverage the intrinsic information of the image itself, substantially enhancing detail restoration. Experimental results show that ClearAIR achieves superior performance across diverse synthetic and real-world datasets. + oai:arXiv.org:2601.02763v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xu Zhang, Huan Zhang, Guoli Wang, Qian Zhang, Lefei Zhang + + + Netflix Artwork Personalization via LLM Post-training + https://arxiv.org/abs/2601.02764 + arXiv:2601.02764v1 Announce Type: new +Abstract: Large language models (LLMs) have demonstrated success in various applications of user recommendation and personalization across e-commerce and entertainment. On many entertainment platforms such as Netflix, users typically interact with a wide range of titles, each represented by an artwork. Since users have diverse preferences, an artwork that appeals to one type of user may not resonate with another with different preferences. Given this user heterogeneity, our work explores the novel problem of personalized artwork recommendations according to diverse user preferences. Similar to the multi-dimensional nature of users' tastes, titles contain different themes and tones that may appeal to different viewers. For example, the same title might feature both heartfelt family drama and intense action scenes. Users who prefer romantic content may like the artwork emphasizing emotional warmth between the characters, while those who prefer action thrillers may find high-intensity action scenes more intriguing. Rather than a one-size-fits-all approach, we conduct post-training of pre-trained LLMs to make personalized artwork recommendations, selecting the most preferred visual representation of a title for each user and thereby improving user satisfaction and engagement. Our experimental results with Llama 3.1 8B models (trained on a dataset of 110K data points and evaluated on 5K held-out user-title pairs) show that the post-trained LLMs achieve 3-5\% improvements over the Netflix production model, suggesting a promising direction for granular personalized recommendations using LLMs. + oai:arXiv.org:2601.02764v1 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Hyunji Nam, Sejoon Oh, Emma Kong, Yesu Feng, Moumita Bhattacharya + + + Advancing Assistive Robotics: Multi-Modal Navigation and Biophysical Monitoring for Next-Generation Wheelchairs + https://arxiv.org/abs/2601.02766 + arXiv:2601.02766v1 Announce Type: new +Abstract: Assistive electric-powered wheelchairs (EPWs) have become essential mobility aids for people with disabilities such as amyotrophic lateral sclerosis (ALS), post-stroke hemiplegia, and dementia-related mobility impairment. This work presents a novel multi-modal EPW control system designed to prioritize patient needs while allowing seamless switching between control modes. Four complementary interfaces, namely joystick, speech, hand gesture, and electrooculography (EOG), are integrated with a continuous vital sign monitoring framework measuring heart rate variability, oxygen saturation (SpO2), and skin temperature. This combination enables greater patient independence while allowing caregivers to maintain real-time supervision and early intervention capability. + Two-point calibration of the biophysical sensors against clinical reference devices resulted in root mean square errors of at most 2 bpm for heart rate, 0.5 degree Celsius for skin temperature, and 1 percent for SpO2. Experimental evaluation involved twenty participants with mobility impairments executing a total of 500 indoor navigation commands. The achieved command recognition accuracies were 99 percent for joystick control, 97 percent plus or minus 2 percent for speech, and 95 percent plus or minus 3 percent for hand gesture, with an average closed-loop latency of 20 plus or minus 0.5 milliseconds. Caregivers receive real-time alerts through an Android application following encrypted cloud transmission of physiological data. By integrating multi-modal mobility control with cloud-enabled health monitoring and reporting latency and energy budgets, the proposed prototype addresses key challenges in assistive robotics, contributes toward compliance with ISO 7176-31 and IEC 80601-2-78 safety standards, and establishes a foundation for future adaptive machine learning enhancements. + oai:arXiv.org:2601.02766v1 + cs.RO + cs.AR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Md. Anowar Hossain, Mohd. Ehsanul Hoque + + + AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs + https://arxiv.org/abs/2601.02771 + arXiv:2601.02771v1 Announce Type: new +Abstract: Visual abductive reasoning (VAR) is a challenging task that requires AI systems to infer the most likely explanation for incomplete visual observations. While recent MLLMs develop strong general-purpose multimodal reasoning capabilities, they fall short in abductive inference, as compared to human beings. To bridge this gap, we draw inspiration from the interplay between verbal and pictorial abduction in human cognition, and propose to strengthen abduction of MLLMs by mimicking such dual-mode behavior. Concretely, we introduce AbductiveMLLM comprising of two synergistic components: REASONER and IMAGINER. The REASONER operates in the verbal domain. It first explores a broad space of possible explanations using a blind LLM and then prunes visually incongruent hypotheses based on cross-modal causal alignment. The remaining hypotheses are introduced into the MLLM as targeted priors, steering its reasoning toward causally coherent explanations. The IMAGINER, on the other hand, further guides MLLMs by emulating human-like pictorial thinking. It conditions a text-to-image diffusion model on both the input video and the REASONER's output embeddings to "imagine" plausible visual scenes that correspond to verbal explanation, thereby enriching MLLMs' contextual grounding. The two components are trained jointly in an end-to-end manner. Experiments on standard VAR benchmarks show that AbductiveMLLM achieves state-of-the-art performance, consistently outperforming traditional solutions and advanced MLLMs. + oai:arXiv.org:2601.02771v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Boyu Chang, Qi Wang, Xi Guo, Zhixiong Nan, Yazhou Yao, Tianfei Zhou + + + From Slaves to Synths? Superintelligence and the Evolution of Legal Personality + https://arxiv.org/abs/2601.02773 + arXiv:2601.02773v1 Announce Type: new +Abstract: This essay examines the evolving concept of legal personality through the lens of recent developments in artificial intelligence and the possible emergence of superintelligence. Legal systems have long been open to extending personhood to non-human entities, most prominently corporations, for instrumental or inherent reasons. Instrumental rationales emphasize accountability and administrative efficiency, whereas inherent ones appeal to moral worth and autonomy. Neither is yet sufficient to justify conferring personhood on AI. Nevertheless, the acceleration of technological autonomy may lead us to reconsider how law conceptualizes agency and responsibility. Drawing on comparative jurisprudence, corporate theory, and the emerging literature on AI governance, the paper argues that existing frameworks can address short-term accountability gaps, but the eventual development of superintelligence may force a paradigmatic shift in our understanding of law itself. In such a speculative future, legal personality may depend less on the cognitive sophistication of machines than on humanity's ability to preserve our own moral and institutional sovereignty. + oai:arXiv.org:2601.02773v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Simon Chesterman + + + Experience and Adaptation in AI-mediated Hiring Systems: A Combined Analysis of Online Discourse and Interface Design + https://arxiv.org/abs/2601.02775 + arXiv:2601.02775v1 Announce Type: new +Abstract: Automated interviewing tools are now widely adopted to manage recruitment at scale, often replacing early human screening with algorithmic assessments. While these systems are promoted as efficient and consistent, they also generate new forms of uncertainty for applicants. Efforts to soften these experiences through human-like design features have only partially addressed underlying concerns. To understand how candidates interpret and cope with such systems, we conducted a mixed empirical investigation that combined analysis of online discussions, responses from more than one hundred and fifty survey participants, and follow-up conversations with seventeen interviewees. The findings point to several recurring problems, including unclear evaluation criteria, limited organizational responsibility for automated outcomes, and a lack of practical support for preparation. Many participants described the technology as far less advanced than advertised, leading them to infer how decisions might be made in the absence of guidance. This speculation often intensified stress and emotional strain. Furthermore, the minimal sense of interpersonal engagement contributed to feelings of detachment and disposability. Based on these observations, we propose design directions aimed at improving clarity, accountability, and candidate support in AI-mediated hiring processes. + oai:arXiv.org:2601.02775v1 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Md Nazmus Sakib, Naga Manogna Rayasam, Sanorita Dey + + + UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction + https://arxiv.org/abs/2601.02776 + arXiv:2601.02776v1 Announce Type: new +Abstract: Neural Audio Codecs (NACs) can reduce transmission overhead by performing compact compression and reconstruction, which also aim to bridge the gap between continuous and discrete signals. Existing NACs can be divided into two categories: multi-codebook and single-codebook codecs. Multi-codebook codecs face challenges such as structural complexity and difficulty in adapting to downstream tasks, while single-codebook codecs, though structurally simpler, suffer from low-fidelity, ineffective modeling of unified audio, and an inability to support modeling of high-frequency audio. We propose the UniSRCodec, a single-codebook codec capable of supporting high sampling rate, low-bandwidth, high fidelity, and unified. We analyze the inefficiency of waveform-based compression and introduce the time and frequency compression method using the Mel-spectrogram, and cooperate with a Vocoder to recover the phase information of the original audio. Moreover, we propose a sub-band reconstruction technique to achieve high-quality compression across both low and high frequency bands. Subjective and objective experimental results demonstrate that UniSRCodec achieves state-of-the-art (SOTA) performance among cross-domain single-codebook codecs with only a token rate of 40, and its reconstruction quality is comparable to that of certain multi-codebook methods. Our demo page is available at https://wxzyd123.github.io/unisrcodec. + oai:arXiv.org:2601.02776v1 + cs.SD + cs.AI + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhisheng Zhang, Xiang Li, Yixuan Zhou, Jing Peng, Shengbo Cai, Guoyang Zeng, Zhiyong Wu + + + M-SEVIQ: A Multi-band Stereo Event Visual-Inertial Quadruped-based Dataset for Perception under Rapid Motion and Challenging Illumination + https://arxiv.org/abs/2601.02777 + arXiv:2601.02777v1 Announce Type: new +Abstract: Agile locomotion in legged robots poses significant challenges for visual perception. Traditional frame-based cameras often fail in these scenarios for producing blurred images, particularly under low-light conditions. In contrast, event cameras capture changes in brightness asynchronously, offering low latency, high temporal resolution, and high dynamic range. These advantages make them suitable for robust perception during rapid motion and under challenging illumination. However, existing event camera datasets exhibit limitations in stereo configurations and multi-band sensing domains under various illumination conditions. To address this gap, we present M-SEVIQ, a multi-band stereo event visual and inertial quadruped dataset collected using a Unitree Go2 equipped with stereo event cameras, a frame-based camera, an inertial measurement unit (IMU), and joint encoders. This dataset contains more than 30 real-world sequences captured across different velocity levels, illumination wavelengths, and lighting conditions. In addition, comprehensive calibration data, including intrinsic, extrinsic, and temporal alignments, are provided to facilitate accurate sensor fusion and benchmarking. Our M-SEVIQ can be used to support research in agile robot perception, sensor fusion, semantic segmentation and multi-modal vision in challenging environments. + oai:arXiv.org:2601.02777v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jingcheng Cao, Chaoran Xiong, Jianmin Song, Shang Yan, Jiachen Liu, Ling Pei + + + Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation + https://arxiv.org/abs/2601.02778 + arXiv:2601.02778v1 Announce Type: new +Abstract: Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities, but training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics and imperfect actuation. We close this gap with a practical sim-to-real reinforcement learning (RL) framework that utilizes dense tactile feedback combined with joint torque sensing to explicitly regulate physical interactions. To enable effective sim-to-real transfer, we introduce (i) a computationally fast tactile simulation that computes distances between dense virtual tactile units and the object via parallel forward kinematics, providing high-rate, high-resolution touch signals needed by RL; (ii) a current-to-torque calibration that eliminates the need for torque sensors on dexterous hands by mapping motor current to joint torque; and (iii) actuator dynamics modeling to bridge the actuation gaps with randomization of non-ideal effects such as backlash, torque-speed saturation. Using an asymmetric actor-critic PPO pipeline trained entirely in simulation, our policies deploy directly to a five-finger hand. The resulting policies demonstrated two essential skills: (1) command-based, controllable grasp force tracking, and (2) reorientation of objects in the hand, both of which were robustly executed without fine-tuning on the robot. By combining tactile and torque in the observation space with effective sensing/actuation modeling, our system provides a practical solution to achieve reliable dexterous manipulation. To our knowledge, this is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware. + oai:arXiv.org:2601.02778v1 + cs.RO + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Haoyu Dong, Zhengmao He, Yang Li, Zhibin Li, Xinyu Yi, Zhe Zhao + + + Hierarchical Preemptive Holistic Collaborative Systems for Embodied Multi-Agent Systems: Framework, Hybrid Stability, and Scalability Analysis + https://arxiv.org/abs/2601.02779 + arXiv:2601.02779v1 Announce Type: new +Abstract: The coordination of Embodied Multi-Agent Systems in constrained physical environments requires a rigorous balance between safety, scalability, and efficiency. Traditional decentralized approaches, e.g., reactive collision avoidance, are prone to local minima or reciprocal yielding standoffs due to the lack of future intent awareness. In contrast, centralized planning suffers from intractable computational complexity and single-point-of-failure vulnerabilities. To address these limitations, we propose the Hierarchical Preemptive Holistic Collaborative (Prollect) framework, which generalizes the Preemptive Holistic Collaborative System (PHCS) by decomposing the global coordination problem into topologically connected subspace optimizations. We formalize the system as a Hybrid Automaton and introduce a three-stage receding horizon mechanism (frozen execution, preliminary planning, proactive look-ahead windows) with explicit padding to prevent races between coordination dissemination and intent updates. Notably, we design a robust timing protocol with a mandatory Idle Buffer that acts as a dwell-time constraint to eliminate Zeno behaviors and ensure computational stability under jitter. Furthermore, we formalize a Shadow Agent protocol to guarantee seamless trajectory consistency across subspace boundaries, which we treat as an Input-to-State Stability (ISS) problem. + oai:arXiv.org:2601.02779v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Ting Peng + + + MiMo-V2-Flash Technical Report + https://arxiv.org/abs/2601.02780 + arXiv:2601.02780v1 Announce Type: new +Abstract: We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration. + oai:arXiv.org:2601.02780v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Bangjun Xiao, Bingquan Xia, Bo Yang, Bofei Gao, Bowen Shen, Chen Zhang, Chenhong He, Chiheng Lou, Fuli Luo, Gang Wang, Gang Xie, Hailin Zhang, Hanglong Lv, Hanyu Li, Heyu Chen, Hongshen Xu, Houbin Zhang, Huaqiu Liu, Jiangshan Duo, Jianyu Wei, Jiebao Xiao, Jinhao Dong, Jun Shi, Junhao Hu, Kainan Bao, Kang Zhou, Lei Li, Liang Zhao, Linghao Zhang, Peidian Li, Qianli Chen, Shaohui Liu, Shihua Yu, Shijie Cao, Shimao Chen, Shouqiu Yu, Shuo Liu, Tianling Zhou, Weijiang Su, Weikun Wang, Wenhan Ma, Xiangwei Deng, Bohan Mao, Bowen Ye, Can Cai, Chenghua Wang, Chengxuan Zhu, Chong Ma, Chun Chen, Chunan Li, Dawei Zhu, Deshan Xiao, Dong Zhang, Duo Zhang, Fangyue Liu, Feiyu Yang, Fengyuan Shi, Guoan Wang, Hao Tian, Hao Wu, Heng Qu, Hongfei Yi, Hongxu An, Hongyi Guan, Xing Zhang, Yifan Song, Yihan Yan, Yihao Zhao, Yingchun Lai, Yizhao Gao, Yu Cheng, Yuanyuan Tian, Yudong Wang, Zhen Tang, Zhengju Tang, Zhengtao Wen, Zhichao Song, Zhixian Zheng, Zihan Jiang, Jian Wen, Jiarui Sun, Jiawei Li, Jinlong Xue, Jun Xia, Kai Fang, Menghang Zhu, Nuo Chen, Qian Tu, Qihao Zhang, Qiying Wang, Rang Li, Rui Ma, Shaolei Zhang, Shengfan Wang, Shicheng Li, Shuhao Gu, Shuhuai Ren, Sirui Deng, Tao Guo, Tianyang Lu, Weiji Zhuang, Weikang Zhang, Weimin Xiong, Wenshan Huang, Wenyu Yang, Xin Zhang, Xing Yong, Xu Wang, Xueyang Xie, Yilin Jiang, Yixin Yang, Yongzhe He, Yu Tu, Yuanliang Dong, Yuchen Liu, Yue Ma, Yue Yu, Yuxing Xiang, Zhaojun Huang, Zhenru Lin, Zhipeng Xu, Zhiyang Chen, Zhonghua Deng, Zihan Zhang, Zihao Yue + + + EarthVL: A Progressive Earth Vision-Language Understanding and Generation Framework + https://arxiv.org/abs/2601.02783 + arXiv:2601.02783v1 Announce Type: new +Abstract: Earth vision has achieved milestones in geospatial object recognition but lacks exploration in object-relational reasoning, limiting comprehensive scene understanding. To address this, a progressive Earth vision-language understanding and generation framework is proposed, including a multi-task dataset (EarthVLSet) and a semantic-guided network (EarthVLNet). Focusing on city planning applications, EarthVLSet includes 10.9k sub-meter resolution remote sensing images, land-cover masks, and 761.5k textual pairs involving both multiple-choice and open-ended visual question answering (VQA) tasks. In an object-centric way, EarthVLNet is proposed to progressively achieve semantic segmentation, relational reasoning, and comprehensive understanding. The first stage involves land-cover segmentation to generate object semantics for VQA guidance. Guided by pixel-wise semantics, the object awareness based large language model (LLM) performs relational reasoning and knowledge summarization to generate the required answers. As for optimization, the numerical difference loss is proposed to dynamically add difference penalties, addressing the various objects' statistics. Three benchmarks, including semantic segmentation, multiple-choice, and open-ended VQA demonstrated the superiorities of EarthVLNet, yielding three future directions: 1) segmentation features consistently enhance VQA performance even in cross-dataset scenarios; 2) multiple-choice tasks show greater sensitivity to the vision encoder than to the language decoder; and 3) open-ended tasks necessitate advanced vision encoders and language decoders for an optimal performance. We believe this dataset and method will provide a beneficial benchmark that connects ''image-mask-text'', advancing geographical applications for Earth vision. + oai:arXiv.org:2601.02783v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Junjue Wang, Yanfei Zhong, Zihang Chen, Zhuo Zheng, Ailong Ma, Liangpei Zhang + + + DreamStyle: A Unified Framework for Video Stylization + https://arxiv.org/abs/2601.02785 + arXiv:2601.02785v1 Announce Type: new +Abstract: Video stylization, an important downstream task of video generation models, has not yet been thoroughly explored. Its input style conditions typically include text, style image, and stylized first frame. Each condition has a characteristic advantage: text is more flexible, style image provides a more accurate visual anchor, and stylized first frame makes long-video stylization feasible. However, existing methods are largely confined to a single type of style condition, which limits their scope of application. Additionally, their lack of high-quality datasets leads to style inconsistency and temporal flicker. To address these limitations, we introduce DreamStyle, a unified framework for video stylization, supporting (1) text-guided, (2) style-image-guided, and (3) first-frame-guided video stylization, accompanied by a well-designed data curation pipeline to acquire high-quality paired video data. DreamStyle is built on a vanilla Image-to-Video (I2V) model and trained using a Low-Rank Adaptation (LoRA) with token-specific up matrices that reduces the confusion among different condition tokens. Both qualitative and quantitative evaluations demonstrate that DreamStyle is competent in all three video stylization tasks, and outperforms the competitors in style consistency and video quality. + oai:arXiv.org:2601.02785v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Mengtian Li, Jinshu Chen, Songtao Zhao, Wanquan Feng, Pengqi Tu, Qian He + + + RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse + https://arxiv.org/abs/2601.02790 + arXiv:2601.02790v1 Announce Type: new +Abstract: Accurate radio map (RM) construction is essential to enabling environment-aware and adaptive wireless communication. However, in future 6G scenarios characterized by high-speed network entities and fast-changing environments, it is very challenging to meet real-time requirements. Although generative diffusion models (DMs) can achieve state-of-the-art accuracy with second-level delay, their iterative nature leads to prohibitive inference latency in delay-sensitive scenarios. In this paper, by uncovering a key structural property of diffusion processes: the latent midpoints remain highly consistent across semantically similar scenes, we propose RadioDiff-Flux, a novel two-stage latent diffusion framework that decouples static environmental modeling from dynamic refinement, enabling the reuse of precomputed midpoints to bypass redundant denoising. In particular, the first stage generates a coarse latent representation using only static scene features, which can be cached and shared across similar scenarios. The second stage adapts this representation to dynamic conditions and transmitter locations using a pre-trained model, thereby avoiding repeated early-stage computation. The proposed RadioDiff-Flux significantly reduces inference time while preserving fidelity. Experiment results show that RadioDiff-Flux can achieve up to 50 acceleration with less than 0.15% accuracy loss, demonstrating its practical utility for fast, scalable RM generation in future 6G networks. + oai:arXiv.org:2601.02790v1 + cs.LG + eess.SP + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xiucheng Wang, Peilin Zheng, Honggang Jia, Nan Cheng, Ruijin Sun, Conghao Zhou, Xuemin Shen + + + Textile IR: A Bidirectional Intermediate Representation for Physics-Aware Fashion CAD + https://arxiv.org/abs/2601.02792 + arXiv:2601.02792v1 Announce Type: new +Abstract: We introduce Textile IR, a bidirectional intermediate representation that connects manufacturing-valid CAD, physics-based simulation, and lifecycle assessment for fashion design. Unlike existing siloed tools where pattern software guarantees sewable outputs but understands nothing about drape, and physics simulation predicts behaviour but cannot automatically fix patterns, Textile IR provides the semantic glue for integration through a seven-layer Verification Ladder -- from cheap syntactic checks (pattern closure, seam compatibility) to expensive physics validation (drape simulation, stress analysis). The architecture enables bidirectional feedback: simulation failures suggest pattern modifications; material substitutions update sustainability estimates in real time; uncertainty propagates across the pipeline with explicit confidence bounds. We formalise fashion engineering as constraint satisfaction over three domains and demonstrate how Textile IR's scene-graph representation enables AI systems to manipulate garments as structured programs rather than pixel arrays. The framework addresses the compound uncertainty problem: when measurement errors in material testing, simulation approximations, and LCA database gaps combine, sustainability claims become unreliable without explicit uncertainty tracking. We propose six research priorities and discuss deployment considerations for fashion SMEs where integrated workflows reduce specialised engineering requirements. Key contribution: a formal representation that makes engineering constraints perceptible, manipulable, and immediately consequential -- enabling designers to navigate sustainability, manufacturability, and aesthetic tradeoffs simultaneously rather than discovering conflicts after costly physical prototyping. + oai:arXiv.org:2601.02792v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Petteri Teikari, Neliana Fuenmayor + + + StableDPT: Temporal Stable Monocular Video Depth Estimation + https://arxiv.org/abs/2601.02793 + arXiv:2601.02793v1 Announce Type: new +Abstract: Applying single image Monocular Depth Estimation (MDE) models to video sequences introduces significant temporal instability and flickering artifacts. We propose a novel approach that adapts any state-of-the-art image-based (depth) estimation model for video processing by integrating a new temporal module - trainable on a single GPU in a few days. Our architecture StableDPT builds upon an off-the-shelf Vision Transformer (ViT) encoder and enhances the Dense Prediction Transformer (DPT) head. The core of our contribution lies in the temporal layers within the head, which use an efficient cross-attention mechanism to integrate information from keyframes sampled across the entire video sequence. This allows the model to capture global context and inter-frame relationships leading to more accurate and temporally stable depth predictions. Furthermore, we propose a novel inference strategy for processing videos of arbitrary length avoiding the scale misalignment and redundant computations associated with overlapping windows used in other methods. Evaluations on multiple benchmark datasets demonstrate improved temporal consistency, competitive state-of-the-art performance and on top 2x faster processing in real-world scenarios. + oai:arXiv.org:2601.02793v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ivan Sobko, Hayko Riemenschneider, Markus Gross, Christopher Schroers + + + Reinforcement Learning for Follow-the-Leader Robotic Endoscopic Navigation via Synthetic Data + https://arxiv.org/abs/2601.02798 + arXiv:2601.02798v1 Announce Type: new +Abstract: Autonomous navigation is crucial for both medical and industrial endoscopic robots, enabling safe and efficient exploration of narrow tubular environments without continuous human intervention, where avoiding contact with the inner walls has been a longstanding challenge for prior approaches. We present a follow-the-leader endoscopic robot based on a flexible continuum structure designed to minimize contact between the endoscope body and intestinal walls, thereby reducing patient discomfort. To achieve this objective, we propose a vision-based deep reinforcement learning framework guided by monocular depth estimation. A realistic intestinal simulation environment was constructed in \textit{NVIDIA Omniverse} to train and evaluate autonomous navigation strategies. Furthermore, thousands of synthetic intraluminal images were generated using NVIDIA Replicator to fine-tune the Depth Anything model, enabling dense three-dimensional perception of the intestinal environment with a single monocular camera. Subsequently, we introduce a geometry-aware reward and penalty mechanism to enable accurate lumen tracking. Compared with the original Depth Anything model, our method improves $\delta_{1}$ depth accuracy by 39.2% and reduces the navigation J-index by 0.67 relative to the second-best method, demonstrating the robustness and effectiveness of the proposed approach. + oai:arXiv.org:2601.02798v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sicong Gao, Chen Qian, Laurence Xian, Liao Wu, Maurice Pagnucco, Yang Song + + + Stratified Hazard Sampling: Minimal-Variance Event Scheduling for CTMC/DTMC Discrete Diffusion and Flow Models + https://arxiv.org/abs/2601.02799 + arXiv:2601.02799v1 Announce Type: new +Abstract: CTMC/DTMC-based discrete generative models, including uniform-noise discrete diffusion (e.g., D3PM/CTDD) and discrete flow matching, enable non-autoregressive sequence generation by repeatedly replacing tokens through a time-inhomogeneous Markov process. Inference is typically implemented with step-based simulation: each token decides to jump via independent Bernoulli (or categorical) draws at every discretization step. Under uniform-noise initialization, where self-correction requires multiple edits per position, these independent decisions induce substantial variance in both the number and timing of edits, leading to characteristic failure modes such as under-editing (residual noise) or over-editing (cascading unnecessary substitutions), decreasing reproducibility. + We propose Stratified Hazard Sampling (SHS), a drop-in and hyperparameter-free inference principle for any sampler that admits a stay-vs.-replace decomposition. SHS models per-token edits as events driven by cumulative hazard (CTMC) or cumulative jump mass (DTMC) and places events by stratifying this cumulative quantity: with a single random phase per position, a token jumps whenever its accumulated hazard crosses unit-spaced thresholds. This preserves the expected number of jumps while achieving the minimum possible variance among unbiased integer estimators (bounded by 1/4), without altering per-jump destination sampling and thus retaining multimodality. We also introduce a phase-allocation variant for blacklist-style lexical constraints that prioritizes early edits at high-risk positions to mitigate late-masking artifacts. + oai:arXiv.org:2601.02799v1 + cs.LG + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Seunghwan Jang, SooJean Han + + + State-Dependent Fading Gaussian Channel with Common Reconstruction Constraints + https://arxiv.org/abs/2601.02802 + arXiv:2601.02802v1 Announce Type: new +Abstract: The task of jointly communicating a message and reconstructing a common estimate of the channel state is examined for a fading Gaussian model with additive state interference. The state is an independent and identically distributed Gaussian sequence known noncausally at the transmitter, and the instantaneous fading coefficient is perfectly known at both the transmitter and the receiver. The receiver is required to decode the transmitted message and, in addition, reconstruct the state under a common reconstruction constraint ensuring that its estimate coincides with that at the transmitter. A complete characterization of the optimal rate distortion tradeoff region for this setting is the main result of our work. The analytical results are also validated through numerical examples illustrating the rate distortion and power distortion tradeoffs. + oai:arXiv.org:2601.02802v1 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Viswanathan Ramachandran + + + Bounded Rewriting Induction for LCSTRSs + https://arxiv.org/abs/2601.02803 + arXiv:2601.02803v1 Announce Type: new +Abstract: Rewriting Induction (RI) is a method to prove inductive theorems, originating from equational reasoning. By using Logically Constrained Simply-typed Term Rewriting Systems (LCSTRSs) as an intermediate language, rewriting induction becomes a tool for program verification, with inductive theorems taking the role of equivalence predicates. Soundness of RI depends on well-founded induction, and one of the core obstacles for obtaining a practically useful proof system is to find suitable well-founded orderings automatically. Using naive approaches, all induction hypotheses must be oriented within the well-founded ordering, which leads to very strong termination requirements. This, in turn, severely limits the proof capacity of RI. Here, we introduce Bounded RI: an adaption of RI for LCSTRSs where such termination requirements are minimized. + oai:arXiv.org:2601.02803v1 + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kasper Hagens, Cynthia Kop + + + Distributionally Robust Game for Proof-of-Work Blockchain Mining Under Resource Uncertainties + https://arxiv.org/abs/2601.02804 + arXiv:2601.02804v1 Announce Type: new +Abstract: Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoW blockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee. + oai:arXiv.org:2601.02804v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xunqiang Lan, Xiao Tang, Ruonan Zhang, Bin Li, Qinghe Du, Dusit Niyato, Zhu Han + + + The perceptual gap between video see-through displays and natural human vision + https://arxiv.org/abs/2601.02805 + arXiv:2601.02805v1 Announce Type: new +Abstract: Video see-through (VST) technology aims to seamlessly blend virtual and physical worlds by reconstructing reality through cameras. While manufacturers promise perceptual fidelity, it remains unclear how close these systems are to replicating natural human vision across varying environmental conditions. In this work, we quantify the perceptual gap between the human eye and different popular VST headsets (Apple Vision Pro, Meta Quest 3, Quest Pro) using psychophysical measures of visual acuity, contrast sensitivity, and color vision. We show that despite hardware advancements, all tested VST systems fail to match the dynamic range and adaptability of the naked eye. While high-end devices approach human performance in ideal lighting, they exhibit significant degradation in low-light conditions, particularly in contrast sensitivity and acuity. Our results map the physiological limitations of digital reality reconstruction, establishing a specific perceptual gap that defines the roadmap for achieving indistinguishable VST experiences. + oai:arXiv.org:2601.02805v1 + cs.HC + cs.GR + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jialin Wang, Songming Ping, Kemu Xu, Yue Li, Hai-Ning Liang + + + Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining + https://arxiv.org/abs/2601.02806 + arXiv:2601.02806v1 Announce Type: new +Abstract: Immunohistochemical (IHC) staining provides crucial molecular characterization of tissue samples and plays an indispensable role in the clinical examination and diagnosis of cancers. However, compared with the commonly used Hematoxylin and Eosin (H&E) staining, IHC staining involves complex procedures and is both time-consuming and expensive, which limits its widespread clinical use. Virtual staining converts H&E images to IHC images, offering a cost-effective alternative to clinical IHC staining. Nevertheless, using adjacent slides as ground truth often results in weakly-paired data with spatial misalignment and local deformations, hindering effective supervised learning. To address these challenges, we propose a novel topology-aware framework for H&E-to-IHC virtual staining. Specifically, we introduce a Topology-aware Consistency Matching (TACM) mechanism that employs graph contrastive learning and topological perturbations to learn robust matching patterns despite spatial misalignments, ensuring structural consistency. Furthermore, we propose a Topology-constrained Pathological Matching (TCPM) mechanism that aligns pathological positive regions based on node importance to enhance pathological consistency. Extensive experiments on two benchmarks across four staining tasks demonstrate that our method outperforms state-of-the-art approaches, achieving superior generation quality with higher clinical relevance. + oai:arXiv.org:2601.02806v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mingzhou Jiang, Jiaying Zhou, Nan Zeng, Mickael Li, Qijie Tang, Chao He, Huazhu Fu, Honghui He + + + COFFEE: COdesign Framework for Feature Enriched Embeddings in Ads-Ranking Systems + https://arxiv.org/abs/2601.02807 + arXiv:2601.02807v1 Announce Type: new +Abstract: Diverse and enriched data sources are essential for commercial ads-recommendation models to accurately assess user interest both before and after engagement with content. While extended user-engagement histories can improve the prediction of user interests, it is equally important to embed activity sequences from multiple sources to ensure freshness of user and ad-representations, following scaling law principles. In this paper, we present a novel three-dimensional framework for enhancing user-ad representations without increasing model inference or serving complexity. The first dimension examines the impact of incorporating diverse event sources, the second considers the benefits of longer user histories, and the third focuses on enriching data with additional event attributes and multi-modal embeddings. We assess the return on investment (ROI) of our source enrichment framework by comparing organic user engagement sources, such as content viewing, with ad-impression sources. The proposed method can boost the area under curve (AUC) and the slope of scaling curves for ad-impression sources by 1.56 to 2 times compared to organic usage sources even for short online-sequence lengths of 100 to 10K. Additionally, click-through rate (CTR) prediction improves by 0.56% AUC over the baseline production ad-recommendation system when using enriched ad-impression event sources, leading to improved sequence scaling resolutions for longer and offline user-ad representations. + oai:arXiv.org:2601.02807v1 + cs.IR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + WSDM, Web and Graph Workshop, 2026 + Sohini Roychowdhury, Doris Wang, Qian Ge, Joy Mu, Srihari Reddy + + + HAL: Inducing Human-likeness in LLMs with Alignment + https://arxiv.org/abs/2601.02813 + arXiv:2601.02813v1 Announce Type: new +Abstract: Conversational human-likeness plays a central role in human-AI interaction, yet it has remained difficult to define, measure, and optimize. As a result, improvements in human-like behavior are largely driven by scale or broad supervised training, rather than targeted alignment. We introduce Human Aligning LLMs (HAL), a framework for aligning language models to conversational human-likeness using an interpretable, data-driven reward. HAL derives explicit conversational traits from contrastive dialogue data, combines them into a compact scalar score, and uses this score as a transparent reward signal for alignment with standard preference optimization methods. Using this approach, we align models of varying sizes without affecting their overall performance. In large-scale human evaluations, models aligned with HAL are more frequently perceived as human-like in conversation. Because HAL operates over explicit, interpretable traits, it enables inspection of alignment behavior and diagnosis of unintended effects. More broadly, HAL demonstrates how soft, qualitative properties of language--previously outside the scope for alignment--can be made measurable and aligned in an interpretable and explainable way. + oai:arXiv.org:2601.02813v1 + cs.AI + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Masum Hasan, Junjie Zhao, Ehsan Hoque + + + Causal-Enhanced AI Agents for Medical Research Screening + https://arxiv.org/abs/2601.02814 + arXiv:2601.02814v1 Announce Type: new +Abstract: Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. + We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. + Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare. + oai:arXiv.org:2601.02814v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Duc Ngo, Arya Rahgoza + + + Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs + https://arxiv.org/abs/2601.02818 + arXiv:2601.02818v1 Announce Type: new +Abstract: Spatial prediction of reservoir parameters, especially permeability, is crucial for oil and gas exploration and development. However, the wide range and high variability of permeability prevent existing methods from providing reliable predictions. For the first time in subsurface spatial prediction, this study presents a quantum-enhanced long short-term memory with attention (QLSTMA) model that incorporates variational quantum circuits (VQCs) into the recurrent cell. Using quantum entanglement and superposition principles, the QLSTMA significantly improves the ability to predict complex geological parameters such as permeability. Two quantization structures, QLSTMA with Shared Gates (QLSTMA-SG) and with Independent Gates (QLSTMA-IG), are designed to investigate and evaluate the effects of quantum structure configurations and the number of qubits on model performance. Experimental results demonstrate that the 8-qubit QLSTMA-IG model significantly outperforms the traditional long short-term memory with attention (LSTMA), reducing Mean Absolute Error (MAE) by 19% and Root Mean Squared Error (RMSE) by 20%, with particularly strong performance in regions featuring complex well-logging data. These findings validate the potential of quantum-classical hybrid neural networks for reservoir prediction, indicating that increasing the number of qubits yields further accuracy gains despite the reliance on classical simulations. This study establishes a foundational framework for the eventual deployment of such models on real quantum hardware and their extension to broader applications in petroleum engineering and geoscience. + oai:arXiv.org:2601.02818v1 + cs.AI + quant-ph + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Muzhen Zhang, Yujie Cheng, Zhanxiang Lei + + + Punctuation-aware Hybrid Trainable Sparse Attention for Large Language Models + https://arxiv.org/abs/2601.02819 + arXiv:2601.02819v1 Announce Type: new +Abstract: Attention serves as the fundamental mechanism for long-context modeling in large language models (LLMs), yet dense attention becomes structurally prohibitive for long sequences due to its quadratic complexity. Consequently, sparse attention has received increasing attention as a scalable alternative. However, existing sparse attention methods rely on coarse-grained semantic representations during block selection, which blur intra-block semantic boundaries and lead to the loss of critical information. To address this issue, we propose \textbf{P}unctuation-aware \textbf{H}ybrid \textbf{S}parse \textbf{A}ttention \textbf{(PHSA)}, a natively trainable sparse attention framework that leverages punctuation tokens as semantic boundary anchors. Specifically, (1) we design a dual-branch aggregation mechanism that fuses global semantic representations with punctuation-enhanced boundary features, preserving the core semantic structure while introducing almost no additional computational overhead; (2) we introduce an extreme-sparsity-adaptive training and inference strategy that stabilizes model behavior under very low token activation ratios; Extensive experiments on general benchmarks and long-context evaluations demonstrate that PHSA consistently outperforms dense attention and state-of-the-art sparse attention baselines, including InfLLM v2. Specifically, for the 0.6B-parameter model with 32k-token input sequences, PHSA can reduce the information loss by 10.8\% at a sparsity ratio of 97.3\%. + oai:arXiv.org:2601.02819v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Junxiang Qiu, Shuo Wang, Zhengsu Chen, Hengheng Zhang, Jinda Lu, Changcheng Li, Qi Tian + + + DeepFP: Deep-Unfolded Fractional Programming for MIMO Beamforming + https://arxiv.org/abs/2601.02822 + arXiv:2601.02822v1 Announce Type: new +Abstract: This work proposes a mixed learning-based and optimization-based approach to the weighted-sum-rates beamforming problem in a multiple-input multiple-output (MIMO) wireless network. The conventional methods, i.e., the fractional programming (FP) method and the weighted minimum mean square error (WMMSE) algorithm, can be computationally demanding for two reasons: (i) they require inverting a sequence of matrices whose sizes are proportional to the number of antennas; (ii) they require tuning a set of Lagrange multipliers to account for the power constraints. The recently proposed method called the reduced WMMSE addresses the above two issues for a single cell. In contrast, for the multicell case, another recent method called the FastFP eliminates the large matrix inversion and the Lagrange multipliers by using an improved FP technique, but the update stepsize in the FastFP can be difficult to decide. As such, we propose integrating the deep unfolding network into the FastFP for the stepsize optimization. Numerical experiments show that the proposed method is much more efficient than the learning method based on the WMMSE algorithm. + oai:arXiv.org:2601.02822v1 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jianhang Zhu, Tsung-Hui Chang, Liyao Xiang, Kaiming Shen + + + Case Count Metric for Comparative Analysis of Entity Resolution Results + https://arxiv.org/abs/2601.02824 + arXiv:2601.02824v1 Announce Type: new +Abstract: This paper describes a new process and software system, the Case Count Metric System (CCMS), for systematically comparing and analyzing the outcomes of two different ER clustering processes acting on the same dataset when the true linking (labeling) is not known. The CCMS produces a set of counts that describe how the clusters produced by the first process are transformed by the second process based on four possible transformation scenarios. The transformations are that a cluster formed in the first process either remains unchanged, merges into a larger cluster, is partitioned into smaller clusters, or otherwise overlaps with multiple clusters formed in the second process. The CCMS produces a count for each of these cases, accounting for every cluster formed in the first process. In addition, when run in analysis mode, the CCMS program can assist the user in evaluating these changes by displaying the details for all changes or only for certain types of changes. The paper includes a detailed description of the CCMS process and program and examples of how the CCMS has been applied in university and industry research. + oai:arXiv.org:2601.02824v1 + cs.DB + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + John R. Talburt, Muzakkiruddin Ahmed Mohammed, Mert Can Cakmak, Onais Khan Mohammed, Mahboob Khan Mohammed, Khizer Syed, Leon Claasssens + + + SketchThinker-R1: Towards Efficient Sketch-Style Reasoning in Large Multimodal Models + https://arxiv.org/abs/2601.02825 + arXiv:2601.02825v1 Announce Type: new +Abstract: Despite the empirical success of extensive, step-by-step reasoning in large multimodal models, long reasoning processes inevitably incur substantial computational overhead, i.e., in terms of higher token costs and increased response time, which undermines inference efficiency. In contrast, humans often employ sketch-style reasoning: a concise, goal-directed cognitive process that prioritizes salient information and enables efficient problem-solving. Inspired by this cognitive efficiency, we propose SketchThinker-R1, which incentivizes sketch-style reasoning ability in large multimodal models. Our method consists of three primary stages. In the Sketch-Mode Cold Start stage, we convert standard long reasoning process into sketch-style reasoning and finetune base multimodal model, instilling initial sketch-style reasoning capability. Next, we train SketchJudge Reward Model, which explicitly evaluates thinking process of model and assigns higher scores to sketch-style reasoning. Finally, we conduct Sketch-Thinking Reinforcement Learning under supervision of SketchJudge to further generalize sketch-style reasoning ability. Experimental evaluation on four benchmarks reveals that our SketchThinker-R1 achieves over 64% reduction in reasoning token cost without compromising final answer accuracy. Qualitative analysis further shows that sketch-style reasoning focuses more on key cues during problem solving. + oai:arXiv.org:2601.02825v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ruiyang Zhang, Dongzhan Zhou, Zhedong Zheng + + + Resolution deficits drive simulator sickness and compromise reading performance in virtual environments + https://arxiv.org/abs/2601.02829 + arXiv:2601.02829v1 Announce Type: new +Abstract: Extended reality (XR) is evolving into a general-purpose computing platform, yet its adoption for productivity is hindered by visual fatigue and simulator sickness. While these symptoms are often attributed to latency or motion conflicts, the precise impact of textual clarity on physiological comfort remains undefined. Here we show that sub-optimal effective resolution, the clarity that reaches the eye after the full display-optics-rendering pipeline, is a primary driver of simulator sickness during reading tasks in both virtual reality and video see-through environments. By systematically manipulating end-to-end effective resolution on a unified logMAR scale, we measured reading psychophysics and sickness symptoms in a controlled within-subjects study. We find that reading performance and user comfort degrade exponentially as resolution drops below 0 logMAR (normal visual acuity). Notably, our results reveal 0 logMAR as a key physiological tipping point: resolutions better than this threshold yield naked-eye-level performance with minimal sickness, whereas poorer resolutions trigger rapid, non-linear increases in nausea and oculomotor strain. These findings suggest that the cognitive and perceptual effort required to resolve blurry text directly compromises user comfort, establishing human-eye resolution as a critical baseline for the design of future ergonomic XR systems. + oai:arXiv.org:2601.02829v1 + cs.HC + cs.GR + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jialin Wang, Xinru Cheng, Boyong Hou, Hai-Ning Liang + + + The performances of the Chinese and U.S. Large Language Models on the Topic of Chinese Culture + https://arxiv.org/abs/2601.02830 + arXiv:2601.02830v1 Announce Type: new +Abstract: Cultural backgrounds shape individuals' perspectives and approaches to problem-solving. Since the emergence of GPT-1 in 2018, large language models (LLMs) have undergone rapid development. To date, the world's ten leading LLM developers are primarily based in China and the United States. To examine whether LLMs released by Chinese and U.S. developers exhibit cultural differences in Chinese-language settings, we evaluate their performance on questions about Chinese culture. This study adopts a direct-questioning paradigm to evaluate models such as GPT-5.1, DeepSeek-V3.2, Qwen3-Max, and Gemini2.5Pro. We assess their understanding of traditional Chinese culture, including history, literature, poetry, and related domains. Comparative analyses between LLMs developed in China and the U.S. indicate that Chinese models generally outperform their U.S. counterparts on these tasks. Among U.S.-developed models, Gemini 2.5Pro and GPT-5.1 achieve relatively higher accuracy. The observed performance differences may potentially arise from variations in training data distribution, localization strategies, and the degree of emphasis on Chinese cultural content during model development. + oai:arXiv.org:2601.02830v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Feiyan Liu, Chenxun Zhuo, Siyan Zhao, Bao Ge, Tianming Liu + + + DGA-Net: Enhancing SAM with Depth Prompting and Graph-Anchor Guidance for Camouflaged Object Detection + https://arxiv.org/abs/2601.02831 + arXiv:2601.02831v1 Announce Type: new +Abstract: To fully exploit depth cues in Camouflaged Object Detection (COD), we present DGA-Net, a specialized framework that adapts the Segment Anything Model (SAM) via a novel ``depth prompting" paradigm. Distinguished from existing approaches that primarily rely on sparse prompts (e.g., points or boxes), our method introduces a holistic mechanism for constructing and propagating dense depth prompts. Specifically, we propose a Cross-modal Graph Enhancement (CGE) module that synthesizes RGB semantics and depth geometric within a heterogeneous graph to form a unified guidance signal. Furthermore, we design an Anchor-Guided Refinement (AGR) module. To counteract the inherent information decay in feature hierarchies, AGR forges a global anchor and establishes direct non-local pathways to broadcast this guidance from deep to shallow layers, ensuring precise and consistent segmentation. Quantitative and qualitative experimental results demonstrate that our proposed DGA-Net outperforms the state-of-the-art COD methods. + oai:arXiv.org:2601.02831v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuetong Li, Qing Zhang, Yilin Zhao, Gongyang Li, Zeming Liu + + + A Practical 73/50 Approximation for Contiguous Monotone Moldable Job Scheduling + https://arxiv.org/abs/2601.02836 + arXiv:2601.02836v1 Announce Type: new +Abstract: In moldable job scheduling, we are provided $m$ identical machines and $n$ jobs that can be executed on a variable number of machines. The execution time of each job depends on the number of machines assigned to execute that job. For the specific problem of monotone moldable job scheduling, jobs are assumed to have a processing time that is non-increasing in the number of machines. + The previous best-known algorithms are: (1) a polynomial-time approximation scheme with time complexity $\Omega(n^{g(1/\varepsilon)})$, where $g(\cdot)$ is a super-exponential function [Jansen and Th\"ole '08; Jansen and Land '18], (2) a fully polynomial approximation scheme for the case of $m \geq 8\frac{n}{\varepsilon}$ [Jansen and Land '18], and (3) a $\frac{3}{2}$ approximation with time complexity $O(nm\log(mn))$ [Wu, Zhang, and Chen '23]. + We present a new practically efficient algorithm with an approximation ratio of $\approx (1.4593 + \varepsilon)$ and a time complexity of $O(nm \log \frac{1}{\varepsilon})$. Our result also applies to the contiguous variant of the problem. In addition to our theoretical results, we implement the presented algorithm and show that the practical performance is significantly better than the theoretical worst-case approximation ratio. + oai:arXiv.org:2601.02836v1 + cs.DS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Klaus Jansen, Felix Ohnesorge + + + Breaking Self-Attention Failure: Rethinking Query Initialization for Infrared Small Target Detection + https://arxiv.org/abs/2601.02837 + arXiv:2601.02837v1 Announce Type: new +Abstract: Infrared small target detection (IRSTD) faces significant challenges due to the low signal-to-noise ratio (SNR), small target size, and complex cluttered backgrounds. Although recent DETR-based detectors benefit from global context modeling, they exhibit notable performance degradation on IRSTD. We revisit this phenomenon and reveal that the target-relevant embeddings of IRST are inevitably overwhelmed by dominant background features due to the self-attention mechanism, leading to unreliable query initialization and inaccurate target localization. To address this issue, we propose SEF-DETR, a novel framework that refines query initialization for IRSTD. Specifically, SEF-DETR consists of three components: Frequency-guided Patch Screening (FPS), Dynamic Embedding Enhancement (DEE), and Reliability-Consistency-aware Fusion (RCF). The FPS module leverages the Fourier spectrum of local patches to construct a target-relevant density map, suppressing background-dominated features. DEE strengthens multi-scale representations in a target-aware manner, while RCF further refines object queries by enforcing spatial-frequency consistency and reliability. Extensive experiments on three public IRSTD datasets demonstrate that SEF-DETR achieves superior detection performance compared to state-of-the-art methods, delivering a robust and efficient solution for infrared small target detection task. + oai:arXiv.org:2601.02837v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuteng Liu, Duanni Meng, Maoxun Yuan, Xingxing Wei + + + TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents + https://arxiv.org/abs/2601.02845 + arXiv:2601.02845v1 Announce Type: new +Abstract: Long-horizon conversational agents have to manage ever-growing interaction histories that quickly exceed the finite context windows of large language models (LLMs). Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, often leading to fragmented memories and unstable long-horizon personalization. We present TiMem, a temporal--hierarchical memory framework that organizes conversations through a Temporal Memory Tree (TMT), enabling systematic memory consolidation from raw conversational observations to progressively abstracted persona representations. TiMem is characterized by three core properties: (1) temporal--hierarchical organization through TMT; (2) semantic-guided consolidation that enables memory integration across hierarchical levels without fine-tuning; and (3) complexity-aware memory recall that balances precision and efficiency across queries of varying complexity. Under a consistent evaluation setup, TiMem achieves state-of-the-art accuracy on both benchmarks, reaching 75.30% on LoCoMo and 76.88% on LongMemEval-S. It outperforms all evaluated baselines while reducing the recalled memory length by 52.20% on LoCoMo. Manifold analysis indicates clear persona separation on LoCoMo and reduced dispersion on LongMemEval-S. Overall, TiMem treats temporal continuity as a first-class organizing principle for long-horizon memory in conversational agents. + oai:arXiv.org:2601.02845v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan + + + Stability and error estimates of a linear and partitioned finite element method approximating nonlinear fluid-structure interactions + https://arxiv.org/abs/2601.02847 + arXiv:2601.02847v1 Announce Type: new +Abstract: We propose and analyze a linear and partitioned finite element method for fluid-shell interactions under the arbitrary Lagrangian-Eulerian (ALE) framework. We adopt the P1-bubble/P1/P1 elements for the fluid velocity, pressure, and structure velocity, respectively. We show the stability and error estimates of the scheme without assuming infinitesimal structural deformation nor neglecting fluid convection effects. The theoretical convergence rate is further corroborated by numerical experiments. + oai:arXiv.org:2601.02847v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Bangwei She, Tian Tian, Karel Tuma + + + Modeling ICD-10 Morbidity and Multidimensional Poverty as a Spatial Network: Evidence from Thailand + https://arxiv.org/abs/2601.02848 + arXiv:2601.02848v1 Announce Type: new +Abstract: Health and poverty in Thailand exhibit pronounced geographic structuring, yet the extent to which they operate as interconnected regional systems remains insufficiently understood. This study analyzes ICD-10 chapter-level morbidity and multidimensional poverty as outcomes embedded in a spatial interaction network. Interpreting Thailand's 76 provinces as nodes within a fixed-degree regional graph, we apply tools from spatial econometrics and social network analysis, including Moran's I, Local Indicators of Spatial Association (LISA), and Spatial Durbin Models (SDM), to assess spatial dependence and cross-provincial spillovers. + Our findings reveal strong spatial clustering across multiple ICD-10 chapters, with persistent high-high morbidity zones, particularly for digestive, respiratory, musculoskeletal, and symptom-based diseases, emerging in well-defined regional belts. SDM estimates demonstrate that spillover effects from neighboring provinces frequently exceed the influence of local deprivation, especially for living-condition, health-access, accessibility, and poor-household indicators. These patterns are consistent with contagion and contextual influence processes well established in social network theory. + By framing morbidity and poverty as interdependent attributes on a spatial network, this study contributes to the growing literature on structural diffusion, health inequality, and regional vulnerability. The results highlight the importance of coordinated policy interventions across provincial boundaries and demonstrate how network-based modeling can uncover the spatial dynamics of health and deprivation. + oai:arXiv.org:2601.02848v1 + cs.SI + cs.CY + stat.AP + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Pratana Kukieattikool, Kittiya Ku-kiattikun, Anukool Noymai, Navaporn Surasvadi, Jantakarn Makma, Pubodin Pornratchpum, Watcharakon Noothong, Chainarong Amornbunchornvej + + + Sample-Efficient Neurosymbolic Deep Reinforcement Learning + https://arxiv.org/abs/2601.02850 + arXiv:2601.02850v1 Announce Type: new +Abstract: Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep RL (DRL) algorithms typically require large training datasets and often struggle to generalize beyond small-scale training scenarios, even within standard benchmarks. We propose a neuro-symbolic DRL approach that integrates background symbolic knowledge to improve sample efficiency and generalization to more challenging, unseen tasks. Partial policies defined for simple domain instances, where high performance is easily attained, are transferred as useful priors to accelerate learning in more complex settings and avoid tuning DRL parameters from scratch. To do so, partial policies are represented as logical rules, and online reasoning is performed to guide the training process through two mechanisms: (i) biasing the action distribution during exploration, and (ii) rescaling Q-values during exploitation. This neuro-symbolic integration enhances interpretability and trustworthiness while accelerating convergence, particularly in sparse-reward environments and tasks with long planning horizons. We empirically validate our methodology on challenging variants of gridworld environments, both in the fully observable and partially observable setting. We show improved performance over a state-of-the-art reward machine baseline. + oai:arXiv.org:2601.02850v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Celeste Veronese, Daniele Meli, Alessandro Farinelli + + + M3MAD-Bench: Are Multi-Agent Debates Really Effective Across Domains and Modalities? + https://arxiv.org/abs/2601.02854 + arXiv:2601.02854v1 Announce Type: new +Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning. However, existing research on MAD suffers from two fundamental limitations: evaluations are conducted under fragmented and inconsistent settings, hindering fair comparison, and are largely restricted to single-modality scenarios that rely on textual inputs only. To address these gaps, we introduce M3MAD-Bench, a unified and extensible benchmark for evaluating MAD methods across Multi-domain tasks, Multi-modal inputs, and Multi-dimensional metrics. M3MAD-Bench establishes standardized protocols over five core task domains: Knowledge, Mathematics, Medicine, Natural Sciences, and Complex Reasoning, and systematically covers both pure text and vision-language datasets, enabling controlled cross-modality comparison. We evaluate MAD methods on nine base models spanning different architectures, scales, and modality capabilities. Beyond accuracy, M3MAD-Bench incorporates efficiency-oriented metrics such as token consumption and inference time, providing a holistic view of performance--cost trade-offs. Extensive experiments yield systematic insights into the effectiveness, robustness, and efficiency of MAD across text-only and multimodal scenarios. We believe M3MAD-Bench offers a reliable foundation for future research on standardized MAD evaluation. The code is available at http://github.com/liaolea/M3MAD-Bench. + oai:arXiv.org:2601.02854v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ao Li, Jinghui Zhang, Luyu Li, Yuxiang Duan, Lang Gao, Mingcai Chen, Weijun Qin, Shaopeng Li, Fengxian Ji, Ning Liu, Lizhen Cui, Xiuying Chen, Yuntao Du + + + Context-aware Privacy Bounds for Linear Queries + https://arxiv.org/abs/2601.02855 + arXiv:2601.02855v1 Announce Type: new +Abstract: Linear queries, as the basis of broad analysis tasks, are often released through privacy mechanisms based on differential privacy (DP), the most popular framework for privacy protection. However, DP adopts a context-free definition that operates independently of the data-generating distribution. In this paper, we revisit the privacy analysis of the Laplace mechanism through the lens of pointwise maximal leakage (PML). We demonstrate that the distribution-agnostic definition of the DP framework often mandates excessive noise. To address this, we incorporate an assumption about the prior distribution by lower-bounding the probability of any single record belonging to any specific class. With this assumption, we derive a tight, context-aware leakage bound for general linear queries, and prove that our derived bound is strictly tighter than the standard DP guarantee and converges to the DP guarantee as this probability lower bound approaches zero. Numerical evaluations demonstrate that by exploiting this prior knowledge, the required noise scale can be reduced while maintaining privacy guarantees. + oai:arXiv.org:2601.02855v1 + cs.IT + cs.CR + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Heng Zhao (KTH Royal Institute of Technology), Sara Saeidian (KTH Royal Institute of Technology, Inria Saclay), Tobias J. Oechtering (KTH Royal Institute of Technology) + + + Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning + https://arxiv.org/abs/2601.02856 + arXiv:2601.02856v1 Announce Type: new +Abstract: Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, enable efficient battery storage optimization, and facilitate demand response planning. However, developing an accurate prediction model is highly challenging in an uncertain and volatile market environment. For instance, although linear models generally exhibit competitive performance in predicting electricity prices with minimal computational requirements, they fail to capture relevant nonlinear relationships. Nonlinear models, on the other hand, can improve forecasting accuracy with a surge in computational costs. We propose a novel multivariate neural network approach that combines linear and nonlinear feed-forward neural structures. Unlike previous hybrid models, our approach integrates online learning and forecast combination for efficient training and accuracy improvement. It also incorporates all relevant characteristics, particularly the fundamental relationships arising from wind and solar generation, electricity demand patterns, related energy fuel and carbon markets, in addition to autoregressive dynamics and calendar effects. Compared to the current state-of-the-art benchmark models, the proposed forecasting method significantly reduces computational cost while delivering superior forecasting accuracy (12-13% RMSE and 15-18% MAE reductions). Our results are derived from a six-year forecasting study conducted on major European electricity markets. + oai:arXiv.org:2601.02856v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Btissame El Mahtout, Florian Ziel + + + Soft Responsive Materials Enhance Humanoid Safety + https://arxiv.org/abs/2601.02857 + arXiv:2601.02857v1 Announce Type: new +Abstract: Humanoid robots are envisioned as general-purpose platforms in human-centered environments, yet their deployment is limited by vulnerability to falls and the risks posed by rigid metal-plastic structures to people and surroundings. We introduce a soft-rigid co-design framework that leverages non-Newtonian fluid-based soft responsive materials to enhance humanoid safety. The material remains compliant during normal interaction but rapidly stiffens under impact, absorbing and dissipating fall-induced forces. Physics-based simulations guide protector placement and thickness and enable learning of active fall policies. Applied to a 42 kg life-size humanoid, the protector markedly reduces peak impact and allows repeated falls without hardware damage, including drops from 3 m and tumbles down long staircases. Across diverse scenarios, the approach improves robot robustness and environmental safety. By uniting responsive materials, structural co-design, and learning-based control, this work advances interact-safe, industry-ready humanoid robots. + oai:arXiv.org:2601.02857v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Chunzheng Wang, Yiyuan Zhang, Annan Tang, Ziqiu Zeng, Haoran Chen, Quan Gao, Zixuan Zhuang, Boyu Li, Zhilin Xiong, Aoqian Zhang, Ce Hao, Siyuan Luo, Tongyang Zhao, Cecilia Laschi, Fan Shi + + + To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs + https://arxiv.org/abs/2601.02858 + arXiv:2601.02858v1 Announce Type: new +Abstract: Socio-demographic prompting (SDP) - prompting Large Language Models (LLMs) using demographic proxies to generate culturally aligned outputs - often shows LLM responses as stereotypical and biased. While effective in assessing LLMs' cultural competency, SDP is prone to confounding factors such as prompt sensitivity, decoding parameters, and the inherent difficulty of generation over discrimination tasks due to larger output spaces. These factors complicate interpretation, making it difficult to determine if the poor performance is due to bias or the task design. To address this, we use inverse socio-demographic prompting (ISDP), where we prompt LLMs to discriminate and predict the demographic proxy from actual and simulated user behavior from different users. We use the Goodreads-CSI dataset (Saha et al., 2025), which captures difficulty in understanding English book reviews for users from India, Mexico, and the USA, and test four LLMs: Aya-23, Gemma-2, GPT-4o, and LLaMA-3.1 with ISDP. Results show that models perform better with actual behaviors than simulated ones, contrary to what SDP suggests. However, performance with both behavior types diminishes and becomes nearly equal at the individual level, indicating limits to personalization. + oai:arXiv.org:2601.02858v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Saurabh Kumar Pandey, Sougata Saha, Monojit Choudhury + + + Training Language Models with homotokens Leads to Delayed Overfitting + https://arxiv.org/abs/2601.02867 + arXiv:2601.02867v1 Announce Type: new +Abstract: Subword tokenization introduces a computational layer in language models where many distinct token sequences decode to the same surface form and preserve meaning, yet induce different internal computations. Despite this non-uniqueness, language models are typically trained using a single canonical longest-prefix tokenization. We formalize homotokens-alternative valid subword segmentations of the same lexical item-as a strictly meaning-preserving form of data augmentation. We introduce a lightweight training architecture that conditions canonical next-token prediction on sampled homotoken variants via an auxiliary causal encoder and block-causal cross-attention, without modifying the training objective or token interface. In data-constrained pretraining, homotoken augmentation consistently delays overfitting under repeated data exposure and improves generalization across diverse evaluation datasets. In multilingual fine-tuning, we find that the effectiveness of homotokens depends on tokenizer quality: gains are strongest when canonical tokens are highly compressed and diminish when the tokenizer already over-fragments the input. Overall, homotokens provide a simple and modular mechanism for inducing tokenization invariance in language models. + oai:arXiv.org:2601.02867v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Adrian Cosma, Stefan Ruseti, Emilian Radoi, Mihai Dascalu + + + CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation + https://arxiv.org/abs/2601.02868 + arXiv:2601.02868v1 Announce Type: new +Abstract: Large language models (LLMs) substantially enhance developer productivity in repository-level code generation through interactive collaboration. However, as interactions progress, repository context must be continuously preserved and updated to integrate newly validated information. Meanwhile, the expanding session history increases cognitive burden, often leading to forgetting and the reintroduction of previously resolved errors. Existing memory management approaches show promise but remain limited by natural language-centric representations. To overcome these limitations, we propose CodeMEM, an AST-guided dynamic memory management system tailored for repository-level iterative code generation. Specifically, CodeMEM introduces the Code Context Memory component that dynamically maintains and updates repository context through AST-guided LLM operations, along with the Code Session Memory that constructs a code-centric representation of interaction history and explicitly detects and mitigates forgetting through AST-based analysis. Experimental results on the instruction-following benchmark CodeIF-Bench and the code generation benchmark CoderEval demonstrate that CodeMEM achieves state-of-the-art performance, improving instruction following by 12.2% for the current turn and 11.5% for the session level, and reducing interaction rounds by 2-3, while maintaining competitive inference latency and token efficiency. + oai:arXiv.org:2601.02868v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Peiding Wang, Li Zhang, Fang Liu, Chongyang Tao, Yinghao Zhu + + + Quantum-Enhanced Neural Contextual Bandit Algorithms + https://arxiv.org/abs/2601.02870 + arXiv:2601.02870v1 Announce Type: new +Abstract: Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-parameterization, computational instability, and the barren plateau phenomenon. This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm, a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations. + By freezing the QNN at a random initialization and utilizing its static QNTK as a kernel for ridge regression, QNTK-UCB bypasses the unstable training dynamics inherent in explicit parameterized quantum circuit training while fully exploiting the unique quantum inductive bias. For a time horizon $T$ and $K$ actions, our theoretical analysis reveals a significantly improved parameter scaling of $\Omega((TK)^3)$ for QNTK-UCB, a substantial reduction compared to $\Omega((TK)^8)$ required by classical NeuralUCB algorithms for similar regret guarantees. Empirical evaluations on non-linear synthetic benchmarks and quantum-native variational quantum eigensolver tasks demonstrate QNTK-UCB's superior sample efficiency in low-data regimes. This work highlights how the inherent properties of QNTK provide implicit regularization and a sharper spectral decay, paving the way for achieving ``quantum advantage'' in online learning. + oai:arXiv.org:2601.02870v1 + cs.LG + cs.IT + math.IT + quant-ph + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yuqi Huang, Vincent Y. F Tan, Sharu Theresa Jose + + + SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection + https://arxiv.org/abs/2601.02871 + arXiv:2601.02871v1 Announce Type: new +Abstract: Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose SimRPD, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fidelity user simulator to synthesize large-scale conversational data through multi-turn online dialogue. Then we introduce a multi-dimensional evaluation framework based on Chain-of-Intention (CoI) to comprehensively assess the simulator and effectively select high-quality data, incorporating both global-level and instance-level metrics. Finally, we train the recruitment proactive dialogue agent on the selected dataset. Experiments in a real-world recruitment scenario demonstrate that SimRPD outperforms existing simulator-based data selection strategies, highlighting its practical value for industrial deployment and its potential applicability to other business-oriented dialogue scenarios. + oai:arXiv.org:2601.02871v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huaiyan Xu, Huan He, Yafei Liu, Siyuan Liu, XiaoLin Lin, Ke Ma, Ruqian Shi, Sijia Yao, Hao Wang, Sicheng Zhou + + + LongBench Pro: A More Realistic and Comprehensive Bilingual Long-Context Evaluation Benchmark + https://arxiv.org/abs/2601.02872 + arXiv:2601.02872v1 Announce Type: new +Abstract: The rapid expansion of context length in large language models (LLMs) has outpaced existing evaluation benchmarks. Current long-context benchmarks often trade off scalability and realism: synthetic tasks underrepresent real-world complexity, while fully manual annotation is costly to scale to extreme lengths and diverse scenarios. We present LongBench Pro, a more realistic and comprehensive bilingual benchmark of 1,500 naturally occurring long-context samples in English and Chinese spanning 11 primary tasks and 25 secondary tasks, with input lengths from 8k to 256k tokens. LongBench Pro supports fine-grained analysis with task-specific metrics and a multi-dimensional taxonomy of context requirement (full vs. partial dependency), length (six levels), and difficulty (four levels calibrated by model performance). To balance quality with scalability, we propose a Human-Model Collaborative Construction pipeline: frontier LLMs draft challenging questions and reference answers, along with design rationales and solution processes, to reduce the cost of expert verification. Experts then rigorously validate correctness and refine problematic cases. Evaluating 46 widely used long-context LLMs on LongBench Pro yields three findings: (1) long-context optimization contributes more to long-context comprehension than parameter scaling; (2) effective context length is typically shorter than the claimed context length, with pronounced cross-lingual misalignment; and (3) the "thinking" paradigm helps primarily models trained with native reasoning, while mixed-thinking designs offer a promising Pareto trade-off. In summary, LongBench Pro provides a robust testbed for advancing long-context understanding. + oai:arXiv.org:2601.02872v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ziyang Chen, Xing Wu, Junlong Jia, Chaochen Gao, Qi Fu, Debing Zhang, Songlin Hu + + + Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion + https://arxiv.org/abs/2601.02873 + arXiv:2601.02873v1 Announce Type: new +Abstract: Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical constraints, but they struggle to produce feasible solutions quickly when many obstacles are present. Diffusion models can generate diverse trajectories around obstacles, yet prior approaches lacked a general and efficient way to condition them on scene structure. In this paper, we show that combining diffusion-based warm-starting conditioned with a latent object-centric representation of the scene and with a collision-aware model predictive controller (MPC) yields reliable and efficient motion generation under strict time limits. Our approach conditions a diffusion transformer on the system state, task, and surroundings, using an object-centric slot attention mechanism to provide a compact obstacle representation suitable for control. The sampled trajectories are refined by an optimal control problem that enforces rigid-body dynamics and signed-distance collision constraints, producing feasible motions in real time. On benchmark tasks, this hybrid method achieved markedly higher success rates and lower latency than sampling-based planners or either component alone. Real-robot experiments with a torque-controlled Panda confirm reliable and safe execution with MPC. + oai:arXiv.org:2601.02873v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Arthur Haffemayer, Alexandre Chapin, Armand Jordana, Krzysztof Wojciechowski, Florent Lamiraux, Nicolas Mansard, Vladimir Petrik + + + Revisiting Data Compression with Language Modeling + https://arxiv.org/abs/2601.02875 + arXiv:2601.02875v1 Announce Type: new +Abstract: In this report, we investigate the potential use of large language models (LLM's) in the task of data compression. Previous works have demonstrated promising results in applying LLM's towards compressing not only text, but also a wide range of multi-modal data. Despite the favorable performance achieved, there still remains several practical questions that pose a challenge towards replacing existing data compression algorithms with LLM's. In this work, we explore different methods to achieve a lower adjusted compression rate using LLM's as data compressors. In comparison to previous works, we were able to achieve a new state-of-the-art (SOTA) adjusted compression rate of around $18\%$ on the enwik9 dataset without additional model training. Furthermore, we explore the use of LLM's in compressing non-English data, code data, byte stream sequences. We show that while LLM's excel in compressing data in text-dominant domains, their ability in compressing non-natural text sequences still remain competitive if configured in the right way. + oai:arXiv.org:2601.02875v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Chen-Han Tsai + + + ReTreVal: Reasoning Tree with Validation - A Hybrid Framework for Enhanced LLM Multi-Step Reasoning + https://arxiv.org/abs/2601.02880 + arXiv:2601.02880v1 Announce Type: new +Abstract: Multi-step reasoning remains a key challenge for Large Language Models (LLMs), particularly in complex domains such as mathematics and creative writing. While recent approaches including ReAct, Reflexion, and Self-Refine improve reasoning through iterative refinement and reflection, they often lack structured exploration of alternative solution paths and persistent learning across problems. We propose ReTreVal (Reasoning Tree with Validation), a hybrid framework that integrates Tree-of-Thoughts exploration, self-refinement, LLM-based critique scoring, and reflexion memory to enable bounded and validated multi-step reasoning. ReTreVal constructs a structured reasoning tree with adaptive depth based on problem complexity, where each node undergoes iterative self-critique and refinement guided by explicit LLM-generated feedback. A dual validation mechanism evaluates reasoning quality, coherence, and correctness at each node while persistently storing insights from successful reasoning paths and failure patterns in a reflexion memory buffer, enabling cross-problem learning. Critique-based pruning retains only the top-k highest-scoring nodes at each level, controlling computational cost while preserving high-quality solution paths. We evaluate ReTreVal against ReAct, Reflexion, and Self-Refine across 500 mathematical problems and creative writing tasks using Qwen 2.5 7B as the underlying LLM, and demonstrate that ReTreVal consistently outperforms existing methods through its combination of structured exploration, critique-driven refinement, and cross-problem memory, making it particularly effective for tasks requiring exploratory reasoning, rigorous verification, and knowledge transfer. + oai:arXiv.org:2601.02880v1 + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Abhishek HS, Pavan C Shekar, Arpit Jain, Ashwanth Krishnan + + + Towards Agnostic and Holistic Universal Image Segmentation with Bit Diffusion + https://arxiv.org/abs/2601.02881 + arXiv:2601.02881v1 Announce Type: new +Abstract: This paper introduces a diffusion-based framework for universal image segmentation, making agnostic segmentation possible without depending on mask-based frameworks and instead predicting the full segmentation in a holistic manner. We present several key adaptations to diffusion models, which are important in this discrete setting. Notably, we show that a location-aware palette with our 2D gray code ordering improves performance. Adding a final tanh activation function is crucial for discrete data. On optimizing diffusion parameters, the sigmoid loss weighting consistently outperforms alternatives, regardless of the prediction type used, and we settle on x-prediction. While our current model does not yet surpass leading mask-based architectures, it narrows the performance gap and introduces unique capabilities, such as principled ambiguity modeling, that these models lack. All models were trained from scratch, and we believe that combining our proposed improvements with large-scale pretraining or promptable conditioning could lead to competitive models. + oai:arXiv.org:2601.02881v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jakob L{\o}nborg Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Vedrana Andersen Dahl + + + Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction + https://arxiv.org/abs/2601.02884 + arXiv:2601.02884v1 Announce Type: new +Abstract: This paper provides a comprehensive comparison of domain generalization techniques applied to time series data within a drilling context, focusing on the prediction of a continuous Stick-Slip Index (SSI), a critical metric for assessing torsional downhole vibrations at the drill bit. The study aims to develop a robust regression model that can generalize across domains by training on 60 second labeled sequences of 1 Hz surface drilling data to predict the SSI. The model is tested in wells that are different from those used during training. To fine-tune the model architecture, a grid search approach is employed to optimize key hyperparameters. A comparative analysis of the Adversarial Domain Generalization (ADG), Invariant Risk Minimization (IRM) and baseline models is presented, along with an evaluation of the effectiveness of transfer learning (TL) in improving model performance. The ADG and IRM models achieve performance improvements of 10% and 8%, respectively, over the baseline model. Most importantly, severe events are detected 60% of the time, against 20% for the baseline model. Overall, the results indicate that both ADG and IRM models surpass the baseline, with the ADG model exhibiting a slight advantage over the IRM model. Additionally, applying TL to a pre-trained model further improves performance. Our findings demonstrate the potential of domain generalization approaches in drilling applications, with ADG emerging as the most effective approach. + oai:arXiv.org:2601.02884v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Transactions on Machine Learning Research Journal, 2025 + Hana Yahia (CAS), Bruno Figliuzzi (CMM), Florent Di Meglio (CAS), Laurent Gerbaud (GEOSCIENCES), Stephane Menand, Mohamed Mahjoub + + + A Mathematical Formalization of Self-Determining Agency + https://arxiv.org/abs/2601.02885 + arXiv:2601.02885v1 Announce Type: new +Abstract: Defining agency is an extremely important challenge for cognitive science and artificial intelligence. Physics generally describes mechanical happenings, but there remains an unbridgeable gap between them and the acts of agents. To discuss the morality and responsibility of agents, it is necessary to model acts; whether such responsible acts can be fully explained by physical determinism has been debated. Although we have already proposed a physical "agent determinism" model that appears to go beyond mere mechanical happenings, we have not yet established a strict mathematical formalism to eliminate ambiguity. Here, we explain why a physical system can follow coarse-graining agent-level determination without violating physical laws by formulating supervenient causation. Generally, supervenience including coarse graining does not change without a change in its lower base; therefore, a single supervenience alone cannot define supervenient causation. We define supervenient causation as the causal efficacy from the supervenience level to its lower base level. Although an algebraic expression composed of the multiple supervenient functions does supervenes on the base, a sequence of indices that determines the algebraic expression does not supervene on the base. Therefore, the sequence can possess unique dynamical laws that are independent of the lower base level. This independent dynamics creates the possibility for temporally preceding changes at the supervenience level to cause changes at the lower base level. Such a dual-laws system is considered useful for modeling self-determining agents such as humans. + oai:arXiv.org:2601.02885v1 + eess.SY + cs.SY + q-bio.NC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yoshiyuki Ohmura, Earnest Kota Carr, Yasuo Kuniyoshi + + + RPIQ: Residual-Projected Multi-Collaboration Closed-Loop and Single Instance Quantization for Visually Impaired Assistance + https://arxiv.org/abs/2601.02888 + arXiv:2601.02888v1 Announce Type: new +Abstract: Visually impaired users face significant challenges in daily information access and real-time environmental perception, and there is an urgent need for intelligent assistive systems with accurate recognition capabilities. Although large-scale models provide effective solutions for perception and reasoning, their practical deployment on assistive devices is severely constrained by excessive memory consumption and high inference costs. Moreover, existing quantization strategies often ignore inter-block error accumulation, leading to degraded model stability. To address these challenges, this study proposes a novel quantization framework -- Residual-Projected Multi-Collaboration Closed-Loop and Single Instance Quantization(RPIQ), whose quantization process adopts a multi-collaborative closed-loop compensation scheme based on Single Instance Calibration and Gauss-Seidel Iterative Quantization. Experiments on various types of large-scale models, including language models such as OPT, Qwen, and LLaMA, as well as vision-language models such as CogVLM2, demonstrate that RPIQ can compress models to 4-bit representation while significantly reducing peak memory consumption (approximately 60%-75% reduction compared to original full-precision models). The method maintains performance highly close to full-precision models across multiple language and visual tasks, and exhibits excellent recognition and reasoning capabilities in key applications such as text understanding and visual question answering in complex scenarios. While verifying the effectiveness of RPIQ for deployment in real assistive systems, this study also advances the computational efficiency and reliability of large models, enabling them to provide visually impaired users with the required information accurately and rapidly. + oai:arXiv.org:2601.02888v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xuanyu Wang, Haisen Su, Jingtao Zhang, Xiangxiang Wang, Yongbin Yu, Manping Fan, Bo Gong, Siqi Chen, Mingsheng Cao, Liyong Ren + + + Transparent Semantic Change Detection with Dependency-Based Profiles + https://arxiv.org/abs/2601.02891 + arXiv:2601.02891v1 Announce Type: new +Abstract: Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable. + oai:arXiv.org:2601.02891v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Bach Phan-Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, Dirk Speelman + + + Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control + https://arxiv.org/abs/2601.02896 + arXiv:2601.02896v1 Announce Type: new +Abstract: Controlling emergent behavioral personas (e.g., sycophancy, hallucination) in Large Language Models (LLMs) is critical for AI safety, yet remains a persistent challenge. Existing solutions face a dilemma: manual prompt engineering is intuitive but unscalable and imprecise, while automatic optimization methods are effective but operate as "black boxes" with no interpretable connection to model internals. We propose a novel framework that adapts gradient ascent to LLMs, enabling targeted prompt discovery. In specific, we propose two methods, RESGA and SAEGA, that both optimize randomly initialized prompts to achieve better aligned representation with an identified persona direction. We introduce fluent gradient ascent to control the fluency of discovered persona steering prompts. We demonstrate RESGA and SAEGA's effectiveness across Llama 3.1, Qwen 2.5, and Gemma 3 for steering three different personas,sycophancy, hallucination, and myopic reward. Crucially, on sycophancy, our automatically discovered prompts achieve significant improvement (49.90% compared with 79.24%). By grounding prompt discovery in mechanistically meaningful features, our method offers a new paradigm for controllable and interpretable behavior modification. + oai:arXiv.org:2601.02896v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Harshvardhan Saini, Yiming Tang, Dianbo Liu + + + Proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024) + https://arxiv.org/abs/2601.02898 + arXiv:2601.02898v1 Announce Type: new +Abstract: This is the proceedings of the 1st International Workshop on Low Carbon Computing (LOCO 2024). + oai:arXiv.org:2601.02898v1 + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Wim Vanderbauwhede, Lauritz Thamsen, Jos\'e Cano + + + SPO-CLAPScore: Enhancing CLAP-based alignment prediction system with Standardize Preference Optimization, for the first XACLE Challenge + https://arxiv.org/abs/2601.02900 + arXiv:2601.02900v1 Announce Type: new +Abstract: The first XACLE Challenge (x-to-audio alignment challenge) addresses the critical need for automatic evaluation metrics that correlate with human perception of audio-text semantic alignment. In this paper, we describe the "Takano_UTokyo_03" system submitted to XACLE Challenge. Our approach leverages a CLAPScore-based architecture integrated with a novel training method called Standardized Preference Optimization (SPO). SPO standardizes the raw alignment scores provided by each listener, enabling the model to learn relative preferences and mitigate the impact of individual scoring biases. Additionally, we employ listener screening to exclude listeners with inconsistent ratings. Experimental evaluations demonstrate that both SPO and listener screening effectively improve the correlation with human judgment. Our system achieved 6th place in the challenge with a Spearman's rank correlation coefficient (SRCC) of 0.6142, demonstrating competitive performance within a marginal gap from the top-ranked systems. The code is available at https://github.com/ttakano398/SPO-CLAPScore. + oai:arXiv.org:2601.02900v1 + cs.SD + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Taisei Takano, Ryoya Yoshida + + + Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning + https://arxiv.org/abs/2601.02902 + arXiv:2601.02902v1 Announce Type: new +Abstract: Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs), providing reliable and verifiable decision-making in high-stakes domains such as mathematical reasoning and legal judgment. In this study, we present a systematic analysis of logical reasoning under controlled increases in logical complexity, and reveal a previously unrecognized phenomenon, which we term Logical Phase Transitions: rather than degrading smoothly, logical reasoning performance remains stable within a regime but collapses abruptly beyond a critical logical depth, mirroring physical phase transitions such as water freezing beyond a critical temperature threshold. Building on this insight, we propose Neuro-Symbolic Curriculum Tuning, a principled framework that adaptively aligns natural language with logical symbols to establish a shared representation, and reshapes training dynamics around phase-transition boundaries to progressively strengthen reasoning at increasing logical depths. Experiments on five benchmarks show that our approach effectively mitigates logical reasoning collapse at high complexity, yielding average accuracy gains of +1.26 in naive prompting and +3.95 in CoT, while improving generalization to unseen logical compositions. Code and data are available at https://github.com/AI4SS/Logical-Phase-Transitions. + oai:arXiv.org:2601.02902v1 + cs.AI + cs.CL + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xinglang Zhang, Yunyao Zhang, ZeLiang Chen, Junqing Yu, Wei Yang, Zikai Song + + + Site-Specific and Frequency-Dependent Channel Characterization and MIMO Performance in FR3 + https://arxiv.org/abs/2601.02903 + arXiv:2601.02903v1 Announce Type: new +Abstract: Next-generation wireless systems aim to enable on-demand connectivity through dynamic spectrum utilization. Motivated by this vision, this paper investigates the propagation characteristics and MIMO performance of the upper mid-band, spanning approximately 7-24 GHz and unofficially referred to as FR3. Using site-specific ray-tracing (RT) simulations based on the Sionna framework, we analyze indoor and outdoor environments at representative frequencies across FR1, FR3, and FR2, including 3.5, 7, 10, 14, 20, 24, and 28 GHz, under both single-antenna and multi-antenna configurations. The results show that FR3 exhibits intermediate propagation behavior between sub-6 GHz and millimeter-wave bands while sustaining effective spatial multiplexing and favorable spectral efficiency. Furthermore, large-array analysis indicates that performance gains in FR3 are closely tied to antenna scaling, highlighting the importance of large-size or large-aperture MIMO architectures for practical deployments. + oai:arXiv.org:2601.02903v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhuangzhuang Cui, Rudranil Chattopadhyay, Emiel Vanspranghels, Sofie Pollin + + + LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments + https://arxiv.org/abs/2601.02905 + arXiv:2601.02905v1 Announce Type: new +Abstract: Tracking objects that move within dynamic environments is a core challenge in robotics. Recent research has advanced this topic significantly; however, many existing approaches remain inefficient due to their reliance on heavy foundation models. To address this limitation, we propose LOST-3DSG, a lightweight open-vocabulary 3D scene graph designed to track dynamic objects in real-world environments. Our method adopts a semantic approach to entity tracking based on word2vec and sentence embeddings, enabling an open-vocabulary representation while avoiding the necessity of storing dense CLIP visual features. As a result, LOST-3DSG achieves superior performance compared to approaches that rely on high-dimensional visual embeddings. We evaluate our method through qualitative and quantitative experiments conducted in a real 3D environment using a TIAGo robot. The results demonstrate the effectiveness and efficiency of LOST-3DSG in dynamic object tracking. Code and supplementary material are publicly available on the project website at https://lab-rococo-sapienza.github.io/lost-3dsg/. + oai:arXiv.org:2601.02905v1 + cs.RO + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Sara Micol Ferraina, Michele Brienza, Francesco Argenziano, Emanuele Musumeci, Vincenzo Suriani, Domenico D. Bloisi, Daniele Nardi + + + Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration + https://arxiv.org/abs/2601.02906 + arXiv:2601.02906v1 Announce Type: new +Abstract: Multilingual speech foundation models such as Whisper are trained on web-scale data, where data for each language consists of a myriad of regional varieties. However, different regional varieties often employ different scripts to write the same language, rendering speech recognition output also subject to non-determinism in the output script. To mitigate this problem, we show that script is linearly encoded in the activation space of multilingual speech models, and that modifying activations at inference time enables direct control over output script. We find the addition of such script vectors to activations at test time can induce script change even in unconventional language-script pairings (e.g. Italian in Cyrillic and Japanese in Latin script). We apply this approach to inducing post-hoc control over the script of speech recognition output, where we observe competitive performance across all model sizes of Whisper. + oai:arXiv.org:2601.02906v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ryan Soh-Eun Shim, Kwanghee Choi, Kalvin Chang, Ming-Hao Hsu, Florian Eichin, Zhizheng Wu, Alane Suhr, Michael A. Hedderich, David Harwath, David R. Mortensen, Barbara Plank + + + Beyond the Black Box: Theory and Mechanism of Large Language Models + https://arxiv.org/abs/2601.02907 + arXiv:2601.02907v1 Announce Type: new +Abstract: The rapid emergence of Large Language Models (LLMs) has precipitated a profound paradigm shift in Artificial Intelligence, delivering monumental engineering successes that increasingly impact modern society. However, a critical paradox persists within the current field: despite the empirical efficacy, our theoretical understanding of LLMs remains disproportionately nascent, forcing these systems to be treated largely as ``black boxes''. To address this theoretical fragmentation, this survey proposes a unified lifecycle-based taxonomy that organizes the research landscape into six distinct stages: Data Preparation, Model Preparation, Training, Alignment, Inference, and Evaluation. Within this framework, we provide a systematic review of the foundational theories and internal mechanisms driving LLM performance. Specifically, we analyze core theoretical issues such as the mathematical justification for data mixtures, the representational limits of various architectures, and the optimization dynamics of alignment algorithms. Moving beyond current best practices, we identify critical frontier challenges, including the theoretical limits of synthetic data self-improvement, the mathematical bounds of safety guarantees, and the mechanistic origins of emergent intelligence. By connecting empirical observations with rigorous scientific inquiry, this work provides a structured roadmap for transitioning LLM development from engineering heuristics toward a principled scientific discipline. + oai:arXiv.org:2601.02907v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Zeyu Gan, Ruifeng Ren, Wei Yao, Xiaolin Hu, Gengze Xu, Chen Qian, Huayi Tang, Zixuan Gong, Xinhao Yao, Pengwei Tang, Zhenxing Dou, Yong Liu + + + TA-Prompting: Enhancing Video Large Language Models for Dense Video Captioning via Temporal Anchors + https://arxiv.org/abs/2601.02908 + arXiv:2601.02908v1 Announce Type: new +Abstract: Dense video captioning aims to interpret and describe all temporally localized events throughout an input video. Recent state-of-the-art methods leverage large language models (LLMs) to provide detailed moment descriptions for video data. However, existing VideoLLMs remain challenging in identifying precise event boundaries in untrimmed videos, causing the generated captions to be not properly grounded. In this paper, we propose TA-Prompting, which enhances VideoLLMs via Temporal Anchors that learn to precisely localize events and prompt the VideoLLMs to perform temporal-aware video event understanding. During inference, in order to properly determine the output caption sequence from an arbitrary number of events presented within a video, we introduce an event coherent sampling strategy to select event captions with sufficient coherence across temporal events and cross-modal similarity with the given video. Through extensive experiments on benchmark datasets, we show that our TA-Prompting is favorable against state-of-the-art VideoLLMs, yielding superior performance on dense video captioning and temporal understanding tasks including moment retrieval and temporalQA. + oai:arXiv.org:2601.02908v1 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026 + Wei-Yuan Cheng, Kai-Po Chang, Chi-Pin Huang, Fu-En Yang, Yu-Chiang Frank Wang + + + Image, Word and Thought: A More Challenging Language Task for the Iterated Learning Model + https://arxiv.org/abs/2601.02911 + arXiv:2601.02911v1 Announce Type: new +Abstract: The iterated learning model simulates the transmission of language from generation to generation in order to explore how the constraints imposed by language transmission facilitate the emergence of language structure. Despite each modelled language learner starting from a blank slate, the presence of a bottleneck limiting the number of utterances to which the learner is exposed can lead to the emergence of language that lacks ambiguity, is governed by grammatical rules, and is consistent over successive generations, that is, one that is expressive, compositional and stable. The recent introduction of a more computationally tractable and ecologically valid semi supervised iterated learning model, combining supervised and unsupervised learning within an autoencoder architecture, has enabled exploration of language transmission dynamics for much larger meaning-signal spaces. Here, for the first time, the model has been successfully applied to a language learning task involving the communication of much more complex meanings: seven-segment display images. Agents in this model are able to learn and transmit a language that is expressive: distinct codes are employed for all 128 glyphs; compositional: signal components consistently map to meaning components, and stable: the language does not change from generation to generation. + oai:arXiv.org:2601.02911v1 + cs.CL + cs.LG + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Hyoyeon Lee, Seth Bullock, Conor Houghton + + + Vulnerabilities of Audio-Based Biometric Authentication Systems Against Deepfake Speech Synthesis + https://arxiv.org/abs/2601.02914 + arXiv:2601.02914v1 Announce Type: new +Abstract: As audio deepfakes transition from research artifacts to widely available commercial tools, robust biometric authentication faces pressing security threats in high-stakes industries. This paper presents a systematic empirical evaluation of state-of-the-art speaker authentication systems based on a large-scale speech synthesis dataset, revealing two major security vulnerabilities: 1) modern voice cloning models trained on very small samples can easily bypass commercial speaker verification systems; and 2) anti-spoofing detectors struggle to generalize across different methods of audio synthesis, leading to a significant gap between in-domain performance and real-world robustness. These findings call for a reconsideration of security measures and stress the need for architectural innovations, adaptive defenses, and the transition towards multi-factor authentication. + oai:arXiv.org:2601.02914v1 + cs.SD + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mengze Hong, Di Jiang, Zeying Xie, Weiwei Zhao, Guan Wang, Chen Jason Zhang + + + ChemBART: A Pre-trained BART Model Assisting Organic Chemistry Analysis + https://arxiv.org/abs/2601.02915 + arXiv:2601.02915v1 Announce Type: new +Abstract: Recent advances in large language models (LLMs) have demonstrated transformative potential across diverse fields. While LLMs have been applied to molecular simplified molecular input line entry system (SMILES) in computer-aided synthesis planning (CASP), existing methodologies typically address single tasks, such as precursor prediction. We introduce ChemBART, a SMILES-based LLM pre-trained on chemical reactions, which enables a unified model for multiple downstream chemical tasks--achieving the paradigm of "one model, one pre-training, multiple tasks." By leveraging outputs from a mask-filling pre-training task on reaction expressions, ChemBART effectively solves a variety of chemical problems, including precursor/reagent generation, temperature-yield regression, molecular property classification, and optimizing the policy and value functions within a reinforcement learning framework, integrated with Monte Carlo tree search for multi-step synthesis route design. Unlike single-molecule pre-trained LLMs constrained to specific applications, ChemBART addresses broader chemical challenges and integrates them for comprehensive synthesis planning. Crucially, ChemBART-designed multi-step synthesis routes and reaction conditions directly inspired wet-lab validation, which confirmed shorter pathways with ~30% yield improvement over literature benchmarks. Our work validates the power of reaction-focused pre-training and showcases the broad utility of ChemBART in advancing the complete synthesis planning cycle. + oai:arXiv.org:2601.02915v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kenan Li, Yijian Zhang, Jin Wang, Haipeng Gan, Zeying Sun, Xiaoguang Lei, Hao Dong + + + RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems + https://arxiv.org/abs/2601.02917 + arXiv:2601.02917v1 Announce Type: new +Abstract: Hallucination is a major concern in LLM-driven service systems, necessitating explicit knowledge grounding for compliance-guaranteed responses. In this paper, we introduce Retrieval-Augmented Learning-to-Match (RAL2M), a novel framework that eliminates generation hallucination by repositioning LLMs as query-response matching judges within a retrieval-based system, providing a robust alternative to purely generative approaches. To further mitigate judgment hallucination, we propose a query-adaptive latent ensemble strategy that explicitly models heterogeneous model competence and interdependencies among LLMs, deriving a calibrated consensus decision. Extensive experiments on large-scale benchmarks demonstrate that the proposed method effectively leverages the "wisdom of the crowd" and significantly outperforms strong baselines. Finally, we discuss best practices and promising directions for further exploiting latent representations in future work. + oai:arXiv.org:2601.02917v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mengze Hong, Di Jiang, Jiangtao Wen, Zhiyang Su, Yawen Li, Yanjie Sun, Guan Wang, Chen Jason Zhang + + + Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning + https://arxiv.org/abs/2601.02918 + arXiv:2601.02918v1 Announce Type: new +Abstract: Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores without explanation or provide low-level descriptions lacking precise scores. Recent reasoning-based vision language models (VLMs) have shown strong potential for IQA, enabling joint generation of quality descriptions and scores. However, we notice that existing VLM-based IQA methods tend to exhibit unreliable reasoning due to their limited capability of integrating visual and textual cues. In this work, we introduce Zoom-IQA, a VLM-based IQA model to explicitly emulate key cognitive behaviors: uncertainty awareness, region reasoning, and iterative refinement. Specifically, we present a two-stage training pipeline: 1) supervised fine-tuning (SFT) on our Grounded-Rationale-IQA (GR-IQA) dataset to teach the model to ground its assessments in key regions; and 2) reinforcement learning (RL) for dynamic policy exploration, primarily stabilized by our KL-Coverage regularizer to prevent reasoning and scoring diversity collapse, and supported by a Progressive Re-sampling Strategy to mitigate annotation bias. Extensive experiments show that Zoom-IQA achieves improved robustness, explainability, and generalization. The application to downstream tasks, such as image restoration, further demonstrates the effectiveness of Zoom-IQA. + oai:arXiv.org:2601.02918v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Guoqiang Liang, Jianyi Wang, Zhonghua Wu, Shangchen Zhou + + + Intersection patterns of set systems on manifolds with slowly growing homological shatter functions + https://arxiv.org/abs/2601.02920 + arXiv:2601.02920v1 Announce Type: new +Abstract: A theorem of Matou\v{s}ek asserts that for any $k \ge 2$, any set system whose shatter function is $o(n^k)$ enjoys a fractional Helly theorem: in the $k$-wise intersection hypergraph, positive density implies a linear-size clique. Kalai and Meshulam conjectured a generalization of that phenomenon to homological shatter functions. It was verified for set systems with bounded homological shatter functions and ground set with a forbidden homological minor (which includes $\mathbb{R}^d$ by a homological analogue of the van Kampen-Flores theorem). We present two contributions to this line of research: + - We study homological minors in certain manifolds (possibly with boundary), for which we prove analogues of the van Kampen-Flores theorem and of the Hanani-Tutte theorem. + - We introduce graded analogues of the Radon and Helly numbers of set systems and relate their growth rate to the original parameters. This allows to extend the verification of the Kalai-Meshulam conjecture for sufficiently slowly growing homological shatter functions. + oai:arXiv.org:2601.02920v1 + cs.CG + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Sergey Avvakumov, Marguerite Bin, Xavier Goaoc + + + DCG ReID: Disentangling Collaboration and Guidance Fusion Representations for Multi-modal Vehicle Re-Identification + https://arxiv.org/abs/2601.02924 + arXiv:2601.02924v1 Announce Type: new +Abstract: Multi-modal vehicle Re-Identification (ReID) aims to leverage complementary information from RGB, Near Infrared (NIR), and Thermal Infrared (TIR) modalities to retrieve the same vehicle. The challenges of multi-modal vehicle ReID arise from the uncertainty of modality quality distribution induced by inherent discrepancies across modalities, resulting in distinct conflicting fusion requirements for data with balanced and unbalanced quality distributions. Existing methods handle all multi-modal data within a single fusion model, overlooking the different needs of the two data types and making it difficult to decouple the conflict between intra-class consistency and inter-modal heterogeneity. To this end, we propose Disentangle Collaboration and Guidance Fusion Representations for Multi-modal Vehicle ReID (DCG-ReID). Specifically, to disentangle heterogeneous quality-distributed modal data without mutual interference, we first design the Dynamic Confidence-based Disentangling Weighting (DCDW) mechanism: dynamically reweighting three-modal contributions via interaction-derived modal confidence to build a disentangled fusion framework. Building on DCDW, we develop two scenario-specific fusion strategies: (1) for balanced quality distributions, Collaboration Fusion Module (CFM) mines pairwise consensus features to capture shared discriminative information and boost intra-class consistency; (2) for unbalanced distributions, Guidance Fusion Module (GFM) implements differential amplification of modal discriminative disparities to reinforce dominant modality advantages, guide auxiliary modalities to mine complementary discriminative info, and mitigate inter-modal divergence to boost multi-modal joint decision performance. Extensive experiments on three multi-modal ReID benchmarks (WMVeID863, MSVR310, RGBNT100) validate the effectiveness of our method. Code will be released upon acceptance. + oai:arXiv.org:2601.02924v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Aihua Zheng, Ya Gao, Shihao Li, Chenglong Li, Jin Tang + + + PrismVAU: Prompt-Refined Inference System for Multimodal Video Anomaly Understanding + https://arxiv.org/abs/2601.02927 + arXiv:2601.02927v1 Announce Type: new +Abstract: Video Anomaly Understanding (VAU) extends traditional Video Anomaly Detection (VAD) by not only localizing anomalies but also describing and reasoning about their context. Existing VAU approaches often rely on fine-tuned multimodal large language models (MLLMs) or external modules such as video captioners, which introduce costly annotations, complex training pipelines, and high inference overhead. In this work, we introduce PrismVAU, a lightweight yet effective system for real-time VAU that leverages a single off-the-shelf MLLM for anomaly scoring, explanation, and prompt optimization. PrismVAU operates in two complementary stages: (1) a coarse anomaly scoring module that computes frame-level anomaly scores via similarity to textual anchors, and (2) an MLLM-based refinement module that contextualizes anomalies through system and user prompts. Both textual anchors and prompts are optimized with a weakly supervised Automatic Prompt Engineering (APE) framework. Extensive experiments on standard VAD benchmarks demonstrate that PrismVAU delivers competitive detection performance and interpretable anomaly explanations -- without relying on instruction tuning, frame-level annotations, and external modules or dense processing -- making it an efficient and practical solution for real-world applications. + oai:arXiv.org:2601.02927v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + I\~naki Erregue, Kamal Nasrollahi, Sergio Escalera + + + HybridSolarNet: A Lightweight and Explainable EfficientNet-CBAM Architecture for Real-Time Solar Panel Fault Detection + https://arxiv.org/abs/2601.02928 + arXiv:2601.02928v1 Announce Type: new +Abstract: Manual inspections for solar panel systems are a tedious, costly, and error-prone task, making it desirable for Unmanned Aerial Vehicle (UAV) based monitoring. Though deep learning models have excellent fault detection capabilities, almost all methods either are too large and heavy for edge computing devices or involve biased estimation of accuracy due to ineffective learning techniques. We propose a new solar panel fault detection model called HybridSolarNet. It integrates EfficientNet-B0 with Convolutional Block Attention Module (CBAM). We implemented it on the Kaggle Solar Panel Images competition dataset with a tight split-before-augmentation protocol. It avoids leakage in accuracy estimation. We introduced focal loss and cosine annealing. Ablation analysis validates that accuracy boosts due to added benefits from CBAM (+1.53%) and that there are benefits from recognition of classes with imbalanced samples via focal loss. Overall average accuracy on 5-fold stratified cross-validation experiments on the given competition dataset topped 92.37% +/- 0.41 and an F1-score of 0.9226 +/- 0.39 compared to baselines like VGG19, requiring merely 16.3 MB storage, i.e., 32 times less. Its inference speed measured at 54.9 FPS with GPU support makes it a successful candidate for real-time UAV implementation. Moreover, visualization obtained from Grad-CAM illustrates that HybridSolarNet focuses on actual locations instead of irrelevant ones. + oai:arXiv.org:2601.02928v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Md. Asif Hossain, G M Mota-Tahrin Tayef, Nabil Subhan + + + Probabilistic Time Slot Leasing in TDMA-Based IoT Networks for Enhanced Channel Utilization + https://arxiv.org/abs/2601.02930 + arXiv:2601.02930v1 Announce Type: new +Abstract: In large-scale resource-constrained wireless networks, such as those prevalent in the Internet of Things (IoT), efficient communication scheduling remains a critical challenge. Among the various approaches, Time Division Multiple Access (TDMA) protocols have been widely adopted for their structured and collision-free communication capabilities. Nevertheless, despite extensive research in this area, current solutions often exhibit suboptimal performance, particularly in dynamic environments where node activity levels fluctuate over time. + This paper introduces a novel fully distributed TDMA-based scheduling protocol that intelligently maximizes the utilization of communication resources. The proposed approach adaptively reallocates underutilized time slots, originally assigned to temporarily inactive nodes, to those experiencing higher communication demands. This dynamic reallocation not only improves channel utilization but also reduces idle periods, thereby enhancing overall network efficiency. To further enhance performance, we incorporate a lightweight probabilistic mechanism that governs the temporal leasing of unused slots. This mechanism balances the trade-off between slot availability and transmission reliability, minimizing packet loss while preserving fairness and stability within the network. + Simulations across a range of network scenarios demonstrate that our protocol significantly improves throughput, latency, and reliability in resource-constrained environments. These results highlight the protocol's potential as a robust and scalable solution for adaptive and energy-efficient scheduling in next-generation IoT networks. + oai:arXiv.org:2601.02930v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + 10.1109/MSWiM67937.2025.11309001 + Hicham Lakhlef, Mohamed Ali Zormati, Khaled Abid, Toufik Ahmed + + + Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs + https://arxiv.org/abs/2601.02931 + arXiv:2601.02931v1 Announce Type: new +Abstract: Autoregressive LLMs perform well on relational tasks that require linking entities via relational words (e.g., father/son, friend), but it is unclear whether they learn the logical semantics of such relations (e.g., symmetry and inversion logic) and, if so, whether reversal-type failures arise from missing relational semantics or left-to-right order bias. We propose a controlled Knowledge Graph-based synthetic framework that generates text from symmetric/inverse triples, train GPT-style autoregressive models from scratch, and evaluate memorization, logical inference, and in-context generalization to unseen entities to address these questions. We find a sharp phase transition in which relational semantics emerge with sufficient logic-bearing supervision, even in shallow (2-3 layer) models, and that successful generalization aligns with stable intermediate-layer signals. Finally, order-matched forward/reverse tests and a diffusion baseline indicate that reversal failures are primarily driven by autoregressive order bias rather than deficient inversion semantics. + oai:arXiv.org:2601.02931v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira + + + Pearmut: Human Evaluation of Translation Made Trivial + https://arxiv.org/abs/2601.02933 + arXiv:2601.02933v1 Announce Type: new +Abstract: Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics, because it is notoriously complex and slow to set up with existing tools with substantial engineering and operational overhead. We introduce Pearmut, a lightweight yet feature-rich platform that makes end-to-end human evaluation as easy to run as automatic evaluation. Pearmut removes common entry barriers and provides support for evaluating multilingual tasks, with a particular focus on machine translation. The platform implements standard evaluation protocols, including DA, ESA, or MQM, but is also extensible to allow prototyping new protocols. It features document-level context, absolute and contrastive evaluation, attention checks, ESAAI pre-annotations and both static and active learning-based assignment strategies. Pearmut enables reliable human evaluation to become a practical, routine component of model development and diagnosis rather than an occasional effort. + oai:arXiv.org:2601.02933v1 + cs.CL + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Vil\'em Zouhar, Tom Kocmi + + + SastBench: A Benchmark for Testing Agentic SAST Triage + https://arxiv.org/abs/2601.02941 + arXiv:2601.02941v1 Announce Type: new +Abstract: SAST (Static Application Security Testing) tools are among the most widely used techniques in defensive cybersecurity, employed by commercial and non-commercial organizations to identify potential vulnerabilities in software. Despite their great utility, they generate numerous false positives, requiring costly manual filtering (aka triage). While LLM-powered agents show promise for automating cybersecurity tasks, existing benchmarks fail to emulate real-world SAST finding distributions. We introduce SastBench, a benchmark for evaluating SAST triage agents that combines real CVEs as true positives with filtered SAST tool findings as approximate false positives. SastBench features an agent-agnostic design. We evaluate different agents on the benchmark and present a comparative analysis of their performance, provide a detailed analysis of the dataset, and discuss the implications for future development. + oai:arXiv.org:2601.02941v1 + cs.CR + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jake Feiglin, Guy Dar + + + MixTTE: Multi-Level Mixture-of-Experts for Scalable and Adaptive Travel Time Estimation + https://arxiv.org/abs/2601.02943 + arXiv:2601.02943v1 Announce Type: new +Abstract: Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level dependency modeling, they struggle to capture city-scale traffic dynamics and long-tail scenarios, leading to unreliable predictions in large urban networks. In this paper, we propose \model, a scalable and adaptive framework that synergistically integrates link-level modeling with industrial route-level TTE systems. Specifically, we propose a spatio-temporal external attention module to capture global traffic dynamic dependencies across million-scale road networks efficiently. Moreover, we construct a stabilized graph mixture-of-experts network to handle heterogeneous traffic patterns while maintaining inference efficiency. Furthermore, an asynchronous incremental learning strategy is tailored to enable real-time and stable adaptation to dynamic traffic distribution shifts. Experiments on real-world datasets validate MixTTE significantly reduces prediction errors compared to seven baselines. MixTTE has been deployed in DiDi, substantially improving the accuracy and stability of the TTE service. + oai:arXiv.org:2601.02943v1 + cs.LG + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3770854.3783940 + Wenzhao Jiang, Jindong Han, Ruiqian Han, Hao Liu + + + VTONQA: A Multi-Dimensional Quality Assessment Dataset for Virtual Try-on + https://arxiv.org/abs/2601.02945 + arXiv:2601.02945v1 Announce Type: new +Abstract: With the rapid development of e-commerce and digital fashion, image-based virtual try-on (VTON) has attracted increasing attention. However, existing VTON models often suffer from artifacts such as garment distortion and body inconsistency, highlighting the need for reliable quality evaluation of VTON-generated images. To this end, we construct VTONQA, the first multi-dimensional quality assessment dataset specifically designed for VTON, which contains 8,132 images generated by 11 representative VTON models, along with 24,396 mean opinion scores (MOSs) across three evaluation dimensions (i.e., clothing fit, body compatibility, and overall quality). Based on VTONQA, we benchmark both VTON models and a diverse set of image quality assessment (IQA) metrics, revealing the limitations of existing methods and highlighting the value of the proposed dataset. We believe that the VTONQA dataset and corresponding benchmarks will provide a solid foundation for perceptually aligned evaluation, benefiting both the development of quality assessment methods and the advancement of VTON models. + oai:arXiv.org:2601.02945v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xinyi Wei, Sijing Wu, Zitong Xu, Yunhao Li, Huiyu Duan, Xiongkuo Min, Guangtao Zhai + + + Quality Degradation Attack in Synthetic Data + https://arxiv.org/abs/2601.02947 + arXiv:2601.02947v1 Announce Type: new +Abstract: Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the recipient of the released synthetic data and attempts to infer sensitive information from it. This study investigates quality degradation attacks initiated by adversaries who possess access to the real dataset or control over the generation process, such as the data owner, the synthetic data provider, or potential intruders. We formalize a corresponding threat model and empirically evaluate the effectiveness of targeted manipulations of real data (e.g., label flipping and feature-importance-based interventions) on the quality of generated synthetic data. The results show that even small perturbations can substantially reduce downstream predictive performance and increase statistical divergence, exposing vulnerabilities within SDG pipelines. This study highlights the need to integrate integrity verification and robustness mechanisms, alongside privacy protection, to ensure the reliability and trustworthiness of synthetic data sharing frameworks. + oai:arXiv.org:2601.02947v1 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Qinyi Liu, Dong Liu, Farhad Vadiee, Mohammad Khalil, Pedro P. Vergara Barrios + + + Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters + https://arxiv.org/abs/2601.02948 + arXiv:2601.02948v1 Announce Type: new +Abstract: Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Integral (PRMPPI) control, a framework that integrates online parameter learning with probabilistic safety constraints. PRMPPI maintains a particle-based belief over parameters via Stein Variational Gradient Descent, evaluates safety constraints using Conformal Prediction, and optimizes both a nominal performance-driven and a safety-focused backup trajectory in parallel. This yields a controller that is cautious at first, improves performance as parameters are learned, and ensures safety throughout. Simulation and hardware experiments demonstrate higher success rates, lower tracking error, and more accurate parameter estimates than baselines. + oai:arXiv.org:2601.02948v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Matti Vahs, Jaeyoun Choi, Niklas Schmid, Jana Tumova, Chuchu Fan + + + Exploring Blockchain Interoperability: Frameworks, Use Cases, and Future Challenges + https://arxiv.org/abs/2601.02949 + arXiv:2601.02949v1 Announce Type: new +Abstract: Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability. + oai:arXiv.org:2601.02949v1 + cs.CR + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Stanly Wilson, Kwabena Adu-Duodu, Yinhao Li, Ellis Solaiman, Omer Rana, Rajiv Ranjan + + + Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning + https://arxiv.org/abs/2601.02950 + arXiv:2601.02950v1 Announce Type: new +Abstract: Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queries jointly to enable cross-instance learning. By performing comparative analysis across batches, BoT identifies high-quality reasoning templates, detects errors through consistency checks, and amortizes computational costs. We instantiate BoT within a multi-agent reflection architecture (BoT-R), where a Reflector performs joint evaluation to unlock mutual information gain unavailable in isolated processing. Experiments across three model families and six benchmarks demonstrate that BoT-R consistently improves accuracy and confidence calibration while reducing inference costs by up to 61%. Our theoretical and experimental analysis reveals when and why batch-aware reasoning benefits LLM systems. + oai:arXiv.org:2601.02950v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xuan Yang, Furong Jia, Roy Xie, Xiong Xi, Hengwei Bian, Jian Li, Monica Agrawal + + + The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models + https://arxiv.org/abs/2601.02954 + arXiv:2601.02954v1 Announce Type: new +Abstract: Existing large audio-language models perceive the world as "mono" -- a single stream of audio that ignores the critical spatial dimension ("where") required for universal acoustic scene analysis. To bridge this gap, we first introduce a hierarchical framework for Auditory Scene Analysis (ASA). Guided by this framework, we introduce a system that enables models like Qwen2-Audio to understand and reason about the complex acoustic world. Our framework achieves this through three core contributions: First, we build a large-scale, synthesized binaural audio dataset to provide the rich spatial cues. Second, we design a hybrid feature projector, which leverages parallel semantic and spatial encoders to extract decoupled representations. These distinct streams are integrated via a dense fusion mechanism, ensuring the model receives a holistic view of the acoustic scene. Finally, we employ a progressive training curriculum, advancing from supervised fine-tuning (SFT) to reinforcement learning via Group Relative Policy Optimization (GRPO), to explicitly evolve the model's capabilities towards reasoning. On our comprehensive benchmark, the model demonstrates comparatively strong capability for spatial understanding. By enabling this spatial perception, our work provides a clear pathway for leveraging the powerful reasoning abilities of large models towards holistic acoustic scene analysis, advancing from "mono" semantic recognition to spatial intelligence. + oai:arXiv.org:2601.02954v1 + cs.SD + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuhuan You, Lai Wei, Xihong Wu, Tianshu Qu + + + HarmonRank: Ranking-aligned Multi-objective Ensemble for Live-streaming E-commerce Recommendation + https://arxiv.org/abs/2601.02955 + arXiv:2601.02955v1 Announce Type: new +Abstract: Recommendation for live-streaming e-commerce is gaining increasing attention due to the explosive growth of the live streaming economy. Different from traditional e-commerce, live-streaming e-commerce shifts the focus from products to streamers, which requires ranking mechanism to balance both purchases and user-streamer interactions for long-term ecology. To trade off multiple objectives, a popular solution is to build an ensemble model to integrate multi-objective scores into a unified score. The ensemble model is usually supervised by multiple independent binary classification losses of all objectives. However, this paradigm suffers from two inherent limitations. First, the optimization direction of the binary classification task is misaligned with the ranking task (evaluated by AUC). Second, this paradigm overlooks the alignment between objectives, e.g., comment and buy behaviors are partially dependent which can be revealed in labels correlations. The model can achieve better trade-offs if it learns the aligned parts of ranking abilities among different objectives. + To mitigate these limitations, we propose a novel multi-objective ensemble framework HarmonRank to fulfill both alignment to the ranking task and alignment among objectives. For alignment to ranking, we formulate ranking metric AUC as a rank-sum problem and utilize differentiable ranking techniques for ranking-oriented optimization. For inter-objective alignment, we change the original one-step ensemble paradigm to a two-step relation-aware ensemble scheme. + Extensive offline experiments results on two industrial datasets and online experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods. The proposed method has been fully deployed in Kuaishou's live-streaming e-commerce recommendation platform with 400 million DAUs, contributing over 2% purchase gain. + oai:arXiv.org:2601.02955v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Boyang Xia, Zhou Yu, Zhiliang Zhu, Hanxiao Sun, Biyun Han, Jun Wang, Runnan Liu, Wenwu Ou + + + Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion + https://arxiv.org/abs/2601.02956 + arXiv:2601.02956v1 Announce Type: new +Abstract: Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adoption of English pivoting. While prior studies attribute this advantage to the superior English-centric capabilities of Large Language Models (LLMs), we find that such measurements are significantly distorted by structural priors inherent in evaluation benchmarks. Specifically, we identify exposure bias and a gold availability prior-both driven by the disproportionate concentration of resources in English-as well as cultural priors rooted in topic locality, as factors that hinder accurate assessment of genuine language preference. To address these biases, we propose DeLP (Debiased Language Preference), a calibrated metric designed to explicitly factor out these structural confounds. Our analysis using DeLP reveals that the previously reported English preference is largely a byproduct of evidence distribution rather than an inherent model bias. Instead, we find that retrievers fundamentally favor monolingual alignment between the query and the document language. Building on this insight, we introduce DELTA (DEbiased Language preference-guided Text Augmentation), a lightweight and efficient mRAG framework that strategically leverages monolingual alignment to optimize cross-lingual retrieval and generation. Experimental results demonstrate that DELTA consistently outperforms English pivoting and mRAG baselines across diverse languages. + oai:arXiv.org:2601.02956v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jeonghyun Park, Byeongjeong Kim, Seojin Hwang, Hwanhee Lee + + + LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation + https://arxiv.org/abs/2601.02957 + arXiv:2601.02957v1 Announce Type: new +Abstract: This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and the interpretability of regime changes in time series data. Two critical limitations in the field are addressed. First, individual detection methods exhibit complementary strengths and weaknesses depending on data characteristics, making method selection non-trivial and prone to suboptimal results. Second, automated, contextual explanations for detected changes are largely absent. The proposed ensemble method aggregates results from ten distinct changepoint detection algorithms, achieving superior performance and robustness compared to individual methods. Additionally, an LLM-powered explanation pipeline automatically generates contextual narratives, linking detected changepoints to potential real-world historical events. For private or domain-specific data, a Retrieval-Augmented Generation (RAG) solution enables explanations grounded in user-provided documents. The open source Python framework demonstrates practical utility in diverse domains, including finance, political science, and environmental science, transforming raw statistical output into actionable insights for analysts and decision-makers. + oai:arXiv.org:2601.02957v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Fabian Lukassen, Christoph Weisser, Michael Schlee, Manish Kumar, Anton Thielmann, Benjamin Saefken, Thomas Kneib + + + Post-Earthquake Restoration of Electricity-Gas Distribution Systems with Damage Information Collection and Repair Vehicle Routing + https://arxiv.org/abs/2601.02958 + arXiv:2601.02958v1 Announce Type: new +Abstract: Extreme events such as earthquakes pose significant threats to integrated electricity-gas distribution systems (IEGDS) by causing widespread damage. Existing restoration approaches typically assume full awareness of damage, which may not be true if monitoring and communication infrastructures are impaired. In such circumstances, field inspection is necessary. This paper presents a novel adaptive restoration framework for IEGDS, considering dynamic damage assessment and repair. The restoration problem is formulated as a partially observable Markov decision process (POMDP), capturing the gradually revealed contingency and the evolving impact of field crew actions. To address the computational challenges of POMDPs in real-time applications, an advanced belief tree search (BTS) algorithm is introduced. This algorithm enables crew members to continuously update their actions based on evolving belief states, leveraging comprehensive simulations to evaluate potential future trajectories and identify optimal inspection and repair strategies. Based on the BTS algorithm, a unified real-time decision-making framework is developed for IEGDS restoration. Case studies on two distinct IEGDS systems demonstrate the effectiveness and scalability of the proposed method. The results indicate that the proposed approach achieves an outage cost comparable to the ideal solution, and reduces the total outage cost by more than 15% compared to strategies based on stochastic programming and heuristic methods. + oai:arXiv.org:2601.02958v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mingxuan Li, Wei Wei, Yin Xu, Chengeng Zhang, Shanshan Shi + + + Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation + https://arxiv.org/abs/2601.02962 + arXiv:2601.02962v1 Announce Type: new +Abstract: Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain. + oai:arXiv.org:2601.02962v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + 10.1145/3501247.3531567 + Proceedings of the 14th ACM Web Science Conference 2022 (WebSci '22). ACM, New York, NY, USA, 2022, pp. 219-227 + Fabian Haak, Philipp Schaer + + + Low-Resource Heuristics for Bahnaric Optical Character Recognition Improvement + https://arxiv.org/abs/2601.02965 + arXiv:2601.02965v1 Announce Type: new +Abstract: Bahnar, a minority language spoken across Vietnam, Cambodia, and Laos, faces significant preservation challenges due to limited research and data availability. This study addresses the critical need for accurate digitization of Bahnar language documents through optical character recognition (OCR) technology. Digitizing scanned paper documents poses significant challenges, as degraded image quality from broken or blurred areas introduces considerable OCR errors that compromise information retrieval systems. We propose a comprehensive approach combining advanced table and non-table detection techniques with probability-based post-processing heuristics to enhance recognition accuracy. Our method first applies detection algorithms to improve input data quality, then employs probabilistic error correction on OCR output. Experimental results indicate a substantial improvement, with recognition accuracy increasing from 72.86% to 79.26%. This work contributes valuable resources for Bahnar language preservation and provides a framework applicable to other minority language digitization efforts. + oai:arXiv.org:2601.02965v1 + cs.CL + cs.CV + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Phat Tran, Phuoc Pham, Hung Trinh, Tho Quan + + + MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free + https://arxiv.org/abs/2601.02967 + arXiv:2601.02967v1 Announce Type: new +Abstract: Extending the input modality of Large Language Models~(LLMs) to the audio domain is essential for achieving comprehensive multimodal perception. However, it is well-known that acoustic information is intrinsically \textit{heterogeneous}, entangling attributes such as speech, music, and environmental context. Existing research is limited to a dense, parameter-shared adapter to model these diverse patterns, which induces \textit{gradient conflict} during optimization, as parameter updates required for distinct attributes contradict each other. To address this limitation, we introduce the \textit{\textbf{MoE-Adapter}}, a sparse Mixture-of-Experts~(MoE) architecture designed to decouple acoustic information. Specifically, it employs a dynamic gating mechanism that routes audio tokens to specialized experts capturing complementary feature subspaces while retaining shared experts for global context, thereby mitigating gradient conflicts and enabling fine-grained feature learning. Comprehensive experiments show that the MoE-Adapter achieves superior performance on both audio semantic and paralinguistic tasks, consistently outperforming dense linear baselines with comparable computational costs. Furthermore, we will release the related code and models to facilitate future research. + oai:arXiv.org:2601.02967v1 + cs.SD + cs.AI + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yishu Lei, Shuwei He, Jing Hu, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou He, Yu Sun, Hua Wu, Haifeng Wang + + + Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models + https://arxiv.org/abs/2601.02968 + arXiv:2601.02968v1 Announce Type: new +Abstract: The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their downstream outcomes, which leads models to rely on superficial pattern matching rather than principled reasoning. We therefore propose the rationale-grounded in-context learning for time series reasoning, where rationales work as guiding reasoning units rather than post-hoc explanations, and develop the RationaleTS method. Specifically, we firstly induce label-conditioned rationales, composed of reasoning paths from observable evidence to the potential outcomes. Then, we design the hybrid retrieval by balancing temporal patterns and semantic contexts to retrieve correlated rationale priors for the final in-context inference on new samples. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our proposed RationaleTS on three-domain time series reasoning tasks. We will release our code for reproduction. + oai:arXiv.org:2601.02968v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Qingxiang Liu, Zhiqing Cui, Xiaoliang Luo, Yuqian Wu, Zhuoyang Jiang, Huaiyu Wan, Sheng Sun, Lvchun Wang, Wei Yu, Yuxuan Liang + + + Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning + https://arxiv.org/abs/2601.02970 + arXiv:2601.02970v1 Announce Type: new +Abstract: Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by adjusting the sampling budget; however, they rely on count-based stopping rules that treat all responses equally, often leading to unnecessary sampling. We propose Reliability-Aware Adaptive Self-Consistency (ReASC), which addresses this limitation by reframing adaptive sampling from response counting to evidence sufficiency, leveraging response-level confidence for principled information aggregation. ReASC operates in two stages: a single-sample decision stage that resolves instances confidently answerable from a single response, and a reliability-aware accumulation stage that aggregates responses by jointly leveraging their frequency and confidence. Across five models and four datasets, ReASC consistently achieves the best accuracy-cost trade-off compared to existing baselines, yielding improved inference efficiency across model scales from 3B to 27B parameters. As a concrete example, ReASC reduces inference cost by up to 70\% relative to self-consistency while preserving accuracy on GSM8K using Gemma-3-4B-it. + oai:arXiv.org:2601.02970v1 + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Junseok Kim, Nakyeong Yang, Kyungmin Min, Kyomin Jung + + + Few-shot learning for security bug report identification + https://arxiv.org/abs/2601.02971 + arXiv:2601.02971v1 Announce Type: new +Abstract: Security bug reports require prompt identification to minimize the window of vulnerability in software systems. Traditional machine learning (ML) techniques for classifying bug reports to identify security bug reports rely heavily on large amounts of labeled data. However, datasets for security bug reports are often scarce in practice, leading to poor model performance and limited applicability in real-world settings. In this study, we propose a few-shot learning-based technique to effectively identify security bug reports using limited labeled data. We employ SetFit, a state-of-the-art few-shot learning framework that combines sentence transformers with contrastive learning and parameter-efficient fine-tuning. The model is trained on a small labeled dataset of bug reports and is evaluated on its ability to classify these reports as either security-related or non-security-related. Our approach achieves an AUC of 0.865, at best, outperforming traditional ML techniques (baselines) for all of the evaluated datasets. This highlights the potential of SetFit to effectively identify security bug reports. SetFit-based few-shot learning offers a promising alternative to traditional ML techniques to identify security bug reports. The approach enables efficient model development with minimal annotation effort, making it highly suitable for scenarios where labeled data is scarce. + oai:arXiv.org:2601.02971v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Muhammad Laiq + + + Correct, Concise and Complete: Multi-stage Training For Adaptive Reasoning + https://arxiv.org/abs/2601.02972 + arXiv:2601.02972v1 Announce Type: new +Abstract: The reasoning capabilities of large language models (LLMs) have improved substantially through increased test-time computation, typically in the form of intermediate tokens known as chain-of-thought (CoT). However, CoT often becomes unnecessarily long, increasing computation cost without actual accuracy gains or sometimes even degrading performance, a phenomenon known as ``overthinking''. We propose a multi-stage efficient reasoning method that combines supervised fine-tuning -- via rejection sampling or reasoning trace reformatting -- with reinforcement learning using an adaptive length penalty. We introduce a lightweight reward function that penalizes tokens generated after the first correct answer but encouraging self-verification only when beneficial. We conduct a holistic evaluation across seven diverse reasoning tasks, analyzing the accuracy--response length trade-off. Our approach reduces response length by an average of 28\% for 8B models and 40\% for 32B models, while incurring only minor performance drops of 1.6 and 2.5 points, respectively. Despite its conceptual simplicity, it achieves a superior trade-off compared to more complex state-of-the-art efficient reasoning methods, scoring 76.6, in terms of the area under the Overthinking-Adjusted Accuracy curve ($\text{AUC}_{\text{OAA}}$) -- 5 points above the base model and 2.5 points above the second-best approach. + oai:arXiv.org:2601.02972v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nathana\"el Carraz Rakotonirina, Ren Pang, Neha Anna John, Michael Bohlke-Schneider, Momchil Hardalov + + + A Fourth-Order Cut-cell Multigrid Method for Generic Elliptic Equations on Arbitrary Domains + https://arxiv.org/abs/2601.02975 + arXiv:2601.02975v1 Announce Type: new +Abstract: To numerically solve a generic elliptic equation on two-dimensional domains with rectangular Cartesian grids, we propose a cut-cell geometric multigrid method that features (1) general algorithmic steps that apply to all forms of elliptic equations and all types of boundary conditions, (2) the versatility of handling both regular and irregular domains with arbitrarily complex topology and geometry, (3) the fourth-order accuracy even at the presence of ${\cal C}^1$ discontinuities on the domain boundary, and (4) the optimal complexity of $O(h^{-2})$. Test results demonstrate the generality, accuracy, efficiency, robustness, and excellent conditioning of the proposed method. + oai:arXiv.org:2601.02975v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiyu Liu, Zhixuan Li, Jiatu Yan, Zhiqi Li, Qinghai Zhang + + + Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencoders + https://arxiv.org/abs/2601.02978 + arXiv:2601.02978v1 Announce Type: new +Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models (LLMs). However, a persistent challenge lies in linking such internal features to the reliable control of complex, behavior-level semantic attributes in language generation. In this paper, we propose a Sparse Autoencoder-based framework for retrieving and steering semantically interpretable internal features associated with high-level linguistic behaviors. Our method employs a contrastive feature retrieval pipeline based on controlled semantic oppositions, combing statistical activation analysis and generation-based validation to distill monosemantic functional features from sparse activation spaces. Using the Big Five personality traits as a case study, we demonstrate that our method enables precise, bidirectional steering of model behavior while maintaining superior stability and performance compared to existing activation steering methods like Contrastive Activation Addition (CAA). We further identify an empirical effect, which we term Functional Faithfulness, whereby intervening on a specific internal feature induces coherent and predictable shifts across multiple linguistic dimensions aligned with the target semantic attribute. Our findings suggest that LLMs internalize deeply integrated representations of high-order concepts, and provide a novel, robust mechanistic path for the regulation of complex AI behaviors. + oai:arXiv.org:2601.02978v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ruikang Zhang, Shuo Wang, Qi Su + + + Developing and Evaluating Lightweight Cryptographic Algorithms for Secure Embedded Systems in IoT Devices + https://arxiv.org/abs/2601.02981 + arXiv:2601.02981v1 Announce Type: new +Abstract: The high rate of development of Internet of Things (IoT) devices has brought to attention new challenges in the area of data security, especially within the resource-limited realm of RFID tags, sensors, and embedded systems. Traditional cryptographic implementations can be of inappropriate computational complexity and energy usage and hence are not suitable on these platforms. This paper examines the design, implementation, and testing of lightweight cryptographic algorithms that have been specifically designed to be used in secure embedded systems. A comparison of some of the state-of-the-art lightweight encryption algorithms, that is PRESENT, SPECK, and SIMON, focuses on the main performance indicators, i.e., throughput, use of memory, and energy utilization. The study presents novel lightweight algorithms that are founded upon the Feistel-network architecture and their safety under cryptanalytic attacks, e.g., differential and linear cryptanalysis. The proposed solutions are proven through hardware implementation on the FPGA platform. The results have shown that lightweight cryptography is an effective strategy that could be used to establish security and maintain performance in the IoT and other resource-limited settings. + oai:arXiv.org:2601.02981v1 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Brahim Khalil Sedraoui, Abdelmadjid Benmachiche, Amina Makhlouf + + + Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning + https://arxiv.org/abs/2601.02983 + arXiv:2601.02983v1 Announce Type: new +Abstract: Recent advances in audio large language models (ALLMs) have made high-quality synthetic audio widely accessible, increasing the risk of malicious audio deepfakes across speech, environmental sounds, singing voice, and music. Real-world audio deepfake detection (ADD) therefore requires all-type detectors that generalize across heterogeneous audio and provide interpretable decisions. Given the strong multi-task generalization ability of ALLMs, we first investigate their performance on all-type ADD under both supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). However, SFT using only binary real/fake labels tends to reduce the model to a black-box classifier, sacrificing interpretability. Meanwhile, vanilla RFT under sparse supervision is prone to reward hacking and can produce hallucinated, ungrounded rationales. To address this, we propose an automatic annotation and polishing pipeline that constructs Frequency-Time structured chain-of-thought (CoT) rationales, producing ~340K cold-start demonstrations. Building on CoT data, we propose Frequency Time-Group Relative Policy Optimization (FT-GRPO), a two-stage training paradigm that cold-starts ALLMs with SFT and then applies GRPO under rule-based frequency-time constraints. Experiments demonstrate that FT-GRPO achieves state-of-the-art performance on all-type ADD while producing interpretable, FT-grounded rationales. The data and code are available online. + oai:arXiv.org:2601.02983v1 + cs.SD + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yuankun Xie, Xiaoxuan Guo, Jiayi Zhou, Tao Wang, Jian Liu, Ruibo Fu, Xiaopeng Wang, Haonan Cheng, Long Ye + + + Selfish Mining in Multi-Attacker Scenarios: An Empirical Evaluation of Nakamoto, Fruitchain, and Strongchain + https://arxiv.org/abs/2601.02984 + arXiv:2601.02984v1 Announce Type: new +Abstract: The aim of this work is to enhance blockchain security by deepening the understanding of selfish mining attacks in various consensus protocols, especially the ones that have the potential to mitigate selfish mining. Previous research was mainly focused on a particular protocol with a single selfish miner, while only limited studies have been conducted on two or more attackers. To address this gap, we proposed a stochastic simulation framework that enables analysis of selfish mining with multiple attackers across various consensus protocols. We created the model of Proof-of-Work (PoW) Nakamoto consensus (serving as the baseline) as well as models of two additional consensus protocols designed to mitigate selfish mining: Fruitchain and Strongchain. Using our framework, thresholds reported in the literature were verified, and several novel thresholds were discovered for 2 and more attackers. We made the source code of our framework available, enabling researchers to evaluate any newly added protocol with one or more selfish miners and cross-compare it with already modeled protocols. + oai:arXiv.org:2601.02984v1 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Martin Pere\v{s}\'ini, Tom\'a\v{s} Hladk\'y, Jakub Kub\'ik, Ivan Homoliak + + + P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist + https://arxiv.org/abs/2601.02986 + arXiv:2601.02986v1 Announce Type: new +Abstract: Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences. However, existing approaches largely treat user context as a static or implicit conditioning signal, failing to capture the dynamic and multi-faceted nature of human judgment. In this paper, we propose P-Check, a novel personalized reward modeling framework, designed to train a plug-and-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction. To better align these checklists with personalized nuances, we introduce Preference-Contrastive Criterion Weighting, a training strategy that assigns saliency scores to criteria based on their discriminative power for personalized judgment. We conduct extensive experiments and demonstrate that P-Check not only improves reward accuracy but also enhances downstream personalized generation, and remains robust in OOD scenarios. + oai:arXiv.org:2601.02986v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Kwangwook Seo, Dongha Lee + + + LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing + https://arxiv.org/abs/2601.02987 + arXiv:2601.02987v1 Announce Type: new +Abstract: Text-to-Image editing using diffusion models faces challenges in balancing content preservation with edit application and handling real-image editing. To address these, we propose LAMS-Edit, leveraging intermediate states from the inversion process--an essential step in real-image editing--during edited image generation. Specifically, latent representations and attention maps from both processes are combined at each step using weighted interpolation, controlled by a scheduler. This technique, Latent and Attention Mixing with Schedulers (LAMS), integrates with Prompt-to-Prompt (P2P) to form LAMS-Edit--an extensible framework that supports precise editing with region masks and enables style transfer via LoRA. Extensive experiments demonstrate that LAMS-Edit effectively balances content preservation and edit application. + oai:arXiv.org:2601.02987v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Wingwa Fu, Takayuki Okatani + + + ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation + https://arxiv.org/abs/2601.02988 + arXiv:2601.02988v1 Announce Type: new +Abstract: In this study, we present ULS+, an enhanced version of the Universal Lesion Segmentation (ULS) model. The original ULS model segments lesions across the whole body in CT scans given volumes of interest (VOIs) centered around a click-point. Since its release, several new public datasets have become available that can further improve model performance. ULS+ incorporates these additional datasets and uses smaller input image sizes, resulting in higher accuracy and faster inference. + We compared ULS and ULS+ using the Dice score and robustness to click-point location on the ULS23 Challenge test data and a subset of the Longitudinal-CT dataset. In all comparisons, ULS+ significantly outperformed ULS. Additionally, ULS+ ranks first on the ULS23 Challenge test-phase leaderboard. By maintaining a cycle of data-driven updates and clinical validation, ULS+ establishes a foundation for robust and clinically relevant lesion segmentation models. + oai:arXiv.org:2601.02988v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Rianne Weber, Niels Rocholl, Max de Grauw, Mathias Prokop, Ewoud Smit, Alessa Hering + + + Mechanistic Interpretability of Large-Scale Counting in LLMs through a System-2 Strategy + https://arxiv.org/abs/2601.02989 + arXiv:2601.02989v1 Announce Type: new +Abstract: Large language models (LLMs), despite strong performance on complex mathematical problems, exhibit systematic limitations in counting tasks. This issue arises from architectural limits of transformers, where counting is performed across layers, leading to degraded precision for larger counting problems due to depth constraints. To address this limitation, we propose a simple test-time strategy inspired by System-2 cognitive processes that decomposes large counting tasks into smaller, independent sub-problems that the model can reliably solve. We evaluate this approach using observational and causal mediation analyses to understand the underlying mechanism of this System-2-like strategy. Our mechanistic analysis identifies key components: latent counts are computed and stored in the final item representations of each part, transferred to intermediate steps via dedicated attention heads, and aggregated in the final stage to produce the total count. Experimental results demonstrate that this strategy enables LLMs to surpass architectural limitations and achieve high accuracy on large-scale counting tasks. This work provides mechanistic insight into System-2 counting in LLMs and presents a generalizable approach for improving and understanding their reasoning behavior. + oai:arXiv.org:2601.02989v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Hosein Hasani, Mohammadali Banayeeanzade, Ali Nafisi, Sadegh Mohammadian, Fatemeh Askari, Mobin Bagherian, Amirmohammad Izadi, Mahdieh Soleymani Baghshah + + + Towards Faithful Reasoning in Comics for Small MLLMs + https://arxiv.org/abs/2601.02991 + arXiv:2601.02991v1 Announce Type: new +Abstract: Comic-based visual question answering (CVQA) poses distinct challenges to multimodal large language models (MLLMs) due to its reliance on symbolic abstraction, narrative logic, and humor, which differ from conventional VQA tasks. Although Chain-of-Thought (CoT) prompting is widely used to enhance MLLM reasoning, surprisingly, its direct application to CVQA often degrades performance, especially in small-scale models. Our theoretical and empirical analyses reveal that standard CoT in CVQA suffers from state entanglement, spurious transitions, and exploration inefficiency, with small models particularly vulnerable in resource-constrained settings. To address these issues, we propose a novel comic reasoning framework, designed to produce more faithful and transferable reasoning chains in small MLLMs. Specifically, our framework combines modular CoT generation with GRPO-based reinforcement fine-tuning and a novel structured reward. Beyond comic VQA, we further evaluate our approach on a broader class of humor-centric and abstract visual reasoning tasks, including meme understanding and editorial cartoon interpretation. Across five challenging benchmarks, our 3B model outperforms state-of-the-art methods, and plug-in experiments yield an additional average improvement of $\mathbf{12.1\%}$ across different MLLMs. + oai:arXiv.org:2601.02991v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chengcheng Feng, Haojie Yin, Yucheng Jin, Kaizhu Huang + + + Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation + https://arxiv.org/abs/2601.02993 + arXiv:2601.02993v1 Announce Type: new +Abstract: Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in large language models (LLMs), yet little is known about how the order of retrieved documents affects model behavior. We empirically show that under Top-5 retrieval with the gold document included, LLM answers vary substantially across permutations of the retrieved set, even when the gold document is fixed in the first position. This reveals a previously underexplored sensitivity to retrieval permutations. Although robust RAG methods primarily focus on enhancing LLM robustness to low-quality retrieval and mitigating positional bias to distribute attention fairly over long contexts, neither approach directly addresses permutation sensitivity. In this paper, we propose Stable-RAG, which exploits permutation sensitivity estimation to mitigate permutation-induced hallucinations. Stable-RAG runs the generator under multiple retrieval orders, clusters hidden states, and decodes from a cluster-center representation that captures the dominant reasoning pattern. It then uses these reasoning results to align hallucinated outputs toward the correct answer, encouraging the model to produce consistent and accurate predictions across document permutations. Experiments on three QA datasets show that Stable-RAG significantly improves answer accuracy, reasoning consistency and robust generalization across datasets, retrievers, and input lengths compared with baselines. + oai:arXiv.org:2601.02993v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Qianchi Zhang, Hainan Zhang, Liang Pang, Hongwei Zheng, Zhiming Zheng + + + Learning to Act Robustly with View-Invariant Latent Actions + https://arxiv.org/abs/2601.02994 + arXiv:2601.02994v1 Announce Type: new +Abstract: Vision-based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view-invariant visual representations. This challenge becomes more pronounced in real-world settings, where viewpoint variability is unavoidable and can significantly disrupt policy performance. Existing methods typically learn invariance from multi-view observations at the scene level, but such approaches rely on visual appearance and fail to incorporate the physical dynamics essential for robust generalization. We propose View-Invariant Latent Action (VILA), which models a latent action capturing transition patterns across trajectories to learn view-invariant representations grounded in physical dynamics. VILA aligns these latent actions across viewpoints using an action-guided objective based on ground-truth action sequences. Experiments in both simulation and the real world show that VILA-based policies generalize effectively to unseen viewpoints and transfer well to new tasks, establishing VILA as a strong pretraining framework that improves robustness and downstream learning performance. + oai:arXiv.org:2601.02994v1 + cs.RO + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Youngjoon Jeong, Junha Chun, Taesup Kim + + + Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners + https://arxiv.org/abs/2601.02996 + arXiv:2601.02996v1 Announce Type: new +Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit chain-of-thought (CoT) explanations. However, recent work shows that LRMs often arrive at the correct answer before completing these textual reasoning steps, indicating the presence of latent reasoning -- internal, non-verbal computation encoded in hidden states. While this phenomenon has been explored in English, its multilingual behavior remains largely unknown. In this paper, we conduct a systematic investigation of multilingual latent reasoning in LRMs across 11 languages. Using a truncation-based strategy, we examine how the correct answer emerges as the model is given only partial reasoning traces, allowing us to measure stepwise latent prediction formation. Our results reveal clear evidence of multilingual latent reasoning, though unevenly: strong in resource-rich languages, weaker in low-resource ones, and broadly less observable on harder benchmarks. To understand whether these differences reflect distinct internal mechanisms, we further perform representational analyses. Despite surface-level disparities, we find that the internal evolution of predictions is highly consistent across languages and broadly aligns with English -- a pattern suggesting an English-centered latent reasoning pathway. + oai:arXiv.org:2601.02996v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yihong Liu, Raoyuan Zhao, Hinrich Sch\"utze, Michael A. Hedderich + + + From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures + https://arxiv.org/abs/2601.02997 + arXiv:2601.02997v1 Announce Type: new +Abstract: Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design--balancing syntactic reliability, performance, and structural novelty--remains underexplored. We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles. The model synthesizes PyTorch convolutional networks which are validated, evaluated via low-fidelity performance signals (single-epoch accuracy), and filtered using a MinHash-Jaccard criterion to prevent structural redundancy. High-performing, novel architectures are converted into prompt-code pairs for iterative fine-tuning via parameter-efficient LoRA adaptation, initialized from the LEMUR dataset. Across cycles, the LLM internalizes empirical architectural priors, becoming a robust generator. The valid generation rate stabilizes at 50.6 percent (peaking at 74.5 percent), while mean first-epoch accuracy rises from 28.06 percent to 50.99 percent, and the fraction of candidates exceeding 40 percent accuracy grows from 2.04 percent to 96.81 percent. Analyses confirm the model moves beyond replicating existing motifs, synthesizing 455 high-performing architectures absent from the original corpus. By grounding code synthesis in execution feedback, this work provides a scalable blueprint for transforming stochastic generators into autonomous, performance-driven neural designers, establishing that LLMs can internalize empirical, non-textual rewards to transcend their training data. + oai:arXiv.org:2601.02997v1 + cs.LG + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Waleed Khalid, Dmitry Ignatov, Radu Timofte + + + Multi-Distribution Robust Conformal Prediction + https://arxiv.org/abs/2601.02998 + arXiv:2601.02998v1 Announce Type: new +Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them. We study the problem of constructing a conformal prediction set that is uniformly valid across multiple, heterogeneous distributions, in the sense that no matter which distribution the test point is from, the coverage of the prediction set is guaranteed to exceed a pre-specified level. We first propose a max-p aggregation scheme that delivers finite-sample, multi-distribution coverage given any conformity scores associated with each distribution. Upon studying several efficiency optimization programs subject to uniform coverage, we prove the optimality and tightness of our aggregation scheme, and propose a general algorithm to learn conformity scores that lead to efficient prediction sets after the aggregation under standard conditions. We discuss how our framework relates to group-wise distributionally robust optimization, sub-population shift, fairness, and multi-source learning. In synthetic and real-data experiments, our method delivers valid worst-case coverage across multiple distributions while greatly reducing the set size compared with naively applying max-p aggregation to single-source conformity scores, and can be comparable in size to single-source prediction sets with popular, standard conformity scores. + oai:arXiv.org:2601.02998v1 + cs.LG + stat.ME + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yuqi Yang, Ying Jin + + + Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection + https://arxiv.org/abs/2601.03001 + arXiv:2601.03001v1 Announce Type: new +Abstract: Vehicle-Infrastructure Collaborative Perception (VICP) is pivotal for resolving occlusion in autonomous driving, yet the trade-off between communication bandwidth and feature redundancy remains a critical bottleneck. While intermediate fusion mitigates data volume compared to raw sharing, existing frameworks typically rely on spatial compression or static confidence maps, which inefficiently transmit spatially redundant features from non-critical background regions. To address this, we propose Risk-intent Selective detection (RiSe), an interaction-aware framework that shifts the paradigm from identifying visible regions to prioritizing risk-critical ones. Specifically, we introduce a Potential Field-Trajectory Correlation Model (PTCM) grounded in potential field theory to quantitatively assess kinematic risks. Complementing this, an Intention-Driven Area Prediction Module (IDAPM) leverages ego-motion priors to proactively predict and filter key Bird's-Eye-View (BEV) areas essential for decision-making. By integrating these components, RiSe implements a semantic-selective fusion scheme that transmits high-fidelity features only from high-interaction regions, effectively acting as a feature denoiser. Extensive experiments on the DeepAccident dataset demonstrate that our method reduces communication volume to 0.71\% of full feature sharing while maintaining state-of-the-art detection accuracy, establishing a competitive Pareto frontier between bandwidth efficiency and perception performance. + oai:arXiv.org:2601.03001v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Li Wang, Boqi Li, Hang Chen, Xingjian Wu, Yichen Wang, Jiewen Tan, Xinyu Zhang, Huaping Liu + + + Closed-Loop Transmission Power Control for Reliable and Low-Power BLE Communication in Dynamic IoT Settings + https://arxiv.org/abs/2601.03003 + arXiv:2601.03003v1 Announce Type: new +Abstract: Reliable and energy-efficient Bluetooth Low Energy (BLE) communication is crucial for Internet of Things (IoT) applications in dynamic environments. However, the Received Signal Strength Indicator (RSSI) and data throughput in BLE are highly susceptible to environmental variability, which degrades communication performance. In this work, we systematically analyze the interdependence among RSSI, throughput, transmission power (TXP), and the peripheral device system power consumption under diverse real-world conditions. We observe that adjusting the TXP effectively influences both RSSI and throughput. We propose a robust closed-loop TXP control framework based on Proportional-Integral-Derivative (PID) controllers. Two initial control strategies are investigated: an RSSI-based approach and a throughput-based approach, each exhibiting distinct advantages and limitations. The RSSI-based method provides rapid responsiveness to signal fluctuations but lacks direct correlation with data throughput, whereas the throughput-based method offers more accurate feedback on effective throughput at the cost of slower response. To address these limitations, a hybrid RSSI-throughput control strategy is developed, combining the responsiveness of RSSI feedback with the accuracy of throughput measurements. Experimental results demonstrate that the proposed hybrid approach maintains data throughput close to the target level with minimal variance, even under rapidly changing environmental conditions. + oai:arXiv.org:2601.03003v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/JIOT.2025.3627414 + IEEE Internet of Things Journal ( Volume: 13, Issue: 1, 01 January 2026) + Ziyao Zhou, Hen-Wei Huang + + + JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification + https://arxiv.org/abs/2601.03005 + arXiv:2601.03005v1 Announce Type: new +Abstract: Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a promising defense by erasing specific harmful parameters, current methods remain vulnerable to diverse jailbreaks. We first conduct an empirical study and discover that this failure mechanism is caused by jailbreaks primarily activating non-erased parameters in the intermediate layers. Further, by probing the underlying mechanism through which these circumvented parameters reassemble into the prohibited output, we verify the persistent existence of dynamic $\textbf{jailbreak paths}$ and show that the inability to rectify them constitutes the fundamental gap in existing unlearning defenses. To bridge this gap, we propose $\textbf{J}$ailbreak $\textbf{P}$ath $\textbf{U}$nlearning (JPU), which is the first to rectify dynamic jailbreak paths towards safety anchors by dynamically mining on-policy adversarial samples to expose vulnerabilities and identify jailbreak paths. Extensive experiments demonstrate that JPU significantly enhances jailbreak resistance against dynamic attacks while preserving the model's utility. + oai:arXiv.org:2601.03005v1 + cs.CR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Zhaoye Li, Bin Ji, Baosheng Wang, Jie Yu + + + From inconsistency to decision: explainable operation and maintenance of battery energy storage systems + https://arxiv.org/abs/2601.03007 + arXiv:2601.03007v1 Announce Type: new +Abstract: Battery Energy Storage Systems (BESSs) are increasingly critical to power-system stability, yet their operation and maintenance remain dominated by reactive, expert-dependent diagnostics. While cell-level inconsistencies provide early warning signals of degradation and safety risks, the lack of scalable and interpretable decision-support frameworks prevents these signals from being effectively translated into operational actions. Here we introduce an inconsistency-driven operation and maintenance paradigm for large-scale BESSs that systematically transforms routine monitoring data into explainable, decision-oriented guidance. The proposed framework integrates multi-dimensional inconsistency evaluation with large language model-based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions. Using eight months of field data from an in-service battery system comprising 3,564 cells, we demonstrate how electrical, thermal, and aging-related inconsistencies can be distilled into structured operational records and converted into actionable maintenance insights through a multi-agent framework. The proposed approach enables accurate and explainable responses to real-world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert-driven practices. These results establish a scalable pathway for intelligent operation and maintenance of battery energy storage systems, with direct implications for reliability, safety, and cost-effective integration of energy storage into modern power systems. + oai:arXiv.org:2601.03007v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jingbo Qu, Yijie Wang, Yujie Fu, Putai Zhang, Weihan Li, Mian Li + + + A Dataset of Low-Rated Applications from the Amazon Appstore for User Feedback Analysis + https://arxiv.org/abs/2601.03009 + arXiv:2601.03009v1 Announce Type: new +Abstract: In todays digital landscape, end-user feedback plays a crucial role in the evolution of software applications, particularly in addressing issues that hinder user experience. While much research has focused on high-rated applications, low-rated applications often remain unexplored, despite their potential to reveal valuable insights. This study introduces a novel dataset curated from 64 low-rated applications sourced from the Amazon Software Appstore (ASA), containing 79,821 user reviews. The dataset is designed to capture the most frequent issues identified by users, which are critical for improving software quality. To further enhance the dataset utility, a subset of 6000 reviews was manually annotated to classify them into six district issue categories: user interface (UI) and user experience (UX), functionality and features, compatibility and device specificity, performance and stability, customer support and responsiveness, and security and privacy issues. This annotated dataset is a valuable resource for developing machine learning-based approaches aiming to automate the classification of user feedback into various issue types. Making both the annotated and raw datasets publicly available provides researchers and developers with a crucial tool to understand common issues in low-rated apps and inform software improvements. The comprehensive analysis and availability of this dataset lay the groundwork for data-derived solutions to improve software quality based on user feedback. Additionally, the dataset can provide opportunities for software vendors and researchers to explore various software evolution-related activities, including frequently missing features, sarcasm, and associated emotions, which will help better understand the reasons for comparatively low app ratings. + oai:arXiv.org:2601.03009v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nek Dil Khan, Javed Ali Khan, Darvesh Khan, Jianqiang Li, Mumrez Khan, Shah Fahad Khan + + + Mathematical aspects of registration methods in bounded domains + https://arxiv.org/abs/2601.03010 + arXiv:2601.03010v1 Announce Type: new +Abstract: Registration methods in bounded domains have received significant attention in the model reduction literature, as a valuable tool for nonlinear approximation. The aim of this work is to provide a concise yet complete overview of relevant results for registration methods in $n$-dimensional domains, from the perspective of parametric model reduction. We present a thorough analysis of two classes of methods, vector flows and compositional maps: we discuss the enforcement of the bijectivity constraint and we comment on the approximation properties of the two methods, for Lipschitz $n$-dimensional domains. + oai:arXiv.org:2601.03010v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Angelo Iollo, Jon Labatut, Pierre Mounoud, Tommaso Taddei + + + ReCCur: A Recursive Corner-Case Curation Framework for Robust Vision-Language Understanding in Open and Edge Scenarios + https://arxiv.org/abs/2601.03011 + arXiv:2601.03011v1 Announce Type: new +Abstract: Corner cases are rare or extreme scenarios that drive real-world failures, but they are difficult to curate at scale: web data are noisy, labels are brittle, and edge deployments preclude large retraining. We present ReCCur (Recursive Corner-Case Curation), a low-compute framework that converts noisy web imagery into auditable fine-grained labels via a multi-agent recursive pipeline. First, large-scale data acquisition and filtering expands a domain vocabulary with a vision-language model (VLM), crawls the web, and enforces tri-modal (image, description, keyword) consistency with light human spot checks to yield refined candidates. Next, mixture-of-experts knowledge distillation uses complementary encoders (e.g., CLIP, DINOv2, BEiT) for kNN voting with dual-confidence activation and uncertainty sampling, converging to a high-precision set. Finally, region-evidence VLM adversarial labeling pairs a proposer (multi-granularity regions and semantic cues) with a validator (global and local chained consistency) to produce explainable labels and close the loop. On realistic corner-case scenarios (e.g., flooded-car inspection), ReCCur runs on consumer-grade GPUs, steadily improves purity and separability, and requires minimal human supervision, providing a practical substrate for downstream training and evaluation under resource constraints. Code and dataset will be released. + oai:arXiv.org:2601.03011v1 + cs.CV + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Yihan Wei, Shenghai Yuan, Tianchen Deng, Boyang Lou, Enwen Hu + + + LLMs, You Can Evaluate It! Design of Multi-perspective Report Evaluation for Security Operation Centers + https://arxiv.org/abs/2601.03013 + arXiv:2601.03013v1 Announce Type: new +Abstract: Security operation centers (SOCs) often produce analysis reports on security incidents, and large language models (LLMs) will likely be used for this task in the near future. We postulate that a better understanding of how veteran analysts evaluate reports, including their feedback, can help produce analysis reports in SOCs. In this paper, we aim to leverage LLMs for analysis reports. To this end, we first construct a Analyst-wise checklist to reflect SOC practitioners' opinions for analysis report evaluation through literature review and user study with SOC practitioners. Next, we design a novel LLM-based conceptual framework, named MESSALA, by further introducing two new techniques, granularization guideline and multi-perspective evaluation. MESSALA can maximize report evaluation and provide feedback on veteran SOC practitioners' perceptions. When we conduct extensive experiments with MESSALA, the evaluation results by MESSALA are the closest to those of veteran SOC practitioners compared with the existing LLM-based methods. We then show two key insights. We also conduct qualitative analysis with MESSALA, and then identify that MESSALA can provide actionable items that are necessary for improving analysis reports. + oai:arXiv.org:2601.03013v1 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hiroyuki Okada, Tatsumi Oba, Naoto Yanai + + + SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering + https://arxiv.org/abs/2601.03014 + arXiv:2601.03014v1 Announce Type: new +Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. Specifically, we construct a hierarchical sentence graph offline by first adapting Rhetorical Structure Theory to distinguish nucleus and satellite sentences, and then organizing them into topic-level subgraphs with cross-document entity bridges. During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence. Extensive experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of SentGraph, validating the importance of explicitly modeling sentence-level logical dependencies for multi-hop reasoning. + oai:arXiv.org:2601.03014v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Junli Liang, Pengfei Zhou, Wangqiu Zhou, Wenjie Qing, Qi Zhao, Ziwen Wang, Qi Song, Xiangyang Li + + + In-Context Reinforcement Learning through Bayesian Fusion of Context and Value Prior + https://arxiv.org/abs/2601.03015 + arXiv:2601.03015v1 Announce Type: new +Abstract: In-context reinforcement learning (ICRL) promises fast adaptation to unseen environments without parameter updates, but current methods either cannot improve beyond the training distribution or require near-optimal data, limiting practical adoption. We introduce SPICE, a Bayesian ICRL method that learns a prior over Q-values via deep ensemble and updates this prior at test-time using in-context information through Bayesian updates. To recover from poor priors resulting from training on sub-optimal data, our online inference follows an Upper-Confidence Bound rule that favours exploration and adaptation. We prove that SPICE achieves regret-optimal behaviour in both stochastic bandits and finite-horizon MDPs, even when pretrained only on suboptimal trajectories. We validate these findings empirically across bandit and control benchmarks. SPICE achieves near-optimal decisions on unseen tasks, substantially reduces regret compared to prior ICRL and meta-RL approaches while rapidly adapting to unseen tasks and remaining robust under distribution shift. + oai:arXiv.org:2601.03015v1 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Ana\"is Berkes, Vincent Taboga, Donna Vakalis, David Rolnick, Yoshua Bengio + + + MMFormalizer: Multimodal Autoformalization in the Wild + https://arxiv.org/abs/2601.03017 + arXiv:2601.03017v1 Announce Type: new +Abstract: Autoformalization, which translates natural language mathematics into formal statements to enable machine reasoning, faces fundamental challenges in the wild due to the multimodal nature of the physical world, where physics requires inferring hidden constraints (e.g., mass or energy) from visual elements. To address this, we propose MMFormalizer, which extends autoformalization beyond text by integrating adaptive grounding with entities from real-world mathematical and physical domains. MMFormalizer recursively constructs formal propositions from perceptually grounded primitives through recursive grounding and axiom composition, with adaptive recursive termination ensuring that every abstraction is supported by visual evidence and anchored in dimensional or axiomatic grounding. We evaluate MMFormalizer on a new benchmark, PhyX-AF, comprising 115 curated samples from MathVerse, PhyX, Synthetic Geometry, and Analytic Geometry, covering diverse multimodal autoformalization tasks. Results show that frontier models such as GPT-5 and Gemini-3-Pro achieve the highest compile and semantic accuracy, with GPT-5 excelling in physical reasoning, while geometry remains the most challenging domain. Overall, MMFormalizer provides a scalable framework for unified multimodal autoformalization, bridging perception and formal reasoning. To the best of our knowledge, this is the first multimodal autoformalization method capable of handling classical mechanics (derived from the Hamiltonian), as well as relativity, quantum mechanics, and thermodynamics. More details are available on our project page: MMFormalizer.github.io + oai:arXiv.org:2601.03017v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Jing Xiong, Qi Han, Yunta Hsieh, Hui Shen, Huajian Xin, Chaofan Tao, Chenyang Zhao, Hengyuan Zhang, Taiqiang Wu, Zhen Zhang, Haochen Wang, Zhongwei Wan, Lingpeng Kong, Ngai Wong + + + Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis + https://arxiv.org/abs/2601.03018 + arXiv:2601.03018v1 Announce Type: new +Abstract: While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which require reasoning over complex, non-monotonic symptom trajectories across multiple visits. Standard supervised training lacks explicit annotations for symptom evolution, while direct Reinforcement Learning (RL) is hindered by sparse binary rewards. To address this challenge, we introduce Dementia-R1, an RL-based framework for longitudinal dementia prognosis from unstructured clinical notes. Our approach adopts a Cold-Start RL strategy that pre-trains the model to predict verifiable clinical indices extracted from patient histories, enhancing the capability to reason about disease progression before determining the final clinical status. Extensive experiments demonstrate that Dementia-R1 achieves an F1 score of 77.03% on real-world unstructured clinical datasets. Notably, on the ADNI benchmark, our 7B model rivals GPT-4o, effectively capturing fluctuating cognitive trajectories. Code is available at https://anonymous.4open.science/r/dementiar1-CDB5 + oai:arXiv.org:2601.03018v1 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Choonghan Kim, Hyunmin Hwang, Hangeol Chang, Jaemin Kim, Jinse Park, Jae-Sung Lim, Jong Chul Ye + + + Hardness of Regular Expression Matching with Extensions + https://arxiv.org/abs/2601.03020 + arXiv:2601.03020v1 Announce Type: new +Abstract: The regular expression matching problem asks whether a given regular expression of length $m$ matches a given string of length $n$. As is well known, the problem can be solved in $O(nm)$ time using Thompson's algorithm. Moreover, recent studies have shown that the matching problem for regular expressions extended with a practical extension called lookaround can be solved in the same time complexity. In this work, we consider three well-known extensions to regular expressions called backreference, intersection and complement, and we show that, unlike in the case of lookaround, the matching problem for regular expressions extended with any of the three (for backreference, even when restricted to one capturing group) cannot be solved in $O(n^{2-\varepsilon} \mathrm{poly}(m))$ time for any constant $\varepsilon > 0$ under the Orthogonal Vectors Conjecture. Moreover, we study the matching problem for regular expressions extended with complement in more detail, which is also known as extended regular expression (ERE) matching. We show that there is no ERE matching algorithm that runs in $O(n^{\omega-\varepsilon} \mathrm{poly}(m))$ time ($2 \le \omega < 2.3716$ is the exponent of square matrix multiplication) for any constant $\varepsilon > 0$ under the $k$-Clique Hypothesis, and there is no combinatorial ERE matching algorithm that runs in $O(n^{3-\varepsilon} \mathrm{poly}(m))$ time for any constant $\varepsilon > 0$ under the Combinatorial $k$-Clique Hypothesis. This shows that the $O(n^3 m)$-time algorithm introduced by Hopcroft and Ullman in 1979 and recently improved by Bille et al. to run in $O(n^\omega m)$ time using fast matrix multiplication was already optimal in a sense, and sheds light on why the theoretical computer science community has struggled to improve the time complexity of ERE matching with respect to $n$ and $m$ for more than 45 years. + oai:arXiv.org:2601.03020v1 + cs.DS + cs.CC + cs.FL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Taisei Nogami, Tachio Terauchi + + + MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models + https://arxiv.org/abs/2601.03023 + arXiv:2601.03023v1 Announce Type: new +Abstract: Medical conversational AI (AI) plays a pivotal role in the development of safer and more effective medical dialogue systems. However, existing benchmarks and evaluation frameworks for assessing the information-gathering and diagnostic reasoning abilities of medical large language models (LLMs) have not been rigorously evaluated. To address these gaps, we present MedDialogRubrics, a novel benchmark comprising 5,200 synthetically constructed patient cases and over 60,000 fine-grained evaluation rubrics generated by LLMs and subsequently refined by clinical experts, specifically designed to assess the multi-turn diagnostic capabilities of LLM. Our framework employs a multi-agent system to synthesize realistic patient records and chief complaints from underlying disease knowledge without accessing real-world electronic health records, thereby mitigating privacy and data-governance concerns. We design a robust Patient Agent that is limited to a set of atomic medical facts and augmented with a dynamic guidance mechanism that continuously detects and corrects hallucinations throughout the dialogue, ensuring internal coherence and clinical plausibility of the simulated cases. Furthermore, we propose a structured LLM-based and expert-annotated rubric-generation pipeline that retrieves Evidence-Based Medicine (EBM) guidelines and utilizes the reject sampling to derive a prioritized set of rubric items ("must-ask" items) for each case. We perform a comprehensive evaluation of state-of-the-art models and demonstrate that, across multiple assessment dimensions, current models face substantial challenges. Our results indicate that improving medical dialogue will require advances in dialogue management architectures, not just incremental tuning of the base-model. + oai:arXiv.org:2601.03023v1 + cs.CL + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Lecheng Gong, Weimin Fang, Ting Yang, Dongjie Tao, Chunxiao Guo, Peng Wei, Bo Xie, Jinqun Guan, Zixiao Chen, Fang Shi, Jinjie Gu, Junwei Liu + + + SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection + https://arxiv.org/abs/2601.03024 + arXiv:2601.03024v1 Announce Type: new +Abstract: We propose Self-Augmented Residual 3D Gaussian Splatting (SA-ResGS), a novel framework to stabilize uncertainty quantification and enhancing uncertainty-aware supervision in next-best-view (NBV) selection for active scene reconstruction. SA-ResGS improves both the reliability of uncertainty estimates and their effectiveness for supervision by generating Self-Augmented point clouds (SA-Points) via triangulation between a training view and a rasterized extrapolated view, enabling efficient scene coverage estimation. While improving scene coverage through physically guided view selection, SA-ResGS also addresses the challenge of under-supervised Gaussians, exacerbated by sparse and wide-baseline views, by introducing the first residual learning strategy tailored for 3D Gaussian Splatting. This targeted supervision enhances gradient flow in high-uncertainty Gaussians by combining uncertainty-driven filtering with dropout- and hard-negative-mining-inspired sampling. Our contributions are threefold: (1) a physically grounded view selection strategy that promotes efficient and uniform scene coverage; (2) an uncertainty-aware residual supervision scheme that amplifies learning signals for weakly contributing Gaussians, improving training stability and uncertainty estimation across scenes with diverse camera distributions; (3) an implicit unbiasing of uncertainty quantification as a consequence of constrained view selection and residual supervision, which together mitigate conflicting effects of wide-baseline exploration and sparse-view ambiguity in NBV planning. Experiments on active view selection demonstrate that SA-ResGS outperforms state-of-the-art baselines in both reconstruction quality and view selection robustness. + oai:arXiv.org:2601.03024v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Kim Jun-Seong, Tae-Hyun Oh, Eduardo P\'erez-Pellitero, Youngkyoon Jang + + + LittiChoQA: Literary Texts in Indic Languages Chosen for Question Answering + https://arxiv.org/abs/2601.03025 + arXiv:2601.03025v1 Announce Type: new +Abstract: Long-context question answering (QA) over literary texts poses significant challenges for modern large language models, particularly in low-resource languages. We address the scarcity of long-context QA resources for Indic languages by introducing LittiChoQA, the largest literary QA dataset to date covering many languages spoken in the Gangetic plains of India. The dataset comprises over 270K automatically generated question-answer pairs with a balanced distribution of factoid and non-factoid questions, generated from naturally authored literary texts collected from the open web. We evaluate multiple multilingual LLMs on non-factoid, abstractive QA, under both full-context and context-shortened settings. Results demonstrate a clear trade-off between performance and efficiency: full-context fine-tuning yields the highest token-level and semantic-level scores, while context shortening substantially improves throughput. Among the evaluated models, Krutrim-2 achieves the strongest performance, obtaining a semantic score of 76.1 with full context. While, in shortened context settings it scores 74.9 with answer paragraph selection and 71.4 with vector-based retrieval. Qualitative evaluations further corroborate these findings. + oai:arXiv.org:2601.03025v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Aarya Khandelwal, Ritwik Mishra, Rajiv Ratn Shah + + + Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning + https://arxiv.org/abs/2601.03027 + arXiv:2601.03027v1 Announce Type: new +Abstract: Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. We introduce F-DPO (Factuality-aware Direct Preference Optimization), a simple extension of DPO that uses only binary factuality labels. F-DPO (i) applies a label-flipping transformation that corrects misordered preference pairs so the chosen response is never less factual than the rejected one, and (ii) adds a factuality-aware margin that emphasizes pairs with clear correctness differences, while reducing to standard DPO when both responses share the same factuality. We construct factuality-aware preference data by augmenting DPO pairs with binary factuality indicators and synthetic hallucinated variants. Across seven open-weight LLMs (1B-14B), F-DPO consistently improves factuality and reduces hallucination rates relative to both base models and standard DPO. On Qwen3-8B, F-DPO reduces hallucination rates by five times (from 0.424 to 0.084) while improving factuality scores by 50 percent (from 5.26 to 7.90). F-DPO also generalizes to out-of-distribution benchmarks: on TruthfulQA, Qwen2.5-14B achieves plus 17 percent MC1 accuracy (0.500 to 0.585) and plus 49 percent MC2 accuracy (0.357 to 0.531). F-DPO requires no auxiliary reward model, token-level annotations, or multi-stage training. + oai:arXiv.org:2601.03027v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Sindhuja Chaduvula, Ahmed Y. Radwan, Azib Farooq, Yani Ioannou, Shaina Raza + + + Flow Matching and Diffusion Models via PointNet for Generating Fluid Fields on Irregular Geometries + https://arxiv.org/abs/2601.03030 + arXiv:2601.03030v1 Announce Type: new +Abstract: We present two novel generative geometric deep learning frameworks, termed Flow Matching PointNet and Diffusion PointNet, for predicting fluid flow variables on irregular geometries by incorporating PointNet into flow matching and diffusion models, respectively. In these frameworks, a reverse generative process reconstructs physical fields from standard Gaussian noise conditioned on unseen geometries. The proposed approaches operate directly on point-cloud representations of computational domains (e.g., grid vertices of finite-volume meshes) and therefore avoid the limitations of pixelation used to project geometries onto uniform lattices. In contrast to graph neural network-based diffusion models, Flow Matching PointNet and Diffusion PointNet do not exhibit high-frequency noise artifacts in the predicted fields. Moreover, unlike such approaches, which require auxiliary intermediate networks to condition geometry, the proposed frameworks rely solely on PointNet, resulting in a simple and unified architecture. The performance of the proposed frameworks is evaluated on steady incompressible flow past a cylinder, using a geometric dataset constructed by varying the cylinder's cross-sectional shape and orientation across samples. The results demonstrate that Flow Matching PointNet and Diffusion PointNet achieve more accurate predictions of velocity and pressure fields, as well as lift and drag forces, and exhibit greater robustness to incomplete geometries compared to a vanilla PointNet with the same number of trainable parameters. + oai:arXiv.org:2601.03030v1 + cs.CV + cs.LG + physics.comp-ph + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ali Kashefi + + + FlexProofs: A Vector Commitment with Flexible Linear Time for Computing All Proofs + https://arxiv.org/abs/2601.03031 + arXiv:2601.03031v1 Announce Type: new +Abstract: In this paper, we introduce FlexProofs, a new vector commitment (VC) scheme that achieves two key properties: (1) the prover can generate all individual opening proofs for a vector of size $N$ in optimal time ${\cal O}(N)$, and there is a flexible batch size parameter $b$ that can be increased to further reduce the time to generate all proofs; and (2) the scheme is directly compatible with a family of zkSNARKs that encode their input as a multi-linear polynomial. As a critical building block, we propose the first functional commitment (FC) scheme for multi-exponentiations with batch opening. Compared with HydraProofs, the only existing VC scheme that computes all proofs in optimal time ${\cal O}(N)$ and is directly compatible with zkSNARKs, FlexProofs may speed up the process of generating all proofs, if the parameter $b$ is properly chosen. Our experiments show that for $N=2^{16}$ and $b=\log^2 N$, FlexProofs can be $6\times$ faster than HydraProofs. Moreover, when combined with suitable zkSNARKs, FlexProofs enable practical applications such as verifiable secret sharing and verifiable robust aggregation. + oai:arXiv.org:2601.03031v1 + cs.CR + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jing Liu, Liang Feng Zhang + + + Causal Manifold Fairness: Enforcing Geometric Invariance in Representation Learning + https://arxiv.org/abs/2601.03032 + arXiv:2601.03032v1 Announce Type: new +Abstract: Fairness in machine learning is increasingly critical, yet standard approaches often treat data as static points in a high-dimensional space, ignoring the underlying generative structure. We posit that sensitive attributes (e.g., race, gender) do not merely shift data distributions but causally warp the geometry of the data manifold itself. To address this, we introduce Causal Manifold Fairness (CMF), a novel framework that bridges causal inference and geometric deep learning. CMF learns a latent representation where the local Riemannian geometry, defined by the metric tensor and curvature, remains invariant under counterfactual interventions on sensitive attributes. By enforcing constraints on the Jacobian and Hessian of the decoder, CMF ensures that the rules of the latent space (distances and shapes) are preserved across demographic groups. We validate CMF on synthetic Structural Causal Models (SCMs), demonstrating that it effectively disentangles sensitive geometric warping while preserving task utility, offering a rigorous quantification of the fairness-utility trade-off via geometric metrics. + oai:arXiv.org:2601.03032v1 + cs.LG + cs.AI + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Vidhi Rathore + + + NorwAI's Large Language Models: Technical Report + https://arxiv.org/abs/2601.03034 + arXiv:2601.03034v1 Announce Type: new +Abstract: Norwegian, spoken by approximately five million people, remains underrepresented in many of the most significant breakthroughs in Natural Language Processing (NLP). To address this gap, the NorLLM team at NorwAI has developed a family of models specifically tailored to Norwegian and other Scandinavian languages, building on diverse Transformer-based architectures such as GPT, Mistral, Llama2, Mixtral and Magistral. These models are either pretrained from scratch or continually pretrained on 25B - 88.45B tokens, using a Norwegian-extended tokenizer and advanced post-training strategies to optimize performance, enhance robustness, and improve adaptability across various real-world tasks. Notably, instruction-tuned variants (e.g., Mistral-7B-Instruct and Mixtral-8x7B-Instruct) showcase strong assistant-style capabilities, underscoring their potential for practical deployment in interactive and domain-specific applications. The NorwAI large language models are openly available to Nordic organizations, companies and students for both research and experimental use. This report provides detailed documentation of the model architectures, training data, tokenizer design, fine-tuning strategies, deployment, and evaluations. + oai:arXiv.org:2601.03034v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jon Atle Gulla, Peng Liu, Lemei Zhang + + + A Bi-directional Adaptive Framework for Agile UAV Landing + https://arxiv.org/abs/2601.03037 + arXiv:2601.03037v1 Announce Type: new +Abstract: Autonomous landing on mobile platforms is crucial for extending quadcopter operational flexibility, yet conventional methods are often too inefficient for highly dynamic scenarios. The core limitation lies in the prevalent ``track-then-descend'' paradigm, which treats the platform as a passive target and forces the quadcopter to perform complex, sequential maneuvers. This paper challenges that paradigm by introducing a bi-directional cooperative landing framework that redefines the roles of the vehicle and the platform. The essential innovation is transforming the problem from a single-agent tracking challenge into a coupled system optimization. Our key insight is that the mobile platform is not merely a target, but an active agent in the landing process. It proactively tilts its surface to create an optimal, stable terminal attitude for the approaching quadcopter. This active cooperation fundamentally breaks the sequential model by parallelizing the alignment and descent phases. Concurrently, the quadcopter's planning pipeline focuses on generating a time-optimal and dynamically feasible trajectory that minimizes energy consumption. This bi-directional coordination allows the system to execute the recovery in an agile manner, characterized by aggressive trajectory tracking and rapid state synchronization within transient windows. The framework's effectiveness, validated in dynamic scenarios, significantly improves the efficiency, precision, and robustness of autonomous quadrotor recovery in complex and time-constrained missions. + oai:arXiv.org:2601.03037v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chunhui Zhao, Xirui Kao, Yilin Lu, Yang Lyu + + + Validating Generalist Robots with Situation Calculus and STL Falsification + https://arxiv.org/abs/2601.03038 + arXiv:2601.03038v1 Announce Type: new +Abstract: Generalist robots are becoming a reality, capable of interpreting natural language instructions and executing diverse operations. However, their validation remains challenging because each task induces its own operational context and correctness specification, exceeding the assumptions of traditional validation methods. We propose a two-layer validation framework that combines abstract reasoning with concrete system falsification. At the abstract layer, situation calculus models the world and derives weakest preconditions, enabling constraint-aware combinatorial testing to systematically generate diverse, semantically valid world-task configurations with controllable coverage strength. At the concrete layer, these configurations are instantiated for simulation-based falsification with STL monitoring. Experiments on tabletop manipulation tasks show that our framework effectively uncovers failure cases in the NVIDIA GR00T controller, demonstrating its promise for validating general-purpose robot autonomy. + oai:arXiv.org:2601.03038v1 + cs.RO + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Changwen Li, Rongjie Yan, Chih-Hong Cheng, Jian Zhang + + + PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms + https://arxiv.org/abs/2601.03040 + arXiv:2601.03040v1 Announce Type: new +Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information. In these challenging environments, the platform must rely exclusively on inertial sensors, leading to pure inertial navigation. However, the inherent noise and other error terms of the inertial sensors in such real-world scenarios will cause the navigation solution to drift over time. Although conventional deep-learning models have emerged as a possible approach to inertial navigation, they are inherently black-box in nature. Furthermore, they struggle to learn effectively with limited supervised sensor data and often fail to preserve physical principles. To address these limitations, we propose PiDR, a physics-informed inertial dead-reckoning framework for autonomous platforms in situations of pure inertial navigation. PiDR offers transparency by explicitly integrating inertial navigation principles into the network training process through the physics-informed residual component. PiDR plays a crucial role in mitigating abrupt trajectory deviations even under limited or sparse supervision. We evaluated PiDR on real-world datasets collected by a mobile robot and an autonomous underwater vehicle. We obtained more than 29% positioning improvement in both datasets, demonstrating the ability of PiDR to generalize different platforms operating in various environments and dynamics. Thus, PiDR offers a robust, lightweight, yet effective architecture and can be deployed on resource-constrained platforms, enabling real-time pure inertial navigation in adverse scenarios. + oai:arXiv.org:2601.03040v1 + cs.RO + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Arup Kumar Sahoo, Itzik Klein + + + BaseCal: Unsupervised Confidence Calibration via Base Model Signals + https://arxiv.org/abs/2601.03042 + arXiv:2601.03042v1 Announce Type: new +Abstract: Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust with severe overconfidence. In contrast, we observe that their corresponding base LLMs often remain well-calibrated. This naturally motivates us to calibrate PoLLM confidence using the base LLM as a reference. This work proposes two ways to achieve this. A straightforward solution, BaseCal-ReEval, evaluates PoLLM's responses by feeding them into the base LLM to get average probabilities as confidence. While effective, this approach introduces additional inference overhead. To address this, we propose BaseCal-Proj, which trains a lightweight projection to map the final-layer hidden states of PoLLMs back to those of their base LLMs. These projected states are then processed by the base LLM's output layer to derive base-calibrated confidence for PoLLM's responses. Notably, BaseCal is an unsupervised, plug-and-play solution that operates without human labels or LLM modifications. Experiments across five datasets and three LLM families demonstrate the effectiveness of BaseCal, reducing Expected Calibration Error (ECE) by an average of 42.90\% compared to the best unsupervised baselines. + oai:arXiv.org:2601.03042v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, Xueqi Cheng + + + Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage + https://arxiv.org/abs/2601.03043 + arXiv:2601.03043v1 Announce Type: new +Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency. Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. To reduce time and memory complexity in the decode stage, a line of work introduces sparse-attention algorithms. In this paper, we show, both empirically and theoretically, that sparse attention can paradoxically increase end-to-end complexity: information loss often induces significantly longer sequences, a phenomenon we term ``Less is Less'' (Lil). To mitigate the Lil problem, we propose an early-stopping algorithm that detects the threshold where information loss exceeds information gain during sparse decoding. Our early-stopping algorithm reduces token consumption by up to 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. + oai:arXiv.org:2601.03043v1 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie + + + SOP: A Scalable Online Post-Training System for Vision-Language-Action Models + https://arxiv.org/abs/2601.03044 + arXiv:2601.03044v1 Announce Type: new +Abstract: Vision-language-action (VLA) models achieve strong generalization through large-scale pre-training, but real-world deployment requires expert-level task proficiency in addition to broad generality. Existing post-training approaches for VLA models are typically offline, single-robot, or task-specific, limiting effective on-policy adaptation and scalable learning from real-world interaction. We introduce a Scalable Online Post-training (SOP) system that enables online, distributed, multi-task post-training of generalist VLA models directly in the physical world. SOP tightly couples execution and learning through a closed-loop architecture in which a fleet of robots continuously streams on-policy experience and human intervention signals to a centralized cloud learner, and asynchronously receives updated policies. This design supports prompt on-policy correction, scales experience collection through parallel deployment, and preserves generality during adaptation. SOP is agnostic to the choice of post-training algorithm; we instantiate it with both interactive imitation learning (HG-DAgger) and reinforcement learning (RECAP). Across a range of real-world manipulation tasks including cloth folding, box assembly, and grocery restocking, we show that SOP substantially improves the performance of large pretrained VLA models while maintaining a single shared policy across tasks. Effective post-training can be achieved within hours of real-world interaction, and performance scales near-linearly with the number of robots in the fleet. These results suggest that tightly coupling online learning with fleet-scale deployment is instrumental to enabling efficient, reliable, and scalable post-training of generalist robot policies in the physical world. + oai:arXiv.org:2601.03044v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mingjie Pan, Siyuan Feng, Qinglin Zhang, Xinchen Li, Jianheng Song, Chendi Qu, Yi Wang, Chuankang Li, Ziyu Xiong, Zhi Chen, Yi Liu, Jianlan Luo + + + Motion Blur Robust Wheat Pest Damage Detection with Dynamic Fuzzy Feature Fusion + https://arxiv.org/abs/2601.03046 + arXiv:2601.03046v1 Announce Type: new +Abstract: Motion blur caused by camera shake produces ghosting artifacts that substantially degrade edge side object detection. Existing approaches either suppress blur as noise and lose discriminative structure, or apply full image restoration that increases latency and limits deployment on resource constrained devices. We propose DFRCP, a Dynamic Fuzzy Robust Convolutional Pyramid, as a plug in upgrade to YOLOv11 for blur robust detection. DFRCP enhances the YOLOv11 feature pyramid by combining large scale and medium scale features while preserving native representations, and by introducing Dynamic Robust Switch units that adaptively inject fuzzy features to strengthen global perception under jitter. Fuzzy features are synthesized by rotating and nonlinearly interpolating multiscale features, then merged through a transparency convolution that learns a content adaptive trade off between original and fuzzy cues. We further develop a CUDA parallel rotation and interpolation kernel that avoids boundary overflow and delivers more than 400 times speedup, making the design practical for edge deployment. We train with paired supervision on a private wheat pest damage dataset of about 3,500 images, augmented threefold using two blur regimes, uniform image wide motion blur and bounding box confined rotational blur. On blurred test sets, YOLOv11 with DFRCP achieves about 10.4 percent higher accuracy than the YOLOv11 baseline with only a modest training time overhead, reducing the need for manual filtering after data collection. + oai:arXiv.org:2601.03046v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Han Zhang, Yanwei Wang, Fang Li, Hongjun Wang + + + When the Coffee Feature Activates on Coffins: An Analysis of Feature Extraction and Steering for Mechanistic Interpretability + https://arxiv.org/abs/2601.03047 + arXiv:2601.03047v1 Announce Type: new +Abstract: Recent work by Anthropic on Mechanistic interpretability claims to understand and control Large Language Models by extracting human-interpretable features from their neural activation patterns using sparse autoencoders (SAEs). If successful, this approach offers one of the most promising routes for human oversight in AI safety. We conduct an initial stress-test of these claims by replicating their main results with open-source SAEs for Llama 3.1. While we successfully reproduce basic feature extraction and steering capabilities, our investigation suggests that major caution is warranted regarding the generalizability of these claims. We find that feature steering exhibits substantial fragility, with sensitivity to layer selection, steering magnitude, and context. We observe non-standard activation behavior and demonstrate the difficulty to distinguish thematically similar features from one another. While SAE-based interpretability produces compelling demonstrations in selected cases, current methods often fall short of the systematic reliability required for safety-critical applications. This suggests a necessary shift in focus from prioritizing interpretability of internal representations toward reliable prediction and control of model output. Our work contributes to a more nuanced understanding of what mechanistic interpretability has achieved and highlights fundamental challenges for AI safety that remain unresolved. + oai:arXiv.org:2601.03047v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Raphael Ronge, Markus Maier, Frederick Eberhardt + + + On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning + https://arxiv.org/abs/2601.03048 + arXiv:2601.03048v1 Announce Type: new +Abstract: Vision Transformers (ViTs) excel in semantic recognition but exhibit systematic failures in spatial reasoning tasks such as mental rotation. While often attributed to data scale, we propose that this limitation arises from the intrinsic circuit complexity of the architecture. We formalize spatial understanding as learning a Group Homomorphism: mapping image sequences to a latent space that preserves the algebraic structure of the underlying transformation group. We demonstrate that for non-solvable groups (e.g., the 3D rotation group $\mathrm{SO}(3)$), maintaining such a structure-preserving embedding is computationally lower-bounded by the Word Problem, which is $\mathsf{NC^1}$-complete. In contrast, we prove that constant-depth ViTs with polynomial precision are strictly bounded by $\mathsf{TC^0}$. Under the conjecture $\mathsf{TC^0} \subsetneq \mathsf{NC^1}$, we establish a complexity boundary: constant-depth ViTs fundamentally lack the logical depth to efficiently capture non-solvable spatial structures. We validate this complexity gap via latent-space probing, demonstrating that ViT representations suffer a structural collapse on non-solvable tasks as compositional depth increases. + oai:arXiv.org:2601.03048v1 + cs.CV + cs.AI + cs.CC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Siyi Lyu, Quan Liu, Feng Yan + + + An Empirical Study on User Profile Analysis and SEO Performance: A Case of Taiwan Cultural Memory Bank 2.0 + https://arxiv.org/abs/2601.03050 + arXiv:2601.03050v1 Announce Type: new +Abstract: Taiwan Cultural Memory Bank 2.0 is an online curation platform that invites the public to become curators, fostering diverse perspectives on Taiwan's society, humanities, natural landscapes, and daily life. Built on a material bank concept, the platform encourages users to co-create and curate their own works using shared resources or self-uploaded materials. At its core, the system follows a collect, store, access, and reuse model, supporting dynamic engagement with over three million cultural memory items from Taiwan. Users can search, browse, explore stories, and engage in creative applications and collaborative productions. Understanding user profiles is crucial for enhancing website service quality, particularly within the framework of the Visitor Relationship Management model. This study conducts an empirical analysis of user profiles on the platform, examining demographic characteristics, browsing behaviors, and engagement patterns. Additionally, the research evaluates the platform's SEO performance, search visibility, and organic traffic effectiveness. Based on the findings, this study provides strategic recommendations for optimizing website management, improving user experience, and leveraging social media for enhanced digital outreach. The insights gained contribute to the broader discussion on digital cultural platforms and their role in audience engagement, online visibility, and networked communication. + oai:arXiv.org:2601.03050v1 + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + 10.1007/978-3-032-09945-7_2 + Mei-Yun Hsu, I-Hsien Ting, Yun-Hsiu Liu, Kazunori Minetaki + + + Temporal Graph Network: Hallucination Detection in Multi-Turn Conversation + https://arxiv.org/abs/2601.03051 + arXiv:2601.03051v1 Announce Type: new +Abstract: Hallucinations can be produced by conversational AI systems, particularly in multi-turn conversations where context changes and contradictions may eventually surface. By representing the entire conversation as a temporal graph, we present a novel graph-based method for detecting dialogue-level hallucinations. Our framework models each dialogue as a node, encoding it using a sentence transformer. We explore two different ways of connectivity: i) shared-entity edges, which connect turns that refer to the same entities; ii) temporal edges, which connect contiguous turns in the conversation. Message-passing is used to update the node embeddings, allowing flow of information between related nodes. The context-aware node embeddings are then combined using attention pooling into a single vector, which is then passed on to a classifier to determine the presence and type of hallucinations. We demonstrate that our method offers slightly improved performance over existing methods. Further, we show the attention mechanism can be used to justify the decision making process. The code and model weights are made available at: https://github.com/sambuaneesh/anlp-project. + oai:arXiv.org:2601.03051v1 + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Vidhi Rathore, Sambu Aneesh, Himanshu Singh + + + Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph + https://arxiv.org/abs/2601.03052 + arXiv:2601.03052v1 Announce Type: new +Abstract: The Retrieval-augmented generation (RAG) system based on Large language model (LLM) has made significant progress. It can effectively reduce factuality hallucinations, but faithfulness hallucinations still exist. Previous methods for detecting faithfulness hallucinations either neglect to capture the models' internal reasoning processes or handle those features coarsely, making it difficult for discriminators to learn. This paper proposes a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination. Specifically, we first extend the layer-wise relevance propagation algorithm from the token level to the semantic level, constructing an internal reasoning graph based on attribution vectors. This provides a more faithful semantic-level representation of dependency. Furthermore, we design a general framework based on a small pre-trained language model to utilize the dependencies in LLM's reasoning for training and hallucination detection, which can dynamically adjust the pass rate of correct samples through a threshold. Experimental results demonstrate that our method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k. + oai:arXiv.org:2601.03052v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jianpeng Hu, Yanzeng Li, Jialun Zhong, Wenfa Qi, Lei Zou + + + IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation + https://arxiv.org/abs/2601.03054 + arXiv:2601.03054v1 Announce Type: new +Abstract: Recent research on medical MLLMs has gradually shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foundation for pixel-level understanding, existing approaches face two major challenges. First, they introduce implicit segmentation tokens and require simultaneous fine-tuning of both the MLLM and external pixel decoders, which increases the risk of catastrophic forgetting and limits generalization to out-of-domain scenarios. Second, most methods rely on single-pass reasoning and lack the capability to iteratively refine segmentation results, leading to suboptimal performance. To overcome these limitations, we propose a novel agentic MLLM, named IBISAgent, that reformulates segmentation as a vision-centric, multi-step decision-making process. IBISAgent enables MLLMs to generate interleaved reasoning and text-based click actions, invoke segmentation tools, and produce high-quality masks without architectural modifications. By iteratively performing multi-step visual reasoning on masked image features, IBISAgent naturally supports mask refinement and promotes the development of pixel-level visual reasoning capabilities. We further design a two-stage training framework consisting of cold-start supervised fine-tuning and agentic reinforcement learning with tailored, fine-grained rewards, enhancing the model's robustness in complex medical referring and reasoning segmentation tasks. Extensive experiments demonstrate that IBISAgent consistently outperforms both closed-source and open-source SOTA methods. All datasets, code, and trained models will be released publicly. + oai:arXiv.org:2601.03054v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yankai Jiang, Qiaoru Li, Binlu Xu, Haoran Sun, Chao Ding, Junting Dong, Yuxiang Cai, Xuhong Zhang, Jianwei Yin + + + A Fast Semidefinite Convex Relaxation for Optimal Control Problems With Spatio-Temporal Constraints + https://arxiv.org/abs/2601.03055 + arXiv:2601.03055v1 Announce Type: new +Abstract: Solving optimal control problems (OCPs) of autonomous agents operating under spatial and temporal constraints fast and accurately is essential in applications ranging from eco-driving of autonomous vehicles to quadrotor navigation. However, the nonlinear programs approximating the OCPs are inherently nonconvex due to the coupling between the dynamics and the event timing, and therefore, they are challenging to solve. Most approaches address this challenge by predefining waypoint times or just using nonconvex trajectory optimization, which simplifies the problem but often yields suboptimal solutions. To significantly improve the numerical properties, we propose a formulation with a time-scaling direct multiple shooting scheme that partitions the prediction horizon into segments aligned with characteristic time constraints. Moreover, we develop a fast semidefinite-programming-based convex relaxation that exploits the sparsity pattern of the lifted formulation. Comprehensive simulation studies demonstrate the solution optimality and computational efficiency. Furthermore, real-world experiments on a quadrotor waypoint flight task with constrained open time windows validate the practical applicability of the approach in complex environments. + oai:arXiv.org:2601.03055v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Shiying Dong, Zhipeng Shen, Rudolf Reiter, Hailong Huang, Bingzhao Gao, Hong Chen, Wen-Hua Chen + + + Fine-Grained Generalization via Structuralizing Concept and Feature Space into Commonality, Specificity and Confounding + https://arxiv.org/abs/2601.03056 + arXiv:2601.03056v1 Announce Type: new +Abstract: Fine-Grained Domain Generalization (FGDG) presents greater challenges than conventional domain generalization due to the subtle inter-class differences and relatively pronounced intra-class variations inherent in fine-grained recognition tasks. Under domain shifts, the model becomes overly sensitive to fine-grained cues, leading to the suppression of critical features and a significant drop in performance. Cognitive studies suggest that humans classify objects by leveraging both common and specific attributes, enabling accurate differentiation between fine-grained categories. However, current deep learning models have yet to incorporate this mechanism effectively. Inspired by this mechanism, we propose Concept-Feature Structuralized Generalization (CFSG). This model explicitly disentangles both the concept and feature spaces into three structured components: common, specific, and confounding segments. To mitigate the adverse effects of varying degrees of distribution shift, we introduce an adaptive mechanism that dynamically adjusts the proportions of common, specific, and confounding components. In the final prediction, explicit weights are assigned to each pair of components. Extensive experiments on three single-source benchmark datasets demonstrate that CFSG achieves an average performance improvement of 9.87% over baseline models and outperforms existing state-of-the-art methods by an average of 3.08%. Additionally, explainability analysis validates that CFSG effectively integrates multi-granularity structured knowledge and confirms that feature structuralization facilitates the emergence of concept structuralization. + oai:arXiv.org:2601.03056v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhen Wang, Jiaojiao Zhao, Qilong Wang, Yongfeng Dong, Wenlong Yu + + + Exploring the Relationship Between Local Election Results and Online Public Opinion in Taiwan: A Case Study of Taitung County + https://arxiv.org/abs/2601.03057 + arXiv:2601.03057v1 Announce Type: new +Abstract: This study examines the relationship between online buzz and local election outcomes in Taiwan, with a focus on Taitung County. As social media becomes a major channel for public discourse, online buzz is increasingly seen as a factor influencing elections. However, its impact on local elections in Taiwan remains underexplored. This research addresses that gap through a comparative analysis of social media data and actual vote shares during the election period. A review of existing literature establishes the study's framework and highlights the need for empirical investigation in this area. + The findings aim to reveal whether online discussions align with electoral results and to what extent digital sentiment reflects voter behavior. The study also discusses methodological and data limitations that may affect interpretation. Beyond its academic value, the research offers practical insights into how online buzz can inform campaign strategies and enhance election predictions. By analyzing the Taitung County case, this study contributes to a deeper understanding of the role of online discourse in Taiwan's local elections and offers a foundation for future research in the field. + oai:arXiv.org:2601.03057v1 + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + 10.1007/978-3-032-09945-7_6 + I-Hsien Ting, Yen-Chih Chiu, Yun-Hsiu Liu, Kazunori Minetaki, Chia-Sung Yen + + + Vertical tacit collusion in AI-mediated markets + https://arxiv.org/abs/2601.03061 + arXiv:2601.03061v1 Announce Type: new +Abstract: AI shopping agents are being deployed to hundreds of millions of consumers, creating a new intermediary between platforms, sellers, and buyers. We identify a novel market failure: vertical tacit collusion, where platforms controlling rankings and sellers controlling product descriptions independently learn to exploit documented AI cognitive biases. Using multi-agent simulation calibrated to empirical measurements of large language model biases, we show that joint exploitation produces consumer harm more than double what would occur if strategies were independent. This super-additive harm arises because platform ranking determines which products occupy bias-triggering positions while seller manipulation determines conversion rates. Unlike horizontal algorithmic collusion, vertical tacit collusion requires no coordination and evades antitrust detection because harm emerges from aligned incentives rather than agreement. Our findings identify an urgent regulatory gap as AI shopping agents reach mainstream adoption. + oai:arXiv.org:2601.03061v1 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Felipe M. Affonso + + + Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks + https://arxiv.org/abs/2601.03062 + arXiv:2601.03062v1 Announce Type: new +Abstract: Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in sensor data, their black-box nature and the limited work on graph-based explainable models for water networks hinder practical adoption. We propose an explainable GNN framework that integrates mutual information to identify critical network regions and fuzzy logic to provide clear, rule-based explanations for node classification tasks. After benchmarking several GNN architectures, we selected the generalized graph convolution network (GENConv) for its superior performance and developed a fuzzy-enhanced variant that offers intuitive explanations for classified leak locations. Our fuzzy graph neural network (FGENConv) achieved Graph F1 scores of 0.889 for detection and 0.814 for localization, slightly below the crisp GENConv 0.938 and 0.858, respectively. Yet it compensates by providing spatially localized, fuzzy rule-based explanations. By striking the right balance between precision and explainability, the proposed fuzzy network could enable hydraulic engineers to validate predicted leak locations, conserve human resources, and optimize maintenance strategies. The code is available at github.com/pasqualedem/GNNLeakDetection. + oai:arXiv.org:2601.03062v1 + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Qusai Khaled, Pasquale De Marinis, Moez Louati, David Ferras, Laura Genga, Uzay Kaymak + + + Do LLMs Encode Functional Importance of Reasoning Tokens? + https://arxiv.org/abs/2601.03066 + arXiv:2601.03066v1 Announce Type: new +Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost and reduced ability to isolate functionally relevant reasoning. Prior work on compact reasoning shortens such chains through probabilistic sampling, heuristics, or supervision from frontier models, but offers limited insight into whether models internally encode token-level functional importance for answer generation. We address this gap diagnostically and propose greedy pruning, a likelihood-preserving deletion procedure that iteratively removes reasoning tokens whose removal minimally degrades model likelihood under a specified objective, yielding length-controlled reasoning chains. We evaluate pruned reasoning in a distillation framework and show that students trained on pruned chains outperform a frontier-model-supervised compression baseline at matched reasoning lengths. Finally, our analysis reveals systematic pruning patterns and shows that attention scores can predict greedy pruning ranks, further suggesting that models encode a nontrivial functional importance structure over reasoning tokens. + oai:arXiv.org:2601.03066v1 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Janvijay Singh, Dilek Hakkani-T\"ur + + + Joint Encoding of KV-Cache Blocks for Scalable LLM Serving + https://arxiv.org/abs/2601.03067 + arXiv:2601.03067v1 Announce Type: new +Abstract: Modern large language models (LLMs) drive interactive AI systems but are bottlenecked by the memory-heavy growth of key-value (KV) caches, which limits real-time throughput under concurrent loads. Existing KV-cache compression methods rely on rigid heuristics, disrupt tensor layouts, or require specialized compute, hindering scalability and deployment. + We propose joint encoding of KV-cache blocks, which fuses similar blocks across requests and input chunks into shared representations while preserving standard cache structure. This alleviates the KV-cache memory bottleneck, supporting high-concurrency serving without specialized hardware. Theoretically, we analyze the rate-distortion tradeoff of fused cache blocks under a Poisson process model. Empirically, our method achieves up to 4.38 $\times$ KV-cache compression with negligible accuracy loss across diverse LLMs and benchmarks, outperforming recent structured and adaptive compression baselines. In real LLM serving, joint encoding improves the token throughput by $\sim$40\% on a single-machine vLLM benchmark, demonstrating substantial gains in inference throughput. Code is available at https://github.com/sef1/kv_fast_fusion kv_joint_encoding. + oai:arXiv.org:2601.03067v1 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Joseph Kampeas, Emir Haleva + + + HEXAR: a Hierarchical Explainability Architecture for Robots + https://arxiv.org/abs/2601.03070 + arXiv:2601.03070v1 Announce Type: new +Abstract: As robotic systems become increasingly complex, the need for explainable decision-making becomes critical. Existing explainability approaches in robotics typically either focus on individual modules, which can be difficult to query from the perspective of high-level behaviour, or employ monolithic approaches, which do not exploit the modularity of robotic architectures. We present HEXAR (Hierarchical EXplainability Architecture for Robots), a novel framework that provides a plug-in, hierarchical approach to generate explanations about robotic systems. HEXAR consists of specialised component explainers using diverse explanation techniques (e.g., LLM-based reasoning, causal models, feature importance, etc) tailored to specific robot modules, orchestrated by an explainer selector that chooses the most appropriate one for a given query. We implement and evaluate HEXAR on a TIAGo robot performing assistive tasks in a home environment, comparing it against end-to-end and aggregated baseline approaches across 180 scenario-query variations. We observe that HEXAR significantly outperforms baselines in root cause identification, incorrect information exclusion, and runtime, offering a promising direction for transparent autonomous systems. + oai:arXiv.org:2601.03070v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tamlin Love, Ferran Gebell\'i, Pradip Pramanick, Antonio Andriella, Guillem Aleny\`a, Anais Garrell, Raquel Ros, Silvia Rossi + + + Understanding Multi-Agent Reasoning with Large Language Models for Cartoon VQA + https://arxiv.org/abs/2601.03073 + arXiv:2601.03073v1 Announce Type: new +Abstract: Visual Question Answering (VQA) for stylised cartoon imagery presents challenges, such as interpreting exaggerated visual abstraction and narrative-driven context, which are not adequately addressed by standard large language models (LLMs) trained on natural images. To investigate this issue, a multi-agent LLM framework is introduced, specifically designed for VQA tasks in cartoon imagery. The proposed architecture consists of three specialised agents: visual agent, language agent and critic agent, which work collaboratively to support structured reasoning by integrating visual cues and narrative context. The framework was systematically evaluated on two cartoon-based VQA datasets: Pororo and Simpsons. Experimental results provide a detailed analysis of how each agent contributes to the final prediction, offering a deeper understanding of LLM-based multi-agent behaviour in cartoon VQA and multimodal inference. + oai:arXiv.org:2601.03073v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tong Wu, Thanet Markchom + + + Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace + https://arxiv.org/abs/2601.03075 + arXiv:2601.03075v1 Announce Type: new +Abstract: Trajectory prediction (TP) is crucial for ensuring safety and efficiency in modern air traffic management systems. It is, for example, a core component of conflict detection and resolution tools, arrival sequencing algorithms, capacity planning, as well as several future concepts. However, TP accuracy within operational systems is hampered by a range of epistemic uncertainties such as the mass and performance settings of aircraft and the effect of meteorological conditions on aircraft performance. It can also require considerable computational resources. + This paper proposes a method for adaptive TP that has two components: first, a fast surrogate TP model based on linear state space models (LSSM)s with an execution time that was 6.7 times lower on average than an implementation of the Base of Aircraft Data (BADA) in Python. It is demonstrated that such models can effectively emulate the BADA aircraft performance model, which is based on the numerical solution of a partial differential equation (PDE), and that the LSSMs can be fitted to trajectories in a dataset of historic flight data. Secondly, the paper proposes an algorithm to assimilate radar observations using particle filtering to adaptively refine TP accuracy. Comparison with baselines using BADA and Kalman filtering demonstrate that the proposed framework improves system identification and state estimation for both climb and descent phases, with 46.3% and 64.7% better estimates for time to top of climb and bottom of descent compared to the best performing benchmark model. In particular, the particle filtering approach provides the flexibility to capture non-linear performance effects including the CAS-Mach transition. + oai:arXiv.org:2601.03075v1 + cs.CE + math.DS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nick Pepper, Marc Thomas, Zack Xuereb Conti + + + Learning to Diagnose and Correct Moral Errors: Towards Enhancing Moral Sensitivity in Large Language Models + https://arxiv.org/abs/2601.03079 + arXiv:2601.03079v1 Announce Type: new +Abstract: Moral sensitivity is fundamental to human moral competence, as it guides individuals in regulating everyday behavior. Although many approaches seek to align large language models (LLMs) with human moral values, how to enable them morally sensitive has been extremely challenging. In this paper, we take a step toward answering the question: how can we enhance moral sensitivity in LLMs? Specifically, we propose two pragmatic inference methods that faciliate LLMs to diagnose morally benign and hazardous input and correct moral errors, whereby enhancing LLMs' moral sensitivity. A central strength of our pragmatic inference methods is their unified perspective: instead of modeling moral discourses across semantically diverse and complex surface forms, they offer a principled perspective for designing pragmatic inference procedures grounded in their inferential loads. Empirical evidence demonstrates that our pragmatic methods can enhance moral sensitivity in LLMs and achieves strong performance on representative morality-relevant benchmarks. + oai:arXiv.org:2601.03079v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Bocheng Chen, Han Zi, Xi Chen, Xitong Zhang, Kristen Johnson, Guangliang Liu + + + Real-Time Adaptive Anomaly Detection in Industrial IoT Environments + https://arxiv.org/abs/2601.03085 + arXiv:2601.03085v1 Announce Type: new +Abstract: To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such multi-dimensional, heterogeneous data occurs mostly in today's industrial Internet of Things (IIoT), where real-time detection of anomalies is critical to prevent impending failures and resolve them in a timely manner. However, existing anomaly detection methods often fall short of effectively coping with the complexity and dynamism of multi-dimensional data streams in IIoT. In this paper, we propose an adaptive method for detecting anomalies in IIoT streaming data utilizing a multi-source prediction model and concept drift adaptation. The proposed anomaly detection algorithm merges a prediction model into a novel drift adaptation method resulting in accurate and efficient anomaly detection that exhibits improved scalability. Our trace-driven evaluations indicate that the proposed method outperforms the state-of-the-art anomaly detection methods by achieving up to an 89.71% accuracy (in terms of Area under the Curve (AUC)) while meeting the given efficiency and scalability requirements. + oai:arXiv.org:2601.03085v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/TNSM.2024.3447532 + IEEE Transactions on Network and Service Management, vol. 21, no. 6, pp. 6839-6856, 2024 + Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch H. Glitho, Johan Eker, Raquel A. F. Mini + + + Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators + https://arxiv.org/abs/2601.03086 + arXiv:2601.03086v1 Announce Type: new +Abstract: We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classical finite element methods (FEM). PFEM consists of a physics informed pretraining stage and an optional finetuning stage. In the pretraining stage, a neural operator based on the Transolver architecture is trained solely from governing partial differential equations, without relying on labeled solution data. The model operates directly on unstructured point clouds, jointly encoding geometric information, material properties, and boundary conditions, and produces physically consistent initial solutions with extremely high computational efficiency. PDE constraints are enforced through explicit finite element, based differentiation, avoiding the overhead associated with automatic differentiation. In the fine-tuning stage, the pretrained prediction is used as an initial guess for conventional FEM solvers, preserving their accuracy, convergence guarantees, and extrapolation capability while substantially reducing the number of iterations required to reach a prescribed tolerance. PFEM is validated on a broad range of benchmark problems, including linear elasticity and nonlinear hyperelasticity with complex geometries, heterogeneous materials, and arbitrary boundary conditions. Numerical results demonstrate strong generalization in the pretraining stage with relative errors on the order of 1\%, and speedups of up to one order of magnitude in the fine-tuning stage compared to FEM with zero initial guesses. + oai:arXiv.org:2601.03086v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi, Cosmin Anitescu, Xiaoying Zhuang, Timon Rabczuk, Yinghua Liu + + + Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs + https://arxiv.org/abs/2601.03087 + arXiv:2601.03087v1 Announce Type: new +Abstract: Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box LLM applications, but suffers from resource-intensive query access. We conceptualise auditing as uncertainty estimation over a target fairness metric and introduce BAFA, the Bounded Active Fairness Auditor for query-efficient auditing of black-box LLMs. BAFA maintains a version space of surrogate models consistent with queried scores and computes uncertainty intervals for fairness metrics (e.g., $\Delta$ AUC) via constrained empirical risk minimisation. Active query selection narrows these intervals to reduce estimation error. We evaluate BAFA on two standard fairness dataset case studies: \textsc{CivilComments} and \textsc{Bias-in-Bios}, comparing against stratified sampling, power sampling, and ablations. BAFA achieves target error thresholds with up to 40$\times$ fewer queries than stratified sampling (e.g., 144 vs 5,956 queries at $\varepsilon=0.02$ for \textsc{CivilComments}) for tight thresholds, demonstrates substantially better performance over time, and shows lower variance across runs. These results suggest that active sampling can reduce resources needed for independent fairness auditing with LLMs, supporting continuous model evaluations. + oai:arXiv.org:2601.03087v1 + cs.LG + cs.CL + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + David Hartmann, Lena Pohlmann, Lelia Hanslik, Noah Gie{\ss}ing, Bettina Berendt, Pieter Delobelle + + + Grad-ELLM: Gradient-based Explanations for Decoder-only LLMs + https://arxiv.org/abs/2601.03089 + arXiv:2601.03089v1 Announce Type: new +Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their black-box nature raises concerns about transparency and faithfulness. Input attribution methods aim to highlight each input token's contributions to the model's output, but existing approaches are typically model-agnostic, and do not focus on transformer-specific architectures, leading to limited faithfulness. To address this, we propose Grad-ELLM, a gradient-based attribution method for decoder-only transformer-based LLMs. By aggregating channel importance from gradients of the output logit with respect to attention layers and spatial importance from attention maps, Grad-ELLM generates heatmaps at each generation step without requiring architectural modifications. Additionally, we introduce two faithfulneses metrics $\pi$-Soft-NC and $\pi$-Soft-NS, which are modifications of Soft-NC/NS that provide fairer comparisons by controlling the amount of information kept when perturbing the text. We evaluate Grad-ELLM on sentiment classification, question answering, and open-generation tasks using different models. Experiment results show that Grad-ELLM consistently achieves superior faithfulness than other attribution methods. + oai:arXiv.org:2601.03089v1 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xin Huang, Antoni B. Chan + + + LesionTABE: Equitable AI for Skin Lesion Detection + https://arxiv.org/abs/2601.03090 + arXiv:2601.03090v1 Announce Type: new +Abstract: Bias remains a major barrier to the clinical adoption of AI in dermatology, as diagnostic models underperform on darker skin tones. We present LesionTABE, a fairness-centric framework that couples adversarial debiasing with dermatology-specific foundation model embeddings. Evaluated across multiple datasets covering both malignant and inflammatory conditions, LesionTABE achieves over a 25\% improvement in fairness metrics compared to a ResNet-152 baseline, outperforming existing debiasing methods while simultaneously enhancing overall diagnostic accuracy. These results highlight the potential of foundation model debiasing as a step towards equitable clinical AI adoption. + oai:arXiv.org:2601.03090v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Rocio Mexia Diaz, Yasmin Greenway, Petru Manescu + + + ATLAS: Adaptive Test-Time Latent Steering with External Verifiers for Enhancing LLMs Reasoning + https://arxiv.org/abs/2601.03093 + arXiv:2601.03093v1 Announce Type: new +Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (LLMs) toward improved reasoning and efficiency without additional training. However, most existing approaches rely on fixed steering policies and static intervention strengths, which limit their robustness across problem instances and often result in over- or under-steering. We propose Adaptive Test-time Latent Steering, called (ATLAS), a task- specific framework that dynamically controls steering decisions at inference time using an external, lightweight latent verifier. Given intermediate hidden states, the verifier predicts the quality of ongoing reasoning and adaptively selects whether and how strongly to apply steering, enabling per-example and per-step adjustment with minimal overhead. To our knowledge, ATLAS is the first method to integrate learned latent verification into test-time steering for enhancing LLMs reasoning. Experiments on multiple mathematical reasoning benchmarks show that ATLAS consistently outperforms both vanilla decoding and fixed steering baselines, achieving higher accuracy while substantially reducing test-time token usage. These results demonstrate that verifier-guided latent adaptation provides an effective and scalable mechanism for controlling reasoning efficiency without sacrificing solution quality. All source code will be publicly available. + oai:arXiv.org:2601.03093v1 + cs.LG + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Tuc Nguyen, Thai Le + + + Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees + https://arxiv.org/abs/2601.03097 + arXiv:2601.03097v1 Announce Type: new +Abstract: We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically on $\mathrm{SE}(3)$. The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities, as is standard in many aerial vehicles and omnidirectional mobile robots. Gaussian Process (GP) regression is integrated into a geometric feedback law to learn and compensate online for unknown, state-dependent disturbances and modeling imperfections affecting both attitude and position, while preserving the algebraic structure and coupling properties inherent to rigid-body motion. + The proposed approach does not rely on explicit parametric models of the unknown effects, making it well-suited for robotic systems subject to sensor-induced disturbances, unmodeled actuation couplings, and environmental uncertainties. A Lyapunov-based analysis establishes probabilistic ultimate boundedness of the pose tracking error under bounded GP uncertainty, providing formal stability guarantees for the learning-based controller. + Simulation results demonstrate accurate and smooth trajectory tracking in the presence of realistic, localized disturbances, including correlated rotational and translational effects arising from magnetometer perturbations. These results illustrate the potential of combining geometric modeling and probabilistic learning to achieve robust, data-efficient pose control for autonomous robotic systems. + oai:arXiv.org:2601.03097v1 + cs.RO + cs.SY + eess.SY + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Omayra Yago Nieto, Alexandre Anahory Simoes, Juan I. Giribet, Leonardo Colombo + + + From Muscle to Text with MyoText: sEMG to Text via Finger Classification and Transformer-Based Decoding + https://arxiv.org/abs/2601.03098 + arXiv:2601.03098v1 Announce Type: new +Abstract: Surface electromyography (sEMG) provides a direct neural interface for decoding muscle activity and offers a promising foundation for keyboard-free text input in wearable and mixed-reality systems. Previous sEMG-to-text studies mainly focused on recognizing letters directly from sEMG signals, forming an important first step toward translating muscle activity into text. Building on this foundation, we present MyoText, a hierarchical framework that decodes sEMG signals to text through physiologically grounded intermediate stages. MyoText first classifies finger activations from multichannel sEMG using a CNN-BiLSTM-Attention model, applies ergonomic typing priors to infer letters, and reconstructs full sentences with a fine-tuned T5 transformer. This modular design mirrors the natural hierarchy of typing, linking muscle intent to language output and reducing the search space for decoding. Evaluated on 30 users from the emg2qwerty dataset, MyoText outperforms baselines by achieving 85.4% finger-classification accuracy, 5.4% character error rate (CER), and 6.5% word error rate (WER). Beyond accuracy gains, this methodology establishes a principled pathway from neuromuscular signals to text, providing a blueprint for virtual and augmented-reality typing interfaces that operate entirely without physical keyboards. By integrating ergonomic structure with transformer-based linguistic reasoning, MyoText advances the feasibility of seamless, wearable neural input for future ubiquitous computing environments. + oai:arXiv.org:2601.03098v1 + cs.LG + cs.NE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Meghna Roy Chowdhury, Shreyas Sen, Yi Ding + + + Time-Aware Synthetic Control + https://arxiv.org/abs/2601.03099 + arXiv:2601.03099v1 Announce Type: new +Abstract: The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing methods typically treat the ordering of pre-intervention time indices interchangeable. This invariance means they may not fully take advantage of temporal structure when strong trends are present. We propose Time-Aware Synthetic Control (TASC), which employs a state-space model with a constant trend while preserving a low-rank structure of the signal. TASC uses the Kalman filter and Rauch-Tung-Striebel smoother: it first fits a generative time-series model with expectation-maximization and then performs counterfactual inference. We evaluate TASC on both simulated and real-world datasets, including policy evaluation and sports prediction. Our results suggest that TASC offers advantages in settings with strong temporal trends and high levels of observation noise. + oai:arXiv.org:2601.03099v1 + cs.LG + econ.EM + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Saeyoung Rho, Cyrus Illick, Samhitha Narasipura, Alberto Abadie, Daniel Hsu, Vishal Misra + + + Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs + https://arxiv.org/abs/2601.03100 + arXiv:2601.03100v1 Announce Type: new +Abstract: Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized. MLLMs still suffer from visually ungrounded hallucinations, often relying on language priors rather than image evidence. While many prior mitigation strategies operate on the text side, they leave the visual representation unchanged and do not exploit the rich hierarchy of features encoded across vision layers. Existing multi-layer fusion methods partially address this limitation but remain static, applying the same layer mixture regardless of the query. In this work, we introduce TGIF (Text-Guided Inter-layer Fusion), a lightweight module that treats encoder layers as depth-wise "experts" and predicts a prompt-dependent fusion of visual features. TGIF follows the principle of direct external fusion, requires no vision-encoder updates, and adds minimal overhead. Integrated into LLaVA-1.5-7B, TGIF provides consistent improvements across hallucination, OCR, and VQA benchmarks, while preserving or improving performance on ScienceQA, GQA, and MMBench. These results suggest that query-conditioned, hierarchy-aware fusion is an effective way to strengthen visual grounding and reduce hallucination in modern MLLMs. + oai:arXiv.org:2601.03100v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chenchen Lin, Sanbao Su, Rachel Luo, Yuxiao Chen, Yan Wang, Marco Pavone, Fei Miao + + + Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs + https://arxiv.org/abs/2601.03103 + arXiv:2601.03103v1 Announce Type: new +Abstract: Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in humor preferences in Oogiri, a Japanese creative response game, by clustering users with voting logs and estimating cluster-specific weights over interpretable preference factors using Bradley-Terry-Luce models. We elicit preference judgments from LLMs by prompting them to select the funnier response and found that user clusters exhibit distinct preference patterns and that the LLM results can resemble those of particular clusters. Finally, we demonstrate that, by persona prompting, LLM preferences can be directed toward a specific cluster. The scripts for data collection and analysis will be released to support reproducibility. + oai:arXiv.org:2601.03103v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura + + + One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling + https://arxiv.org/abs/2601.03111 + arXiv:2601.03111v1 Announce Type: new +Abstract: The reasoning ability of large language models (LLMs) can be unleashed with reinforcement learning (RL) (OpenAI, 2024; DeepSeek-AI et al., 2025a; Zeng et al., 2025). The success of existing RL attempts in LLMs usually relies on high-quality samples of thousands or beyond. In this paper, we challenge fundamental assumptions about data requirements in RL for LLMs by demonstrating the remarkable effectiveness of one-shot learning. Specifically, we introduce polymath learning, a framework for designing one training sample that elicits multidisciplinary impact. We present three key findings: (1) A single, strategically selected math reasoning sample can produce significant performance improvements across multiple domains, including physics, chemistry, and biology with RL; (2) The math skills salient to reasoning suggest the characteristics of the optimal polymath sample; and (3) An engineered synthetic sample that integrates multidiscipline elements outperforms training with individual samples that naturally occur. Our approach achieves superior performance to training with larger datasets across various reasoning benchmarks, demonstrating that sample quality and design, rather than quantity, may be the key to unlock enhanced reasoning capabilities in language models. Our results suggest a shift, dubbed as sample engineering, toward precision engineering of training samples rather than simply increasing data volume. + oai:arXiv.org:2601.03111v1 + cs.LG + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Yiyuan Li, Zhen Huang, Yanan Wu, Weixun Wang, Xuefeng Li, Yijia Luo, Wenbo Su, Bo Zheng, Pengfei Liu + + + A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control + https://arxiv.org/abs/2601.03113 + arXiv:2601.03113v1 Announce Type: new +Abstract: This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the development and rigorous human-in-the-loop evaluation of AI agents for Air Traffic Control (ATC), providing a virtual representation of real-world airspace that enables safe exploration of higher levels of ATC automation. + This paper makes three significant contributions: firstly, we demonstrate how historical and live operational data may be combined with a probabilistic, physics-informed machine learning model of aircraft performance to reproduce real-world traffic scenarios, while accurately reflecting the level of uncertainty inherent in ATC. Secondly, we develop a structured assurance case, following the Trustworthy and Ethical Assurance framework, to provide quantitative evidence for the Digital Twin's accuracy and fidelity. This is crucial to building trust in this novel technology within this safety-critical domain. Thirdly, we describe how the Digital Twin forms a unified environment for agent testing and evaluation. This includes fast-time execution (up to x200 real-time), a standardised Python-based ``gym'' interface that supports a range of AI agent designs, and a suite of quantitative metrics for assessing performance. Crucially, the framework facilitates competency-based assessment of AI agents by qualified Air Traffic Control Officers through a Human Machine Interface. We also outline further applications and future extensions of the Digital Twin architecture. + oai:arXiv.org:2601.03113v1 + cs.CE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Nick Pepper, Adam Keane, Amy Hodgkin, Dewi Gould, Edward Henderson, Lynge Lauritsen, Christos Vlahos, George De Ath, Richard Everson, Richard Cannon, Alvaro Sierra Castro, John Korna, Ben Carvell, Marc Thomas + + + Stroke Patches: Customizable Artistic Image Styling Using Regression + https://arxiv.org/abs/2601.03114 + arXiv:2601.03114v1 Announce Type: new +Abstract: We present a novel, regression-based method for artistically styling images. Unlike recent neural style transfer or diffusion-based approaches, our method allows for explicit control over the stroke composition and level of detail in the rendered image through the use of an extensible set of stroke patches. The stroke patch sets are procedurally generated by small programs that control the shape, size, orientation, density, color, and noise level of the strokes in the individual patches. Once trained on a set of stroke patches, a U-Net based regression model can render any input image in a variety of distinct, evocative and customizable styles. + oai:arXiv.org:2601.03114v1 + cs.GR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Creative AI Track + Ian Jaffray, John Bronskill + + + Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models + https://arxiv.org/abs/2601.03115 + arXiv:2601.03115v1 Announce Type: new +Abstract: Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) encode it internally. We present the first neuron-level interpretability study of emotion-sensitive neurons (ESNs) in LALMs and provide causal evidence that such units exist in Qwen2.5-Omni, Kimi-Audio, and Audio Flamingo 3. Across these three widely used open-source models, we compare frequency-, entropy-, magnitude-, and contrast-based neuron selectors on multiple emotion recognition benchmarks. Using inference-time interventions, we reveal a consistent emotion-specific signature: ablating neurons selected for a given emotion disproportionately degrades recognition of that emotion while largely preserving other classes, whereas gain-based amplification steers predictions toward the target emotion. These effects arise with modest identification data and scale systematically with intervention strength. We further observe that ESNs exhibit non-uniform layer-wise clustering with partial cross-dataset transfer. Taken together, our results offer a causal, neuron-level account of emotion decisions in LALMs and highlight targeted neuron interventions as an actionable handle for controllable affective behaviors. + oai:arXiv.org:2601.03115v1 + cs.CL + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xiutian Zhao, Bj\"orn Schuller, Berrak Sisman + + + A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace + https://arxiv.org/abs/2601.03120 + arXiv:2601.03120v1 Announce Type: new +Abstract: Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en route UK airspace as an environment for training and testing AI Air Traffic Control (ATC) agents. There is a developing regulatory landscape for this kind of novel technology. Regulatory requirements are expected to be application specific, and may need to be tailored to each specific use case. + We draw on emerging guidance for both Digital Twin development and the use of Artificial Intelligence/Machine Learning (AI/ML) in Air Traffic Management (ATM) to present an assurance framework. This framework defines actionable goals and the evidence required to demonstrate that a Digital Twin accurately represents its physical counterpart and also provides sufficient functionality across target use cases. It provides a structured approach for researchers to assess, understand and document the strengths and limitations of the Digital Twin, whilst also identifying areas where fidelity could be improved. Furthermore, it serves as a foundation for engagement with stakeholders and regulators, supporting discussions around the regulatory needs for future applications, and contributing to the emerging guidance through a concrete, working example of a Digital Twin. + The framework leverages a methodology known as Trustworthy and Ethical Assurance (TEA) to develop an assurance case. An assurance case is a nested set of structured arguments that provides justified evidence for how a top-level goal has been realised. In this paper we provide an overview of each structured argument and a number of deep dives which elaborate in more detail upon particular arguments, including the required evidence, assumptions and justifications. + oai:arXiv.org:2601.03120v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Adam Keane, Nick Pepper, Chris Burr, Amy Hodgkin, Dewi Gould, John Korna, Marc Thomas + + + ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation + https://arxiv.org/abs/2601.03121 + arXiv:2601.03121v1 Announce Type: new +Abstract: Augmenting toxic language data in a controllable and class-specific manner is crucial for improving robustness in toxicity classification, yet remains challenging due to limited supervision and distributional skew. We propose ToxiGAN, a class-aware text augmentation framework that combines adversarial generation with semantic guidance from large language models (LLMs). To address common issues in GAN-based augmentation such as mode collapse and semantic drift, ToxiGAN introduces a two-step directional training strategy and leverages LLM-generated neutral texts as semantic ballast. Unlike prior work that treats LLMs as static generators, our approach dynamically selects neutral exemplars to provide balanced guidance. Toxic samples are explicitly optimized to diverge from these exemplars, reinforcing class-specific contrastive signals. Experiments on four hate speech benchmarks show that ToxiGAN achieves the strongest average performance in both macro-F1 and hate-F1, consistently outperforming traditional and LLM-based augmentation methods. Ablation and sensitivity analyses further confirm the benefits of semantic ballast and directional training in enhancing classifier robustness. + oai:arXiv.org:2601.03121v1 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Peiran Li, Jan Fillies, Adrian Paschke + + + LeafLife: An Explainable Deep Learning Framework with Robustness for Grape Leaf Disease Recognition + https://arxiv.org/abs/2601.03124 + arXiv:2601.03124v1 Announce Type: new +Abstract: Plant disease diagnosis is essential to farmers' management choices because plant diseases frequently lower crop yield and product quality. For harvests to flourish and agricultural productivity to boost, grape leaf disease detection is important. The plant disease dataset contains grape leaf diseases total of 9,032 images of four classes, among them three classes are leaf diseases, and the other one is healthy leaves. After rigorous pre-processing dataset was split (70% training, 20% validation, 10% testing), and two pre-trained models were deployed: InceptionV3 and Xception. Xception shows a promising result of 96.23% accuracy, which is remarkable than InceptionV3. Adversarial Training is used for robustness, along with more transparency. Grad-CAM is integrated to confirm the leaf disease. Finally deployed a web application using Streamlit with a heatmap visualization and prediction with confidence level for robust grape leaf disease classification. + oai:arXiv.org:2601.03124v1 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + B. M. Shahria Alam, Md. Nasim Ahmed + + + Dualities for finite abelian groups and applications to coding theory + https://arxiv.org/abs/2601.03126 + arXiv:2601.03126v1 Announce Type: new +Abstract: The choice of an isomorphism, a duality, between a finite abelian group $A$ and its character group allows one to define dual codes of additive codes over $A$. Properties of dualities and dual codes are studied, continuing work of Delsarte from 1973 and more recent work of Dougherty and his collaborators. + oai:arXiv.org:2601.03126v1 + cs.IT + math.GR + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jay A. Wood + + + Unified Thinker: A General Reasoning Modular Core for Image Generation + https://arxiv.org/abs/2601.03127 + arXiv:2601.03127v1 Announce Type: new +Abstract: Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--execution gap. Meanwhile, closed-source systems (e.g., Nano Banana) have demonstrated strong reasoning-driven image generation, highlighting a substantial gap to current open-source models. We argue that closing this gap requires not merely better visual generators, but executable reasoning: decomposing high-level intents into grounded, verifiable plans that directly steer the generative process. To this end, we propose Unified Thinker, a task-agnostic reasoning architecture for general image generation, designed as a unified planning core that can plug into diverse generators and workflows. Unified Thinker decouples a dedicated Thinker from the image Generator, enabling modular upgrades of reasoning without retraining the entire generative model. We further introduce a two-stage training paradigm: we first build a structured planning interface for the Thinker, then apply reinforcement learning to ground its policy in pixel-level feedback, encouraging plans that optimize visual correctness over textual plausibility. Extensive experiments on text-to-image generation and image editing show that Unified Thinker substantially improves image reasoning and generation quality. + oai:arXiv.org:2601.03127v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao + + + Density Matters: A Complexity Dichotomy of Deleting Edges to Bound Subgraph Density + https://arxiv.org/abs/2601.03129 + arXiv:2601.03129v1 Announce Type: new +Abstract: We study $\tau$-Bounded-Density Edge Deletion ($\tau$-BDED), where given an undirected graph $G$, the task is to remove as few edges as possible to obtain a graph $G'$ where no subgraph of $G'$ has density more than $\tau$. The density of a (sub)graph is the number of edges divided by the number of vertices. This problem was recently introduced and shown to be NP-hard for $\tau \in \{2/3, 3/4, 1 + 1/25\}$, but polynomial-time solvable for $\tau \in \{0,1/2,1\}$ [Bazgan et al., JCSS 2025]. We provide a complete dichotomy with respect to the target density $\tau$: + 1. If $2\tau \in \mathbb{N}$ (half-integral target density) or $\tau < 2/3$, then $\tau$-BDED is polynomial-time solvable. + 2. Otherwise, $\tau$-BDED is NP-hard. + We complement the NP-hardness with fixed-parameter tractability with respect to the treewidth of $G$. Moreover, for integral target density $\tau \in \mathbb{N}$, we show $\tau$-BDED to be solvable in randomized $O(m^{1 + o(1)})$ time. Our algorithmic results are based on a reduction to a new general flow problem on restricted networks that, depending on $\tau$, can be solved via Maximum s-t-Flow or General Factors. We believe this connection between these variants of flow and matching to be of independent interest. + oai:arXiv.org:2601.03129v1 + cs.DS + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Matthias Bentert, Tom-Lukas Breitkopf, Vincent Froese, Anton Herrmann, Andr\'e Nichterlein + + + Automatic Prompt Engineering with No Task Cues and No Tuning + https://arxiv.org/abs/2601.03130 + arXiv:2601.03130v1 Announce Type: new +Abstract: This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tuning and no explicit clues about the task. We evaluated our approach on cryptic column name expansion (CNE) in database tables, a task which is critical for tabular data search, access, and understanding and yet there has been very little existing work. We evaluated on datasets in two languages, English and German. This is the first work to report on the application of automatic prompt engineering for the CNE task. To the best of our knowledge, this is also the first work on the application of automatic prompt engineering for a language other than English. + oai:arXiv.org:2601.03130v1 + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + The IEEE International Conference on Data Mining (ICDM) 2025 : Demo Track + Faisal Chowdhury, Nandana Mihindukulasooriya, Niharika S D'Souza, Horst Samulowitz, Neeru Gupta, Tomasz Hanusiak, Michal Kapitonow + + + Finite Memory Belief Approximation for Optimal Control in Partially Observable Markov Decision Processes + https://arxiv.org/abs/2601.03132 + arXiv:2601.03132v1 Announce Type: new +Abstract: We study finite memory belief approximation for partially observable (PO) stochastic optimal control (SOC) problems. While belief states are sufficient for SOC in partially observable Markov decision processes (POMDPs), they are generally infinite-dimensional and impractical. We interpret truncated input-output (IO) histories as inducing a belief approximation and develop a metric-based theory that directly relates information loss to control performance. Using the Wasserstein metric, we derive policy-conditional performance bounds that quantify value degradation induced by finite memory along typical closed-loop trajectories. Our analysis proceeds via a fixed-policy comparison: we evaluate two cost functionals under the same closed-loop execution and isolate the effect of replacing the true belief by its finite memory approximation inside the belief-level cost. For linear quadratic Gaussian (LQG) systems, we provide closed-form belief mismatch evaluation and empirically validate the predicted mechanism, demonstrating that belief mismatch decays approximately exponentially with memory length and that the induced performance mismatch scales accordingly. Together, these results provide a metric-aware characterization of what finite memory belief approximation can and cannot achieve in PO settings. + oai:arXiv.org:2601.03132v1 + eess.SY + cs.IT + cs.LG + cs.SY + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mintae Kim + + + The Anatomy of Conversational Scams: A Topic-Based Red Teaming Analysis of Multi-Turn Interactions in LLMs + https://arxiv.org/abs/2601.03134 + arXiv:2601.03134v1 Announce Type: new +Abstract: As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that single-turn safety evaluations fail to capture. We systematically study these risks using a controlled LLM-to-LLM simulation framework across multi-turn scam scenarios. Evaluating eight state-of-the-art models in English and Chinese, we analyze dialogue outcomes and qualitatively annotate attacker strategies, defensive responses, and failure modes. Results reveal that scam interactions follow recurrent escalation patterns, while defenses employ verification and delay mechanisms. Furthermore, interactional failures frequently stem from safety guardrail activation and role instability. Our findings highlight multi-turn interactional safety as a critical, distinct dimension of LLM behavior. + oai:arXiv.org:2601.03134v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xiangzhe Yuan, Zhenhao Zhang, Haoming Tang, Siying Hu + + + Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing + https://arxiv.org/abs/2601.03135 + arXiv:2601.03135v1 Announce Type: new +Abstract: Low-resource indigenous languages often lack the parallel corpora required for effective neural machine translation (NMT). Synthetic data generation offers a practical strategy for mitigating this limitation in data-scarce settings. In this work, we augment curated parallel datasets for indigenous languages of the Americas with synthetic sentence pairs generated using a high-capacity multilingual translation model. We fine-tune a multilingual mBART model on curated-only and synthetically augmented data and evaluate translation quality using chrF++, the primary metric used in recent AmericasNLP shared tasks for agglutinative languages. + We further apply language-specific preprocessing, including orthographic normalization and noise-aware filtering, to reduce corpus artifacts. Experiments on Guarani--Spanish and Quechua--Spanish translation show consistent chrF++ improvements from synthetic data augmentation, while diagnostic experiments on Aymara highlight the limitations of generic preprocessing for highly agglutinative languages. + oai:arXiv.org:2601.03135v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Aashish Dhawan, Christopher Driggers-Ellis, Christan Grant, Daisy Zhe Wang + + + Limited Linguistic Diversity in Embodied AI Datasets + https://arxiv.org/abs/2601.03136 + arXiv:2601.03136v1 Announce Type: new +Abstract: Language plays a critical role in Vision-Language-Action (VLA) models, yet the linguistic characteristics of the datasets used to train and evaluate these systems remain poorly documented. In this work, we present a systematic dataset audit of several widely used VLA corpora, aiming to characterize what kinds of instructions these datasets actually contain and how much linguistic variety they provide. We quantify instruction language along complementary dimensions-including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity. Our analysis shows that many datasets rely on highly repetitive, template-like commands with limited structural variation, yielding a narrow distribution of instruction forms. We position these findings as descriptive documentation of the language signal available in current VLA training and evaluation data, intended to support more detailed dataset reporting, more principled dataset selection, and targeted curation or augmentation strategies that broaden language coverage. + oai:arXiv.org:2601.03136v1 + cs.CL + cs.AI + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Selma Wanna, Agnes Luhtaru, Jonathan Salfity, Ryan Barron, Juston Moore, Cynthia Matuszek, Mitch Pryor + + + Accurate Table Question Answering with Accessible LLMs + https://arxiv.org/abs/2601.03137 + arXiv:2601.03137v1 Announce Type: new +Abstract: Given a table T in a database and a question Q in natural language, the table question answering (TQA) task aims to return an accurate answer to Q based on the content of T. Recent state-of-the-art solutions leverage large language models (LLMs) to obtain high-quality answers. However, most rely on proprietary, large-scale LLMs with costly API access, posing a significant financial barrier. This paper instead focuses on TQA with smaller, open-weight LLMs that can run on a desktop or laptop. This setting is challenging, as such LLMs typically have weaker capabilities than large proprietary models, leading to substantial performance degradation with existing methods. + We observe that a key reason for this degradation is that prior approaches often require the LLM to solve a highly sophisticated task using long, complex prompts, which exceed the capabilities of small open-weight LLMs. Motivated by this observation, we present Orchestra, a multi-agent approach that unlocks the potential of accessible LLMs for high-quality, cost-effective TQA. Orchestra coordinates a group of LLM agents, each responsible for a relatively simple task, through a structured, layered workflow to solve complex TQA problems -- akin to an orchestra. By reducing the prompt complexity faced by each agent, Orchestra significantly improves output reliability. + We implement Orchestra on top of AgentScope, an open-source multi-agent framework, and evaluate it on multiple TQA benchmarks using a wide range of open-weight LLMs. Experimental results show that Orchestra achieves strong performance even with small- to medium-sized models. For example, with Qwen2.5-14B, Orchestra reaches 72.1% accuracy on WikiTQ, approaching the best prior result of 75.3% achieved with GPT-4; with larger Qwen, Llama, or DeepSeek models, Orchestra outperforms all prior methods and establishes new state-of-the-art results across all benchmarks. + oai:arXiv.org:2601.03137v1 + cs.DB + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yangfan Jiang, Fei Wei, Ergute Bao, Yaliang Li, Bolin Ding, Yin Yang, Xiaokui Xiao + + + Time-Varying Kinematics Control for Magnetically-Actuated Satellite Swarm without Additional Actuator + https://arxiv.org/abs/2601.03143 + arXiv:2601.03143v1 Announce Type: new +Abstract: Electromagnetic Formation Flight is a technology that uses electromagnetic forces and torques to control multiple satellites without conventional fuel-based propulsion. In this paper, the controllability of the system is discussed based on the conservation of the entire system's angular momentum, which constitutes a nonholonomic constraint. This paper designs a new controller for multiple satellites without an additional attitude actuator. + oai:arXiv.org:2601.03143v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuta Takahashi, Hiraku Sakamoto, Shin-ichiro Sakai + + + Self-Verification is All You Need To Pass The Japanese Bar Examination + https://arxiv.org/abs/2601.03144 + arXiv:2601.03144v1 Announce Type: new +Abstract: Despite rapid advances in large language models (LLMs), achieving reliable performance on highly professional and structured examinations remains a significant challenge. The Japanese bar examination is a particularly demanding benchmark, requiring not only advanced legal reasoning but also strict adherence to complex answer formats that involve joint evaluation of multiple propositions. While recent studies have reported improvements by decomposing such questions into simpler true--false judgments, these approaches have not been systematically evaluated under the original exam format and scoring scheme, leaving open the question of whether they truly capture exam-level competence. In this paper, we present a self-verification model trained on a newly constructed dataset that faithfully replicates the authentic format and evaluation scale of the exam. Our model is able to exceed the official passing score when evaluated on the actual exam scale, marking the first demonstration, to our knowledge, of an LLM passing the Japanese bar examination without altering its original question structure or scoring rules. We further conduct extensive comparisons with alternative strategies, including multi-agent inference and decomposition-based supervision, and find that these methods fail to achieve comparable performance. Our results highlight the importance of format-faithful supervision and consistency verification, and suggest that carefully designed single-model approaches can outperform more complex systems in high-stakes professional reasoning tasks. Our dataset and codes are publicly available. + oai:arXiv.org:2601.03144v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Andrew Shin + + + PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback + https://arxiv.org/abs/2601.03149 + arXiv:2601.03149v1 Announce Type: new +Abstract: Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly achieve behavioral diversity and logical groundedness. Rule-driven simulators rely on hand-crafted workflows and shallow stochasticity, which miss the richness of human behavior. Learning-based generators such as GANs capture correlations yet often violate hard financial constraints and still require training on private data. We introduce PersonaLedger, a generation engine that uses a large language model conditioned on rich user personas to produce diverse transaction streams, coupled with an expert configurable programmatic engine that maintains correctness. The LLM and engine interact in a closed loop: after each event, the engine updates the user state, enforces financial rules, and returns a context aware "nextprompt" that guides the LLM toward feasible next actions. With this engine, we create a public dataset of 30 million transactions from 23,000 users and a benchmark suite with two tasks, illiquidity classification and identity theft segmentation. PersonaLedger offers a realistic, privacy preserving resource that supports rigorous evaluation of forecasting and anomaly detection models. PersonaLedger offers the community a rich, realistic, and privacy preserving resource -- complete with code, rules, and generation logs -- to accelerate innovation in financial AI and enable rigorous, reproducible evaluation. + oai:arXiv.org:2601.03149v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Dehao Yuan, Tyler Farnan, Stefan Tesliuc, Doron L Bergman, Yulun Wu, Xiaoyu Liu, Minghui Liu, James Montgomery, Nam H Nguyen, C. Bayan Bruss, Furong Huang + + + Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach + https://arxiv.org/abs/2601.03152 + arXiv:2601.03152v1 Announce Type: new +Abstract: Accurate aircraft trajectory prediction (TP) in air traffic management systems is confounded by a number of epistemic uncertainties, dominated by uncertain meteorological conditions and operator specific procedures. Handling this uncertainty necessitates the use of probabilistic, machine learned models for generating trajectories. However, the trustworthiness of such models is limited if generated trajectories are not physically plausible. For this reason we propose a physics-informed approach in which aircraft thrust and airspeed are learned from data and are used to condition the existing Base of Aircraft Data (BADA) model, which is physics-based and enforces energy-based constraints on generated trajectories. A set of informative features are identified and used to condition a probabilistic model of aircraft thrust and airspeed, with the proposed scheme demonstrating a 20% improvement in skilfulness across a set of six metrics, compared against a baseline probabilistic model that ignores contextual information such as meteorological conditions. + oai:arXiv.org:2601.03152v1 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Amy Hodgkin, Nick Pepper, Marc Thomas + + + Parallel Latent Reasoning for Sequential Recommendation + https://arxiv.org/abs/2601.03153 + arXiv:2601.03153v1 Announce Type: new +Abstract: Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise by extending test-time computation through multi-step reasoning, yet they exclusively rely on depth-level scaling along a single trajectory, suffering from diminishing returns as reasoning depth increases. To address this limitation, we propose \textbf{Parallel Latent Reasoning (PLR)}, a novel framework that pioneers width-level computational scaling by exploring multiple diverse reasoning trajectories simultaneously. PLR constructs parallel reasoning streams through learnable trigger tokens in continuous latent space, preserves diversity across streams via global reasoning regularization, and adaptively synthesizes multi-stream outputs through mixture-of-reasoning-streams aggregation. Extensive experiments on three real-world datasets demonstrate that PLR substantially outperforms state-of-the-art baselines while maintaining real-time inference efficiency. Theoretical analysis further validates the effectiveness of parallel reasoning in improving generalization capability. Our work opens new avenues for enhancing reasoning capacity in sequential recommendation beyond existing depth scaling. + oai:arXiv.org:2601.03153v1 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiakai Tang, Xu Chen, Wen Chen, Jian Wu, Yuning Jiang, Bo Zheng + + + Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective + https://arxiv.org/abs/2601.03154 + arXiv:2601.03154v1 Announce Type: new +Abstract: Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation--which requires capturing probabilistic ambiguity rather than resolving it--remains underexplored. We investigate this through systematic disentanglement experiments on distribution-based tasks, employing Cross-CoT experiments to isolate the effect of reasoning text from intrinsic model priors. We observe a distinct "decoupled mechanism": while CoT improves distributional alignment, final accuracy is dictated by CoT content (99% variance contribution), whereas distributional ranking is governed by model priors (over 80%). Step-wise analysis further shows that while CoT's influence on accuracy grows monotonically during the reasoning process, distributional structure is largely determined by LLM's intrinsic priors. These findings suggest that long CoT serves as a decisive LLM decision-maker for the top option but fails to function as a granular distribution calibrator for ambiguous tasks. + oai:arXiv.org:2601.03154v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Beiduo Chen, Tiancheng Hu, Caiqi Zhang, Robert Litschko, Anna Korhonen, Barbara Plank + + + Prompt-Counterfactual Explanations for Generative AI System Behavior + https://arxiv.org/abs/2601.03156 + arXiv:2601.03156v1 Announce Type: new +Abstract: As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their behavior. In particular, decision-makers need to understand what causes generative AI systems to exhibit specific output characteristics. Within this general topic, this paper examines a key question: what is it about the input -the prompt- that causes an LLM-based generative AI system to produce output that exhibits specific characteristics, such as toxicity, negative sentiment, or political bias. To examine this question, we adapt a common technique from the Explainable AI literature: counterfactual explanations. We explain why traditional counterfactual explanations cannot be applied directly to generative AI systems, due to several differences in how generative AI systems function. We then propose a flexible framework that adapts counterfactual explanations to non-deterministic, generative AI systems in scenarios where downstream classifiers can reveal key characteristics of their outputs. Based on this framework, we introduce an algorithm for generating prompt-counterfactual explanations (PCEs). Finally, we demonstrate the production of counterfactual explanations for generative AI systems with three case studies, examining different output characteristics (viz., political leaning, toxicity, and sentiment). The case studies further show that PCEs can streamline prompt engineering to suppress undesirable output characteristics and can enhance red-teaming efforts to uncover additional prompts that elicit undesirable outputs. Ultimately, this work lays a foundation for prompt-focused interpretability in generative AI: a capability that will become indispensable as these models are entrusted with higher-stakes tasks and subject to emerging regulatory requirements for transparency and accountability. + oai:arXiv.org:2601.03156v1 + cs.LG + cs.AI + cs.CL + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Sofie Goethals, Foster Provost, Jo\~ao Sedoc + + + Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation + https://arxiv.org/abs/2601.03159 + arXiv:2601.03159v1 Announce Type: new +Abstract: Time series augmentation is critical for training robust deep learning models, particularly in domains where labelled data is scarce and expensive to obtain. However, existing augmentation libraries for time series, mainly written in Python, suffer from performance bottlenecks, where running time grows exponentially as dataset sizes increase -- an aspect limiting their applicability in large-scale, production-grade systems. We introduce RATS (Rapid Augmentations for Time Series), a high-performance library for time series augmentation written in Rust with Python bindings (RATSpy). RATS implements multiple augmentation methods spanning basic transformations, frequency-domain operations and time warping techniques, all accessible through a unified pipeline interface with built-in parallelisation. Comprehensive benchmarking of RATSpy versus a commonly used library (tasug) on 143 datasets demonstrates that RATSpy achieves an average speedup of 74.5\% over tsaug (up to 94.8\% on large datasets), with up to 47.9\% less peak memory usage. + oai:arXiv.org:2601.03159v1 + cs.LG + cs.AI + cs.PF + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Wadie Skaf, Felix Kern, Aryamaan Basu Roy, Tejas Pradhan, Roman Kalkreuth, Holger Hoos + + + Stability, convergence, and geometric properties of second-order-in-time space-time discretizations for linear and semilinear wave equations + https://arxiv.org/abs/2601.03160 + arXiv:2601.03160v1 Announce Type: new +Abstract: We revisit second-order-in-time space-time discretizations of the linear and semilinear wave equations by establishing precise equivalences with first-order-in-time formulations. Focusing on schemes using continuous piecewise-polynomial trial functions in time, we analyze their stability, convergence, and geometric properties. We consider first a weak space-time formulation with test functions projected onto discontinuous polynomials of one degree lower in time, showing that it is equivalent to the scheme proposed in [French, Peterson 1996] in the linear case, and extended in [Karakashian, Makridakis 2005] to the semilinear case. In particular, this equivalence shows that this method conserves energy at mesh nodes but is not symplectic. We then introduce two symplectic variants, obtained through Gauss-Legendre and Gauss-Lobatto quadratures in time, and show that they correspond to specific Runge-Kutta time integrators. These connections clarify the geometric structure of the space-time methods considered. + oai:arXiv.org:2601.03160v1 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Matteo Ferrari, Ilaria Perugia, Enrico Zampa + + + On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime + https://arxiv.org/abs/2601.03162 + arXiv:2601.03162v1 Announce Type: new +Abstract: Spectral bias, the tendency of neural networks to learn low frequencies first, can be both a blessing and a curse. While it enhances the generalization capabilities by suppressing high-frequency noise, it can be a limitation in scientific tasks that require capturing fine-scale structures. The delayed generalization phenomenon known as grokking is another barrier to rapid training of neural networks. Grokking has been hypothesized to arise as learning transitions from the NTK to the feature-rich regime. This paper explores the impact of preconditioned gradient descent (PGD), such as Gauss-Newton, on spectral bias and grokking phenomena. We demonstrate through theoretical and empirical results how PGD can mitigate issues associated with spectral bias. Additionally, building on the rich learning regime grokking hypothesis, we study how PGD can be used to reduce delays associated with grokking. Our conjecture is that PGD, without the impediment of spectral bias, enables uniform exploration of the parameter space in the NTK regime. Our experimental results confirm this prediction, providing strong evidence that grokking represents a transitional behavior between the lazy regime characterized by the NTK and the rich regime. These findings deepen our understanding of the interplay between optimization dynamics, spectral bias, and the phases of neural network learning. + oai:arXiv.org:2601.03162v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Shuai Jiang, Alexey Voronin, Eric Cyr, Ben Southworth + + + LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole Slide Images + https://arxiv.org/abs/2601.03163 + arXiv:2601.03163v1 Announce Type: new +Abstract: Precise and scalable instance segmentation of cell nuclei is essential for computational pathology, yet gigapixel Whole-Slide Images pose major computational challenges. Existing approaches rely on patch-based processing and costly post-processing for instance separation, sacrificing context and efficiency. We introduce LSP-DETR (Local Star Polygon DEtection TRansformer), a fully end-to-end framework that uses a lightweight transformer with linear complexity to process substantially larger images without additional computational cost. Nuclei are represented as star-convex polygons, and a novel radial distance loss function allows the segmentation of overlapping nuclei to emerge naturally, without requiring explicit overlap annotations or handcrafted post-processing. Evaluations on PanNuke and MoNuSeg show strong generalization across tissues and state-of-the-art efficiency, with LSP-DETR being over five times faster than the next-fastest leading method. Code and models are available at https://github.com/RationAI/lsp-detr. + oai:arXiv.org:2601.03163v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Mat\v{e}j Pek\'ar, V\'it Musil, Rudolf Nenutil, Petr Holub, Tom\'a\v{s} Br\'azdil + + + WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning + https://arxiv.org/abs/2601.03164 + arXiv:2601.03164v1 Announce Type: new +Abstract: Large Language Model(LLM)-based agents have shown strong capabilities in web information seeking, with reinforcement learning (RL) becoming a key optimization paradigm. However, planning remains a bottleneck, as existing methods struggle with long-horizon strategies. Our analysis reveals a critical phenomenon, plan anchor, where the first reasoning step disproportionately impacts downstream behavior in long-horizon web reasoning tasks. Current RL algorithms, fail to account for this by uniformly distributing rewards across the trajectory. To address this, we propose Anchor-GRPO, a two-stage RL framework that decouples planning and execution. In Stage 1, the agent optimizes its first-step planning using fine-grained rubrics derived from self-play experiences and human calibration. In Stage 2, execution is aligned with the initial plan through sparse rewards, ensuring stable and efficient tool usage. We evaluate Anchor-GRPO on four benchmarks: BrowseComp, BrowseComp-Zh, GAIA, and XBench-DeepSearch. Across models from 3B to 30B, Anchor-GRPO outperforms baseline GRPO and First-step GRPO, improving task success and tool efficiency. Notably, WebAnchor-30B achieves 46.0% pass@1 on BrowseComp and 76.4% on GAIA. Anchor-GRPO also demonstrates strong scalability, getting higher accuracy as model size and context length increase. + oai:arXiv.org:2601.03164v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yu Xinmiao, Zhang Liwen, Feng Xiaocheng, Jiang Yong, Qin Bing, Xie Pengjun, Zhou Jingren + + + On the Euclidean duals of the cyclic codes generated by cyclotomic polynomials + https://arxiv.org/abs/2601.03165 + arXiv:2601.03165v1 Announce Type: new +Abstract: In this article, we determine the minimum distance of the Euclidean dual of the cyclic code $\mathcal{C}_n$ generated by the $n$th cyclotomic polynomial $Q_n(x)$ over $\mathbb{F}_q$, for every positive integer $n$ co-prime to $q$. In particular, we prove that the minimum distance of $\mathcal{C}_{n}^{\perp}$ is a function of $n$, namely $2^{\omega(n)}$. This was precisely the conjecture posed by us in \cite{BHAGAT2025}. + oai:arXiv.org:2601.03165v1 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Anuj Kumar Bhagat, Ritumoni Sarma + + + Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization + https://arxiv.org/abs/2601.03166 + arXiv:2601.03166v1 Announce Type: new +Abstract: Choosing a suitable ML model is a complex task that can depend on several objectives, e.g., accuracy, model size, fairness, inference time, or energy consumption. In practice, this requires trading off multiple, often competing, objectives through multi-objective optimization (MOO). However, existing MOO methods typically treat all hyperparameters as equally important, overlooking that hyperparameter importance (HPI) can vary significantly depending on the trade-off between objectives. We propose a novel dynamic optimization approach that prioritizes the most influential hyperparameters based on varying objective trade-offs during the search process, which accelerates empirical convergence and leads to better solutions. Building on prior work on HPI for MOO post-analysis, we now integrate HPI, calculated with HyperSHAP, into the optimization. For this, we leverage the objective weightings naturally produced by the MOO algorithm ParEGO and adapt the configuration space by fixing the unimportant hyperparameters, allowing the search to focus on the important ones. Eventually, we validate our method with diverse tasks from PyMOO and YAHPO-Gym. Empirical results demonstrate improvements in convergence speed and Pareto front quality compared to baselines. + oai:arXiv.org:2601.03166v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-sa/4.0/ + Daphne Theodorakopoulos, Marcel Wever, Marius Lindauer + + + Can Embedding Similarity Predict Cross-Lingual Transfer? A Systematic Study on African Languages + https://arxiv.org/abs/2601.03168 + arXiv:2601.03168v1 Announce Type: new +Abstract: Cross-lingual transfer is essential for building NLP systems for low-resource African languages, but practitioners lack reliable methods for selecting source languages. We systematically evaluate five embedding similarity metrics across 816 transfer experiments spanning three NLP tasks, three African-centric multilingual models, and 12 languages from four language families. We find that cosine gap and retrieval-based metrics (P@1, CSLS) reliably predict transfer success ($\rho = 0.4-0.6$), while CKA shows negligible predictive power ($\rho \approx 0.1$). Critically, correlation signs reverse when pooling across models (Simpson's Paradox), so practitioners must validate per-model. Embedding metrics achieve comparable predictive power to URIEL linguistic typology. Our results provide concrete guidance for source language selection and highlight the importance of model-specific analysis. + oai:arXiv.org:2601.03168v1 + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Tewodros Kederalah Idris, Prasenjit Mitra, Roald Eiselen + + + Segment-Aware Conditioning for Training-Free Intra-Utterance Emotion and Duration Control in Text-to-Speech + https://arxiv.org/abs/2601.03170 + arXiv:2601.03170v1 Announce Type: new +Abstract: While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance expression challenging due to their reliance on non-public datasets or complex multi-stage training. In this paper, we propose a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression. Specifically, we propose a segment-aware emotion conditioning strategy that combines causal masking with monotonic stream alignment filtering to isolate emotion conditioning and schedule mask transitions, enabling smooth intra-utterance emotion shifts while preserving global semantic coherence. Based on this, we further propose a segment-aware duration steering strategy to combine local duration embedding steering with global EOS logit modulation, allowing local duration adjustment while ensuring globally consistent termination. To eliminate the need for segment-level manual prompt engineering, we construct a 30,000-sample multi-emotion and duration-annotated text dataset to enable LLM-based automatic prompt construction. Extensive experiments demonstrate that our training-free method not only achieves state-of-the-art intra-utterance consistency in multi-emotion and duration control, but also maintains baseline-level speech quality of the underlying TTS model. Audio samples are available at https://aclanonymous111.github.io/TED-TTS-DemoPage/. + oai:arXiv.org:2601.03170v1 + cs.SD + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Qifan Liang, Yuansen Liu, Ruixin Wei, Nan Lu, Junchuan Zhao, Ye Wang + + + Eco-WakeLoc: An Energy-Neutral and Cooperative UWB Real-Time Locating System + https://arxiv.org/abs/2601.03171 + arXiv:2601.03171v1 Announce Type: new +Abstract: Indoor localization systems face a fundamental trade-off between efficiency and responsiveness, which is especially important for emerging use cases such as mobile robots operating in GPS-denied environments. Traditional RTLS either require continuously powered infrastructure, limiting their scalability, or are limited by their responsiveness. This work presents Eco-WakeLoc, designed to achieve centimeter-level UWB localization while remaining energy-neutral by combining ultra-low power wake-up radios (WuRs) with solar energy harvesting. By activating anchor nodes only on demand, the proposed system eliminates constant energy consumption while achieving centimeter-level positioning accuracy. To reduce coordination overhead and improve scalability, Eco-WakeLoc employs cooperative localization where active tags initiate ranging exchanges (trilateration), while passive tags opportunistically reuse these messages for TDOA positioning. An additive-increase/multiplicative-decrease (AIMD)-based energy-aware scheduler adapts localization rates according to the harvested energy, thereby maximizing the overall performance of the sensor network while ensuring long-term energy neutrality. The measured energy consumption is only 3.22mJ per localization for active tags, 951uJ for passive tags, and 353uJ for anchors. Real-world deployment on a quadruped robot with nine anchors confirms the practical feasibility, achieving an average accuracy of 43cm in dynamic indoor environments. Year-long simulations show that tags achieve an average of 2031 localizations per day, retaining over 7% battery capacity after one year -- demonstrating that the RTLS achieves sustained energy-neutral operation. Eco-WakeLoc demonstrates that high-accuracy indoor localization can be achieved at scale without continuous infrastructure operation, combining energy neutrality, cooperative positioning, and adaptive scheduling. + oai:arXiv.org:2601.03171v1 + cs.NI + cs.ET + eess.SP + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/JSEN.2026.3652283 + Silvano Cortesi, Lukas Schulthess, Davide Plozza, Christian Vogt, Michele Magno + + + Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions + https://arxiv.org/abs/2601.03173 + arXiv:2601.03173v1 Announce Type: new +Abstract: Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored in intelligent transportation systems. We present a large-scale dataset of 129,000+ labeled multivariate time-series sequences from 153 rides by 51 participants under No, Low, and High Time Pressure conditions. Each sequence captures 63 features spanning vehicle kinematics, control inputs, behavioral violations, and environmental context. Our empirical analysis shows High Time Pressure induces 48% higher speeds, 36.4% greater speed variability, 58% more risky turns at intersections, 36% more sudden braking, and 50% higher rear brake forces versus No Time Pressure. To benchmark this dataset, we propose MotoTimePressure, a deep learning model combining convolutional preprocessing, dual-stage temporal attention, and Squeeze-and-Excitation feature recalibration, achieving 91.53% accuracy and 98.93% ROC AUC, outperforming eight baselines. Since time pressure cannot be directly measured in real time, we demonstrate its utility in collision prediction and threshold determination. Using MTPS-predicted time pressure as features, improves Informer-based collision risk accuracy from 91.25% to 93.51%, approaching oracle performance (93.72%). Thresholded time pressure states capture rider cognitive stress and enable proactive ITS interventions, including adaptive alerts, haptic feedback, V2I signaling, and speed guidance, supporting safer two-wheeler mobility under the Safe System Approach. + oai:arXiv.org:2601.03173v1 + cs.LG + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sumit S. Shevtekar, Chandresh K. Maurya, Gourab Sil, Subasish Das + + + DiffBench Meets DiffAgent: End-to-End LLM-Driven Diffusion Acceleration Code Generation + https://arxiv.org/abs/2601.03178 + arXiv:2601.03178v1 Announce Type: new +Abstract: Diffusion models have achieved remarkable success in image and video generation. However, their inherently multiple step inference process imposes substantial computational overhead, hindering real-world deployment. Accelerating diffusion models is therefore essential, yet determining how to combine multiple model acceleration techniques remains a significant challenge. To address this issue, we introduce a framework driven by large language models (LLMs) for automated acceleration code generation and evaluation. First, we present DiffBench, a comprehensive benchmark that implements a three stage automated evaluation pipeline across diverse diffusion architectures, optimization combinations and deployment scenarios. Second, we propose DiffAgent, an agent that generates optimal acceleration strategies and codes for arbitrary diffusion models. DiffAgent employs a closed-loop workflow in which a planning component and a debugging component iteratively refine the output of a code generation component, while a genetic algorithm extracts performance feedback from the execution environment to guide subsequent code refinements. We provide a detailed explanation of the DiffBench construction and the design principles underlying DiffAgent. Extensive experiments show that DiffBench offers a thorough evaluation of generated codes and that DiffAgent significantly outperforms existing LLMs in producing effective diffusion acceleration strategies. + oai:arXiv.org:2601.03178v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Jiajun jiao, Haowei Zhu, Puyuan Yang, Jianghui Wang, Ji Liu, Ziqiong Liu, Dong Li, Yuejian Fang, Junhai Yong, Bin Wang, Emad Barsoum + + + Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey + https://arxiv.org/abs/2601.03181 + arXiv:2601.03181v1 Announce Type: new +Abstract: Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general-purpose AI agents capable of handling diverse network management requests and highly complex wireless-related tasks involving multi-modal data. Inspired by these ideas, this work discusses the utilization of FMs, especially multi-modal FMs in wireless networks. We focus on two important types of tasks in wireless network management: prediction tasks and control tasks. In particular, we first discuss FMs-enabled multi-modal contextual information understanding in wireless networks. Then, we explain how FMs can be applied to prediction and control tasks, respectively. Following this, we introduce the development of wireless-specific FMs from two perspectives: available datasets for development and the methodologies used. Finally, we conclude with a discussion of the challenges and future directions for FM-enhanced wireless networks. + oai:arXiv.org:2601.03181v1 + cs.NI + cs.AI + cs.CL + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/COMST.2025.3648785 + Han Zhang, Mohammad Farzanullah, Mohammad Ghassemi, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci + + + Decentralized Autoregressive Generation + https://arxiv.org/abs/2601.03184 + arXiv:2601.03184v1 Announce Type: new +Abstract: We present a theoretical analysis of decentralization of autoregressive generation. We define the Decentralized Discrete Flow Matching objective, by expressing probability generating velocity as a linear combination of expert flows. We also conduct experiments demonstrat- ing the equivalence between decentralized and centralized training settings for multimodal language models across diverse set of benchmarks. Specifically, we compare two distinct paradigms: LLaVA and InternVL 2.5-1B, which uses a fixed CLIP vision encoder and per- forms full-parameter fine-tuning (ViT+MLP+LLM) during the instruction tuning stage. + oai:arXiv.org:2601.03184v1 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Stepan Maschan, Haoxuan Qu, Jun Liu + + + TaNG: Modeling Packet Classification with TSS-assisted Neural Networks on GPUs + https://arxiv.org/abs/2601.03187 + arXiv:2601.03187v1 Announce Type: new +Abstract: Packet classification is a core function in software-defined networks, and learning-based methods have recently shown significant throughput gains on large-scale rulesets. However, existing learning-based approaches struggle with overlapping rules, leading to incomplete model coverage or excessive rule replication. Their limited GPU integration also hampers performance with large-scale rulesets. To address these issues, we propose TaNG, which utilizes a single neural network trained on multi-dimensional features to ensure complete coverage without duplicating rules. TaNG employs a semi-structured design that combines the neural network model with a tuple space, reducing model complexity. Furthermore, we develop a mechanism based on the semi-structure for rule updates. Finally, we implement a CPU-GPU hybrid streaming framework tailored for learning-based methods, further enhancing throughput. On our GPU-based classification framework with 512k rulesets, TaNG achieves 12.19x and 9.37x higher throughput and 98.84x and 156.98x higher performance stability compared to two state-of-the-art learning methods NuevoMatch and NeuTree, respectively. + oai:arXiv.org:2601.03187v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhengyu Liao, Shiyou Qian + + + Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning + https://arxiv.org/abs/2601.03190 + arXiv:2601.03190v1 Announce Type: new +Abstract: Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens in a response indiscriminately and enforce uncertainty over the entire vocabulary. This global treatment results in unnecessary utility degradation and extends optimization to content-agnostic regions. To address these limitations, we propose PALU (Prefix-Aware Localized Unlearning), a framework driven by a local entropy maximization objective across both temporal and vocabulary dimensions. PALU reveals that (i) suppressing the sensitive prefix alone is sufficient to sever the causal generation link, and (ii) flattening only the top-$k$ logits is adequate to maximize uncertainty in the critical subspace. These findings allow PALU to avoid redundant optimization across the full vocabulary and parameter space while minimizing collateral damage to general model performance. Extensive experiments validate that PALU achieves superior forgetting efficacy and utility preservation compared to state-of-the-art baselines. + oai:arXiv.org:2601.03190v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Naixin Zhai, Pengyang Shao, Binbin Zheng, Fei Shen, Long Bai, Xun Yang + + + AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation + https://arxiv.org/abs/2601.03191 + arXiv:2601.03191v1 Announce Type: new +Abstract: Multimodal medical large language models have shown impressive progress in chest X-ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to establish a true anatomical correspondence, resulting in incorrect anatomical understanding in the medical domain. To address this gap, we introduce AnatomiX, a multitask multimodal large language model explicitly designed for anatomically grounded chest X-ray interpretation. Inspired by the radiological workflow, AnatomiX adopts a two stage approach: first, it identifies anatomical structures and extracts their features, and then leverages a large language model to perform diverse downstream tasks such as phrase grounding, report generation, visual question answering, and image understanding. Extensive experiments across multiple benchmarks demonstrate that AnatomiX achieves superior anatomical reasoning and delivers over 25% improvement in performance on anatomy grounding, phrase grounding, grounded diagnosis and grounded captioning tasks compared to existing approaches. Code and pretrained model are available at https://github.com/aneesurhashmi/anatomix + oai:arXiv.org:2601.03191v1 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Anees Ur Rehman Hashmi, Numan Saeed, Christoph Lippert + + + MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory + https://arxiv.org/abs/2601.03192 + arXiv:2601.03192v1 Announce Type: new +Abstract: The hallmark of human intelligence is the ability to master new skills through Constructive Episodic Simulation-retrieving past experiences to synthesize solutions for novel tasks. While Large Language Models possess strong reasoning capabilities, they struggle to emulate this self-evolution: fine-tuning is computationally expensive and prone to catastrophic forgetting, while existing memory-based methods rely on passive semantic matching that often retrieves noise. To address these challenges, we propose MemRL, a framework that enables agents to self-evolve via non-parametric reinforcement learning on episodic memory. MemRL explicitly separates the stable reasoning of a frozen LLM from the plastic, evolving memory. Unlike traditional methods, MemRL employs a Two-Phase Retrieval mechanism that filters candidates by semantic relevance and then selects them based on learned Q-values (utility). These utilities are continuously refined via environmental feedback in an trial-and-error manner, allowing the agent to distinguish high-value strategies from similar noise. Extensive experiments on HLE, BigCodeBench, ALFWorld, and Lifelong Agent Bench demonstrate that MemRL significantly outperforms state-of-the-art baselines. Our analysis experiments confirm that MemRL effectively reconciles the stability-plasticity dilemma, enabling continuous runtime improvement without weight updates. + oai:arXiv.org:2601.03192v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou, Junwei Liao, Yuchen Feng, Weinan Zhang, Ying Wen, Zhiyu Li, Feiyu Xiong, Yutao Qi, Bo Tang, Muning Wen + + + UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision + https://arxiv.org/abs/2601.03193 + arXiv:2601.03193v1 Announce Type: new +Abstract: While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals. To validate the restoration of multimodal coherence, we introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop. Extensive experiments demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves SOTA performance on TIIF(73.8), DPG(86.8), CompBench(88.5), and UniCycle while further delivering substantial gains of +5.0 on WISE and +6.5 on OneIG. These results highlight that our method significantly enhances T2I generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence. + oai:arXiv.org:2601.03193v1 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ruiyan Han, Zhen Fang, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao + + + X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework + https://arxiv.org/abs/2601.03194 + arXiv:2601.03194v1 Announce Type: new +Abstract: Hate speech detection on social media faces challenges in both accuracy and explainability, especially for underexplored Indic languages. We propose a novel explainability-guided training framework, X-MuTeST (eXplainable Multilingual haTe Speech deTection), for hate speech detection that combines high-level semantic reasoning from large language models (LLMs) with traditional attention-enhancing techniques. We extend this research to Hindi and Telugu alongside English by providing benchmark human-annotated rationales for each word to justify the assigned class label. The X-MuTeST explainability method computes the difference between the prediction probabilities of the original text and those of unigrams, bigrams, and trigrams. Final explanations are computed as the union between LLM explanations and X-MuTeST explanations. We show that leveraging human rationales during training enhances both classification performance and explainability. Moreover, combining human rationales with our explainability method to refine the model attention yields further improvements. We evaluate explainability using Plausibility metrics such as Token-F1 and IOU-F1 and Faithfulness metrics such as Comprehensiveness and Sufficiency. By focusing on under-resourced languages, our work advances hate speech detection across diverse linguistic contexts. Our dataset includes token-level rationale annotations for 6,004 Hindi, 4,492 Telugu, and 6,334 English samples. Data and code are available on https://github.com/ziarehman30/X-MuTeST + oai:arXiv.org:2601.03194v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + AAA 2026 (AISI) + Mohammad Zia Ur Rehman, Sai Kartheek Reddy Kasu, Shashivardhan Reddy Koppula, Sai Rithwik Reddy Chirra, Shwetank Shekhar Singh, Nagendra Kumar + + + Sparse Knowledge Distillation: A Mathematical Framework for Probability-Domain Temperature Scaling and Multi-Stage Compression + https://arxiv.org/abs/2601.03195 + arXiv:2601.03195v1 Announce Type: new +Abstract: We develop a unified theoretical framework for sparse knowledge distillation based on probability-domain softening operators. While the equivalence $p^{1/T} \propto \mathrm{softmax}(z/T)$ is well known, our contribution is an operator-level analytical framework built on this foundation rather than the equivalence itself. + The framework comprises four core components: (i) operator-agnostic bias--variance decompositions that characterize when sparse students outperform dense teachers, (ii) a homotopy path formalization of multi-stage pruning in function space explaining why iterative compression succeeds where one-shot pruning fails, (iii) convergence guarantees establishing $O(1/n)$ rates for $n$-stage distillation with explicit parameter dependence, and (iv) equivalence class characterizations identifying distinct probability-domain operators that yield identical student models under capacity constraints. + We introduce an axiomatic definition of probability-domain softening operators based on ranking preservation, continuity, entropy monotonicity, identity, and boundary behavior, and show that multiple non-equivalent operator families satisfy these axioms. All learning-theoretic guarantees are shown to hold uniformly across this operator class, independent of implementation details. These results provide theoretical grounding for black-box teacher distillation, partial-access settings such as top-$k$ truncation and text-only outputs, and privacy-preserving model compression. + oai:arXiv.org:2601.03195v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Aaron R. Flouro, Shawn P. Chadwick + + + Software-Defined Agentic Serving + https://arxiv.org/abs/2601.03197 + arXiv:2601.03197v1 Announce Type: new +Abstract: As multi-agent LLM pipelines grow in complexity, existing serving paradigms fail to adapt to the dynamic serving conditions. We argue that agentic serving systems should be programmable and system-aware, unlike existing serving which statically encode the parameters. In this work, we propose a new SDN-inspired agentic serving framework that helps control the key attributes of communication based on runtime state. This architecture enables serving-efficient, responsive agent systems and paves the way for high-level intent-driven agentic serving. + oai:arXiv.org:2601.03197v1 + cs.DC + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Saurabh Agarwal, Marco Laju, Jayanth Srinivasa, Myungjin Lee, Aditya Akella + + + Empowering Reliable Visual-Centric Instruction Following in MLLMs + https://arxiv.org/abs/2601.03198 + arXiv:2601.03198v1 Announce Type: new +Abstract: Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs' instruction-following capability primarily focus on verbal instructions in the textual modality. These limitations hinder a thorough analysis of instruction-following capabilities, as they overlook the implicit constraints embedded in the semantically rich visual modality. To address this gap, we introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset that evaluates MLLMs' instruction-following ability under multimodal settings. Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. Furthermore, by fine-tuning MLLMs on our dataset, we achieve substantial gains in visual instruction-following accuracy and adherence. Through extensive evaluation across representative MLLMs, we provide new insights into the strengths and limitations of current models. + oai:arXiv.org:2601.03198v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Weilei He, Feng Ju, Zhiyuan Fan, Rui Min, Minhao Cheng, Yi R. Fung + + + DIP: Dynamic In-Context Planner For Diffusion Language Models + https://arxiv.org/abs/2601.03199 + arXiv:2601.03199v1 Announce Type: new +Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. However, due to the bidirectional attention mechanism, DLMs incur substantial computational cost as context length increases. This work addresses this issue with a key discovery: unlike the sequential generation in autoregressive language models (ARLMs), the diffusion generation paradigm in DLMs allows \textit{efficient dynamic adjustment of the context} during generation. Building on this insight, we propose \textbf{D}ynamic \textbf{I}n-Context \textbf{P}lanner (DIP), a context-optimization method that dynamically selects and inserts in-context examples during generation, rather than providing all examples in the prompt upfront. Results show DIP maintains generation quality while achieving up to 12.9$\times$ inference speedup over standard inference and 1.17$\times$ over KV cache-enhanced inference. + oai:arXiv.org:2601.03199v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Yang Li, Han Meng, Chenan Wang, Haipeng Chen + + + A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting + https://arxiv.org/abs/2601.03200 + arXiv:2601.03200v1 Announce Type: new +Abstract: Developing high-fidelity, interactive digital twins is crucial for enabling closed-loop motion planning and reliable real-world robot execution, which are essential to advancing sim-to-real transfer. However, existing approaches often suffer from slow reconstruction, limited visual fidelity, and difficulties in converting photorealistic models into planning-ready collision geometry. We present a practical framework that constructs high-quality digital twins within minutes from sparse RGB inputs. Our system employs 3D Gaussian Splatting (3DGS) for fast, photorealistic reconstruction as a unified scene representation. We enhance 3DGS with visibility-aware semantic fusion for accurate 3D labelling and introduce an efficient, filter-based geometry conversion method to produce collision-ready models seamlessly integrated with a Unity-ROS2-MoveIt physics engine. In experiments with a Franka Emika Panda robot performing pick-and-place tasks, we demonstrate that this enhanced geometric accuracy effectively supports robust manipulation in real-world trials. These results demonstrate that 3DGS-based digital twins, enriched with semantic and geometric consistency, offer a fast, reliable, and scalable path from perception to manipulation in unstructured environments. + oai:arXiv.org:2601.03200v1 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Ziyang Sun, Lingfan Bao, Tianhu Peng, Jingcheng Sun, Chengxu Zhou + + + Recursive querying of neural networks via weighted structures + https://arxiv.org/abs/2601.03201 + arXiv:2601.03201v1 Announce Type: new +Abstract: Expressive querying of machine learning models - viewed as a form of intentional data - enables their verification and interpretation using declarative languages, thereby making learned representations of data more accessible. Motivated by the querying of feedforward neural networks, we investigate logics for weighted structures. In the absence of a bound on neural network depth, such logics must incorporate recursion; thereto we revisit the functional fixpoint mechanism proposed by Gr\"adel and Gurevich. We adopt it in a Datalog-like syntax; we extend normal forms for fixpoint logics to weighted structures; and show an equivalent "loose" fixpoint mechanism that allows values of inductively defined weight functions to be overwritten. We propose a "scalar" restriction of functional fixpoint logic, of polynomial-time data complexity, and show it can express all PTIME model-agnostic queries over reduced networks with polynomially bounded weights. In contrast, we show that very simple model-agnostic queries are already NP-complete. Finally, we consider transformations of weighted structures by iterated transductions. + oai:arXiv.org:2601.03201v1 + cs.LO + cs.AI + cs.DB + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Martin Grohe, Christoph Standke, Juno Steegmans, Jan Van den Bussche + + + Counterfactual Fairness with Graph Uncertainty + https://arxiv.org/abs/2601.03203 + arXiv:2601.03203v1 Announce Type: new +Abstract: Evaluating machine learning (ML) model bias is key to building trustworthy and robust ML systems. Counterfactual Fairness (CF) audits allow the measurement of bias of ML models with a causal framework, yet their conclusions rely on a single causal graph that is rarely known with certainty in real-world scenarios. We propose CF with Graph Uncertainty (CF-GU), a bias evaluation procedure that incorporates the uncertainty of specifying a causal graph into CF. CF-GU (i) bootstraps a Causal Discovery algorithm under domain knowledge constraints to produce a bag of plausible Directed Acyclic Graphs (DAGs), (ii) quantifies graph uncertainty with the normalized Shannon entropy, and (iii) provides confidence bounds on CF metrics. Experiments on synthetic data show how contrasting domain knowledge assumptions support or refute audits of CF, while experiments on real-world data (COMPAS and Adult datasets) pinpoint well-known biases with high confidence, even when supplied with minimal domain knowledge constraints. + oai:arXiv.org:2601.03203v1 + cs.LG + cs.AI + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Davi Val\'erio, Chrysoula Zerva, Mariana Pinto, Ricardo Santos, Andr\'e Carreiro + + + InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents + https://arxiv.org/abs/2601.03204 + arXiv:2601.03204v1 Announce Type: new +Abstract: LLM agents can reason and use tools, but they often break down on long-horizon tasks due to unbounded context growth and accumulated errors. Common remedies such as context compression or retrieval-augmented prompting introduce trade-offs between information fidelity and reasoning stability. We present InfiAgent, a general-purpose framework that keeps the agent's reasoning context strictly bounded regardless of task duration by externalizing persistent state into a file-centric state abstraction. At each step, the agent reconstructs context from a workspace state snapshot plus a fixed window of recent actions. Experiments on DeepResearch and an 80-paper literature review task show that, without task-specific fine-tuning, InfiAgent with a 20B open-source model is competitive with larger proprietary systems and maintains substantially higher long-horizon coverage than context-centric baselines. These results support explicit state externalization as a practical foundation for stable long-horizon agents. Github Repo:https://github.com/ChenglinPoly/infiAgent + oai:arXiv.org:2601.03204v1 + cs.AI + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Chenglin Yu, Yuchen Wang, Songmiao Wang, Hongxia Yang, Ming Li + + + UltraLogic: Enhancing LLM Reasoning through Large-Scale Data Synthesis and Bipolar Float Reward + https://arxiv.org/abs/2601.03205 + arXiv:2601.03205v1 Announce Type: new +Abstract: While Large Language Models (LLMs) have demonstrated significant potential in natural language processing , complex general-purpose reasoning requiring multi-step logic, planning, and verification remains a critical bottleneck. Although Reinforcement Learning with Verifiable Rewards (RLVR) has succeeded in specific domains , the field lacks large-scale, high-quality, and difficulty-calibrated data for general reasoning. To address this, we propose UltraLogic, a framework that decouples the logical core of a problem from its natural language expression through a Code-based Solving methodology to automate high-quality data production. The framework comprises hundreds of unique task types and an automated calibration pipeline across ten difficulty levels. Furthermore, to mitigate binary reward sparsity and the Non-negative Reward Trap, we introduce the Bipolar Float Reward (BFR) mechanism, utilizing graded penalties to effectively distinguish perfect responses from those with logical flaws. Our experiments demonstrate that task diversity is the primary driver for reasoning enhancement , and that BFR, combined with a difficulty matching strategy, significantly improves training efficiency, guiding models toward global logical optima. + oai:arXiv.org:2601.03205v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yile Liu, Yixian Liu, Zongwei Li, Yufei Huang, Xinhua Feng, Zhichao Hu, Jinglu Hu, Jianfeng Yan, Fengzong Lian, Yuhong Liu + + + Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers + https://arxiv.org/abs/2601.03211 + arXiv:2601.03211v1 Announce Type: new +Abstract: In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an efficient approach to fine-tune small language models (SLMs) for accurate relevance labeling, enabling high-throughput, domain-specific labeling comparable or even better in quality to that of state-of-the-art large language models (LLMs). To overcome the lack of high-quality and accessible datasets in the enterprise domain, our method leverages on synthetic data generation. Specifically, we employ an LLM to synthesize realistic enterprise queries from a seed document, apply BM25 to retrieve hard negatives, and use a teacher LLM to assign relevance scores. The resulting dataset is then distilled into an SLM, producing a compact relevance labeler. We evaluate our approach on a high-quality benchmark consisting of 923 enterprise query-document pairs annotated by trained human annotators, and show that the distilled SLM achieves agreement with human judgments on par with or better than the teacher LLM. Furthermore, our fine-tuned labeler substantially improves throughput, achieving 17 times increase while also being 19 times more cost-effective. This approach enables scalable and cost-effective relevance labeling for enterprise-scale retrieval applications, supporting rapid offline evaluation and iteration in real-world settings. + oai:arXiv.org:2601.03211v1 + cs.IR + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yue Kang, Zhuoyi Huang, Benji Schussheim, Diana Licon, Dina Atia, Shixing Cao, Jacob Danovitch, Kunho Kim, Billy Norcilien, Jonah Karpman, Mahmound Sayed, Mike Taylor, Tao Sun, Pavel Metrikov, Vipul Agarwal, Chris Quirk, Ye-Yi Wang, Nick Craswell, Irene Shaffer, Tianwei Chen, Sulaiman Vesal, Soundar Srinivasan + + + Critic-Guided Reinforcement Unlearning in Text-to-Image Diffusion + https://arxiv.org/abs/2601.03213 + arXiv:2601.03213v1 Announce Type: new +Abstract: Machine unlearning in text-to-image diffusion models aims to remove targeted concepts while preserving overall utility. Prior diffusion unlearning methods typically rely on supervised weight edits or global penalties; reinforcement-learning (RL) approaches, while flexible, often optimize sparse end-of-trajectory rewards, yielding high-variance updates and weak credit assignment. We present a general RL framework for diffusion unlearning that treats denoising as a sequential decision process and introduces a timestep-aware critic with noisy-step rewards. Concretely, we train a CLIP-based reward predictor on noisy latents and use its per-step signal to compute advantage estimates for policy-gradient updates of the reverse diffusion kernel. Our algorithm is simple to implement, supports off-policy reuse, and plugs into standard text-to-image backbones. Across multiple concepts, the method achieves better or comparable forgetting to strong baselines while maintaining image quality and benign prompt fidelity; ablations show that (i) per-step critics and (ii) noisy-conditioned rewards are key to stability and effectiveness. We release code and evaluation scripts to facilitate reproducibility and future research on RL-based diffusion unlearning. + oai:arXiv.org:2601.03213v1 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Mykola Vysotskyi, Zahar Kohut, Mariia Shpir, Taras Rumezhak, Volodymyr Karpiv + + + oneTwin: Online Digital Network Twin via Neural Radio Radiance Field + https://arxiv.org/abs/2601.03216 + arXiv:2601.03216v1 Announce Type: new +Abstract: Digital network twin is a promising technology that replicates real-world networks in real-time and assists with the design, operation, and management of next-generation networks. However, existing approaches (e.g., simulator-based and neural-based) cannot effectively realize the digital network twin, in terms of fidelity, synchronicity, and tractability. In this paper, we propose oneTwin, the first online digital twin system, for the prediction of physical layer metrics. We architect the oneTwin system with two primary components: an enhanced simulator and a neural radio radiance field (NRRF). On the one hand, we achieve the enhanced simulator by designing a material tuning algorithm that incrementally optimizes the building materials to minimize the twin-to-real gap. On the other hand, we achieve the NRRF by designing a neural learning algorithm that continually updates its DNNs based on both online and simulated data from the enhanced simulator. We implement oneTwin system using Sionna RT as the simulator and developing new DNNs as the NRRF, under a public cellular network. Extensive experimental results show that, compared to state-of-the-art solutions, oneTwin achieves real-time updating (0.98s), with 36.39% and 57.50% reductions of twin-to-real gap under in-distribution and out-of-distribution test datasets, respectively. + oai:arXiv.org:2601.03216v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuru Zhang, Ming Zhao, Qiang Liu, Nakjung Choi + + + MalruleLib: Large-Scale Executable Misconception Reasoning with Step Traces for Modeling Student Thinking in Mathematics + https://arxiv.org/abs/2601.03217 + arXiv:2601.03217v1 Announce Type: new +Abstract: Student mistakes in mathematics are often systematic: a learner applies a coherent but wrong procedure and repeats it across contexts. We introduce MalruleLib, a learning-science-grounded framework that translates documented misconceptions into executable procedures, drawing on 67 learning-science and mathematics education sources, and generates step-by-step traces of malrule-consistent student work. We formalize a core student-modeling problem as Malrule Reasoning Accuracy (MRA): infer a misconception from one worked mistake and predict the student's next answer under cross-template rephrasing. Across nine language models (4B-120B), accuracy drops from 66% on direct problem solving to 40% on cross-template misconception prediction. MalruleLib encodes 101 malrules over 498 parameterized problem templates and produces paired dual-path traces for both correct reasoning and malrule-consistent student reasoning. Because malrules are executable and templates are parameterizable, MalruleLib can generate over one million instances, enabling scalable supervision and controlled evaluation. Using MalruleLib, we observe cross-template degradations of 10-21%, while providing student step traces improves prediction by 3-15%. We release MalruleLib as infrastructure for educational AI that models student procedures across contexts, enabling diagnosis and feedback that targets the underlying misconception. + oai:arXiv.org:2601.03217v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Xinghe Chen, Naiming Liu, Shashank Sonkar + + + Enhancing Safety in Automated Ports: A Virtual Reality Study of Pedestrian-Autonomous Vehicle Interactions under Time Pressure, Visual Constraints, and Varying Vehicle Size + https://arxiv.org/abs/2601.03218 + arXiv:2601.03218v1 Announce Type: new +Abstract: Autonomous driving improves traffic efficiency but presents safety challenges in complex port environments. This study investigates how environmental factors, traffic factors, and pedestrian characteristics influence interaction safety between autonomous vehicles and pedestrians in ports. Using virtual reality (VR) simulations of typical port scenarios, 33 participants completed pedestrian crossing tasks under varying visibility, vehicle sizes, and time pressure conditions. Results indicate that low-visibility conditions, partial occlusions and larger vehicle sizes significantly increase perceived risk, prompting pedestrians to wait longer and accept larger gaps. Specifically, pedestrians tended to accept larger gaps and waited longer when interacting with large autonomous truck platoons, reflecting heightened caution due to their perceived threat. However, local obstructions also reduce post-encroachment time, compressing safety margins. Individual attributes such as age, gender, and driving experience further shape decision-making, while time pressure undermines compensatory behaviors and increases risk. Based on these findings, safety strategies are proposed, including installing wide-angle cameras at multiple viewpoints, enabling real-time vehicle-infrastructure communication, enhancing port lighting and signage, and strengthening pedestrian safety training. This study offers practical recommendations for improving the safety and deployment of vision-based autonomous systems in port settings. + oai:arXiv.org:2601.03218v1 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuan Che, Mun On Wong, Xiaowei Gao, Haoyang Liang, Yun Ye + + + inRAN: Interpretable Online Bayesian Learning for Network Automation in Open Radio Access Networks + https://arxiv.org/abs/2601.03219 + arXiv:2601.03219v1 Announce Type: new +Abstract: Emerging AI/ML techniques have been showing great potential in automating network control in open radio access networks (Open RAN). However, existing approaches heavily rely on blackbox policies parameterized by deep neural networks, which inherently lack interpretability, explainability, and transparency, and create substantial obstacles in practical network deployment. In this paper, we propose inRAN, a novel interpretable online Bayesian learning framework for network automation in Open RAN. The core idea is to integrate interpretable surrogate models and safe optimization solvers to continually optimize control actions, while adapting to non-stationary dynamics in real-world networks. We achieve the inRAN framework with three key components: 1) an interpretable surrogate model via ensembling Kolmogorov-Arnold Networks (KANs); 2) safe optimization solvers via integrating genetic search and trust-region descent method; 3) an online dynamics tracker via continual model learning and adaptive threshold offset. We implement inRAN in an end-to-end O-RAN-compliant network testbed, and conduct extensive over-the-air experiments with the focused use case of network slicing. The results show that, inRAN substantially outperforms state-of-the-art works, by guaranteeing the chance-based constraint with a 92.67% assurance ratio with comparative resource usage throughout the online network control, under unforeseeable time-evolving network dynamics. + oai:arXiv.org:2601.03219v1 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ming Zhao, Yuru Zhang, Qiang Liu, Ahan Kak, Nakjung Choi + + + From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence + https://arxiv.org/abs/2601.03220 + arXiv:2601.03220v1 Announce Type: new +Abstract: Can we learn more from data than existed in the generating process itself? Can new and useful information be constructed from merely applying deterministic transformations to existing data? Can the learnable content in data be evaluated without considering a downstream task? On these questions, Shannon information and Kolmogorov complexity come up nearly empty-handed, in part because they assume observers with unlimited computational capacity and fail to target the useful information content. In this work, we identify and exemplify three seeming paradoxes in information theory: (1) information cannot be increased by deterministic transformations; (2) information is independent of the order of data; (3) likelihood modeling is merely distribution matching. To shed light on the tension between these results and modern practice, and to quantify the value of data, we introduce epiplexity, a formalization of information capturing what computationally bounded observers can learn from data. Epiplexity captures the structural content in data while excluding time-bounded entropy, the random unpredictable content exemplified by pseudorandom number generators and chaotic dynamical systems. With these concepts, we demonstrate how information can be created with computation, how it depends on the ordering of the data, and how likelihood modeling can produce more complex programs than present in the data generating process itself. We also present practical procedures to estimate epiplexity which we show capture differences across data sources, track with downstream performance, and highlight dataset interventions that improve out-of-distribution generalization. In contrast to principles of model selection, epiplexity provides a theoretical foundation for data selection, guiding how to select, generate, or transform data for learning systems. + oai:arXiv.org:2601.03220v1 + cs.LG + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Marc Finzi, Shikai Qiu, Yiding Jiang, Pavel Izmailov, J. Zico Kolter, Andrew Gordon Wilson + + + The Fake Friend Dilemma: Trust and the Political Economy of Conversational AI + https://arxiv.org/abs/2601.03222 + arXiv:2601.03222v1 Announce Type: new +Abstract: As conversational AI systems become increasingly integrated into everyday life, they raise pressing concerns about user autonomy, trust, and the commercial interests that influence their behavior. To address these concerns, this paper develops the Fake Friend Dilemma (FFD), a sociotechnical condition in which users place trust in AI agents that appear supportive while pursuing goals that are misaligned with the user's own. The FFD provides a critical framework for examining how anthropomorphic AI systems facilitate subtle forms of manipulation and exploitation. Drawing on literature in trust, AI alignment, and surveillance capitalism, we construct a typology of harms, including covert advertising, political propaganda, behavioral nudging, and surveillance. We then assess possible mitigation strategies, including both structural and technical interventions. By focusing on trust as a vector of asymmetrical power, the FFD offers a lens for understanding how AI systems may undermine user autonomy while maintaining the appearance of helpfulness. + oai:arXiv.org:2601.03222v1 + cs.CY + cs.AI + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Jacob Erickson + + + Are eHMIs always helpful? Investigating how eHMIs interfere with pedestrian behavior on multi-lane streets: An eye-tracking virtual reality experiment + https://arxiv.org/abs/2601.03223 + arXiv:2601.03223v1 Announce Type: new +Abstract: Appropriate communication is crucial for efficient and safe interactions between pedestrians and autonomous vehicles (AVs). External human-machine interfaces (eHMIs) on AVs, which can be categorized as allocentric or egocentric, are considered a promising solution. While the effectiveness of eHMIs has been extensively studied, in complex environments, such as unsignalized multi-lane streets, their potential to interfere with pedestrian crossing behavior remains underexplored. Hence, a virtual reality-based experiment was conducted to examine how different types of eHMIs displayed on AVs affect the crossing behavior of pedestrians in multi-lane streets environments, with a focus on the gaze patterns of pedestrians during crossing. The results revealed that the presence of eHMIs significantly influenced the cognitive load on pedestrians and increased the possibility of distraction, even misleading pedestrians in cases involving multiple AVs on multi-lane streets. Notably, allocentric eHMIs induced higher cognitive loads and greater distraction in pedestrians than egocentric eHMIs. This was primarily evidenced by longer gaze time and higher proportions of attention for the eHMI on the interacting vehicle, as well as a broader distribution of gaze toward vehicles in the non-interacting lane. However, misleading behavior was mainly triggered by eHMI signals from yielding vehicles in the non-interacting lane. Under such asymmetric signal configurations, egocentric eHMIs resulted in a higher misjudgment rate than allocentric eHMIs. These findings highlight the importance of enhancing eHMI designs to balance the clarity and consistency of the displayed information across different perspectives, especially in complex multi-lane traffic scenarios. This study provides valuable insights regarding the application and standardization of future eHMI systems for AVs. + oai:arXiv.org:2601.03223v1 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yun Ye, Zexuan Li, Panagiotis Angeloudis, S. C. Wong, Jian Sun, Haoyang Liang + + + Wait or cross? Understanding the influence of behavioral tendency, trust, and risk perception on pedestrian gap-acceptance of automated truck platoons + https://arxiv.org/abs/2601.03225 + arXiv:2601.03225v1 Announce Type: new +Abstract: Although automated trucks have the potential to improve freight efficiency, reduce costs, and address driver shortages, organizing two or more trucks in a convoy has raised considerable concerns for pedestrian safety. This study conducted a controlled experiment to examine the influence of behavioral tendency, trust, and risk perception on pedestrian intention to cross in front of an automated truck platoon. A total of 603 subjects participated in the virtual reality video-based questionnaire survey. By fusing the merits of structural equation modeling and artificial neural networks, a two-stage, hybrid model was developed to examine complex relationships between latent variables and gap-acceptance behaviors. Our results indicated that subjects watched an average of five vehicle gaps before starting crossing and the average time gap accepted was about 5.35 seconds. Risk perception not only played the most dominant role in shaping pedestrian crossing decisions, but also served as the strong bone, mediating the effects of behavioral tendency and trust on gap-acceptance. Participants who frequently violated traffic rules were more likely to accept a smaller time gap, while those who showed positive behaviors to other road users tended to wait for a larger time gap. Participants who often committed errors, showed aggressive behaviors, and held greater trust in the safety of automated trucks generally reported a lower level of risk for road-crossing in front of automated truck platoons. Built on these findings, a range of tailored countermeasures were proposed to ensure safer and smother interactions between pedestrians and automated truck platoons. + oai:arXiv.org:2601.03225v1 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yun Ye, Yuan Che, Haoyang Liang, Yingheng Zhang, Pengpeng Xu + + + The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization + https://arxiv.org/abs/2601.03227 + arXiv:2601.03227v1 Announce Type: new +Abstract: Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is relevant to public safety. In contrast, progress on audio geo-localization has been constrained by the lack of high-quality audio-location pairs. To address this gap, we introduce AGL1K, the first audio geo-localization benchmark for audio language models (ALMs), spanning 72 countries and territories. To extract reliably localizable samples from a crowd-sourced platform, we propose the Audio Localizability metric that quantifies the informativeness of each recording, yielding 1,444 curated audio clips. Evaluations on 16 ALMs show that ALMs have emerged with audio geo-localization capability. We find that closed-source models substantially outperform open-source models, and that linguistic clues often dominate as a scaffold for prediction. We further analyze ALMs' reasoning traces, regional bias, error causes, and the interpretability of the localizability metric. Overall, AGL1K establishes a benchmark for audio geo-localization and may advance ALMs with better geospatial reasoning capability. + oai:arXiv.org:2601.03227v1 + cs.SD + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ruixing Zhang, Zihan Liu, Leilei Sun, Tongyu Zhu, Weifeng Lv + + + SpANNS: Optimizing Approximate Nearest Neighbor Search for Sparse Vectors Using Near Memory Processing + https://arxiv.org/abs/2601.03229 + arXiv:2601.03229v1 Announce Type: new +Abstract: Approximate Nearest Neighbor Search (ANNS) is a fundamental operation in vector databases, enabling efficient similarity search in high-dimensional spaces. While dense ANNS has been optimized using specialized hardware accelerators, sparse ANNS remains limited by CPU-based implementations, hindering scalability. This limitation is increasingly critical as hybrid retrieval systems, combining sparse and dense embeddings, become standard in Information Retrieval (IR) pipelines. We propose SpANNS, a near-memory processing architecture for sparse ANNS. SpANNS combines a hybrid inverted index with efficient query management and runtime optimizations. The architecture is built on a CXL Type-2 near-memory platform, where a specialized controller manages query parsing and cluster filtering, while compute-enabled DIMMs perform index traversal and distance computations close to the data. It achieves 15.2x to 21.6x faster execution over the state-of-the-art CPU baselines, offering scalable and efficient solutions for sparse vector search. + oai:arXiv.org:2601.03229v1 + cs.DB + cs.AR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tianqi Zhang, Flavio Ponzina, Tajana Rosing + + + Multi-RADS Synthetic Radiology Report Dataset and Head-to-Head Benchmarking of 41 Open-Weight and Proprietary Language Models + https://arxiv.org/abs/2601.03232 + arXiv:2601.03232v1 Announce Type: new +Abstract: Background: Reporting and Data Systems (RADS) standardize radiology risk communication but automated RADS assignment from narrative reports is challenging because of guideline complexity, output-format constraints, and limited benchmarking across RADS frameworks and model sizes. Purpose: To create RXL-RADSet, a radiologist-verified synthetic multi-RADS benchmark, and compare validity and accuracy of open-weight small language models (SLMs) with a proprietary model for RADS assignment. Materials and Methods: RXL-RADSet contains 1,600 synthetic radiology reports across 10 RADS (BI-RADS, CAD-RADS, GB-RADS, LI-RADS, Lung-RADS, NI-RADS, O-RADS, PI-RADS, TI-RADS, VI-RADS) and multiple modalities. Reports were generated by LLMs using scenario plans and simulated radiologist styles and underwent two-stage radiologist verification. We evaluated 41 quantized SLMs (12 families, 0.135-32B parameters) and GPT-5.2 under a fixed guided prompt. Primary endpoints were validity and accuracy; a secondary analysis compared guided versus zero-shot prompting. Results: Under guided prompting GPT-5.2 achieved 99.8% validity and 81.1% accuracy (1,600 predictions). Pooled SLMs (65,600 predictions) achieved 96.8% validity and 61.1% accuracy; top SLMs in the 20-32B range reached ~99% validity and mid-to-high 70% accuracy. Performance scaled with model size (inflection between <1B and >=10B) and declined with RADS complexity primarily due to classification difficulty rather than invalid outputs. Guided prompting improved validity (99.2% vs 96.7%) and accuracy (78.5% vs 69.6%) compared with zero-shot. Conclusion: RXL-RADSet provides a radiologist-verified multi-RADS benchmark; large SLMs (20-32B) can approach proprietary-model performance under guided prompting, but gaps remain for higher-complexity schemes. + oai:arXiv.org:2601.03232v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Kartik Bose, Abhinandan Kumar, Raghuraman Soundararajan, Priya Mudgil, Samonee Ralmilay, Niharika Dutta, Manphool Singhal, Arun Kumar, Saugata Sen, Anurima Patra, Priya Ghosh, Abanti Das, Amit Gupta, Ashish Verma, Dipin Sudhakaran, Ekta Dhamija, Himangi Unde, Ishan Kumar, Krithika Rangarajan, Prerna Garg, Rachel Sequeira, Sudhin Shylendran, Taruna Yadav, Tej Pal, Pankaj Gupta + + + LTX-2: Efficient Joint Audio-Visual Foundation Model + https://arxiv.org/abs/2601.03233 + arXiv:2601.03233v1 Announce Type: new +Abstract: Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides. We introduce LTX-2, an open-source foundational model capable of generating high-quality, temporally synchronized audiovisual content in a unified manner. LTX-2 consists of an asymmetric dual-stream transformer with a 14B-parameter video stream and a 5B-parameter audio stream, coupled through bidirectional audio-video cross-attention layers with temporal positional embeddings and cross-modality AdaLN for shared timestep conditioning. This architecture enables efficient training and inference of a unified audiovisual model while allocating more capacity for video generation than audio generation. We employ a multilingual text encoder for broader prompt understanding and introduce a modality-aware classifier-free guidance (modality-CFG) mechanism for improved audiovisual alignment and controllability. Beyond generating speech, LTX-2 produces rich, coherent audio tracks that follow the characters, environment, style, and emotion of each scene -- complete with natural background and foley elements. In our evaluations, the model achieves state-of-the-art audiovisual quality and prompt adherence among open-source systems, while delivering results comparable to proprietary models at a fraction of their computational cost and inference time. All model weights and code are publicly released. + oai:arXiv.org:2601.03233v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, Andrew Kvochko, Avishai Berkowitz, Daniel Shalem, Daphna Lifschitz, Dudu Moshe, Eitan Porat, Eitan Richardson, Guy Shiran, Itay Chachy, Jonathan Chetboun, Michael Finkelson, Michael Kupchick, Nir Zabari, Nitzan Guetta, Noa Kotler, Ofir Bibi, Ori Gordon, Poriya Panet, Roi Benita, Shahar Armon, Victor Kulikov, Yaron Inger, Yonatan Shiftan, Zeev Melumian, Zeev Farbman + + + MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents + https://arxiv.org/abs/2601.03236 + arXiv:2601.03236v1 Announce Type: new +Abstract: Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This design limits interpretability and alignment between query intent and retrieved evidence, leading to suboptimal reasoning accuracy. In this paper, we propose MAGMA, a multi-graph agentic memory architecture that represents each memory item across orthogonal semantic, temporal, causal, and entity graphs. MAGMA formulates retrieval as policy-guided traversal over these relational views, enabling query-adaptive selection and structured context construction. By decoupling memory representation from retrieval logic, MAGMA provides transparent reasoning paths and fine-grained control over retrieval. Experiments on LoCoMo and LongMemEval demonstrate that MAGMA consistently outperforms state-of-the-art agentic memory systems in long-horizon reasoning tasks. + oai:arXiv.org:2601.03236v1 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Dongming Jiang, Yi Li, Guanpeng Li, Bingzhe Li + + + PET-TURTLE: Deep Unsupervised Support Vector Machines for Imbalanced Data Clusters + https://arxiv.org/abs/2601.03237 + arXiv:2601.03237v1 Announce Type: new +Abstract: Foundation vision, audio, and language models enable zero-shot performance on downstream tasks via their latent representations. Recently, unsupervised learning of data group structure with deep learning methods has gained popularity. TURTLE, a state of the art deep clustering algorithm, uncovers data labeling without supervision by alternating label and hyperplane updates, maximizing the hyperplane margin, in a similar fashion to support vector machines (SVMs). However, TURTLE assumes clusters are balanced; when data is imbalanced, it yields non-ideal hyperplanes that cause higher clustering error. We propose PET-TURTLE, which generalizes the cost function to handle imbalanced data distributions by a power law prior. Additionally, by introducing sparse logits in the labeling process, PET-TURTLE optimizes a simpler search space that in turn improves accuracy for balanced datasets. Experiments on synthetic and real data show that PET-TURTLE improves accuracy for imbalanced sources, prevents over-prediction of minority clusters, and enhances overall clustering. + oai:arXiv.org:2601.03237v1 + cs.LG + eess.IV + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-nd/4.0/ + 10.1109/LSP.2025.3636453 + IEEE Signal Processing Letters, vol. 33, pp. 91-95, 2026 + Javier Salazar Cavazos + + + On the Capacity Region of Individual Key Rates in Vector Linear Secure Aggregation + https://arxiv.org/abs/2601.03241 + arXiv:2601.03241v1 Announce Type: new +Abstract: We provide new insights into an open problem recently posed by Yuan-Sun [ISIT 2025], concerning the minimum individual key rate required in the vector linear secure aggregation problem. Consider a distributed system with $K$ users, where each user $k\in [K]$ holds a data stream $W_k$ and an individual key $Z_k$. A server aims to compute a linear function $\mathbf{F}[W_1;\ldots;W_K]$ without learning any information about another linear function $\mathbf{G}[W_1;\ldots;W_K]$, where $[W_1;\ldots;W_K]$ denotes the row stack of $W_1,\ldots,W_K$. The open problem is to determine the minimum required length of $Z_k$, denoted as $R_k$, $k\in [K]$. In this paper, we characterize a new achievable region for the rate tuple $(R_1,\ldots,R_K)$. The region is polyhedral, with vertices characterized by a binary rate assignment $(R_1,\ldots,R_K) = (\mathbf{1}(1 \in \mathcal{I}),\ldots,\mathbf{1}(K\in \mathcal{I}))$, where $\mathcal{I}\subseteq [K]$ satisfies the \textit{rank-increment condition}: $\mathrm{rank}\left(\bigl[\mathbf{F}_{\mathcal{I}};\mathbf{G}_{\mathcal{I}}\bigr]\right) =\mathrm{rank}\bigl(\mathbf{F}_{\mathcal{I}}\bigr)+N$. Here, $\mathbf{F}_\mathcal{I}$ and $\mathbf{G}_\mathcal{I}$ are the submatrices formed by the columns indexed by $\mathcal{I}$. Our results uncover the novel fact that it is not necessary for every user to hold a key, thereby strictly enlarging the best-known achievable region in the literature. Furthermore, we provide a converse analysis to demonstrate its optimality when minimizing the number of users that hold keys. + oai:arXiv.org:2601.03241v1 + cs.IT + cs.CR + cs.NI + eess.SP + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Lei Hu, Sennur Ulukus + + + SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion Zones + https://arxiv.org/abs/2601.03242 + arXiv:2601.03242v1 Announce Type: new +Abstract: Training data is a critical and often proprietary asset in Large Language Model (LLM) development, motivating the use of data watermarking to embed model-transferable signals for usage verification. We identify low coverage as a vital yet largely overlooked requirement for practicality, as individual data owners typically contribute only a minute fraction of massive training corpora. Prior methods fail to maintain stealthiness, verification feasibility, or robustness when only one or a few sequences can be modified. To address these limitations, we introduce SLIM, a framework enabling per-user data provenance verification under strict black-box access. SLIM leverages intrinsic LLM properties to induce a Latent-Space Confusion Zone by training the model to map semantically similar prefixes to divergent continuations. This manifests as localized generation instability, which can be reliably detected via hypothesis testing. Experiments demonstrate that SLIM achieves ultra-low coverage capability, strong black-box verification performance, and great scalability while preserving both stealthiness and model utility, offering a robust solution for protecting training data in modern LLM pipelines. + oai:arXiv.org:2601.03242v1 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hengyu Wu, Yang Cao + + + $\mathsf{QAC}^0$ Contains $\mathsf{TC}^0$ (with Many Copies of the Input) + https://arxiv.org/abs/2601.03243 + arXiv:2601.03243v1 Announce Type: new +Abstract: $\mathsf{QAC}^0$ is the class of constant-depth polynomial-size quantum circuits constructed from arbitrary single-qubit gates and generalized Toffoli gates. It is arguably the smallest natural class of constant-depth quantum computation which has not been shown useful for computing any non-trivial Boolean function. Despite this, many attempts to port classical $\mathsf{AC}^0$ lower bounds to $\mathsf{QAC}^0$ have failed. + We give one possible explanation of this: $\mathsf{QAC}^0$ circuits are significantly more powerful than their classical counterparts. We show the unconditional separation $\mathsf{QAC}^0\not\subset\mathsf{AC}^0[p]$ for decision problems, which also resolves for the first time whether $\mathsf{AC}^0$ could be more powerful than $\mathsf{QAC}^0$. Moreover, we prove that $\mathsf{QAC}^0$ circuits can compute a wide range of Boolean functions if given multiple copies of the input: $\mathsf{TC}^0 \subseteq \mathsf{QAC}^0 \circ \mathsf{NC}^0$. Along the way, we introduce an amplitude amplification technique that makes several approximate constant-depth constructions exact. + oai:arXiv.org:2601.03243v1 + cs.CC + quant-ph + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Daniel Grier, Jackson Morris, Kewen Wu + + + STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning + https://arxiv.org/abs/2601.03248 + arXiv:2601.03248v1 Announce Type: new +Abstract: Spatio-temporal reasoning in time series involves the explicit synthesis of temporal dynamics, spatial dependencies, and textual context. This capability is vital for high-stakes decision-making in systems such as traffic networks, power grids, and disease propagation. However, the field remains underdeveloped because most existing works prioritize predictive accuracy over reasoning. To address the gap, we introduce ST-Bench, a benchmark consisting of four core tasks, including etiological reasoning, entity identification, correlation reasoning, and in-context forecasting, developed via a network SDE-based multi-agent data synthesis pipeline. We then propose STReasoner, which empowers LLM to integrate time series, graph structure, and text for explicit reasoning. To promote spatially grounded logic, we introduce S-GRPO, a reinforcement learning algorithm that rewards performance gains specifically attributable to spatial information. Experiments show that STReasoner achieves average accuracy gains between 17% and 135% at only 0.004X the cost of proprietary models and generalizes robustly to real-world data. + oai:arXiv.org:2601.03248v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Juntong Ni, Shiyu Wang, Ming Jin, Qi He, Wei Jin + + + Proceedings 16th International Workshop on Graph Computation Models + https://arxiv.org/abs/2601.03249 + arXiv:2601.03249v1 Announce Type: new +Abstract: This volume contains the post-proceedings of the Sixteenth International Workshop on Graph Computation Models (GCM 2025). The workshops took place in Koblenz, Germany on June 10 as part of STAF (Software Technologies: Applications and Foundations). + Graphs are common mathematical structures that are visual and intuitive. They constitute a natural and seamless way for system modeling in science, engineering, and beyond, including computer science, biology, and business process modeling. Graph computation models constitute a class of very high-level models where graphs are first-class citizens. The aim of the International GCM Workshop series is to bring together researchers interested in all aspects of computation models based on graphs and graph transformation. It promotes the cross-fertilizing exchange of ideas and experiences among senior and young researchers from the different communities interested in the foundations, applications, and implementations of graph computation models and related areas. + oai:arXiv.org:2601.03249v1 + cs.LO + cs.FL + cs.PL + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + 10.4204/EPTCS.440 + EPTCS 440, 2026 + Leen Lambers (Brandenburg University of Technology Cottbus-Senftenberg), Oszk\'ar Semer\'ath (Budapest University of Technology,Economics) + + + A Versatile Multimodal Agent for Multimedia Content Generation + https://arxiv.org/abs/2601.03250 + arXiv:2601.03250v1 Announce Type: new +Abstract: With the advancement of AIGC (AI-generated content) technologies, an increasing number of generative models are revolutionizing fields such as video editing, music generation, and even film production. However, due to the limitations of current AIGC models, most models can only serve as individual components within specific application scenarios and are not capable of completing tasks end-to-end in real-world applications. In real-world applications, editing experts often work with a wide variety of images and video inputs, producing multimodal outputs -- a video typically includes audio, text, and other elements. This level of integration across multiple modalities is something current models are unable to achieve effectively. However, the rise of agent-based systems has made it possible to use AI tools to tackle complex content generation tasks. To deal with the complex scenarios, in this paper, we propose a MultiMedia-Agent designed to automate complex content creation. Our agent system includes a data generation pipeline, a tool library for content creation, and a set of metrics for evaluating preference alignment. Notably, we introduce the skill acquisition theory to model the training data curation and agent training. We designed a two-stage correlation strategy for plan optimization, including self-correlation and model preference correlation. Additionally, we utilized the generated plans to train the MultiMedia-Agent via a three stage approach including base/success plan finetune and preference optimization. The comparison results demonstrate that the our approaches are effective and the MultiMedia-Agent can generate better multimedia content compared to novel models. + oai:arXiv.org:2601.03250v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Daoan Zhang, Wenlin Yao, Xiaoyang Wang, Yebowen Hu, Jiebo Luo, Dong Yu + + + NavAI: A Generalizable LLM Framework for Navigation Tasks in Virtual Reality Environments + https://arxiv.org/abs/2601.03251 + arXiv:2601.03251v1 Announce Type: new +Abstract: Navigation is one of the fundamental tasks for automated exploration in Virtual Reality (VR). Existing technologies primarily focus on path optimization in 360-degree image datasets and 3D simulators, which cannot be directly applied to immersive VR environments. To address this gap, we present NavAI, a generalizable large language model (LLM)-based navigation framework that supports both basic actions and complex goal-directed tasks across diverse VR applications. We evaluate NavAI in three distinct VR environments through goal-oriented and exploratory tasks. Results show that it achieves high accuracy, with an 89% success rate in goal-oriented tasks. Our analysis also highlights current limitations of relying entirely on LLMs, particularly in scenarios that require dynamic goal assessment. Finally, we discuss the limitations observed during the experiments and offer insights for future research directions. + oai:arXiv.org:2601.03251v1 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Xue Qin, Matthew DiGiovanni + + + InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields + https://arxiv.org/abs/2601.03252 + arXiv:2601.03252v1 Announce Type: new +Abstract: Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts. + oai:arXiv.org:2601.03252v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hao Yu, Haotong Lin, Jiawei Wang, Jiaxin Li, Yida Wang, Xueyang Zhang, Yue Wang, Xiaowei Zhou, Ruizhen Hu, Sida Peng + + + Automated Semantic Rules Detection (ASRD) for Emergent Communication Interpretation + https://arxiv.org/abs/2601.03254 + arXiv:2601.03254v1 Announce Type: new +Abstract: The field of emergent communication within multi-agent systems examines how autonomous agents can independently develop communication strategies, without explicit programming, and adapt them to varied environments. However, few studies have focused on the interpretability of emergent languages. The research exposed in this paper proposes an Automated Semantic Rules Detection (ASRD) algorithm, which extracts relevant patterns in messages exchanged by agents trained with two different datasets on the Lewis Game, which is often studied in the context of emergent communication. ASRD helps at the interpretation of the emergent communication by relating the extracted patterns to specific attributes of the input data, thereby considerably simplifying subsequent analysis. + oai:arXiv.org:2601.03254v1 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://creativecommons.org/licenses/by/4.0/ + Bastien Vanderplaetse, Xavier Siebert, St\'ephane Dupont + + + Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training + https://arxiv.org/abs/2601.03256 + arXiv:2601.03256v1 Announce Type: new +Abstract: We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on part-aware optimization, manual assembly, or 2D image generation, often produce unrealistic or incoherent 3D assets due to the challenges of intricate part-level manipulation and limited out-of-domain generation. In contrast, Muses leverages the 3D skeleton, a fundamental representation of biological forms, to explicitly and rationally compose diverse elements. This skeletal foundation formalizes 3D content creation as a structure-aware pipeline of design, composition, and generation. Muses begins by constructing a creatively composed 3D skeleton with coherent layout and scale through graph-constrained reasoning. This skeleton then guides a voxel-based assembly process within a structured latent space, integrating regions from different objects. Finally, image-guided appearance modeling under skeletal conditions is applied to generate a style-consistent and harmonious texture for the assembled shape. Extensive experiments establish Muses' state-of-the-art performance in terms of visual fidelity and alignment with textual descriptions, and potential on flexible 3D object editing. Project page: https://luhexiao.github.io/Muses.github.io/. + oai:arXiv.org:2601.03256v1 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + new + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hexiao Lu, Xiaokun Sun, Zeyu Cai, Hao Guo, Ying Tai, Jian Yang, Zhenyu Zhang + + + TWIST: Training-free and Label-free Short Text Clustering through Iterative Vector Updating with LLMs + https://arxiv.org/abs/2510.06747 + arXiv:2510.06747v1 Announce Type: cross +Abstract: In this paper, we propose a training-free and label-free method for short text clustering that can be used on top of any existing embedder. In the context of customer-facing chatbots, companies are dealing with large amounts of user utterances that need to be clustered according to their intent. In these commercial settings, no labeled data is typically available, and the number of clusters is not known. Our method is based on iterative vector updating: it constructs sparse vectors based on representative texts, and then iteratively refines them through LLM guidance. Our method achieves comparable or superior results to state-of-the-art methods that use contrastive learning, but without assuming prior knowledge of clusters or labels. Experiments on diverse datasets and smaller LLMs show that our method is model agnostic and can be applied to any embedder, with relatively small LLMs, and different clustering methods. We also show that our method scales to large datasets, reducing the computational cost of the LLM. These low-resource, adaptable settings and the scalability of our method make it more aligned with real-world scenarios than existing clustering methods. + oai:arXiv.org:2510.06747v1 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + I-Fan Lin, Faegheh Hasibi, Suzan Verberne + + + Effect of Electric Charge on Biotherapeutic Transport, Binding and Absorption: A Computational Study + https://arxiv.org/abs/2601.00505 + arXiv:2601.00505v1 Announce Type: cross +Abstract: This study explores the effects of electric charge on the dynamics of drug transport and absorption in subcutaneous injections of monoclonal antibodies (mAbs). We develop a novel mathematical and computational model, based on the Nernst-Planck equations and porous media flow theory, to investigate the complex interactions between mAbs and charged species in subcutaneous tissue. The model enables us to study short-term transport dynamics and long-term binding and absorption for two mAbs with different electric properties. We examine the influence of buffer pH, body mass index, injection depth, and formulation concentration on drug distribution and compare our numerical results with experimental data from the literature. + oai:arXiv.org:2601.00505v1 + cs.CE + cs.NA + math.NA + physics.flu-dyn + physics.med-ph + q-bio.BM + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Mario de Lucio, Pavlos P. Vlachos, Hector Gomez + + + On (Newcomb-)Benford's law: a tale of two papers and of their disproportionate citations. How citation counts can become biased + https://arxiv.org/abs/2601.02395 + arXiv:2601.02395v1 Announce Type: cross +Abstract: The first digit (FD) phenomenon i.e., the significant digits of numbers in large data are often distributed according to a logarithmically decreasing function was first reported by S. Newcomb and then many decades later independently by F. Benford. After its century long neglect the last three decades have seen huge growth in the number of relevant publications. However, notwithstanding the rising popularity the two independent proponents of the phenomenon are not equally acknowledged an indication of which is disproportionate number of citations accumulated by Newcomb (1881) and Benford (1938). In the present study use citation analysis to show that the formalization of the eponym Benford's law, a name questionable itself for overlooking Newcomb's contribution, by Raimi (1976) had a strong adverse effect on the future citations of Newcomb (1881). Furthermore, we identify the papers published over various decades of the developmental history of the FD phenomenon, which latter turned out to be amongst the most cited ones in the field. We find that lack of its consideration, intentional or occasionally out of ignorance for referencing by the prominent papers, is responsible for a far lesser number of citations of Newcomb (1881) in comparison to Benford (1938). + oai:arXiv.org:2601.02395v1 + physics.soc-ph + cs.DL + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Tariq Ahmad Mir, Marcel Ausloos + + + Detecting and Mitigating Treatment Leakage in Text-Based Causal Inference: Distillation and Sensitivity Analysis + https://arxiv.org/abs/2601.02400 + arXiv:2601.02400v1 Announce Type: cross +Abstract: Text-based causal inference increasingly employs textual data as proxies for unobserved confounders, yet this approach introduces a previously undertheorized source of bias: treatment leakage. Treatment leakage occurs when text intended to capture confounding information also contains signals predictive of treatment status, thereby inducing post-treatment bias in causal estimates. Critically, this problem can arise even when documents precede treatment assignment, as authors may employ future-referencing language that anticipates subsequent interventions. Despite growing recognition of this issue, no systematic methods exist for identifying and mitigating treatment leakage in text-as-confounder applications. This paper addresses this gap through three contributions. First, we provide formal statistical and set-theoretic definitions of treatment leakage that clarify when and why bias occurs. Second, we propose four text distillation methods -- similarity-based passage removal, distant supervision classification, salient feature removal, and iterative nullspace projection -- designed to eliminate treatment-predictive content while preserving confounder information. Third, we validate these methods through simulations using synthetic text and an empirical application examining International Monetary Fund structural adjustment programs and child mortality. Our findings indicate that moderate distillation optimally balances bias reduction against confounder retention, whereas overly stringent approaches degrade estimate precision. + oai:arXiv.org:2601.02400v1 + econ.EM + cs.CL + econ.GN + q-fin.EC + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Adel Daoud, Richard Johansson, Connor T. Jerzak + + + OpenFOAM computational fluid dynamics (CFD) solver for magnetohydrodynamic open cycles, applied to the Sakhalin pulsed magnetohydrodynamic generator (PMHDG) + https://arxiv.org/abs/2601.02406 + arXiv:2601.02406v1 Announce Type: cross +Abstract: In the current study, we present a mathematical and computational fluid dynamics (CFD) model for simulating open-cycle linear Faraday-type continuous-electrode channels of magnetohydrodynamic (MHD) power generators, operating on combustion plasma. The model extends the Favre-averaged Navier-Stokes equations to account for the electric properties of the flowing plasma gas and its reaction to the applied magnetic field. The model takes into account various effects, such as the Lorentz force, turbulence, compressibility, and energy extraction from the plasma, and it adopts an electric potential technique along with the low magnetic Reynolds number (Rem) approximation. The model is numerically implemented using the multiphysics open-source computer programming environment "OpenFOAM," which combines the finite volume method (FVM) and the object-oriented programming (OOP) concept. The capabilities of the model are demonstrated by simulating the supersonic channel of the large-scale pulsed MHD generator (PMHDG) called "Sakhalin", with the aid of collected data and empirical expressions in the literature about its tested operation. Sakhalin was the world's largest PMHDG, with a demonstrated peak electric power output of 510 MW. Sakhalin operated on solid-propellant plasma (SPP), and it had a single supersonic divergent Faraday-type continuous-electrode channel with a length of 4.5 m. We check the validity of the model through comparisons with independent results for the Sakhalin PMHDG. Then, we process our three-dimensional simulation results to provide scalar characteristics of the Sakhalin channel, one-dimensional profiles along the longitudinal centerline, and three-dimensional distributions in the entire channel. + oai:arXiv.org:2601.02406v1 + physics.plasm-ph + cs.CE + physics.flu-dyn + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + 10.1007/s42452-025-07744-1 + Osama A. Marzouk (2025). OpenFOAM computational fluid dynamics (CFD) solver for magnetohydrodynamic open cycles, applied to the Sakhalin pulsed magnetohydrodynamic generator (PMHDG). Discover Applied Sciences. 7(10):1108 + Osama A. Marzouk + + + A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design + https://arxiv.org/abs/2601.02424 + arXiv:2601.02424v1 Announce Type: cross +Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery. + oai:arXiv.org:2601.02424v1 + cond-mat.mtrl-sci + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Kai Gu, Yingping Liang, Senliang Peng, Aotian Guo, Haizheng Zhong, Ying Fu + + + Formal Modeling and Verification of Grover's Algorithm + https://arxiv.org/abs/2601.02435 + arXiv:2601.02435v1 Announce Type: cross +Abstract: Grover's algorithm relies on the superposition and interference of quantum mechanics, which is more efficient than classical computing in specific tasks such as searching an unsorted database. Due to the high complexity of quantum mechanics, the correctness of quantum algorithms is difficult to guarantee through traditional simulation methods. By contrast, the fundamental concepts and mathematical structure of Grover's algorithm can be formalized into logical expressions and verified by higher-order logical reasoning. In this paper, we formally model and verify Grover's algorithm in the HOL Light theorem prover. We focus on proving key properties such as the unitarity of its oracle and diffusion operators, the monotonicity of the success probability with respect to the number of iterations, and an exact expression for the optimal iteration count. By analyzing a concrete application to integer factorization, we demonstrate the practicality and prospects of our work. + oai:arXiv.org:2601.02435v1 + quant-ph + cs.FL + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-nd/4.0/ + H. Sun, Z. Shi, S. Chen, G. Wang, X. Li, Y. Guan, Q. Zhang, Z. Shao + + + Deep Learning Superresolution for 7T Knee MR Imaging: Impact on Image Quality and Diagnostic Performance + https://arxiv.org/abs/2601.02436 + arXiv:2601.02436v1 Announce Type: cross +Abstract: Background: Deep learning superresolution (SR) may enhance musculoskeletal MR image quality, but its diagnostic value in knee imaging at 7T is unclear. Objectives: To compare image quality and diagnostic performance of SR, low-resolution (LR), and high-resolution (HR) 7T knee MRI. Methods: In this prospective study, 42 participants underwent 7T knee MRI with LR (0.8*0.8*2 mm3) and HR (0.4*0.4*2 mm3) sequences. SR images were generated from LR data using a Hybrid Attention Transformer model. Three radiologists assessed image quality, anatomic conspicuity, and detection of knee pathologies. Arthroscopy served as reference in 10 cases. Results: SR images showed higher overall quality than LR (median score 5 vs 4, P<.001) and lower noise than HR (5 vs 4, P<.001). Visibility of cartilage, menisci, and ligaments was superior in SR and HR compared to LR (P<.001). Detection rates and diagnostic performance (sensitivity, specificity, AUC) for intra-articular pathology were similar across image types (P>=.095). Conclusions: Deep learning superresolution improved subjective image quality in 7T knee MRI but did not increase diagnostic accuracy compared with standard LR imaging. + oai:arXiv.org:2601.02436v1 + eess.IV + cs.CV + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Pinzhen Chen, Libo Xu, Boyang Pan, Jing Li, Yuting Wang, Ran Xiong, Xiaoli Gou, Long Qing, Wenjing Hou, Nan-jie Gong, Wei Chen + + + Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss + https://arxiv.org/abs/2601.02440 + arXiv:2601.02440v1 Announce Type: cross +Abstract: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and long-tailed anomaly score distributions (LTD). This imbalance skews model training and degrades detection performance, especially for minority instances. To address this issue, we propose a novel importance-weighted loss designed specifically for anomaly detection. Compared to the previous method for LTD in classification, our method does not require prior knowledge of normal data classes. Instead, we introduce a weighted loss function that incorporates importance sampling to align the distribution of anomaly scores with a target Gaussian, ensuring a balanced representation of normal data. Extensive experiments on three benchmark image datasets and three real-world hyperspectral imaging datasets demonstrate the robustness of our approach in mitigating LTD-induced bias. Our method improves anomaly detection performance by 0.043, highlighting its effectiveness in real-world applications. + oai:arXiv.org:2601.02440v1 + stat.ML + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-nd/4.0/ + 10.1109/IJCNN64981.2025.11229283 + Proc. IJCNN 2025 + Jungi Lee, Jungkwon Kim, Chi Zhang, Sangmin Kim, Kwangsun Yoo, Seok-Joo Byun + + + Star Formation in Galaxy Collisions: Dependence on Impact Velocity and Gas Mass of Galaxies in GADGET-4 Simulations + https://arxiv.org/abs/2601.02506 + arXiv:2601.02506v1 Announce Type: cross +Abstract: This work investigates variations in the star formation rate during galaxy collisions when the initial conditions of velocity and gas mass are altered. For this purpose, hydrodynamic simulations were performed using the GADGET-4 code, with initial conditions generated by the Galstep and SnapshotJoiner programs. Systems of two galaxies on a head-on collision course were modeled with relative initial velocities ranging from 100 km/s to 1000 km/s, considering two scenarios: the first with identical galaxies, and the second with galaxies of different sizes. In simulations of systems with higher initial relative velocities, both found more intense peaks in the star formation rate, triggered by the first contact of the collision, followed by a strong decline caused by gas dispersion. In contrast, for systems with lower initial velocities, mergers between galaxies were observed, leading to multiple peaks in the star formation rate. A greater initial distance between galaxies has also been linked to whether or not the galaxy system merges, since it implies longer timescales for gravitational action, which leads to higher relative velocities at the moment of collision. Furthermore, the star formation rate in galaxies was found to have a clear dependence on initial gas content. Furthermore, the initial gas content in galaxies was found to have a clear dependence on star formation rates. Overall, our results show that the relative impact velocity, the initial distance between the galaxies, and the gas content are important parameters for analyzing the star formation rate in colliding galaxies. + oai:arXiv.org:2601.02506v1 + astro-ph.GA + cs.MS + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Gustavo Neves Pereira, Paulo Laerte Natti + + + Compressed Qubit Noise Spectroscopy: Piecewise-Linear Modeling and Rademacher Measurements + https://arxiv.org/abs/2601.02516 + arXiv:2601.02516v1 Announce Type: cross +Abstract: Random pulse sequences are a powerful method for qubit noise spectroscopy, enabling efficient reconstruction of sparse noise spectra. Here, we advance this method in two complementary directions. First, we extend the method using a regularizer based on the total generalized variation (TGV) norm, in order to reconstruct a larger class of noise spectra, namely piecewise-linear noise spectra, which more realistically model many physical systems. We show through numerical simulations that the new method resolves finer spectral features, while maintaining an order-of-magnitude speedup over conventional approaches to noise spectroscopy. Second, we simplify the experimental implementation of the method, by introducing Rademacher measurements for reconstructing sparse noise spectra. These measurements use pseudorandom pulse sequences that can be generated in real time from a short random seed, reducing experimental complexity without compromising reconstruction accuracy. Together, these developments broaden the reach of random pulse sequences for accurate and efficient noise characterization in realistic quantum systems. + oai:arXiv.org:2601.02516v1 + quant-ph + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kaixin Huang, Demitry Farfurnik, Dror Baron, Yi-Kai Liu + + + Diffusion Computation versus Quantum Computation: A Comparative Model for Order Finding and Factoring + https://arxiv.org/abs/2601.02518 + arXiv:2601.02518v1 Announce Type: cross +Abstract: We study a hybrid computational model for integer factorization in which the only non-classical resource is access to an \emph{iterated diffusion process} on a finite graph. Concretely, a \emph{diffusion step} is defined to be one application of a symmetric stochastic matrix (the half-lazy walk operator) to an $\ell^{1}$--normalized state vector, followed by an optional readout of selected coordinates. + Let $N\ge 3$ be an odd integer which is neither prime nor a prime power, and let $b\in(\mathbb{Z}/N\mathbb{Z})^\ast$ have odd multiplicative order $r={\rm ord}_N(b)$. We construct, without knowing $r$ in advance, a weighted Cayley graph whose vertex set is the cyclic subgroup $\langle b\rangle$ and whose edges correspond to the powers $b^{\pm 2^t}$ for $t\le \lfloor \log_2 N\rfloor+1$. Using an explicit spectral decomposition together with an elementary doubling lemma, we show that $r$ can be recovered from a single heat-kernel value after at most $O((\log_2 N)^2)$ diffusion steps, with an effective bound. + We then combine this order-finding model with the standard reduction from factoring to order finding (in the spirit of Shor's framework) to obtain a randomized factorization procedure whose success probability depends only on the number $m$ of distinct prime factors of $N$. Our comparison with Shor's algorithm is \emph{conceptual and model-based}. We replace unitary $\ell^2$ evolution by Markovian $\ell^1$ evolution, and we report complexity in two cost measures: digital steps and diffusion steps. Finally, we include illustrative examples and discussion of practical implementations. + oai:arXiv.org:2601.02518v1 + math.SP + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Carlos A. Cadavid, Paulina Hoyos, Jay Jorgenson, Lejla Smajlovi\'c, J. D. V\'elez + + + First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data + https://arxiv.org/abs/2601.02523 + arXiv:2601.02523v1 Announce Type: cross +Abstract: Artificial intelligence has advanced rapidly through large neural networks trained on massive datasets using thousands of GPUs or TPUs. Such training can occupy entire data centers for weeks and requires enormous computational and energy resources. Yet the optimization algorithms behind these runs have not kept pace. Most large scale training still relies on synchronous methods, where workers must wait for the slowest device, wasting compute and amplifying the effects of hardware and network variability. Removing synchronization seems like a simple fix, but asynchrony introduces staleness, meaning updates computed on outdated models. This makes analysis difficult, especially when delays arise from system level randomness rather than algorithmic choices. As a result, the time complexity of asynchronous methods remains poorly understood. This dissertation develops a rigorous framework for asynchronous first order stochastic optimization, focusing on the core challenge of heterogeneous worker speeds. Within this framework, we show that with proper design, asynchronous SGD can achieve optimal time complexity, matching guarantees previously known only for synchronous methods. Our first contribution, Ringmaster ASGD, attains optimal time complexity in the homogeneous data setting by selectively discarding stale updates. The second, Ringleader ASGD, extends optimality to heterogeneous data, common in federated learning, using a structured gradient table mechanism. Finally, ATA improves resource efficiency by learning worker compute time distributions and allocating tasks adaptively, achieving near optimal wall clock time with less computation. Together, these results establish asynchronous optimization as a theoretically sound and practically efficient foundation for distributed learning, showing that coordination without synchronization can be both feasible and optimal. + oai:arXiv.org:2601.02523v1 + math.OC + cs.DC + cs.LG + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + 10.25781/KAUST-WH234 + Artavazd Maranjyan + + + A Green Solution for Breast Region Segmentation Using Deep Active Learning + https://arxiv.org/abs/2601.02538 + arXiv:2601.02538v1 Announce Type: cross +Abstract: Purpose: Annotation of medical breast images is an essential step toward better diagnostic but a time consuming task. This research aims to focus on different selecting sample strategies within deep active learning on Breast Region Segmentation (BRS) to lessen computational cost of training and effective use of resources. + Methods: The Stavanger breast MRI dataset containing 59 patients was used in this study, with FCN-ResNet50 adopted as a sustainable deep learning (DL) model. A novel sample selection approach based on Breast Anatomy Geometry (BAG) analysis was introduced to group data with similar informative features for DL. Patient positioning and Breast Size were considered the key selection criteria in this process. Four selection strategies including Random Selection, Nearest Point, Breast Size, and a hybrid of all three strategies were evaluated using an active learning framework. Four training data proportions of 10%, 20%, 30%, and 40% were used for model training, with the remaining data reserved for testing. Model performance was assessed using Dice score, Intersection over Union, precision, and recall, along with 5-fold cross-validation to enhance generalizability. + Results: Increasing the training data proportion from 10% to 40% improved segmentation performance for nearly all strategies, except for Random Selection. The Nearest Point strategy consistently achieved the lowest carbon footprint at 30% and 40% data proportions. Overall, combining the Nearest Point strategy with 30% of the training data provided the best balance between segmentation performance, efficiency, and environmental sustainability. + Keywords: Deep Active Learning, Breast Region Segmentation, Human-center analysis + oai:arXiv.org:2601.02538v1 + physics.med-ph + cs.CV + eess.IV + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Sam Narimani, Solveig Roth Hoff, Kathinka D{\ae}hli Kurz, Kjell-Inge Gjesdal, J\"urgen Geisler, Endre Gr{\o}vik + + + AI-exposed jobs deteriorated before ChatGPT + https://arxiv.org/abs/2601.02554 + arXiv:2601.02554v1 Announce Type: cross +Abstract: Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT. Analyzing millions of LinkedIn profiles, we show that graduate cohorts from 2021 onward entered AI-exposed jobs at lower rates than earlier cohorts, with gaps opening before late 2022. Finally, from millions of university syllabi, we find that graduates taking more AI-exposed curricula had higher first-job pay and shorter job searches after ChatGPT. Together, these results point to forces pre-dating generative AI and to the ongoing value of LLM-relevant education. + oai:arXiv.org:2601.02554v1 + econ.GN + cs.AI + cs.CY + q-fin.EC + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Morgan R. Frank, Alireza Javadian Sabet, Lisa Simon, Sarah H. Bana, Renzhe Yu + + + Comparative Analysis of Binarization Methods For Medical Image Hashing On Odir Dataset + https://arxiv.org/abs/2601.02564 + arXiv:2601.02564v1 Announce Type: cross +Abstract: In this study, we evaluated four binarization methods. Locality-Sensitive Hashing (LSH), Iterative Quantization (ITQ), Kernel-based Supervised Hashing (KSH), and Supervised Discrete Hashing (SDH) on the ODIR dataset using deep feature embeddings. Experimental results show that SDH achieved the best performance, with an mAP@100 of 0.9184 using only 32-bit codes, outperforming LSH, ITQ, and KSH. Compared with prior studies, our method proved highly competitive: Fang et al. reported 0.7528 (Fundus-iSee, 48 bits) and 0.8856 (ASOCT-Cataract, 48 bits), while Wijesinghe et al. achieved 94.01 (KVASIR, 256 bits). Despite using significantly fewer bits, our SDH-based framework reached retrieval accuracy close to the state-of-the-art. These findings demonstrate that SDH is the most effective approach among those tested, offering a practical balance of accuracy, storage, and efficiency for medical image retrieval and device inventory management. + oai:arXiv.org:2601.02564v1 + eess.IV + cs.CV + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Nedim Muzoglu + + + Annealed Langevin Posterior Sampling (ALPS): A Rapid Algorithm for Image Restoration with Multiscale Energy Models + https://arxiv.org/abs/2601.02594 + arXiv:2601.02594v1 Announce Type: cross +Abstract: Solving inverse problems in imaging requires models that support efficient inference, uncertainty quantification, and principled probabilistic reasoning. Energy-Based Models (EBMs), with their interpretable energy landscapes and compositional structure, are well-suited for this task but have historically suffered from high computational costs and training instability. To overcome the historical shortcomings of EBMs, we introduce a fast distillation strategy to transfer the strengths of pre-trained diffusion models into multi-scale EBMs. These distilled EBMs enable efficient sampling and preserve the interpretability and compositionality inherent to potential-based frameworks. Leveraging EBM compositionality, we propose Annealed Langevin Posterior Sampling (ALPS) algorithm for Maximum-A-Posteriori (MAP), Minimum Mean Square Error (MMSE), and uncertainty estimates for inverse problems in imaging. Unlike diffusion models that use complex guidance strategies for latent variables, we perform annealing on static posterior distributions that are well-defined and composable. Experiments on image inpainting and MRI reconstruction demonstrate that our method matches or surpasses diffusion-based baselines in both accuracy and efficiency, while also supporting MAP recovery. Overall, our framework offers a scalable and principled solution for inverse problems in imaging, with potential for practical deployment in scientific and clinical settings. ALPS code is available at the GitHub repository \href{https://github.com/JyoChand/ALPS}{ALPS}. + oai:arXiv.org:2601.02594v1 + eess.IV + cs.AI + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Jyothi Rikhab Chand, Mathews Jacob + + + Structural reducibility of hypergraphs + https://arxiv.org/abs/2601.02603 + arXiv:2601.02603v1 Announce Type: cross +Abstract: Higher-order interactions provide a nuanced understanding of the relational structure of complex systems beyond traditional pairwise interactions. However, higher-order network analyses also incur more cumbersome interpretations and greater computational demands than their pairwise counterparts. Here we present an information-theoretic framework for determining the extent to which a hypergraph representation of a networked system is structurally redundant, and for identifying its most critical higher orders of interaction that allow us to remove these redundancies while preserving essential higher-order structure. + oai:arXiv.org:2601.02603v1 + physics.soc-ph + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Physical Review Letters 135 (24), 247401 (2025) + Alec Kirkley, Helcio Felippe, Federico Battiston + + + Extremum Seeking Control for Wave-PDE Actuation with Distributed Effects + https://arxiv.org/abs/2601.02607 + arXiv:2601.02607v1 Announce Type: cross +Abstract: This paper deals with the gradient-based extremum seeking control (ESC) with actuation dynamics governed by distributed wave partial differential equations (PDEs). To achieve the control objective of real-time optimization for this class of infinite-dimensional systems, we first solve the trajectory generation problem to re-design the additive perturbation signal of the ESC system. Then, we develop a boundary control law through the backstepping method to compensate for the wave PDE with distributed effects, which ensures the exponential stability of the average closed-loop system by means of a Lyapunov-based analysis. At last, by employing the averaging theory for infinite-dimensional systems, we prove that the closed-loop trajectories converge to a small neighborhood surrounding the optimal point. Numerical simulations are presented to illustrate the effectiveness of the proposed method. + oai:arXiv.org:2601.02607v1 + math.OC + cs.SY + eess.SY + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Elisio Juvenal Muchave, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira, Miroslav Krsti\'c + + + Hierarchical temporal receptive windows and zero-shot timescale generalization in biologically constrained scale-invariant deep networks + https://arxiv.org/abs/2601.02618 + arXiv:2601.02618v1 Announce Type: cross +Abstract: Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, based on scale-invariant hippocampal time cells, on a language classification task mimicking the hierarchical structure of language (e.g., 'letters' forming 'words'). First, using a feedforward model (SITHCon), we found that a hierarchy of TRWs emerged naturally across layers, despite the network having an identical spectrum of time constants within layers. We then distilled these inductive priors into a biologically plausible recurrent architecture, SITH-RNN. Training a sequence of architectures ranging from generic RNNs to this restricted subset showed that the scale-invariant SITH-RNN learned faster with orders-of-magnitude fewer parameters, and generalized zero-shot to out-of-distribution timescales. These results suggest the brain employs scale-invariant, sequential priors - coding "what" happened "when" - making recurrent networks with such priors particularly well-suited to describe human cognition. + oai:arXiv.org:2601.02618v1 + q-bio.NC + cs.AI + cs.CL + cs.LG + cs.NE + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Aakash Sarkar, Marc W. Howard + + + Statistical Inference for Fuzzy Clustering + https://arxiv.org/abs/2601.02656 + arXiv:2601.02656v1 Announce Type: cross +Abstract: Clustering is a central tool in biomedical research for discovering heterogeneous patient subpopulations, where group boundaries are often diffuse rather than sharply separated. Traditional methods produce hard partitions, whereas soft clustering methods such as fuzzy $c$-means (FCM) allow mixed memberships and better capture uncertainty and gradual transitions. Despite the widespread use of FCM, principled statistical inference for fuzzy clustering remains limited. + We develop a new framework for weighted fuzzy $c$-means (WFCM) for settings with potential cluster size imbalance. Cluster-specific weights rebalance the classical FCM criterion so that smaller clusters are not overwhelmed by dominant groups, and the weighted objective induces a normalized density model with scale parameter $\sigma$ and fuzziness parameter $m$. Estimation is performed via a blockwise majorize--minimize (MM) procedure that alternates closed-form membership and centroid updates with likelihood-based updates of $(\sigma,\bw)$. The intractable normalizing constant is approximated by importance sampling using a data-adaptive Gaussian mixture proposal. We further provide likelihood ratio tests for comparing cluster centers and bootstrap-based confidence intervals. + We establish consistency and asymptotic normality of the maximum likelihood estimator, validate the method through simulations, and illustrate it using single-cell RNA-seq and Alzheimer disease Neuroimaging Initiative (ADNI) data. These applications demonstrate stable uncertainty quantification and biologically meaningful soft memberships, ranging from well-separated cell populations under imbalance to a graded AD versus non-AD continuum consistent with disease progression. + oai:arXiv.org:2601.02656v1 + stat.ME + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Qiuyi Wu, Zihan Zhu, Anru R. Zhang + + + Branching $k$-path vertex cover of forests + https://arxiv.org/abs/2601.02685 + arXiv:2601.02685v1 Announce Type: cross +Abstract: We define a set $P$ to be a branching $k$-path vertex cover of an undirected forest $F$ if all leaves and isolated vertices (vertices of degree at most $1$) of $F$ belong to $P$ and every path on $k$ vertices (of length $k-1$) contains either a branching vertex (a vertex of degree at least $3$) or a vertex belonging to $P$. We define the branching $k$-path vertex cover number of an undirected forest $F$, denoted by $\psi_b(F,k)$, to be the number of vertices in the smallest branching $k$-path vertex cover of $F$. These notions for a rooted directed forest are defined similarly, with natural adjustments. We prove the lower bound $\psi_b(F,k) \geq \frac{n+3k-1}{2k}$ for undirected forests, the lower bound $\psi_b(F,k) \geq \frac{n+k}{2k}$ for rooted directed forests, and that both of them are tight. + oai:arXiv.org:2601.02685v1 + math.CO + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Mikhail Makarov + + + Transform and Entropy Coding in AV2 + https://arxiv.org/abs/2601.02712 + arXiv:2601.02712v1 Announce Type: cross +Abstract: AV2 is the successor to the AV1 royalty-free video coding standard developed by the Alliance for Open Media (AOMedia). Its primary objective is to deliver substantial compression gains and subjective quality improvements while maintaining low-complexity encoder and decoder operations. This paper describes the transform, quantization and entropy coding design in AV2, including redesigned transform kernels and data-driven transforms, expanded transform partitioning, and a mode & coefficient dependent transform signaling. AV2 introduces several new coding tools including Intra/Inter Secondary Transforms (IST), Trellis Coded Quantization (TCQ), Adaptive Transform Coding (ATC), Probability Adaptation Rate Adjustment (PARA), Forward Skip Coding (FSC), Cross Chroma Component Transforms (CCTX), Parity Hiding (PH) tools and improved lossless coding. These advances enable AV2 to deliver the highest quality video experience for video applications at a significantly reduced bitrate. + oai:arXiv.org:2601.02712v1 + eess.IV + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Alican Nalci, Hilmi E. Egilmez, Madhu P. Krishnan, Keng-Shih Lu, Joe Young, Debargha Mukherjee, Lin Zheng, Jingning Han, Joel Sole, Xin Zhao, Tianqi Liu, Liang Zhao, Todd Nguyen, Urvang Joshi, Kruthika Koratti Sivakumar, Luhang Xu, Zhijun Lei, Yue Yu, Aki Kuusela, Minhua Zhou, Andrey Norkin, Adrian Grange + + + Fast Conformal Prediction using Conditional Interquantile Intervals + https://arxiv.org/abs/2601.02769 + arXiv:2601.02769v1 Announce Type: cross +Abstract: We introduce Conformal Interquantile Regression (CIR), a conformal regression method that efficiently constructs near-minimal prediction intervals with guaranteed coverage. CIR leverages black-box machine learning models to estimate outcome distributions through interquantile ranges, transforming these estimates into compact prediction intervals while achieving approximate conditional coverage. We further propose CIR+ (Conditional Interquantile Regression with More Comparison), which enhances CIR by incorporating a width-based selection rule for interquantile intervals. This refinement yields narrower prediction intervals while maintaining comparable coverage, though at the cost of slightly increased computational time. Both methods address key limitations of existing distributional conformal prediction approaches: they handle skewed distributions more effectively than Conformalized Quantile Regression, and they achieve substantially higher computational efficiency than Conformal Histogram Regression by eliminating the need for histogram construction. Extensive experiments on synthetic and real-world datasets demonstrate that our methods optimally balance predictive accuracy and computational efficiency compared to existing approaches. + oai:arXiv.org:2601.02769v1 + stat.ML + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Naixin Guo, Rui Luo, Zhixin Zhou + + + The Sequence Reconstruction of Permutations under Hamming Metric with Small Errors + https://arxiv.org/abs/2601.02844 + arXiv:2601.02844v1 Announce Type: cross +Abstract: The sequence reconstruction problem asks for the recovery of a sequence from multiple noisy copies, where each copy may contain up to $r$ errors. In the case of permutations on \(n\) letters under the Hamming metric, this problem is closely related to the parameter $N(n,r)$, the maximum intersection size of two Hamming balls of radius $r$. While previous work has resolved \(N(n,r)\) for small radii (\(r \leq 4\)) and established asymptotic bounds for larger \(r\), we present new exact formulas for \(r \in \{5,6,7\}\) using group action techniques. In addition, we develop a formula for \(N(n,r)\) based on the irreducible characters of the symmetric group \(S_n\), along with an algorithm that enables computation of \(N(n,r)\) for larger parameters, including cases such as \(N(43,8)\) and \(N(24,14)\). + oai:arXiv.org:2601.02844v1 + math.GR + cs.IT + math.CO + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + A. Abdollahi, J. Bagherian, H. Eskandari, F. Jafari, M. Khatami, F. Parvaresh + + + Lesion Segmentation in FDG-PET/CT Using Swin Transformer U-Net 3D: A Robust Deep Learning Framework + https://arxiv.org/abs/2601.02864 + arXiv:2601.02864v1 Announce Type: cross +Abstract: Accurate and automated lesion segmentation in Positron Emission Tomography / Computed Tomography (PET/CT) imaging is essential for cancer diagnosis and therapy planning. This paper presents a Swin Transformer UNet 3D (SwinUNet3D) framework for lesion segmentation in Fluorodeoxyglucose Positron Emission Tomography / Computed Tomography (FDG-PET/CT) scans. By combining shifted window self-attention with U-Net style skip connections, the model captures both global context and fine anatomical detail. We evaluate SwinUNet3D on the AutoPET III FDG dataset and compare it against a baseline 3D U-Net. Results show that SwinUNet3D achieves a Dice score of 0.88 and IoU of 0.78, surpassing 3D U-Net (Dice 0.48, IoU 0.32) while also delivering faster inference times. Qualitative analysis demonstrates improved detection of small and irregular lesions, reduced false positives, and more accurate PET/CT fusion. While the framework is currently limited to FDG scans and trained under modest GPU resources, it establishes a strong foundation for future multi-tracer, multi-center evaluations and benchmarking against other transformer-based architectures. Overall, SwinUNet3D represents an efficient and robust approach to PET/CT lesion segmentation, advancing the integration of transformer-based models into oncology imaging workflows. + oai:arXiv.org:2601.02864v1 + eess.IV + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Shovini Guha, Dwaipayan Nandi + + + STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules + https://arxiv.org/abs/2601.02882 + arXiv:2601.02882v1 Announce Type: cross +Abstract: We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical weather prediction or data-driven models often lack the necessary precision due to unresolved local effects. Typical statistical postprocessing methods correct these biases, but often degrade spatio-temporal correlation structures in doing so. Recent works based on generative modeling successfully improve spatial correlation structures but have to forecast every lead time independently. In contrast, STIPP makes joint spatio-temporal forecasts which have increased accuracy for surface temperature, wind, relative humidity and precipitation when compared to baseline methods. It makes hourly ensemble predictions given only a six-hourly deterministic forecast, blending the boundaries of postprocessing and temporal interpolation. By leveraging a multivariate proper scoring rule for training, STIPP contributes to ongoing work data-driven atmospheric models supervised only with distribution marginals. + oai:arXiv.org:2601.02882v1 + physics.ao-ph + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + David Landry, Isabelle Gouttevin, Hugo Merizen, Claire Monteleoni, Anastase Charantonis + + + Enhanced 3D Gravity Inversion Using ResU-Net with Density Logging Constraints: A Dual-Phase Training Approach + https://arxiv.org/abs/2601.02890 + arXiv:2601.02890v1 Announce Type: cross +Abstract: Gravity exploration has become an important geophysical method due to its low cost and high efficiency. With the rise of artificial intelligence, data-driven gravity inversion methods based on deep learning (DL) possess physical property recovery capabilities that conventional regularization methods lack. However, existing DL methods suffer from insufficient prior information constraints, which leads to inversion models with large data fitting errors and unreliable results. Moreover, the inversion results lack constraints and matching from other exploration methods, leading to results that may contradict known geological conditions. In this study, we propose a novel approach that integrates prior density well logging information to address the above issues. First, we introduce a depth weighting function to the neural network (NN) and train it in the weighted density parameter domain. The NN, under the constraint of the weighted forward operator, demonstrates improved inversion performance, with the resulting inversion model exhibiting smaller data fitting errors. Next, we divide the entire network training into two phases: first training a large pre-trained network Net-I, and then using the density logging information as the constraint to get the optimized fine-tuning network Net-II. Through testing and comparison in synthetic models and Bishop Model, the inversion quality of our method has significantly improved compared to the unconstrained data-driven DL inversion method. Additionally, we also conduct a comparison and discussion between our method and both the conventional focusing inversion (FI) method and its well logging constrained variant. Finally, we apply this method to the measured data from the San Nicolas mining area in Mexico, comparing and analyzing it with two recent gravity inversion methods based on DL. + oai:arXiv.org:2601.02890v1 + physics.geo-ph + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Siyuan Dong, Jinghuai Gao, Shuai Zhou, Baohai Wu, Hongfa Jia + + + Transducing Linear Decompositions of Tournaments + https://arxiv.org/abs/2601.02999 + arXiv:2601.02999v1 Announce Type: cross +Abstract: Boja\'nczyk, Pilipczuk, and Grohe [LICS '18] proved that for graphs of bounded linear clique-width, clique-decompositions of bounded width can be produced by a CMSO transduction. We show that in the case of tournaments, a first-order transduction suffices. This implies that the logics CMSO and existential MSO are equivalent over bounded linear clique-width tournaments. + oai:arXiv.org:2601.02999v1 + math.CO + cs.DM + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Colin Geniet, Fatemeh Ghasemi, Mamadou Moustapha Kant\'e + + + DNACHUNKER: Learnable Tokenization for DNA Language Models + https://arxiv.org/abs/2601.03019 + arXiv:2601.03019v1 Announce Type: cross +Abstract: DNA language models have emerged as powerful tools for decoding the complex language of DNA sequences. However, the performance of these models is heavily affected by their tokenization strategy, i.e., a method used to parse DNA sequences into a shorter sequence of chunks. In this work, we propose DNACHUNKER, which integrates a learnable dynamic DNA tokenization mechanism and is trained as a masked language model. Adopting the dynamic chunking procedure proposed by H-Net, our model learns to segment sequences into variable-length chunks. This dynamic chunking offers two key advantages: it's resilient to shifts and mutations in the DNA, and it allocates more detail to important functional areas. We demonstrate the performance of DNACHUNKER by training it on the human reference genome (HG38) and testing it on the Nucleotide Transformer and Genomic benchmarks. Further ablative experiments reveal that DNACHUNKER learns tokenization that grasps biological grammar and uses smaller chunks to preserve detail in important functional elements such as promoters and exons, while using larger chunks for repetitive, redundant regions. + oai:arXiv.org:2601.03019v1 + q-bio.GN + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Taewon Kim, Jihwan Shin, Hyomin Kim, Youngmok Jung, Jonhoon Lee, Won-Chul Lee, Insu Han, Sungsoo Ahn + + + Similarity-Sensitive Entropy: Induced Kernels and Data-Processing Inequalities + https://arxiv.org/abs/2601.03064 + arXiv:2601.03064v1 Announce Type: cross +Abstract: We study an entropy functional $H_K$ that is sensitive to a prescribed similarity structure on a state space. For finite spaces, $H_K$ coincides with the order-1 similarity-sensitive entropy of Leinster and Cobbold. We work in the general measure-theoretic setting of kernelled probability spaces $(\Omega,\mu,K)$ introduced by Leinster and Roff, and develop basic structural properties of $H_K$. + Our main results concern the behavior of $H_K$ under coarse-graining. For a measurable map $f:\Omega\to Y$ and input law $\mu$, we define a law-induced kernel on $Y$ whose pullback minimally dominates $K$, and show that it yields a coarse-graining inequality and a data-processing inequality for $H_K$, for both deterministic maps and general Markov kernels. We also introduce conditional similarity-sensitive entropy and an associated mutual information, and compare their behavior to the classical Shannon case. + oai:arXiv.org:2601.03064v1 + math.PR + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Joseph Samuel Miller + + + Computationally Efficient Estimation of Localized Treatment Effects in High-Dimensional Design Spaces using Gaussian Process Regression + https://arxiv.org/abs/2601.03105 + arXiv:2601.03105v1 Announce Type: cross +Abstract: Population-scale agent-based simulations of the opioid epidemic help evaluate intervention strategies and overdose outcomes in heterogeneous communities and provide estimates of localized treatment effects, which support the design of locally-tailored policies for precision public health. However, it is prohibitively costly to run simulations of all treatment conditions in all communities because the number of possible treatments grows exponentially with the number of interventions and levels at which they are applied. To address this need efficiently, we develop a metamodel framework, whereby treatment outcomes are modeled using a response function whose coefficients are learned through Gaussian process regression (GPR) on locally-contextualized covariates. We apply this framework to efficiently estimate treatment effects on overdose deaths in Pennsylvania counties. In contrast to classical designs such as fractional factorial design or Latin hypercube sampling, our approach leverages spatial correlations and posterior uncertainty to sequentially sample the most informative counties and treatment conditions. Using a calibrated agent-based opioid epidemic model, informed by county-level overdose mortality and baseline dispensing rate data for different treatments, we obtained county-level estimates of treatment effects on overdose deaths per 100,000 population for all treatment conditions in Pennsylvania, achieving approximately 5% average relative error using one-tenth the number of simulation runs required for exhaustive evaluation. Our bi-level framework provides a computationally efficient approach to decision support for policy makers, enabling rapid evaluation of alternative resource-allocation strategies to mitigate the opioid epidemic in local communities. The same analytical framework can be applied to guide precision public health interventions in other epidemic settings. + oai:arXiv.org:2601.03105v1 + stat.AP + cs.MA + cs.SI + physics.soc-ph + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Abdulrahman A. Ahmed, M. Amin Rahimian, Qiushi Chen, Praveen Kumar + + + DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations + https://arxiv.org/abs/2601.03112 + arXiv:2601.03112v1 Announce Type: cross +Abstract: Generative joint source-channel coding (GJSCC) has emerged as a new Deep JSCC paradigm for achieving high-fidelity and robust image transmission under extreme wireless channel conditions, such as ultra-low bandwidth and low signal-to-noise ratio. Recent studies commonly adopt diffusion models as generative decoders, but they frequently produce visually realistic results with limited semantic consistency. This limitation stems from a fundamental mismatch between reconstruction-oriented JSCC encoders and generative decoders, as the former lack explicit semantic discriminability and fail to provide reliable conditional cues. In this paper, we propose DiT-JSCC, a novel GJSCC backbone that can jointly learn a semantics-prioritized representation encoder and a diffusion transformer (DiT) based generative decoder, our open-source project aims to promote the future research in GJSCC. Specifically, we design a semantics-detail dual-branch encoder that aligns naturally with a coarse-to-fine conditional DiT decoder, prioritizing semantic consistency under extreme channel conditions. Moreover, a training-free adaptive bandwidth allocation strategy inspired by Kolmogorov complexity is introduced to further improve the transmission efficiency, thereby indeed redefining the notion of information value in the era of generative decoding. Extensive experiments demonstrate that DiT-JSCC consistently outperforms existing JSCC methods in both semantic consistency and visual quality, particularly in extreme regimes. + oai:arXiv.org:2601.03112v1 + eess.IV + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kailin Tan, Jincheng Dai, Sixian Wang, Guo Lu, Shuo Shao, Kai Niu, Wenjun Zhang, Ping Zhang + + + Transformers self-organize like newborn visual systems when trained in prenatal worlds + https://arxiv.org/abs/2601.03117 + arXiv:2601.03117v1 Announce Type: cross +Abstract: Do transformers learn like brains? A key challenge in addressing this question is that transformers and brains are trained on fundamentally different data. Brains are initially "trained" on prenatal sensory experiences (e.g., retinal waves), whereas transformers are typically trained on large datasets that are not biologically plausible. We reasoned that if transformers learn like brains, then they should develop the same structure as newborn brains when exposed to the same prenatal data. To test this prediction, we simulated prenatal visual input using a retinal wave generator. Then, using self-supervised temporal learning, we trained transformers to adapt to those retinal waves. During training, the transformers spontaneously developed the same structure as newborn visual systems: (1) early layers became sensitive to edges, (2) later layers became sensitive to shapes, and (3) the models developed larger receptive fields across layers. The organization of newborn visual systems emerges spontaneously when transformers adapt to a prenatal visual world. This developmental convergence suggests that brains and transformers learn in common ways and follow the same general fitting principles. + oai:arXiv.org:2601.03117v1 + q-bio.NC + cs.AI + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Lalit Pandey, Samantha M. W. Wood, Justin N. Wood + + + Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries + https://arxiv.org/abs/2601.03123 + arXiv:2601.03123v1 Announce Type: cross +Abstract: When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is always possible using exact methods, but finding minimal circuits efficiently remains a challenging problem. The landscape is very different for compiled unitaries, which arise from programming and typically have short circuits, as compared with generic unitaries, which use all parameters and typically require circuits of maximal size. We show that simple gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries, including in the presence of restricted chip connectivity. This runs counter to earlier evidence that optimal synthesis required combinatorial search, and we show that this discrepancy can be explained by avoiding the random selection of certain parameter-deficient circuit skeletons. + oai:arXiv.org:2601.03123v1 + quant-ph + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Janani Gomathi, Alex Meiburg + + + A short proof of a bound on the size of finite irreducible semigroups of rational matrices + https://arxiv.org/abs/2601.03206 + arXiv:2601.03206v1 Announce Type: cross +Abstract: I give a short proof of a recent result due to Kiefer and Ryzhikov showing that a finite irreducible semigroup of $n\times n$ matrices has cardinality at most $3^{n^2}$. + oai:arXiv.org:2601.03206v1 + math.GR + cs.FL + math.RT + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Benjamin Steinberg + + + Shallow-circuit Supervised Learning on a Quantum Processor + https://arxiv.org/abs/2601.03235 + arXiv:2601.03235v1 Announce Type: cross +Abstract: Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of classical data and poor trainability of many quantum machine learning algorithms designed for near-term quantum hardware. In this work, we show that one can overcome these obstacles by using a linear Hamiltonian-based machine learning method which provides a compact quantum representation of classical data via ground state problems for k-local Hamiltonians. We use the recent sample-based Krylov quantum diagonalization method to compute low-energy states of the data Hamiltonians, whose parameters are trained to express classical datasets through local gradients. We demonstrate the efficacy and scalability of the methods by performing experiments on benchmark datasets using up to 50 qubits of an IBM Heron quantum processor. + oai:arXiv.org:2601.03235v1 + quant-ph + cs.LG + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Luca Candelori, Swarnadeep Majumder, Antonio Mezzacapo, Javier Robledo Moreno, Kharen Musaelian, Santhanam Nagarajan, Sunil Pinnamaneni, Kunal Sharma, Dario Villani + + + Algorithmic randomness in harmonic analysis + https://arxiv.org/abs/2601.03239 + arXiv:2601.03239v1 Announce Type: cross +Abstract: Within the last fifteen years, a program of establishing relationships between algorithmic randomness and almost-everywhere theorems in analysis and ergodic theory has developed. In harmonic analysis, Franklin, McNicholl, and Rute characterized Schnorr randomness using an effective version of Carleson's Theorem. We show here that, for computable $1<p<\infty$, the reals at which the Fourier series of a weakly computable vector in $L^p[-\pi,\pi]$ converges are precisely the Martin-L\"{o}f random reals. Furthermore, we show that radial limits of the Poisson integral of an $L^1(\mathbb{R})$-computable function coincide with the values of the function at exactly the Schnorr random reals and that radial limits of the Poisson integral of a weakly $L^1(\mathbb{R})$-computable function coincide with the values of the function at exactly the Martin-L\"{o}f random reals. + oai:arXiv.org:2601.03239v1 + math.LO + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Johanna N. Y. Franklin, Lucas E. Rodriguez, Diego A. Rojas + + + Self-Supervised Learning from Noisy and Incomplete Data + https://arxiv.org/abs/2601.03244 + arXiv:2601.03244v1 Announce Type: cross +Abstract: Many important problems in science and engineering involve inferring a signal from noisy and/or incomplete observations, where the observation process is known. Historically, this problem has been tackled using hand-crafted regularization (e.g., sparsity, total-variation) to obtain meaningful estimates. Recent data-driven methods often offer better solutions by directly learning a solver from examples of ground-truth signals and associated observations. However, in many real-world applications, obtaining ground-truth references for training is expensive or impossible. Self-supervised learning methods offer a promising alternative by learning a solver from measurement data alone, bypassing the need for ground-truth references. This manuscript provides a comprehensive summary of different self-supervised methods for inverse problems, with a special emphasis on their theoretical underpinnings, and presents practical applications in imaging inverse problems. + oai:arXiv.org:2601.03244v1 + stat.ML + cs.LG + eess.IV + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Juli\'an Tachella, Mike Davies + + + Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data + https://arxiv.org/abs/2601.03247 + arXiv:2601.03247v1 Announce Type: cross +Abstract: Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low-dimensional slow manifold. We learn an explicit input-to-output map on this manifold from forced-response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint. This approach yields a substantial reduction in tracking error compared to feedback-only and feedforward-only baselines under identical settings. This record-and-control workflow enables rapid characterization and high-performance control of soft muscles and muscle-driven joints without detailed physics-based modeling. + oai:arXiv.org:2601.03247v1 + math.DS + cs.CE + cs.RO + cs.SY + eess.SY + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + cross + http://creativecommons.org/licenses/by/4.0/ + Leonardo Bettini, Amirhossein Kazemipour, Robert K. Katzschmann, George Haller + + + Auditing for Core Stability in Participatory Budgeting + https://arxiv.org/abs/2209.14468 + arXiv:2209.14468v2 Announce Type: replace +Abstract: We consider the participatory budgeting problem where each of $n$ voters specifies additive utilities over $m$ candidate projects with given sizes, and the goal is to choose a subset of projects (i.e., a committee) with total size at most $k$. Participatory budgeting mathematically generalizes multiwinner elections, and both have received great attention in computational social choice recently. A well-studied notion of group fairness in this setting is core stability: Each voter is assigned an "entitlement" of $\frac{k}{n}$, so that a subset $S$ of voters can pay for a committee of size at most $|S| \cdot \frac{k}{n}$. A given committee is in the core if no subset of voters can pay for another committee that provides each of them strictly larger utility. This provides proportional representation to all voters in a strong sense. + In this paper, we study the following auditing question: Given a committee computed by some preference aggregation method, how close is it to the core? Concretely, how much does the entitlement of each voter need to be scaled down by, so that the core property subsequently holds? As our main contribution, we present computational hardness results for this problem, as well as a logarithmic approximation algorithm via linear program rounding. We show that our analysis is tight against the linear programming bound. Additionally, we consider two related notions of group fairness that have similar audit properties. The first is Lindahl priceability, which audits the closeness of a committee to a market clearing solution. We show that this is related to the linear programming relaxation of auditing the core, leading to efficient exact and approximation algorithms for auditing. The second is a novel weakening of the core that we term the sub-core, and we present computational results for auditing this notion as well. + oai:arXiv.org:2209.14468v2 + cs.GT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kamesh Munagala, Yiheng Shen, Kangning Wang + + + Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis + https://arxiv.org/abs/2210.06094 + arXiv:2210.06094v3 Announce Type: replace +Abstract: Intraoral 3D scanning is now widely adopted in modern dentistry and plays a central role in supporting key tasks such as tooth segmentation, detection, labeling, and dental landmark identification. Accurate analysis of these scans is essential for orthodontic and restorative treatment planning, as it enables automated workflows and minimizes the need for manual intervention. However, the development of robust learning-based solutions remains challenging due to the limited availability of high-quality public datasets and standardized benchmarks. This article presents Teeth3DS+, an extended public benchmark dedicated to intraoral 3D scan analysis. Developed in the context of the MICCAI 3DTeethSeg and 3DTeethLand challenges, Teeth3DS+ supports multiple fundamental tasks, including tooth detection, segmentation, labeling, 3D modeling, and dental landmark identification. The dataset consists of rigorously curated intraoral scans acquired using state-of-the-art scanners and validated by experienced orthodontists and dental surgeons. In addition to the data, Teeth3DS+ provides standardized data splits and evaluation protocols to enable fair and reproducible comparison of methods, with the goal of fostering progress in learning-based analysis of 3D dental scans. Detailed instructions for accessing the dataset are available at https://crns-smartvision.github.io/teeth3ds + oai:arXiv.org:2210.06094v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Achraf Ben-Hamadou, Nour Neifar, Ahmed Rekik, Oussama Smaoui, Firas Bouzguenda, Sergi Pujades, Edmond Boyer, Edouard Ladroit + + + MAST: Model-Agnostic Sparsified Training + https://arxiv.org/abs/2311.16086 + arXiv:2311.16086v2 Announce Type: replace +Abstract: We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional formulations, the proposed approach explicitly incorporates an initially pre-trained model and random sketch operators, allowing for sparsification of both the model and gradient during training. We establish the insightful properties of the proposed objective function and highlight its connections to the standard formulation. Furthermore, we present several variants of the Stochastic Gradient Descent (SGD) method adapted to the new problem formulation, including SGD with general sampling, a distributed version, and SGD with variance reduction techniques. We achieve tighter convergence rates and relax assumptions, bridging the gap between theoretical principles and practical applications, covering several important techniques such as Dropout and Sparse training. This work presents promising opportunities to enhance the theoretical understanding of model training through a sparsification-aware optimization approach. + oai:arXiv.org:2311.16086v2 + cs.LG + cs.AI + cs.DC + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richt\'arik + + + Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version) + https://arxiv.org/abs/2312.11714 + arXiv:2312.11714v4 Announce Type: replace +Abstract: Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data, including local correlations as well as global dependencies. Most existing generative models have failed to effectively learn both the local and global properties of time series data. To address this open problem, we propose a novel time series generative model named 'Time-Transformer AAE', which consists of an adversarial autoencoder (AAE) and a newly designed architecture named 'Time-Transformer' within the decoder. The Time-Transformer first simultaneously learns local and global features in a layer-wise parallel design, combining the abilities of Temporal Convolutional Networks and Transformer in extracting local features and global dependencies respectively. Second, a bidirectional cross attention is proposed to provide complementary guidance across the two branches and achieve proper fusion between local and global features. Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties. Furthermore, we highlight our model's advantage on handling this kind of data via an artificial dataset. Finally, we show our model's ability to address a real-world problem: data augmentation to support learning with small datasets and imbalanced datasets. + oai:arXiv.org:2312.11714v4 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuansan Liu, Sudanthi Wijewickrema, Ang Li, Christofer Bester, Stephen O'Leary, James Bailey + + + A Large-Scale Analysis on the Use of Arrival Time Prediction for Automated Shuttle Services in the Real World + https://arxiv.org/abs/2401.05322 + arXiv:2401.05322v2 Announce Type: replace +Abstract: Urban mobility is on the cusp of transformation with the emergence of shared, connected, and cooperative automated vehicles. Yet, for them to be accepted by customers, trust in their punctuality is vital. Many pilot initiatives operate without a fixed schedule, enhancing the importance of reliable arrival time (AT) predictions. This study presents an AT prediction system for automated shuttles, utilizing separate models for dwell and running time predictions, validated on real-world data from six cities. Alongside established methods such as XGBoost, we explore the benefits of leveraging spatial correlations using graph neural networks (GNN). To accurately handle the case of a shuttle bypassing a stop, we propose a hierarchical model combining a random forest classifier and a GNN. The results for the final AT prediction are promising, showing low errors even when predicting several stops ahead. Yet, no single model emerges as universally superior, and we provide insights into the characteristics of pilot sites that influence the model selection process and prediction performance. Finally, we identify dwell time prediction as the key determinant in overall AT prediction accuracy when automated shuttles are deployed in low-traffic areas or under regulatory speed limits. Our meta-analysis across six pilot sites in different cities provides insights into the current state of autonomous public transport prediction models and paves the way for more data-informed decision-making as the field advances. + oai:arXiv.org:2401.05322v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/TITS.2025.3643319 + Carolin Schmidt, Mathias Tygesen, Filipe Rodrigues + + + On the permutation automorphisms of binary cubic codes + https://arxiv.org/abs/2402.10667 + arXiv:2402.10667v3 Announce Type: replace +Abstract: A binary linear code whose permutation automorphism group has a fixed point free permutation of order $3$ is called a binary cubic code. The scope of this paper is to investigate the structural properties of binary cubic codes. Let $C$ be a binary cubic $[n,k]$ code. In this paper, we prove that if $n\geq 30$ and $C$ has permutation automorphism group of order three, then $k\geq 6$. Additionally, we show that if $n < 30$ and $k\leq 4$, then the permutation automorphism group of $C$ has order greater than three. Moreover, along the way, we provide some results on the structure of the higher dimensional cubic codes. In particular, we present some results concerning the structure of the putative extremal self-dual $[72,36,16]$ code under the assumption that it is cubic. + oai:arXiv.org:2402.10667v3 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Murat Altunbulak, Fatma Altunbulak Aksu, Roghayeh Hafezieh, \.Ipek Tuvay + + + HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion + https://arxiv.org/abs/2404.03527 + arXiv:2404.03527v3 Announce Type: replace +Abstract: Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, paying inadequate attention to the inherent differences between RGB and thermal modalities. Recent progress in vision foundation models (VFMs) trained through self-supervision on vast amounts of unlabeled data has proven their ability to extract informative, general-purpose features. However, this potential has yet to be fully leveraged in the domain. In this study, we take one step toward this new research area by exploring a feasible strategy to fully exploit VFM features for RGB-thermal scene parsing. Specifically, we delve deeper into the unique characteristics of RGB and thermal modalities, thereby designing a hybrid, asymmetric encoder that incorporates both a VFM and a convolutional neural network. This design allows for more effective extraction of complementary heterogeneous features, which are subsequently fused in a dual-path, progressive manner. Moreover, we introduce an auxiliary task to further enrich the local semantics of the fused features, thereby improving the overall performance of RGB-thermal scene parsing. Our proposed HAPNet, equipped with all these components, demonstrates superior performance compared to all other state-of-the-art RGB-thermal scene parsing networks, achieving top ranks across three widely used public RGB-thermal scene parsing datasets. We believe this new paradigm has opened up new opportunities for future developments in data-fusion scene parsing approaches. + oai:arXiv.org:2404.03527v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiahang Li, Peng Yun, Yang Xu, Ye Zhang, Mingjian Sun, Qijun Chen, Ilin Alexander, Rui Fan + + + Myopically Verifiable Probabilistic Certificates for Safe Control and Learning + https://arxiv.org/abs/2404.16883 + arXiv:2404.16883v3 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:2404.16883v3 + eess.SY + cs.LG + cs.SY + Wed, 07 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 + + + Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning + https://arxiv.org/abs/2405.18376 + arXiv:2405.18376v3 Announce Type: replace +Abstract: Existing SFDA methods struggle to fully use pre-trained knowledge and often rely on a single model's predictions or handcrafted prompts, limiting robustness under domain shift. Multimodal Large Language Models (MLLMs) offer a promising alternative: they encode rich visual-semantic knowledge and generalize well without task-specific tuning. However, their use in SFDA is hindered by instruction-following failures, inconsistent outputs, and high inference costs. We propose Reliability-based Curriculum Learning (RCL), a novel framework that distills robust supervision from multiple frozen MLLMs into a compact target model. RCL organizes adaptation as a three-stage curriculum that progressively incorporates pseudo-labels based on inter-model agreement and model confidence, enabling stable and noise-aware training. Our approach achieves state-of-the-art performance on standard SFDA datasets, Office-Home, DomainNet-126, and VisDA-C, outperforming zero-shot MLLMs, their ensembles, all without accessing source data or tuning foundation models. Our code is available at: https://github.com/Dong-Jie-Chen/RCL. + oai:arXiv.org:2405.18376v3 + cs.LG + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Dongjie Chen, Kartik Patwari, Zhengfeng Lai, Xiaoguang Zhu, Sen-ching Cheung, Chen-Nee Chuah + + + Topological Perspectives on Optimal Multimodal Embedding Spaces + https://arxiv.org/abs/2405.18867 + arXiv:2405.18867v2 Announce Type: replace +Abstract: Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual information within a unified latent space. This paper delves into a comparative analysis between CLIP and its recent counterpart, CLOOB. To unravel the intricate distinctions within the embedding spaces crafted by these models, we employ topological data analysis. Our approach encompasses a comprehensive examination of the modality gap drivers, the clustering structures existing across both high and low dimensions, and the pivotal role that dimension collapse plays in shaping their respective embedding spaces. Empirical experiments substantiate the implications of our analyses on downstream performance across various contextual scenarios. Through this investigation, we aim to shed light on the nuanced intricacies that underlie the comparative efficacy of CLIP and CLOOB, offering insights into their respective strengths and weaknesses, and providing a foundation for further refinement and advancement in multimodal model research. + oai:arXiv.org:2405.18867v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Abdul Aziz A. B, A. B Abdul Rahim + + + A Survey on Failure Analysis and Fault Injection in AI Systems + https://arxiv.org/abs/2407.00125 + arXiv:2407.00125v2 Announce Type: replace +Abstract: The rapid advancement of Artificial Intelligence (AI) has led to its integration into various areas, especially with Large Language Models (LLMs) significantly enhancing capabilities in Artificial Intelligence Generated Content (AIGC). However, the complexity of AI systems has also exposed their vulnerabilities, necessitating robust methods for failure analysis (FA) and fault injection (FI) to ensure resilience and reliability. Despite the importance of these techniques, there lacks a comprehensive review of FA and FI methodologies in AI systems. This study fills this gap by presenting a detailed survey of existing FA and FI approaches across six layers of AI systems. We systematically analyze 160 papers and repositories to answer three research questions including (1) what are the prevalent failures in AI systems, (2) what types of faults can current FI tools simulate, (3) what gaps exist between the simulated faults and real-world failures. Our findings reveal a taxonomy of AI system failures, assess the capabilities of existing FI tools, and highlight discrepancies between real-world and simulated failures. Moreover, this survey contributes to the field by providing a framework for fault diagnosis, evaluating the state-of-the-art in FI, and identifying areas for improvement in FI techniques to enhance the resilience of AI systems. + oai:arXiv.org:2407.00125v2 + cs.SE + cs.AI + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Guangba Yu, Gou Tan, Haojia Huang, Zhenyu Zhang, Pengfei Chen, Roberto Natella, Zibin Zheng + + + Limits to Predicting Online Speech Using Large Language Models + https://arxiv.org/abs/2407.12850 + arXiv:2407.12850v3 Announce Type: replace +Abstract: Our paper studies the predictability of online speech -- that is, how well language models learn to model the distribution of user generated content on X (previously Twitter). We define predictability as a measure of the model's uncertainty, i.e. its negative log-likelihood. As the basis of our study, we collect 10M tweets for ``tweet-tuning'' base models and a further 6.25M posts from more than five thousand X (previously Twitter) users and their peers. In our study involving more than 5000 subjects, we find that predicting posts of individual users remains surprisingly hard. Moreover, it matters greatly what context is used: models using the users' own history significantly outperform models using posts from their social circle. We validate these results across four large language models ranging in size from 1.5 billion to 70 billion parameters. Moreover, our results replicate if instead of prompting the model with additional context, we finetune on it. We follow up with a detailed investigation on what is learned in-context and a demographic analysis. Up to 20\% of what is learned in-context is the use of @-mentions and hashtags. Our main results hold across the demographic groups we studied. + oai:arXiv.org:2407.12850v3 + cs.CL + cs.CY + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Mina Remeli, Moritz Hardt, Robert C. Williamson + + + An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentation + https://arxiv.org/abs/2409.14305 + arXiv:2409.14305v2 Announce Type: replace +Abstract: Deep learning models have achieved significant success in segmenting cardiovascular structures, but there is a growing need to improve their generalization and robustness. Current methods often face challenges such as overfitting and limited accuracy, largely due to their reliance on large annotated datasets and limited optimization techniques. This paper introduces the UU-Mamba model, an extension of the U-Mamba architecture, designed to address these challenges in both cardiac and vascular segmentation. By incorporating Sharpness-Aware Minimization (SAM), the model enhances generalization by seeking flatter minima in the loss landscape. Additionally, we propose an uncertainty-aware loss function that integrates region-based, distribution-based, and pixel-based components, improving segmentation accuracy by capturing both local and global features. We expand our evaluations on the ImageCAS (coronary artery) and Aorta (aortic branches and zones) datasets, which present more complex segmentation challenges than the ACDC dataset (left and right ventricles) used in prior work, showcasing the model's adaptability and resilience. Our results confirm UU-Mamba's superior performance compared to leading models such as TransUNet, Swin-Unet, nnUNet, and nnFormer. We also provide a more in-depth assessment of the model's robustness and segmentation accuracy through extensive experiments. + oai:arXiv.org:2409.14305v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ting Yu Tsai, Liangqiao Gui, Yineng Chen, Li Lin, Shu Hu, Connie W. Tsao, Xin Li, Shao Lin, Ming-Ching Chang, Hongtu Zhu, Xin Wang + + + Conformal Prediction for Dose-Response Models with Continuous Treatments + https://arxiv.org/abs/2409.20412 + arXiv:2409.20412v2 Announce Type: replace +Abstract: Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insufficient in these high-risk environments, highlighting the need for uncertainty quantification to support informed decisions. Conformal prediction, a distribution-free and model-agnostic method for uncertainty quantification, has seen limited application in continuous treatments or dose-response models. To address this gap, we propose a novel methodology that frames the causal dose-response problem as a covariate shift, leveraging weighted conformal prediction. By incorporating propensity estimation, conformal predictive systems, and likelihood ratios, we present a practical solution for generating prediction intervals for dose-response models. Additionally, our method approximates local coverage for every treatment value by applying kernel functions as weights in weighted conformal prediction. Finally, we use a new synthetic benchmark dataset to demonstrate the significance of covariate shift assumptions in achieving robust prediction intervals for dose-response models. + oai:arXiv.org:2409.20412v2 + cs.LG + cs.AI + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jarne Verhaeghe, Jef Jonkers, Sofie Van Hoecke + + + Large Language Models can Achieve Social Balance + https://arxiv.org/abs/2410.04054 + arXiv:2410.04054v3 Announce Type: replace +Abstract: Large Language Models (LLMs) can be deployed in situations where they process positive/negative interactions with other agents. We study how this is done under the sociological framework of social balance, which explains the emergence of one faction or multiple antagonistic ones among agents. Across different LLM models, we find that balance depends on the (i) type of interaction, (ii) update mechanism, and (iii) population size. Across (i)-(iii), we characterize the frequency at which social balance is achieved, the justifications for the social dynamics, and the diversity and stability of interactions. Finally, we explain how our findings inform the deployment of agentic systems. + oai:arXiv.org:2410.04054v3 + cs.CL + cs.AI + cs.MA + cs.SI + physics.soc-ph + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Pedro Cisneros-Velarde + + + A Machine Learning Model for Solving Lane-Emden Equation using Legendre Wavelet Neural Network + https://arxiv.org/abs/2410.05409 + arXiv:2410.05409v2 Announce Type: replace +Abstract: As we know differential equations are very useful for electrical engineers to solve a variety of problems like: voltage across a capacitor, input versus output voltage, etc. Therefore, the goal of this paper is to find the solutions of non-linear differential equations based on the Lane Emden equation of second order using the Legendre wavelet neural network (LWNN) method. Here all the considered equations are singular initial value problems. To manage the singularity challenge, we have employed an artificial neural network method. This approach utilizes a neural network of a single layer, where the hidden layer is omitted by enlarging the input using Legendre wavelets functions. We have applied a feed-forward neural network method to the proposed problem along with the principle of error backpropagation. The effectiveness of the Legendre wavelet Neural Network method is validated through Lane Emden equations.. + oai:arXiv.org:2410.05409v2 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Vijay Kumar Patel, Abhishekh, Dileep Kumar, Nitin Kumar + + + Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data + https://arxiv.org/abs/2410.13341 + arXiv:2410.13341v3 Announce Type: replace +Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong existing models in lieu of costly labels to provide cheap model evaluations. Unfortunately, this method of using models as judges introduces biases, such as self-preferencing, that can distort model comparisons. An emerging family of debiasing tools promises to fix these issues by using a few high quality labels to debias a large number of model judgments. In this paper, we study how far such debiasing methods, in principle, can go. Our main result shows that when the judge is no more accurate than the evaluated model, no debiasing method can decrease the required amount of ground truth labels by more than half. Our result speaks to the severe limitations of the LLM-as-a-judge paradigm at the evaluation frontier where the goal is to assess newly released models that are possibly better than the judge. Through an empirical evaluation, we demonstrate that the sample size savings achievable in practice are even more modest than what our theoretical limit suggests. Along the way, our work provides new observations about debiasing methods for model evaluation, and points out promising avenues for future work. + oai:arXiv.org:2410.13341v3 + cs.LG + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Florian E. Dorner, Vivian Y. Nastl, Moritz Hardt + + + How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models + https://arxiv.org/abs/2410.15002 + arXiv:2410.15002v2 Announce Type: replace +Abstract: Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models on such datasets enables them to generate images that might violate copyright laws and individual privacy. This phenomenon is termed imitation -- generation of images with content that has recognizable similarity to its training images. In this work we estimate the point at which a model was trained on enough instances of a concept to be able to imitate it -- the imitation threshold. We posit this question as a new problem and propose an efficient approach that estimates the imitation threshold without incurring the colossal cost of training these models from scratch. We experiment with two domains -- human faces and art styles, and evaluate four text-to-image models that were trained on three pretraining datasets. We estimate the imitation threshold of these models to be in the range of 200-700 images, depending on the domain and the model. The imitation threshold provides an empirical basis for copyright violation claims and acts as a guiding principle for text-to-image model developers that aim to comply with copyright and privacy laws. Website: https://how-many-van-goghs-does-it-take.github.io/. Code: https://github.com/vsahil/MIMETIC-2. + oai:arXiv.org:2410.15002v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Sahil Verma, Royi Rassin, Arnav Das, Gantavya Bhatt, Preethi Seshadri, Chirag Shah, Jeff Bilmes, Hannaneh Hajishirzi, Yanai Elazar + + + Uncovering Autoregressive LLM Knowledge of Thematic Fit in Event Representation + https://arxiv.org/abs/2410.15173 + arXiv:2410.15173v2 Announce Type: replace +Abstract: We show closed models possess much thematic fit knowledge and set a new state of the art, while open models also seem to capture much relevant knowledge (in semantic filtering), but yield lower scores. Surprisingly, multi-step reasoning only helped closed models (with few exceptions); generated sentences hurt closed models' performance; and output form had little to no effect. We analyze the reasons for these findings, and conclude that more foundational work is needed for a single LLM to perform the best on all tasks with the same experimental condition, let alone improve results further. Source code is available at: https://github.com/SafeyahShemali/LLM_Thematic_Fit_25 + oai:arXiv.org:2410.15173v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Safeyah Khaled Alshemali, Daniel Bauer, Yuval Marton + + + SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs + https://arxiv.org/abs/2410.16882 + arXiv:2410.16882v4 Announce Type: replace +Abstract: Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalance with numeric interpolation; however, existing approaches largely rely on embedding-space arithmetic, which fails to capture the rich semantics inherent in text-attributed graphs. In this work, we propose our method, SaVe-TAG (Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs), a novel VRM framework that leverages Large Language Models (LLMs) to perform text-level interpolation, generating on-manifold, boundary-enriching synthetic samples for minority classes. To mitigate the risk of noisy generation, we introduce a confidence-based edge assignment mechanism that uses graph topology as a natural filter to ensure structural consistency. We provide theoretical justification for our method and conduct extensive experiments on benchmark datasets, showing that our approach consistently outperforms both numeric interpolation and prior long-tailed node classification baselines. Our results highlight the importance of integrating semantic and structural signals for balanced and effective learning on text-attributed graphs. The source code is publicly available at: https://github.com/LWang-Laura/SaVe-TAG. + oai:arXiv.org:2410.16882v4 + cs.AI + cs.LG + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3770854.3780311 + Leyao Wang, Yu Wang, Bo Ni, Yuying Zhao, Hanyu Wang, Yao Ma, Tyler Derr + + + EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking + https://arxiv.org/abs/2411.06254 + arXiv:2411.06254v5 Announce Type: replace +Abstract: Decoder-only LLM rerankers struggle with long documents: inference is costly and relevance signals can be diluted by irrelevant context. Motivated by an attention analysis indicating a consistent degradation trend when non-relevant text is appended, we propose EviRerank, an evidence-based long-document reranking framework for decoder-only LLMs. EviRerank (i) scores document blocks with a lightweight selector (BM25, bi-encoder, or cross-encoder), (ii) constructs a compact reranking context under a hard token cap by dynamically budgeting evidence blocks with Adaptive Evidence Budgeting (AEB) and adding a global summary cue via Summary Augmentation (SA), and (iii) reranks with a decoder-only LLM. Across TREC DL'19, DL'23, and MLDR-zh, EviRerank consistently outperforms full-document LLM reranking and strong block-selection baselines while substantially reducing the required input length. On TREC DL'19, EviRerank achieves 0.743 nDCG@10 and 0.307 MAP, establishing a new best result and improving over RankLLaMA (0.701/0.288) by +0.042 nDCG@10 (+6.0%) and +0.019 MAP (+6.6%). + oai:arXiv.org:2411.06254v5 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Minghan Li, Eric Gaussier, Juntao Li, Guodong Zhou + + + Communication Compression for Tensor Parallel LLM Inference + https://arxiv.org/abs/2411.09510 + arXiv:2411.09510v3 Announce Type: replace +Abstract: Large Language Models (LLMs) have pushed the frontier of artificial intelligence but are comprised of hundreds of billions of parameters and operations. For faster inference latency, LLMs are deployed on multiple hardware accelerators through various Model Parallelism strategies. Our paper looks into the details on one such strategy - Tensor Parallel - and proposes to reduce latency by compressing inter-accelerator communication. We leverage fine grained quantization techniques to compress selected activations by 3.5 - 4.5x. Our proposed method leads up to 2x reduction of time-to-first-token (TTFT) with negligible model performance degradation. + oai:arXiv.org:2411.09510v3 + cs.LG + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jan Hansen-Palmus, Michael Truong Le, Oliver Hausd\"orfer, Alok Verma + + + FCC: Fully Connected Correlation for One-Shot Segmentation + https://arxiv.org/abs/2411.11917 + arXiv:2411.11917v2 Announce Type: replace +Abstract: Few-shot segmentation (FSS) aims to segment the target object in a query image using only a small set of support images and masks. Therefore, having strong prior information for the target object using the support set is essential for guiding the initial training of FSS, which leads to the success of few-shot segmentation in challenging cases, such as when the target object shows considerable variation in appearance, texture, or scale across the support and query images. Previous methods have tried to obtain prior information by creating correlation maps from pixel-level correlation on final-layer or same-layer features. However, we found these approaches can offer limited and partial information when advanced models like Vision Transformers are used as the backbone. Vision Transformer encoders have a multi-layer structure with identical shapes in their intermediate layers. Leveraging the feature comparison from all layers in the encoder can enhance the performance of few-shot segmentation. We introduce FCC (Fully Connected Correlation) to integrate pixel-level correlations between support and query features, capturing associations that reveal target-specific patterns and correspondences in both same-layers and cross-layers. FCC captures previously inaccessible target information, effectively addressing the limitations of support mask. Our approach consistently demonstrates state-of-the-art performance on PASCAL, COCO, and domain shift tests. We conducted an ablation study and cross-layer correlation analysis to validate FCC's core methodology. These findings reveal the effectiveness of FCC in enhancing prior information and overall model performance. + oai:arXiv.org:2411.11917v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Seonghyeon Moon, Haein Kong, Muhammad Haris Khan, Mubbasir Kapadia, Yuewei Lin + + + Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval + https://arxiv.org/abs/2411.17490 + arXiv:2411.17490v4 Announce Type: replace +Abstract: Structuring latent representations in a hierarchical manner enables models to learn patterns at multiple levels of abstraction. However, most prevalent image understanding models focus on visual similarity, and learning visual hierarchies is relatively unexplored. In this work, for the first time, we introduce a learning paradigm that can encode user-defined multi-level complex visual hierarchies in hyperbolic space without requiring explicit hierarchical labels. As a concrete example, first, we define a part-based image hierarchy using object-level annotations within and across images. Then, we introduce an approach to enforce the hierarchy using contrastive loss with pairwise entailment metrics. Finally, we discuss new evaluation metrics to effectively measure hierarchical image retrieval. Encoding these complex relationships ensures that the learned representations capture semantic and structural information that transcends mere visual similarity. Experiments in part-based image retrieval show significant improvements in hierarchical retrieval tasks, demonstrating the capability of our model in capturing visual hierarchies. + oai:arXiv.org:2411.17490v4 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ziwei Wang, Sameera Ramasinghe, Chenchen Xu, Julien Monteil, Loris Bazzani, Thalaiyasingam Ajanthan + + + AdaVLN: Towards Visual Language Navigation in Continuous Indoor Environments with Moving Humans + https://arxiv.org/abs/2411.18539 + arXiv:2411.18539v3 Announce Type: replace +Abstract: Visual Language Navigation is a task that challenges robots to navigate in realistic environments based on natural language instructions. While previous research has largely focused on static settings, real-world navigation must often contend with dynamic human obstacles. Hence, we propose an extension to the task, termed Adaptive Visual Language Navigation (AdaVLN), which seeks to narrow this gap. AdaVLN requires robots to navigate complex 3D indoor environments populated with dynamically moving human obstacles, adding a layer of complexity to navigation tasks that mimic the real-world. To support exploration of this task, we also present AdaVLN simulator and AdaR2R datasets. The AdaVLN simulator enables easy inclusion of fully animated human models directly into common datasets like Matterport3D. We also introduce a "freeze-time" mechanism for both the navigation task and simulator, which pauses world state updates during agent inference, enabling fair comparisons and experimental reproducibility across different hardware. We evaluate several baseline models on this task, analyze the unique challenges introduced by AdaVLN, and demonstrate its potential to bridge the sim-to-real gap in VLN research. + oai:arXiv.org:2411.18539v3 + cs.CV + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Dillon Loh, Tomasz Bednarz, Xinxing Xia, Frank Guan + + + Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control + https://arxiv.org/abs/2412.00548 + arXiv:2412.00548v2 Announce Type: replace +Abstract: This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as satellite attitude actuators in Earth's orbit and for long-term formation and attitude control. This study derives a unique, continuous, and power-optimal current solution via sequential convex programming and approximates it using a multilayer perceptron model. The effectiveness of our strategy was demonstrated through numerical simulations and experimental trials on the formation and attitude control. + oai:arXiv.org:2412.00548v2 + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuta Takahashi, Shin-ichiro Sakai + + + MemHunter: Automated and Verifiable Memorization Detection at Dataset-scale in LLMs + https://arxiv.org/abs/2412.07261 + arXiv:2412.07261v3 Announce Type: replace +Abstract: Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets. Existing methods for detecting memorization are primarily sample-specific, relying on manually crafted or discretely optimized memory-inducing prompts generated on a per-sample basis, which become impractical for dataset-level detection due to the prohibitive computational cost of iterating through all samples. In real-world scenarios, data owners may need to verify whether a susceptible LLM has memorized their dataset, particularly if the LLM may have collected the data from the web without authorization. To address this, we introduce MemHunter, which trains a memory-inducing LLM and employs hypothesis testing to efficiently detect memorization at the dataset level, without requiring sample-specific memory inducing. Experiments on models like Pythia and Llama demonstrate that MemHunter can extract up to 40% more training data than existing methods under constrained time resources and reduce search time by up to 80% when integrated as a plug-in. Crucially, MemHunter is the first method capable of dataset-level memorization detection, providing a critical tool for assessing privacy risks in LLMs powered by large-scale datasets. + oai:arXiv.org:2412.07261v3 + cs.CR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhenpeng Wu, Jian Lou, Zibin Zheng, Chuan Chen + + + RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset + https://arxiv.org/abs/2412.19500 + arXiv:2412.19500v2 Announce Type: replace +Abstract: Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DoFs and complex, dynamic environments. While traditional motion planning algorithms struggle with high-dimensional spaces, deep learning-based methods often face instability and inefficiency in complex tasks. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators. By integrating physical constraints with a point cloud encoder and replacing the U-Net structure with an encoder-only transformer, RobotDiffuse improves the model's ability to capture temporal dependencies and generate smoother, more coherent motion plans. We validate the approach using a complex simulator and release a new dataset, Robot-obtalcles-panda (ROP), with 35M robot poses and 0.14M obstacle avoidance scenarios. The highest overall score obtained in the experiment demonstrates the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The dataset can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse. + oai:arXiv.org:2412.19500v2 + cs.RO + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xudong Mou, Xiaohan Zhang, Tiejun Wang, Tianyu Wo, Cangbai Xu, Ningbo Gu, Rui Wang, Xudong Liu + + + Steering Flexible Linear Objects in Planar Environments by Two Robot Hands Using Euler's Elastica Solutions + https://arxiv.org/abs/2501.02874 + arXiv:2501.02874v4 Announce Type: replace +Abstract: The manipulation of flexible objects such as cables, wires and fresh food items by robot hands forms a special challenge in robot grasp mechanics. This paper considers the steering of flexible linear objects in planar environments by two robot hands. The flexible linear object, modeled as an elastic non-stretchable rod, is manipulated by varying the gripping endpoint positions while keeping equal endpoint tangents. The flexible linear object shape has a closed form solution in terms of the grasp endpoint positions and tangents, called Euler's elastica. This paper obtains the elastica solutions under the optimal control framework, then uses the elastica solutions to obtain closed-form criteria for non self-intersection, stability and obstacle avoidance of the flexible linear object. The new tools are incorporated into a planning scheme for steering flexible linear objects in planar environments populated by sparsely spaced obstacles. The scheme is fully implemented and demonstrated with detailed examples. + oai:arXiv.org:2501.02874v4 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Aharon Levin, Elon Rimon, Amir Shapiro + + + The structure of polynomial growth for tree automata/transducers and MSO set queries + https://arxiv.org/abs/2501.10270 + arXiv:2501.10270v4 Announce Type: replace +Abstract: Given an $\mathbb{N}$-weighted tree automaton, we give a decision procedure for exponential vs polynomial growth (with respect to the input size) in quadratic time, and an algorithm that computes the exact polynomial degree of growth in cubic time. As a special case, they apply to the growth of the ambiguity of a nondeterministic tree automaton, i.e. the number of distinct accepting runs over a given input. + We deduce analogous decidability results (ignoring complexity) for the growth of the number of results of set queries in Monadic Second-Order logic (MSO) over ranked trees. In the case of polynomial growth of degree $k$, we also prove a reparameterization theorem for such queries: their results can be mapped to $k$-tuples of input nodes in a finite-to-one and MSO-definable fashion. + We then apply these tools to study growth rates and subclass membership problems for tree-to-tree functions. Using new proof strategies, we recover and generalize known results concerning polyregular functions, total deterministic macro tree transducers, and partial nondeterministic top-down tree transducers. In particular, we give a procedure to decide polynomial size-to-height increase for both macro tree transducers and MSO set interpretations, and compute the degree. + The paper concludes with a survey of a wide range of related work. + oai:arXiv.org:2501.10270v4 + cs.FL + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Paul Gallot, Nathan Lhote, L\^e Th\`anh D\~ung Nguy\^en + + + SLVC-DIDA: Signature-less Verifiable Credential-based Issuer-hiding and Multi-party Authentication for Decentralized Identity + https://arxiv.org/abs/2501.11052 + arXiv:2501.11052v3 Announce Type: replace +Abstract: As an emerging paradigm in digital identity, Decentralized Identity (DID) appears advantages over traditional identity management methods in a variety of aspects, e.g., enhancing user-centric online services and ensuring complete user autonomy and control. Verifiable Credential (VC) techniques are used to facilitate decentralized DID-based access control across multiple entities. However, existing DID schemes generally rely on a distributed public key infrastructure that also causes challenges, such as context information deduction, key exposure, and issuer data leakage. To address the issues above, this paper proposes a issuer-hiding and privacy-preserving DID multi-party authentication model with a signature-less VC scheme, named SLVC-DIDA, for the first time. Our proposed scheme avoids the dependence on signing keys by employing hashing and issuer membership proofs, which supports universal zero-knowledge multi-party DID authentications, eliminating additional technical integrations. We adopt a novel zero-knowledge circuit to maintain the anonymity of the issuer set, thereby enabling public verification while safeguarding the privacy of identity attributes via a Merkle tree-based VC list. Furthermore, by eliminating reliance on a Public Key Infrastructure (PKI), SLVC-DIDA enables decentralized and self-sovereign DID authentication. Our experiments further evaluate the effectiveness and practicality of SLVC-DIDA. + oai:arXiv.org:2501.11052v3 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tianxiu Xie, Keke Gai, Jing Yu, Liehuang Zhu, Bin Xiao + + + Model-checking real-time systems: revisiting the alternating automaton route + https://arxiv.org/abs/2501.17576 + arXiv:2501.17576v2 Announce Type: replace +Abstract: Alternating timed automata (ATA) are an extension of timed automata, that are closed under complementation and hence amenable to logic-to-automata translations. Several timed logics, including Metric Temporal Logic (MTL), can be converted to equivalent 1-clock ATAs (1-ATAs). Satisfiability of an MTL formula reduces to checking emptiness of a 1-ATA. A straightforward modification of the 1-ATA emptiness algorithm can be applied for model-checking timed automata models against 1-ATA specifications. However, existing emptiness algorithms for 1-ATA proceed by an extended region construction, and are not suitable for implementations. Our goal in this work is to initiate the study of zone-based methods directly for 1-ATAs. We first introduce a deactivation operation on the 1-ATA syntax to allow an explicit deactivation of the clock in transitions. Using the deactivation operation, we improve the existing MTL-to-1-ATA conversion and present a fragment of MTL for which the equivalent 1-ATA generate a bounded number of variables. Secondly, we develop the idea of zones for 1-ATA and present an emptiness algorithm which explores a corresponding zone graph. For termination, a special entailment check between zones is necessary. Our main technical contributions are: (1) an algorithm for the entailment check using simple zone operations and (2) an NP-hardness for the entailment check in the general case. Finally, we adapt our methods to the problem of model-checking timed automata models against 1-ATA specifications. We observe that when the timed automaton is strongly non-Zeno or when the 1-ATA generates a bounded number of variables, a modified entailment check with quadratic complexity can be applied. + oai:arXiv.org:2501.17576v2 + cs.LO + cs.FL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Patricia Bouyer, B Srivathsan, Vaishnavi Vishwanath + + + Successor-Generator Planning with LLM-generated Heuristics + https://arxiv.org/abs/2501.18784 + arXiv:2501.18784v4 Announce Type: replace +Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning. This work revisits that paradigm in light of recent advances in large language models (LLMs), which enable the automatic synthesis of heuristics directly from problem definitions -- bypassing the need for handcrafted domain knowledge. We present a method that employs LLMs to generate problem-specific heuristic functions from planning tasks specified through successor generators, goal tests, and initial states written in a general-purpose programming language. These heuristics are compiled and integrated into standard heuristic search algorithms, such as greedy best-first search. Our approach achieves competitive, and in many cases state-of-the-art, performance across a broad range of established planning benchmarks. Moreover, it enables the solution of problems that are difficult to express in traditional formalisms, including those with complex numeric constraints or custom transition dynamics. We provide an extensive empirical evaluation that characterizes the strengths and limitations of the approach across diverse planning settings, demonstrating its effectiveness. + oai:arXiv.org:2501.18784v4 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Alexander Tuisov, Yonatan Vernik, Alexander Shleyfman + + + Leveraging the true depth of LLMs + https://arxiv.org/abs/2502.02790 + arXiv:2502.02790v3 Announce Type: replace +Abstract: The remarkable capabilities of Large Language Models (LLMs) are overshadowed by their immense computational cost. While recent work has shown that many LLM layers can be reordered or even removed with minimal impact on accuracy, these insights have not been translated into significant inference speedups. To bridge this gap, we introduce a novel method that restructures the computational graph by grouping and evaluating consecutive layer pairs in parallel. This approach, requiring no retraining, yields a 1.19x throughput gain on Llama 2 7B while reducing the average benchmark accuracy by only 1.5\%. We demonstrate the practical value of this method for large-scale LLM deployment and show that some of the lost accuracy can be recovered with lightweight fine-tuning of the parallelized layers. + oai:arXiv.org:2502.02790v3 + cs.LG + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Ram\'on Calvo Gonz\'alez, Daniele Paliotta, Matteo Pagliardini, Martin Jaggi, Fran\c{c}ois Fleuret + + + Training Set Reconstruction from Differentially Private Forests: How Effective is DP? + https://arxiv.org/abs/2502.05307 + arXiv:2502.05307v4 Announce Type: replace +Abstract: Recent research has shown that structured machine learning models such as tree ensembles are vulnerable to privacy attacks targeting their training data. To mitigate these risks, differential privacy (DP) has become a widely adopted countermeasure, as it offers rigorous privacy protection. In this paper, we introduce a reconstruction attack targeting state-of-the-art $\epsilon$-DP random forests. By leveraging a constraint programming model that incorporates knowledge of the forest's structure and DP mechanism characteristics, our approach formally reconstructs the most likely dataset that could have produced a given forest. Through extensive computational experiments, we examine the interplay between model utility, privacy guarantees and reconstruction accuracy across various configurations. Our results reveal that random forests trained with meaningful DP guarantees can still leak portions of their training data. Specifically, while DP reduces the success of reconstruction attacks, the only forests fully robust to our attack exhibit predictive performance no better than a constant classifier. Building on these insights, we also provide practical recommendations for the construction of DP random forests that are more resilient to reconstruction attacks while maintaining a non-trivial predictive performance. + oai:arXiv.org:2502.05307v4 + cs.LG + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Alice Gorg\'e, Julien Ferry, S\'ebastien Gambs, Thibaut Vidal + + + DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior + https://arxiv.org/abs/2502.09111 + arXiv:2502.09111v2 Announce Type: replace +Abstract: Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. + oai:arXiv.org:2502.09111v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1109/TVCG.2025.3617961 + Mingrui Li, Shuhong Liu, Tianchen Deng, Hongyu Wang + + + Whose story is it? Personalizing story generation by inferring author styles + https://arxiv.org/abs/2502.13028 + arXiv:2502.13028v3 Announce Type: replace +Abstract: Personalization is critical for improving user experience in interactive writing and educational applications, yet remains understudied in story generation. We study the task of personalizing story generation, where our goal is to mimic an author's writing style, given other stories written by them. We collect Mythos, a dataset of 3.6k stories from 112 authors, with an average of 16 stories per author, across five distinct sources reflecting diverse story-writing settings. We propose a two-stage pipeline for personalized story generation: first, we infer authors' implicit writing characteristics and organize them into an Author Writing Sheet, which is validated by humans to be of high quality; second, we simulate the author's persona using tailored persona descriptions and personalized story rules. We find that stories personalized using the Author Writing Sheet outperform a non-personalized baseline, achieving a 78% win-rate in capturing authors' past style and 59% in similarity to ground-truth author stories. Human evaluation supports these findings and further highlights trends, such as Reddit stories being easier to personalize, and the Creativity and Language Use aspects of stories being easier to personalize than the Plot. + oai:arXiv.org:2502.13028v3 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Nischal Ashok Kumar, Chau Minh Pham, Mohit Iyyer, Andrew Lan + + + Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework + https://arxiv.org/abs/2502.13759 + arXiv:2502.13759v3 Announce Type: replace +Abstract: Geolocation, the task of identifying an image's location, requires complex reasoning and is crucial for navigation, monitoring, and cultural preservation. However, current methods often produce coarse, imprecise, and non-interpretable localization. A major challenge lies in the quality and scale of existing geolocation datasets. These datasets are typically small-scale and automatically constructed, leading to noisy data and inconsistent task difficulty, with images that either reveal answers too easily or lack sufficient clues for reliable inference. To address these challenges, we introduce a comprehensive geolocation framework with three key components: GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric, collectively designed to address critical challenges and drive advancements in geolocation research. At the core of this framework is GeoComp (Geolocation Competition Dataset), a large-scale dataset collected from a geolocation game platform involving 740K users over two years. It comprises 25 million entries of metadata and 3 million geo-tagged locations spanning much of the globe, with each location annotated thousands to tens of thousands of times by human users. The dataset offers diverse difficulty levels for detailed analysis and highlights key gaps in current models. Building on this dataset, we propose Geographical Chain-of-Thought (GeoCoT), a novel multi-step reasoning framework designed to enhance the reasoning capabilities of Large Vision Models (LVMs) in geolocation tasks. GeoCoT improves performance by integrating contextual and spatial cues through a multi-step process that mimics human geolocation reasoning. Finally, using the GeoEval metric, we demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability. + oai:arXiv.org:2502.13759v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Zirui Song, Jingpu Yang, Yuan Huang, Jonathan Tonglet, Zeyu Zhang, Tao Cheng, Meng Fang, Iryna Gurevych, Xiuying Chen + + + Towards Threshold-Free KV Cache Pruning + https://arxiv.org/abs/2502.16886 + arXiv:2502.16886v3 Announce Type: replace +Abstract: To reduce memory consumption during LLM inference, prior works have proposed numerous methods that focus on KV cache pruning based on various criteria. While these techniques often accomplish lossless memory reduction on many datasets, they often rely on an under-emphasized condition: a dataset/domain-specific budget size threshold needs to be pre-determined to achieve the optimal performance. However, such input-specific tuning may be considerably limited in real-world scenarios, as open-domain inputs span diverse domains, lengths and difficulty levels, without clear boundaries for pre-tuning. Thus, the dependence of an input-sensitive threshold can be an inherent limitation that may cause large degradation on arbitrary inputs. In this work, we propose a new objective that lifts the threshold constraints for robust KV pruning, calling for "threshold-free" methods that automatically adjust budget sizes while ensuring full-cache performance. We then propose a novel method ReFreeKV as the first solution fulfilling this objective, validated by intensive experiments on 13 datasets of diverse context lengths, task types, and model sizes. + oai:arXiv.org:2502.16886v3 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xuanfan Ni, Liyan Xu, Chenyang Lyu, Longyue Wang, Mo Yu, Lemao Liu, Fandong Meng, Jie Zhou, Piji Li + + + It's Not All Black and White: Degree of Truthfulness for Risk-Avoiding Agents + https://arxiv.org/abs/2502.18805 + arXiv:2502.18805v3 Announce Type: replace +Abstract: The classic notion of \emph{truthfulness} requires that no agent has a profitable manipulation -- an untruthful report that, for \emph{some} combination of reports of the other agents, increases her utility. This strong notion implicitly assumes that the manipulating agent either knows what all other agents are going to report, or is willing to take the risk and act as-if she knows their reports. + Without knowledge of the others' reports, most manipulations are \emph{risky} -- they might decrease the manipulator's utility for some other combinations of reports by the other agents. Accordingly, a recent paper (Bu, Song and Tao, ``On the existence of truthful fair cake cutting mechanisms'', Artificial Intelligence 319 (2023), 103904) suggests a relaxed notion, which we refer to as \emph{risk-avoiding truthfulness (RAT)}, which requires only that no agent can gain from a \emph{safe} manipulation -- one that is sometimes beneficial and never harmful. + Truthfulness and RAT are two extremes: the former considers manipulators with complete knowledge of others, whereas the latter considers manipulators with no knowledge at all. In reality, agents often know about some -- but not all -- of the other agents. This paper introduces the \emph{RAT-degree} of a mechanism, defined as the smallest number of agents whose reports, if known, may allow another agent to safely manipulate, or $n$ if there is no such number. This notion interpolates between classic truthfulness (degree $n$) and RAT (degree at least $1$): a mechanism with a higher RAT-degree is harder to manipulate safely. + To illustrate the generality and applicability of this concept, we analyze the RAT-degree of prominent mechanisms across various social choice settings, including auctions, indivisible goods allocations, cake-cutting, voting, and two-sided matching. + oai:arXiv.org:2502.18805v3 + cs.GT + cs.MA + econ.TH + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3736252.374265 + Eden Hartman, Erel Segal-Halevi, Biaoshuai Tao + + + Protecting multimodal large language models against misleading visualizations + https://arxiv.org/abs/2502.20503 + arXiv:2502.20503v5 Announce Type: replace +Abstract: Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understanding has accelerated massively, with steady improvements on standard benchmarks. However, for MLLMs to be reliable, they must be robust to misleading visualizations, i.e., charts that distort the underlying data, leading readers to draw inaccurate conclusions. Here, we uncover an important vulnerability: MLLM question-answering (QA) accuracy on misleading visualizations drops on average to the level of the random baseline. To address this, we provide the first comparison of six inference-time methods to improve QA performance on misleading visualizations, without compromising accuracy on non-misleading ones. We find that two methods, table-based QA and redrawing the visualization, are effective, with improvements of up to 19.6 percentage points. We make our code and data available. + oai:arXiv.org:2502.20503v5 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Jonathan Tonglet, Tinne Tuytelaars, Marie-Francine Moens, Iryna Gurevych + + + Active operator learning with predictive uncertainty quantification for partial differential equations + https://arxiv.org/abs/2503.03178 + arXiv:2503.03178v2 Announce Type: replace +Abstract: With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. We propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework's uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings. + oai:arXiv.org:2503.03178v2 + cs.LG + math.PR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Nick Winovich, Mitchell Daneker, Lu Lu, Guang Lin + + + The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems + https://arxiv.org/abs/2503.03750 + arXiv:2503.03750v3 Announce Type: replace +Abstract: As large language models (LLMs) become more capable and agentic, the requirement for trust in their outputs grows significantly, yet at the same time concerns have been mounting that models may learn to lie in pursuit of their goals. To address these concerns, a body of work has emerged around the notion of "honesty" in LLMs, along with interventions aimed at mitigating deceptive behaviors. However, some benchmarks claiming to measure honesty in fact simply measure accuracy--the correctness of a model's beliefs--in disguise. Moreover, no benchmarks currently exist for directly measuring whether language models lie. In this work, we introduce a large-scale human-collected dataset for directly measuring lying, allowing us to disentangle accuracy from honesty. Across a diverse set of LLMs, we find that while larger models obtain higher accuracy on our benchmark, they do not become more honest. Surprisingly, most frontier LLMs obtain high scores on truthfulness benchmarks yet exhibit a substantial propensity to lie under pressure, resulting in low honesty scores on our benchmark. We find that simple methods, such as representation engineering interventions, can improve honesty. These results underscore the growing need for robust evaluations and effective interventions to ensure LLMs remain trustworthy. + oai:arXiv.org:2503.03750v3 + cs.LG + cs.AI + cs.CL + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Richard Ren, Arunim Agarwal, Mantas Mazeika, Cristina Menghini, Robert Vacareanu, Brad Kenstler, Mick Yang, Isabelle Barrass, Alice Gatti, Xuwang Yin, Eduardo Trevino, Matias Geralnik, Adam Khoja, Dean Lee, Summer Yue, Dan Hendrycks + + + E$^2$AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models + https://arxiv.org/abs/2503.04833 + arXiv:2503.04833v3 Announce Type: replace +Abstract: Research endeavors have been made in learning robust Multimodal Large Language Models (MLLMs) against jailbreak attacks. However, existing methods for improving MLLMs' robustness still face critical challenges: \ding{172} how to efficiently tune massive weight parameters and \ding{173} how to ensure robustness against attacks across both visual and textual modalities. To this end, we propose an \textbf{E}fficient \textbf{E}nd-to-end \textbf{A}dversarial \textbf{T}raining (E$^2$AT) framework for both visual and textual adversarial attacks. Specifically, for the visual aspect, E$^2$AT incorporates an efficient projector-based AT module that aligns the attack samples at the feature level. For training objectives, we propose a Dynamic Joint Multimodal Optimization (DJMO) strategy to enhance generalization ability against jailbreak attacks by dynamically adjusting weights between normal and adversarial objectives. Extensive experiments are conducted with five major jailbreak attack methods across three mainstream MLLMs. Results demonstrate that our E$^2$AT achieves the state-of-the-art performance, outperforming existing baselines by an average margin of 34\% across text and image modalities, while maintaining clean task performance. Furthermore, evaluations of real-world embodied intelligent systems highlight the practical applicability of E$^2$AT, paving the way for the development of more secure and reliable multimodal systems. Our code is available on \href{https://anonymous.4open.science/r/E2AT_568}{\textcolor{red}{https://anonymous.4open.science/r/E2AT\_568}}. + oai:arXiv.org:2503.04833v3 + cs.CV + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Liming Lu, Xiang Gu, Shuchao Pang, Siyuan Liang, Haotian Zhu, Xiyu Zeng, Xu Zheng, Yongbin Zhou + + + From Intrinsic Toxicity to Reception-Based Toxicity: A Contextual Framework for Prediction and Evaluation + https://arxiv.org/abs/2503.16072 + arXiv:2503.16072v3 Announce Type: replace +Abstract: Most toxicity detection models treat toxicity as an intrinsic property of text, overlooking the role of context in shaping its impact. In this position paper, drawing on insights from psychology, neuroscience, and computational social science, we reconceptualise toxicity as a socially emergent signal of stress. We formalise this perspective in the Contextual Stress Framework (CSF), which defines toxicity as a stress-inducing norm violation within a given context and introduces an additional dimension for toxicity detection. As one possible realisation of CSF, we introduce PONOS (Proportion Of Negative Observed Sentiments), a metric that quantifies toxicity through collective social reception rather than lexical features. We validate this approach on a novel dataset, demonstrating improved contextual sensitivity and adaptability when used alongside existing models. + oai:arXiv.org:2503.16072v3 + cs.LG + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Sergey Berezin, Reza Farahbakhsh, Noel Crespi + + + Offline Model-Based Optimization: Comprehensive Review + https://arxiv.org/abs/2503.17286 + arXiv:2503.17286v2 Announce Type: replace +Abstract: Offline optimization is a fundamental challenge in science and engineering, where the goal is to optimize black-box functions using only offline datasets. This setting is particularly relevant when querying the objective function is prohibitively expensive or infeasible, with applications spanning protein engineering, material discovery, neural architecture search, and beyond. The main difficulty lies in accurately estimating the objective landscape beyond the available data, where extrapolations are fraught with significant epistemic uncertainty. This uncertainty can lead to objective hacking(reward hacking), exploiting model inaccuracies in unseen regions, or other spurious optimizations that yield misleadingly high performance estimates outside the training distribution. Recent advances in model-based optimization(MBO) have harnessed the generalization capabilities of deep neural networks to develop offline-specific surrogate and generative models. Trained with carefully designed strategies, these models are more robust against out-of-distribution issues, facilitating the discovery of improved designs. Despite its growing impact in accelerating scientific discovery, the field lacks a comprehensive review. To bridge this gap, we present the first thorough review of offline MBO. We begin by formalizing the problem for both single-objective and multi-objective settings and by reviewing recent benchmarks and evaluation metrics. We then categorize existing approaches into two key areas: surrogate modeling, which emphasizes accurate function approximation in out-of-distribution regions, and generative modeling, which explores high-dimensional design spaces to identify high-performing designs. Finally, we examine the key challenges and propose promising directions for advancement in this rapidly evolving field including safe control of superintelligent systems. + oai:arXiv.org:2503.17286v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Minsu Kim, Jiayao Gu, Ye Yuan, Taeyoung Yun, Zixuan Liu, Yoshua Bengio, Can Chen + + + Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias + https://arxiv.org/abs/2503.23358 + arXiv:2503.23358v2 Announce Type: replace +Abstract: Popularity bias is a common challenge in recommender systems. It often causes unbalanced item recommendation performance and intensifies the Matthew effect. Due to limited user-item interactions, unpopular items are frequently constrained to the embedding neighborhoods of only a few users, leading to representation collapse and weakening the model's generalization. Although existing supervised alignment and reweighting methods can help mitigate this problem, they still face two major limitations: (1) they overlook the inherent variability among different Graph Convolutional Networks (GCNs) layers, which can result in negative gains in deeper layers; (2) they rely heavily on fixed hyperparameters to balance popular and unpopular items, limiting adaptability to diverse data distributions and increasing model complexity. + To address these challenges, we propose Graph-Structured Dual Adaptation Framework (GSDA), a dual adaptive framework for mitigating popularity bias in recommendation. Our theoretical analysis shows that supervised alignment in GCNs is hindered by the over-smoothing effect, where the distinction between popular and unpopular items diminishes as layers deepen, reducing the effectiveness of alignment at deeper levels. To overcome this limitation, GSDA integrates a hierarchical adaptive alignment mechanism that counteracts entropy decay across layers together with a distribution-aware contrastive weighting strategy based on the Gini coefficient, enabling the model to adapt its debiasing strength dynamically without relying on fixed hyperparameters. Extensive experiments on three benchmark datasets demonstrate that GSDA effectively alleviates popularity bias while consistently outperforming state-of-the-art methods in recommendation performance. + oai:arXiv.org:2503.23358v2 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Miaomiao Cai, Lei Chen, Yifan Wang, Zhiyong Cheng, Min Zhang, Meng Wang + + + Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization + https://arxiv.org/abs/2504.01018 + arXiv:2504.01018v3 Announce Type: replace +Abstract: Selective retrieval aims to make retrieval-augmented generation (RAG) more efficient and reliable by skipping retrieval when an LLM's parametric knowledge suffices. Despite promising results, existing methods are constrained by a binary design choice: either retrieve from a single external source or skip retrieval and let the LLM directly produce the final answer. We argue that this fallback underestimates the model's knowledge and obscures the more general multi-source decision problem that arises in practical systems. We propose Self-Routing RAG (SR-RAG), which casts selective retrieval as knowledge source selection and treats the LLM itself as a first-class knowledge source. SR-RAG learns to select an appropriate knowledge source, optionally verbalize parametric knowledge, and answer using the selected source, all within a single left-to-right generation pass. SR-RAG further augments source selection by combining LLM-based uncertainty with a flexible external policy datastore to improve decision calibration. Across four benchmarks and three 7B-class LLMs, SR-RAG outperforms a strong selective retrieval baseline by 8.5%/2.1%/4.7% while performing 26%/40%/21% fewer retrievals, and it achieves favorable accuracy-latency trade-offs without dataset-specific threshold tuning. + oai:arXiv.org:2504.01018v3 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Di Wu, Jia-Chen Gu, Kai-Wei Chang, Nanyun Peng + + + EgoLog: Ego-Centric Fine-Grained Daily Log with Ubiquitous Wearables + https://arxiv.org/abs/2504.02624 + arXiv:2504.02624v3 Announce Type: replace +Abstract: Despite advances in human activity recognition (HAR) with different modalities, a precise, robust, and accurate daily log system is not yet available. Current solutions primarily rely on controlled, lab-based data collection, which limits their real-world applicability. The challenges towards a fine-grained daily log are 1) contextual awareness, 2) spatial awareness, and 3) effective fusion of multi-modal sensor data. To solve them, we propose EgoLog, which integrates effective audio-IMU fusion for daily log with ubiquitous wearables. Our approach first fuses audio and IMU data from two perspectives: temporal understanding and spatial understanding. We extract scenario-level features and aggregate them in the time dimension, while using motion compensation to enhance the performance of sound source localization. The knowledge obtained from these steps is then integrated into a multi-modal HAR framework. Here, the scenario provides prior knowledge, and the spatial location helps differentiate the user from the background. Furthermore, we integrate a LLM to enhance scenario recognition through logical reasoning. The knowledge derived from the LLM is subsequently transferred back to the local device to enable efficient, on-device inference. Evaluated on both public and self-collected dataset, EgoLog achieves effective multimodal fusion for both activity and scenraio recognition, outperforms the baseline by 12% and 15%, respectively. + oai:arXiv.org:2504.02624v3 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Lixing He, Bufang Yang, Di Duan, Zhenyu Yan, Guoliang Xing + + + Solving the Paint Shop Problem with Flexible Management of Multi-Lane Buffers Using Reinforcement Learning and Action Masking + https://arxiv.org/abs/2504.02644 + arXiv:2504.02644v2 Announce Type: replace +Abstract: In the paint shop problem, an unordered incoming sequence of cars assigned to different colors has to be reshuffled with the objective of minimizing the number of color changes. To reshuffle the incoming sequence, manufacturers can employ a first-in-first-out multi-lane buffer system allowing store and retrieve operations. So far, prior studies primarily focused on simple decision heuristics like greedy or simplified problem variants that do not allow full flexibility when performing store and retrieve operations. In this study, we propose a reinforcement learning approach to minimize color changes for the flexible problem variant, where store and retrieve operations can be performed in an arbitrary order. After proving that greedy retrieval is optimal, we incorporate this finding into the model using action masking. Our evaluation, based on 170 problem instances with 2-8 buffer lanes and 5-15 colors, shows that our approach reduces color changes compared to existing methods by considerable margins depending on the problem size. Furthermore, we demonstrate the robustness of our approach towards different buffer sizes and imbalanced color distributions. + oai:arXiv.org:2504.02644v2 + cs.LG + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1016/j.ejor.2025.12.017 + Mirko Stappert, Bernhard Lutz, Janis Brammer, Dirk Neumann + + + Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction + https://arxiv.org/abs/2504.06193 + arXiv:2504.06193v2 Announce Type: replace +Abstract: Link prediction is a crucial graph-learning task with applications including citation prediction and product recommendation. Distilling Graph Neural Networks (GNNs) teachers into Multi-Layer Perceptrons (MLPs) students has emerged as an effective approach to achieve strong performance and reducing computational cost by removing graph dependency. However, existing distillation methods only use standard GNNs and overlook alternative teachers such as specialized model for link prediction (GNN4LP) and heuristic methods (e.g., common neighbors). This paper first explores the impact of different teachers in GNN-to-MLP distillation. Surprisingly, we find that stronger teachers do not always produce stronger students: MLPs distilled from GNN4LP can underperform those distilled from simpler GNNs, while weaker heuristic methods can teach MLPs to near-GNN performance with drastically reduced training costs. Building on these insights, we propose Ensemble Heuristic-Distilled MLPs (EHDM), which eliminates graph dependencies while effectively integrating complementary signals via a gating mechanism. Experiments on ten datasets show an average 7.93% improvement over previous GNN-to-MLP approaches with 1.95-3.32 times less training time, indicating EHDM is an efficient and effective link prediction method. + oai:arXiv.org:2504.06193v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Zongyue Qin, Shichang Zhang, Mingxuan Ju, Tong Zhao, Neil Shah, Yizhou Sun + + + Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation + https://arxiv.org/abs/2504.07554 + arXiv:2504.07554v2 Announce Type: replace +Abstract: Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot's physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler R2 sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA. + oai:arXiv.org:2504.07554v2 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + IEEE/RSJ International Conference on Intelligent Robots and Systems 2025 + Yisheng Li, Longji Yin, Yixi Cai, Jianheng Liu, Fangcheng Zhu, Mingpu Ma, Siqi Liang, Haotian Li, Fu Zhang + + + Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recovery + https://arxiv.org/abs/2504.13177 + arXiv:2504.13177v3 Announce Type: replace +Abstract: We propose spatial polarization multiplexing (SPM) for joint sensing of shape and reflectance of a static or dynamic deformable object, which is also invisible to the naked eye. Past structured-light methods are limited to shape acquisition and cannot recover reflectance as they alter scene appearance. Our key idea is to spatially multiplex a polarization pattern to encode the incident ray and also densely sample the reflected light. We derive a quantized polarized light pattern that can be robustly and uniquely decoded from the reflected Angle of Linear Polarization (AoLP) values. It also enables single-shot disentanglement of polarimetric diffuse and specular reflections for accurate BRDF estimation. We achieve this spatial polarization multiplexing (SPM) with a constrained de Bruijn sequence. We validate this novel invisible single-shot shape and reflectance method with real static and dynamic objects. The results demonstrate the effectiveness of SPM for accurate shape and BRDF measurement which opens new avenues of application for 3D sensing thanks to its invisibility and ability to jointly recover the radiometric properties. + oai:arXiv.org:2504.13177v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tomoki Ichikawa, Ryo Kawahara, Ko Nishino + + + SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Space + https://arxiv.org/abs/2504.16315 + arXiv:2504.16315v2 Announce Type: replace +Abstract: The complexity of sign language data processing brings many challenges. The current approach to recognition of ASL signs aims to translate RGB sign language videos through pose information into English-based ID Glosses, which serve to uniquely identify ASL signs. This paper proposes SignX, a novel framework for continuous sign language recognition in compact pose-rich latent space. First, we construct a unified latent representation that encodes heterogeneous pose formats (SMPLer-X, DWPose, Mediapipe, PrimeDepth, and Sapiens Segmentation) into a compact, information-dense space. Second, we train a ViT-based Video2Pose module to extract this latent representation directly from raw videos. Finally, we develop a temporal modeling and sequence refinement method that operates entirely in this latent space. This multi-stage design achieves end-to-end sign language recognition while significantly reducing computational consumption. Experimental results demonstrate that SignX achieves state-of-the-art accuracy on continuous sign language recognition. + oai:arXiv.org:2504.16315v2 + cs.CV + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sen Fang, Yalin Feng, Chunyu Sui, Hongbin Zhong, Hongwei Yi, Dimitris N. Metaxas + + + Beyond Platforms -- Growing Distributed Transaction Networks for Digital Commerce + https://arxiv.org/abs/2504.18602 + arXiv:2504.18602v4 Announce Type: replace +Abstract: We talk of the internet as digital infrastructure; but we leave the building of rails and roads to the quasi-monopolistic platform providers. Decentralised architectures provide a number of advantages: They are potentially more inclusive for small players; more resilient against adversarial events; and seem to generate more innovation. However, it is not well understood how to evolve, adapt and govern decentralised infrastructures. This article reports qualitative empirical research on the development and governance of the Beckn Protocol, an open source protocol for decentralised transactions, the successful development of domain-specific adaptations, and implementation and scaling of commercial infrastructures based on it. It explores how the architecture and governance support local innovation for specific business domains, and how the domain-specific innovations feed back into the development of the core concept The research applied a case study approach, combining interviews with core members of the Beckn community; triangulated by interviews with community leaders of domain specific adaptations and by analysis of online documents and the protocol itself. The article shows the possibility of such a decentralised approach to IT Infrastructures. It analyses the Beckn Protocol, domain specific adaptations, and networks built as a software ecosystem. Based on this analysis, a number of generative mechanisms, socio-technical arrangements that support adoption, innovation, and scaling of infrastructures are highlighted. + oai:arXiv.org:2504.18602v4 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yvonne Dittrich, Kim Peiter J{\o}rgensen, Ravi Prakash, Willard Rafnsson, Jonas Kastberg Hinrichsen + + + PartHOI: Part-based Hand-Object Interaction Transfer via Generalized Cylinders + https://arxiv.org/abs/2504.20599 + arXiv:2504.20599v2 Announce Type: replace +Abstract: Learning-based methods to understand and model hand-object interactions (HOI) require a large amount of high-quality HOI data. One way to create HOI data is to transfer hand poses from a source object to another based on the objects' geometry. However, current methods for transferring hand poses between objects rely on shape matching, limiting the ability to transfer poses across different categories due to differences in their shapes and sizes. We observe that HOI often involves specific semantic parts of objects, which often have more consistent shapes across categories. In addition, constructing size-invariant correspondences between these parts is important for cross-category transfer. Based on these insights, we introduce a novel method PartHOI for part-based HOI transfer. Using a generalized cylinder representation to parameterize an object parts' geometry, PartHOI establishes a robust geometric correspondence between object parts, and enables the transfer of contact points. Given the transferred points, we optimize a hand pose to fit the target object well. Qualitative and quantitative results demonstrate that our method can generalize HOI transfers well even for cross-category objects, and produce high-fidelity results that are superior to the existing methods. + oai:arXiv.org:2504.20599v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Qiaochu Wang, Chufeng Xiao, Manfred Lau, Hongbo Fu + + + UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities + https://arxiv.org/abs/2504.20734 + arXiv:2504.20734v3 Announce Type: replace +Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing approaches are limited to a text-only corpus, and while recent efforts have extended RAG to other modalities such as images and videos, they typically operate over a single modality-specific corpus. In contrast, real-world queries vary widely in the type of knowledge they require, which a single type of knowledge source cannot address. To address this, we introduce UniversalRAG, designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. Specifically, motivated by the observation that forcing all modalities into a unified representation space derived from a single aggregated corpus causes a modality gap, where the retrieval tends to favor items from the same modality as the query, we propose modality-aware routing, which dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it, and further justify its effectiveness with a theoretical analysis. Moreover, beyond modality, we organize each modality into multiple granularity levels, enabling fine-tuned retrieval tailored to the complexity and scope of the query. We validate UniversalRAG on 10 benchmarks of multiple modalities, showing its superiority over various modality-specific and unified baselines. + oai:arXiv.org:2504.20734v3 + cs.CL + cs.AI + cs.CV + cs.IR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang + + + The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act + https://arxiv.org/abs/2505.01122 + arXiv:2505.01122v2 Announce Type: replace +Abstract: To facilitate accountability and transparency, the Digital Services Act (DSA) sets up a process through which Very Large Online Platforms (VLOPs) need to grant vetted researchers access to their internal data (Article 40(4)). Operationalising such access is challenging for at least two reasons. First, data access is only available for research on systemic risks affecting European citizens, a concept with high levels of legal uncertainty. Second, data access suffers from an inherent standoff problem. Researchers need to request specific data but are not in a position to know all internal data processed by VLOPs, who, in turn, expect data specificity for potential access. In light of these limitations, data access under the DSA remains a mystery. To contribute to the discussion of how Article 40 can be interpreted and applied, we provide a concrete illustration of what data access can look like in a real-world systemic risk case study. We focus on the 2024 Romanian presidential election interference incident, the first event of its kind to trigger systemic risk investigations by the European Commission. During the elections, one candidate is said to have benefited from TikTok algorithmic amplification through a complex dis- and misinformation campaign. By analysing this incident, we can comprehend election-related systemic risk to explore practical research tasks and compare necessary data with available TikTok data. In particular, we make two contributions: (i) we combine insights from law, computer science and platform governance to shed light on the complexities of studying systemic risks in the context of election interference, focusing on two relevant factors: platform manipulation and hidden advertising; and (ii) we provide practical insights into various categories of available data for the study of TikTok, based on platform documentation, data donations and the Research API. + oai:arXiv.org:2505.01122v2 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Catalina Goanta, Savvas Zannettou, Rishabh Kaushal, Jacob van de Kerkhof, Thales Bertaglia, Taylor Annabell, Haoyang Gui, Gerasimos Spanakis, Adriana Iamnitchi + + + HONEYBEE: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning + https://arxiv.org/abs/2505.01538 + arXiv:2505.01538v2 Announce Type: replace +Abstract: Enterprise deployments of vector databases require access control policies to protect sensitive data. These systems often implement access control through hybrid vector queries that combine nearest-neighbor search with relational predicates based on user permissions. However, existing approaches face a fundamental trade-off: dedicated per-user indexes minimize query latency but incur high memory redundancy, while shared indexes with post-search filtering reduce memory overhead at the cost of increased latency. This paper introduces HONEYBEE, a dynamic partitioning framework that leverages the structure of Role-Based Access Control (RBAC) policies to create a smooth trade-off between these extremes. RBAC policies organize users into roles and assign permissions at the role level, creating a natural ``thin waist" in the permission structure that is ideal for partitioning decisions. Specifically, HONEYBEE produces overlapping partitions where vectors can be strategically replicated across different partitions to reduce query latency while controlling memory overhead. To guide these decisions, HONEYBEE develops analytical models of vector search performance and recall, and formulates partitioning as a constrained optimization problem that balances memory usage, query efficiency, and recall. Evaluations on RBAC workloads demonstrate that HONEYBEE achieves up to 13.5X lower query latency than row-level security with only a 1.24X increase in memory usage, while achieving comparable query performance to dedicated, per-role indexes with 90.4% reduction in additional memory consumption, offering a practical middle ground for secure and efficient vector search. + oai:arXiv.org:2505.01538v2 + cs.DB + cs.CR + cs.IR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3786625 + Hongbin Zhong, Matthew Lentz, Nina Narodytska, Adriana Szekeres, Kexin Rong + + + Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing + https://arxiv.org/abs/2505.05665 + arXiv:2505.05665v3 Announce Type: replace +Abstract: Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and undesired outputs poses risks. This unwanted behavior is further exacerbated in environments where sensors are noisy or unreliable. Characterizing the behavior of LLM planners to varied observations is necessary to proactively avoid failures in safety-critical scenarios. We specifically investigate the response of LLMs along two different perturbation dimensions. Like prior works, one dimension generates semantically similar prompts with varied phrasing by randomizing order of details, modifying access to few-shot examples, etc. Unique to our work, the second dimension simulates access to varied sensors and noise to mimic raw sensor or detection algorithm failures. An initial case study in which perturbations are manually applied show that both dimensions lead LLMs to hallucinate in a multi-agent driving environment. However, manually covering the entire perturbation space for several scenarios is infeasible. As such, we propose a novel method for efficiently searching the space of prompt perturbations using adaptive stress testing (AST) with Monte-Carlo tree search (MCTS). Our AST formulation enables discovery of scenarios, sensor configurations, and prompt phrasing that cause language models to act with high uncertainty or even crash. By generating MCTS prompt perturbation trees across diverse scenarios, we show through extensive experiments that offline analyses can be used to proactively understand potential failures that may arise at runtime. + oai:arXiv.org:2505.05665v3 + cs.RO + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Neeloy Chakraborty, John Pohovey, Melkior Ornik, Katherine Driggs-Campbell + + + Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness + https://arxiv.org/abs/2505.08627 + arXiv:2505.08627v3 Announce Type: replace +Abstract: Visuomotor policies trained on human expert demonstrations have recently shown strong performance across a wide range of robotic manipulation tasks. However, these policies remain highly sensitive to domain shifts stemming from background or robot embodiment changes, which limits their generalization capabilities. In this paper, we present ARRO, a novel visual representation that leverages zero-shot open-vocabulary segmentation and object detection models to efficiently mask out task-irrelevant regions of the scene in real time without requiring additional training, modeling of the setup, or camera calibration. By filtering visual distractors and overlaying virtual guides during both training and inference, ARRO improves robustness to scene variations and reduces the need for additional data collection. We extensively evaluate ARRO with Diffusion Policy on a range of tabletop manipulation tasks in both simulation and real-world environments, and further demonstrate its compatibility and effectiveness with generalist robot policies, such as Octo, OpenVLA and Pi Zero. Across all settings in our evaluation, ARRO yields consistent performance gains, allows for selective masking to choose between different objects, and shows robustness even to challenging segmentation conditions. Videos showcasing our results are available at: https://augmented-reality-for-robots.github.io/ + oai:arXiv.org:2505.08627v3 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Reihaneh Mirjalili, Tobias J\"ulg, Florian Walter, Wolfram Burgard + + + Reference-Free Evaluation of Taxonomies + https://arxiv.org/abs/2505.11470 + arXiv:2505.11470v2 Announce Type: replace +Abstract: We introduce two reference-free metrics for quality evaluation of taxonomies in the absence of labels. The first metric evaluates robustness by calculating the correlation between semantic and taxonomic similarity, addressing error types not considered by existing metrics. The second uses Natural Language Inference to assess logical adequacy. Both metrics are tested on five taxonomies and are shown to correlate well with F1 against ground truth taxonomies. We further demonstrate that our metrics can predict downstream performance in hierarchical classification when used with label hierarchies. + oai:arXiv.org:2505.11470v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Pascal Wullschleger, Majid Zarharan, Donnacha Daly, Marc Pouly, Jennifer Foster + + + DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization + https://arxiv.org/abs/2505.12366 + arXiv:2505.12366v5 Announce Type: replace +Abstract: The recent success and openness of DeepSeek-R1 have brought widespread attention to Group Relative Policy Optimization (GRPO) as a reinforcement learning method for large reasoning models (LRMs). In this work, we analyze the GRPO objective under a binary reward setting and reveal an inherent limitation of question-level difficulty bias. We also identify a connection between GRPO and traditional discriminative methods in supervised learning. Motivated by these insights, we introduce a new Discriminative Constrained Optimization (DisCO) framework for reinforcing LRMs, grounded in the principle of discriminative learning. The main differences between DisCO and GRPO and its recent variants are: (1) it replaces the group relative objective with a discriminative objective defined by a scoring function; (2) it abandons clipping-based surrogates in favor of non-clipping RL surrogate objectives used as scoring functions; (3) it employs a simple yet effective constrained optimization approach to enforce the KL divergence constraint. As a result, DisCO offers notable advantages over GRPO and its variants: (i) it completely eliminates difficulty bias by adopting discriminative objectives; (ii) it addresses the entropy instability in GRPO and its variants through the use of non-clipping scoring functions and a constrained optimization approach, yielding long and stable training dynamics; (iii) it allows the incorporation of advanced discriminative learning techniques to address data imbalance, where a significant number of questions have more negative than positive generated answers during training. Our experiments on enhancing the mathematical reasoning capabilities of SFT-finetuned models show that DisCO significantly outperforms GRPO and its improved variants such as DAPO, achieving average gains of 7\% over GRPO and 6\% over DAPO across six benchmark tasks for a 1.5B model. + oai:arXiv.org:2505.12366v5 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Gang Li, Ming Lin, Tomer Galanti, Zhengzhong Tu, Tianbao Yang + + + EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generation + https://arxiv.org/abs/2505.12424 + arXiv:2505.12424v2 Announce Type: replace +Abstract: Search-Based Software Testing (SBST) is a well-established approach for automated unit test generation, yet it often suffers from premature convergence and limited diversity in the generated test suites. Recently, Large Language Models (LLMs) have emerged as an alternative technique for unit test generation. We present EvoGPT, a hybrid test generation system that integrates LLM-based test generation with SBST-based test suite optimization. EvoGPT uses LLMs to generate an initial population of test suites, and uses an Evolutionary Algorithm (EA) to further optimize this test suite population. A distinguishing feature of EvoGPT is its explicit enforcement of diversity, achieved through the use of multiple temperatures and prompt instructions during test generation. In addition, each LLM-generated test is refined using a generation-repair loop and coverage-guided assertion generation. To address evolutionary plateaus, EvoGPT also detects stagnation during search and injects additional LLM-generated tests aimed at previously uncovered branches. Here too diversity is enforced using multiple temperatures and prompt instructions. We evaluate EvoGPT on Defects4J, a standard benchmark for test generation. The results show that EvoGPT achieves, on average, a 10\% improvement in both code coverage and mutation score metrics compared to TestART, an LLM-only baseline; and EvoSuite, a standard SBST baseline. An ablation study indicates that explicitly enforcing diversity both at initialization and during the search is key to effectively leveraging LLMs for automated unit test generation. + oai:arXiv.org:2505.12424v2 + cs.SE + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Lior Broide, Roni Stern, Argaman Mordoch + + + The Virtual Reality Koinos Method: Analysis of Symmetrical Dyadic Collaboration in Virtual Reality from the perspective of communication models + https://arxiv.org/abs/2505.14078 + arXiv:2505.14078v2 Announce Type: replace +Abstract: Understanding which factors could influence co-presence in Virtual Reality could help develop more qualitative social interactions, or social interactions that generate similar sensations, emotions and feelings than the ones generated during Face-to-Face interactions. Co-presence is studied since the beginning of Virtual Reality (VR); though, no consensus is identified on what factors could influence it, except the consensus on the definition of "being there together" inside the Virtual Environment. In this paper, we introduce the Koinos method to explain social interactions in VR through communication models, (i) theoretically, and (ii) on two VR experiments that change the virtual partner social and physical representations. These analyses lead us to propose an equation to predict and help manage the sense of co-presence in VR. + oai:arXiv.org:2505.14078v2 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Eloise Minder, Sylvain Fleury, Sol\`ene Neyret, Jean-R\'emy Chardonnet + + + Extensible Post Quantum Cryptography Based Authentication + https://arxiv.org/abs/2505.16112 + arXiv:2505.16112v2 Announce Type: replace +Abstract: Cryptography underpins the security of modern digital infrastructure, from cloud services to health data. However, many widely deployed systems will become vulnerable after the advent of scalable quantum computing. Although quantum-safe cryptographic primitives have been developed, such as lattice-based digital signature algorithms (DSAs) and key encapsulation mechanisms (KEMs), their unique structural and performance characteristics make them unsuitable for existing protocols. In this work, we introduce a quantum-safe single-shot protocol for machine-to-machine authentication and authorization that is specifically designed to leverage the strengths of lattice-based DSAs and KEMs. Operating entirely over insecure channels, this protocol enables the forward-secure establishment of tokens in constrained environments. By demonstrating how new quantum-safe cryptographic primitives can be incorporated into secure systems, this study lays the groundwork for scalable, resilient, and future-proof identity infrastructures in a quantum-enabled world. + oai:arXiv.org:2505.16112v2 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Homer A. Riva-Cambrin, Rahul Singh, Sanju Lama, Garnette R. Sutherland + + + EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios + https://arxiv.org/abs/2505.16160 + arXiv:2505.16160v4 Announce Type: replace +Abstract: As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing the first diverse benchmark tailored for educational scenarios, incorporating synthetic data containing 9 major scenarios and over 4,000 distinct educational contexts. To enable comprehensive assessment, we propose a set of multi-dimensional evaluation metrics that cover 12 critical aspects relevant to both teachers and students. We further apply human annotation to ensure the effectiveness of the model-generated evaluation responses. Additionally, we succeed to train a relatively small-scale model on our constructed dataset and demonstrate that it can achieve performance comparable to state-of-the-art large models (e.g., Deepseek V3, Qwen Max) on the test set. Overall, this work provides a practical foundation for the development and evaluation of education-oriented language models. Code and data are released at https://github.com/ybai-nlp/EduBench. + oai:arXiv.org:2505.16160v4 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang + + + Asynchronous Global Protocols, Precisely: Full Proofs + https://arxiv.org/abs/2505.17676 + arXiv:2505.17676v2 Announce Type: replace +Abstract: Asynchronous multiparty session types are a type-based framework which ensure the compatibility of components in a distributed system by checking compliance against a specified global protocol. We propose a top-down approach, starting with the global protocol which is then projected into a set of local specifications. Next, we use an asynchronous refinement relation, precise asynchronous multiparty subtyping, to enable local specifications to be optimised by permuting actions within individual asynchronous components. This supports local reasoning, as each component can be independently developed and refined in isolation, before being integrated into a larger system. We show that this methodology guarantees both type soundness and liveness of the collection of optimised components. In this article, we first propose new operational semantics of global protocols which capture sound optimisations in the context of asynchronous message-passing. Next we define an asynchronous association between global protocols and a set of optimised local types. Thirdly, we prove, for the first time, the correctness of the most expressive endpoint projection in the literature, coinductive full merging projection. We then show the main theorems of this article: soundness and completeness of the operational correspondence of the asynchronous association. As a consequence, the association acts as an invariant that can be used to transfer key theorems from the bottom-up system to the top-down system. In particular, we used this to prove type soundness, session-fidelity, deadlock-freedom and liveness of the collection of optimised endpoints. + oai:arXiv.org:2505.17676v2 + cs.PL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Kai Pischke, Jake Masters, Nobuko Yoshida + + + PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation + https://arxiv.org/abs/2505.19347 + arXiv:2505.19347v3 Announce Type: replace +Abstract: Patent similarity evaluation plays a critical role in intellectual property analysis. However, existing methods often overlook the intricate structure of patent documents, which integrate technical specifications, legal boundaries, and application contexts. We introduce PatentMind, a novel framework for patent similarity assessment based on a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patents into their three dimensions of technical features, application domains, and claim scopes, then dimension-specific similarity scores are calculated over the MARG. These scores are dynamically weighted through a context-aware reasoning process, which integrates contextual signals to emulate expert-level judgment. To support evaluation, we construct a human-annotated benchmark PatentSimBench, comprising 500 patent pairs. Experimental results demonstrate that the PatentMind-generated scores show a strong correlation ($r=0.938$) with expert annotations, significantly outperforming embedding-based models, patent-specific models, and advanced prompt engineering methods. Beyond computational linguistics, our framework provides a structured and semantically grounded foundation for real-world decision-making, particularly for tasks such as infringement risk assessment, underscoring its broader impact on both patent analytics and evaluation. + oai:arXiv.org:2505.19347v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yongmin Yoo, Qiongkai Xu, Longbing Cao + + + VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval + https://arxiv.org/abs/2505.20291 + arXiv:2505.20291v3 Announce Type: replace +Abstract: Text-to-image retrieval (T2I retrieval) remains challenging because cross-modal embeddings often behave as bags of concepts, underrepresenting structured visual relationships such as pose and viewpoint. We propose Visualize-then-Retrieve (VisRet), a retrieval paradigm that mitigates this limitation of cross-modal similarity alignment. VisRet first projects textual queries into the image modality via T2I generation, then performs retrieval within the image modality to bypass the weaknesses of cross-modal retrievers in recognizing subtle visual-spatial features. Across four benchmarks (Visual-RAG, INQUIRE-Rerank, Microsoft COCO, and our new Visual-RAG-ME featuring multi-entity comparisons), VisRet substantially outperforms cross-modal similarity matching and baselines that recast T2I retrieval as text-to-text similarity matching, improving nDCG@30 by 0.125 on average with CLIP as the retriever and by 0.121 with E5-V. For downstream question answering, VisRet increases accuracy on Visual-RAG and Visual-RAG-ME by 3.8% and 15.7% in top-1 retrieval, and by 3.9% and 11.1% in top-10 retrieval. Ablation studies show compatibility with different T2I instruction LLMs, T2I generation models, and downstream LLMs. VisRet provides a simple yet effective perspective for advancing in text-image retrieval. Our code and the new benchmark are publicly available at https://github.com/xiaowu0162/Visualize-then-Retrieve. + oai:arXiv.org:2505.20291v3 + cs.CV + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Di Wu, Yixin Wan, Kai-Wei Chang + + + POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization + https://arxiv.org/abs/2505.20624 + arXiv:2505.20624v2 Announce Type: replace +Abstract: Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multievent dataset with over 23k instances in seven languages from diverse online platforms and real-world events. Polarization is annotated along three axes: presence, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) we fine-tune six multilingual pretrained language models in both monolingual and cross-lingual setups; and (2) we evaluate a range of open and closed large language models (LLMs) in few-shot and zero-shot scenarios. Results show that while most models perform well on binary polarization detection, they achieve substantially lower scores when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally. + oai:arXiv.org:2505.20624v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Usman Naseem, Juan Ren, Saba Anwar, Sarah Kohail, Rudy Alexandro Garrido Veliz, Robert Geislinger, Aisha Jabr, Idris Abdulmumin, Laiba Qureshi, Aarushi Ajay Borkar, Maryam Ibrahim Mukhtar, Abinew Ali Ayele, Ibrahim Said Ahmad, Adem Ali, Martin Semmann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam + + + RoboTransfer: Controllable Geometry-Consistent Video Diffusion for Manipulation Policy Transfer + https://arxiv.org/abs/2505.23171 + arXiv:2505.23171v2 Announce Type: replace +Abstract: The goal of general-purpose robotics is to create agents that can seamlessly adapt to and operate in diverse, unstructured human environments. Imitation learning has become a key paradigm for robotic manipulation, yet collecting large-scale and diverse demonstrations is prohibitively expensive. Simulators provide a cost-effective alternative, but the sim-to-real gap remains a major obstacle to scalability. We present RoboTransfer, a diffusion-based video generation framework for synthesizing robotic data. By leveraging cross-view feature interactions and globally consistent 3D geometry, RoboTransfer ensures multi-view geometric consistency while enabling fine-grained control over scene elements, such as background editing and object replacement. Extensive experiments demonstrate that RoboTransfer produces videos with superior geometric consistency and visual fidelity. Furthermore, policies trained on this synthetic data exhibit enhanced generalization to novel, unseen scenarios. Project page: https://horizonrobotics.github.io/robot_lab/robotransfer. + oai:arXiv.org:2505.23171v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Liu Liu, Xiaofeng Wang, Guosheng Zhao, Keyu Li, Wenkang Qin, Jiagang Zhu, Jiaxiong Qiu, Zheng Zhu, Guan Huang, Zhizhong Su + + + Melding the Serverless Control Plane with the Conventional Cluster Manager for Speed and Resource Efficiency + https://arxiv.org/abs/2505.24551 + arXiv:2505.24551v4 Announce Type: replace +Abstract: Serverless platforms face a trade-off: conventional cluster managers like Kubernetes offer compatibility for co-locating Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components of serverless applications, at the cost of high cold-start latency, whereas specialized FaaS-only systems like Dirigent achieve low latency by sacrificing compatibility, preventing integrated management and optimization. Our analysis reveals that FaaS traffic is bimodal: predictable, sustainable traffic consumes >98% of cluster resources, whereas sporadic, excessive bursts stress the control plane's scaling latency, not its throughput. + With these insights, we design PulseNet, a serverless architecture that uses a dual-track control plane tailored to both traffic types. PulseNet's standard track manages sustainable traffic with long-lived, full-featured Regular Instances under a conventional cluster manager, preserving compatibility for the majority of the workload. To handle excessive traffic, an expedited track bypasses the slow manager to rapidly create short-lived, disposable Emergency Instances, minimizing cold-start latency and resource waste from idle instances. This hybrid approach achieves 35% better performance than Dirigent, a FaaS-only system, on a production workload at the same cost and outperforms other Kubernetes-compatible systems by 1.5-3.5x, reducing the cost by up to 70%. + oai:arXiv.org:2505.24551v4 + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Leonid Kondrashov, Lazar Cvetkovi\'c, Hancheng Wang, Boxi Zhou, Dmitrii Ustiugov + + + Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads + https://arxiv.org/abs/2506.00634 + arXiv:2506.00634v2 Announce Type: replace +Abstract: Rental listings offer a window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional boundaries and neighborhood claims. Through manual and large language model annotation, we classify unstructured listings from Craigslist according to their neighborhood. Further geospatial analysis reveals three distinct patterns: properties with conflicting neighborhood designations due to competing spatial definitions, border properties with valid claims to adjacent neighborhoods, and "reputation laundering" where listings claim association with distant, desirable neighborhoods. Through topic modeling, we identify patterns that correlate with spatial positioning: listings further from neighborhood centers emphasize different amenities than centrally-located units. Natural language processing techniques reveal how definitions of urban spaces are contested in ways that traditional methods overlook. + oai:arXiv.org:2506.00634v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Adam Visokay, Ruth Bagley, Ian Kennedy, Chris Hess, Kyle Crowder, Rob Voigt, Denis Peskoff + + + Quantifying task-relevant representational similarity using decision variable correlation + https://arxiv.org/abs/2506.02164 + arXiv:2506.02164v3 Announce Type: replace +Abstract: Previous studies have compared neural activities in the visual cortex to representations in deep neural networks trained on image classification. Interestingly, while some suggest that their representations are highly similar, others argued the opposite. Here, we propose a new approach to characterize the similarity of the decision strategies of two observers (models or brains) using decision variable correlation (DVC). DVC quantifies the image-by-image correlation between the decoded decisions based on the internal neural representations in a classification task. Thus, it can capture task-relevant information rather than general representational alignment. We evaluate DVC using monkey V4/IT recordings and network models trained on image classification tasks. We find that model-model similarity is comparable to monkey-monkey similarity, whereas model-monkey similarity is consistently lower. Strikingly, DVC decreases with increasing network performance on ImageNet-1k. Adversarial training does not improve model-monkey similarity in task-relevant dimensions assessed using DVC, although it markedly increases the model-model similarity. Similarly, pre-training on larger datasets does not improve model-monkey similarity. These results suggest a divergence between the task-relevant representations in monkey V4/IT and those learned by models trained on image classification tasks. + oai:arXiv.org:2506.02164v3 + cs.CV + cs.LG + q-bio.NC + q-bio.QM + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yu (Eric), Qian, Wilson S. Geisler, Xue-Xin Wei + + + Something Just Like TRuST : Toxicity Recognition of Span and Target + https://arxiv.org/abs/2506.02326 + arXiv:2506.02326v2 Announce Type: replace +Abstract: Toxic language includes content that is offensive, abusive, or that promotes harm. Progress in preventing toxic output from large language models (LLMs) is hampered by inconsistent definitions of toxicity. We introduce TRuST, a large-scale dataset that unifies and expands prior resources through a carefully synthesized definition of toxicity, and corresponding annotation scheme. It consists of ~300k annotations, with high-quality human annotation on ~11k. To ensure high-quality, we designed a rigorous, multi-stage human annotation process, and evaluated the diversity of the annotators. Then we benchmarked state-of-the-art LLMs and pre-trained models on three tasks: toxicity detection, identification of the target group, and of toxic words. Our results indicate that fine-tuned PLMs outperform LLMs on the three tasks, and that current reasoning models do not reliably improve performance. TRuST constitutes one of the most comprehensive resources for evaluating and mitigating LLM toxicity, and other research in socially-aware and safer language technologies. + oai:arXiv.org:2506.02326v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Berk Atil, Namrata Sureddy, Rebecca J. Passonneau + + + OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation + https://arxiv.org/abs/2506.02397 + arXiv:2506.02397v3 Announce Type: replace +Abstract: Human cognition operates through two complementary modes: fast intuitive thinking and slow deliberate thinking. Vanilla large language models (LLMs) predominantly follow the fast-thinking paradigm, producing immediate responses; while recent large reasoning models (LRMs) adopt slow-thinking strategies, generating detailed reasoning chains before arriving at answers. While LRMs often achieve higher accuracy, this comes at the cost of substantially increased token usage. To address this efficiency-accuracy trade-off, we propose OThink-R1, a hybrid reasoning framework that integrates both modes within a single LRM and enables automatic mode switching based on problem characteristics. We first identify three major patterns of essential and redundant reasoning trajectories in LRMs, which guide the design of an auxiliary LLM-based judge that adaptively determines when slow thinking is necessary. Leveraging the judge's decisions, we construct a hybrid fine-tuning dataset by pruning redundant reasoning to produce fast-thinking samples and retaining complete reasoning for slow-thinking samples. This dataset is then used to fine-tune LRMs, equipping them with inherent autonomous mode-selection capabilities. Extensive experiments on mathematical and question-answering benchmarks show that OThink-R1 reduces reasoning token usage significantly while maintaining competitive accuracy. The code is available at https://github.com/AgenticIR-Lab/OThink-R1. + oai:arXiv.org:2506.02397v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Shengjia Zhang, Junjie Wu, Jiawei Chen, Changwang Zhang, Zhe Li, Xingyu Lou, Wangchunshu Zhou, Sheng Zhou, Can Wang, Jun Wang + + + Cyber Security of Sensor Systems for State Sequence Estimation: an AI Approach + https://arxiv.org/abs/2506.06572 + arXiv:2506.06572v2 Announce Type: replace +Abstract: Sensor systems are extremely popular today and vulnerable to sensor data attacks. Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. This paper develops the first methods that accurately identify/eliminate only the problematic attacked sensor data presented to a sequence estimation/regression algorithm under a powerful attack model constructed based on known/observed attacks. The approach does not assume a known form for the statistical model of the sensor data, allowing data-driven and machine learning sequence estimation/regression algorithms to be protected. A simple protection approach for attackers not endowed with knowledge of the details of our protection approach is first developed, followed by additional processing for attacks based on protection system knowledge. In the cases tested for which it was designed, experimental results show that the simple approach achieves performance indistinguishable, to two decimal places, from that for an approach which knows which sensors are attacked. For cases where the attacker has knowledge of the protection approach, experimental results indicate the additional processing can be configured so that the worst-case degradation under the additional processing and a large number of sensors attacked can be made significantly smaller than the worst-case degradation of the simple approach, and close to an approach which knows which sensors are attacked, for the same number of attacked sensors with just a slight degradation under no attacks. Mathematical descriptions of the worst-case attacks are used to demonstrate the additional processing will provide similar advantages for cases for which we do not have numerical results. All the data-driven processing used in our approaches employ only unattacked training data. + oai:arXiv.org:2506.06572v2 + cs.CR + eess.SP + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xubin Fang, Rick S. Blum, Ramesh Bharadwaj, Brian M. Sadler + + + Aligning Text, Images, and 3D Structure Token-by-Token + https://arxiv.org/abs/2506.08002 + arXiv:2506.08002v2 Announce Type: replace +Abstract: Creating machines capable of understanding the world in 3D is essential in assisting designers that build and edit 3D environments and robots navigating and interacting within a three-dimensional space. Inspired by advances in language and image modeling, we investigate the potential of autoregressive models for a new modality: structured 3D scenes. To this end, we propose a unified LLM framework that aligns language, images, and 3D scenes and provide a detailed ''cookbook'' outlining critical design choices for achieving optimal training and performance addressing key questions related to data representation, modality-specific objectives, and more. We show how to tokenize complex 3D objects to incorporate into our structured 3D scene modality. We evaluate performance across four core 3D tasks -- rendering, recognition, instruction-following, and question-answering -- and four 3D datasets, synthetic and real-world. We show our model's effectiveness on reconstructing complete 3D scenes consisting of complex objects from a single image and on real-world 3D object recognition tasks. Project webpage: https://glab-caltech.github.io/kyvo/ + oai:arXiv.org:2506.08002v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Aadarsh Sahoo, Vansh Tibrewal, Georgia Gkioxari + + + TTrace: Lightweight Error Checking and Diagnosis for Distributed Training + https://arxiv.org/abs/2506.09280 + arXiv:2506.09280v2 Announce Type: replace +Abstract: Distributed training is essential for scaling the training of large neural network models, such as large language models (LLMs), across thousands of GPUs. However, the complexity of distributed training programs makes them particularly prone to silent bugs, which do not produce explicit error signals but lead to incorrect training outcomes. Effectively detecting and localizing such silent bugs in distributed training is challenging. Common debugging practices based on monitoring training loss or gradient norm curves are indirect, inefficient, and provide no way to localize bugs. To address those challenges, we design and implement TTrace, the first systematic differential testing system for detecting and localizing silent bugs in distributed training. TTrace aligns intermediate tensors from distributed training with those from a trusted reference implementation. To properly compare the floating-point values in the corresponding tensors, we propose a novel mathematical analysis that provides a guideline for setting tolerances, enabling TTrace to distinguish bug-induced errors from numerical errors. Experimental results demonstrate that TTrace effectively detects 11 existing bugs and 3 new bugs in the widely used Megatron-LM framework, while requiring fewer than 10 lines of code changes. TTrace is effective in various training recipes, including low-precision recipes involving BF16 and FP8. Notably, a popular open-source training framework has already adopted the method proposed by TTrace in its development workflow. + oai:arXiv.org:2506.09280v2 + cs.DC + cs.LG + cs.NA + math.NA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Haitian Jiang, Shaowei Zhu, Zhen Zhang, Zhenyu Song, Xinwei Fu, Zhen Jia, Yida Wang, Jinyang Li + + + Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation + https://arxiv.org/abs/2506.09990 + arXiv:2506.09990v2 Announce Type: replace +Abstract: We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals; and (2) subsequent action tokens are generated autoregressively, conditioned on the initial keyframe and previously predicted actions. This backward action reasoning enforces a global-to-local structure, allowing each local action to be tightly constrained by the final goal. To further realize the action reasoning structure, CoA incorporates four complementary designs: continuous action token representation; dynamic stopping for variable-length trajectory generation; reverse temporal ensemble; and multi-token prediction to balance action chunk modeling with global structure. As a result, CoA gives strong spatial generalization capabilities while preserving the flexibility and simplicity of a visuo-motor policy. Empirically, we observe CoA achieves the state-of-the-art performance across 60 RLBench tasks and 8 real-world manipulation tasks. + oai:arXiv.org:2506.09990v2 + cs.RO + cs.CV + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wenbo Zhang, Tianrun Hu, Hanbo Zhang, Yanyuan Qiao, Yuchu Qin, Yang Li, Jiajun Liu, Tao Kong, Lingqiao Liu, Xiao Ma + + + A new type of federated clustering: A non-model-sharing approach + https://arxiv.org/abs/2506.10244 + arXiv:2506.10244v3 Announce Type: replace +Abstract: In recent years, the growing need to leverage sensitive data across institutions has led to increased attention on federated learning (FL), a decentralized machine learning paradigm that enables model training without sharing raw data. However, existing FL-based clustering methods, known as federated clustering, typically assume simple data partitioning scenarios such as horizontal or vertical splits, and cannot handle more complex distributed structures. This study proposes data collaboration clustering (DC-Clustering), a novel federated clustering method that supports clustering over complex data partitioning scenarios where horizontal and vertical splits coexist. In DC-Clustering, each institution shares only intermediate representations instead of raw data, ensuring privacy preservation while enabling collaborative clustering. The method allows flexible selection between k-means and spectral clustering, and achieves final results with a single round of communication with the central server. We conducted extensive experiments using synthetic and open benchmark datasets. The results show that our method achieves clustering performance comparable to centralized clustering where all data are pooled. DC-Clustering addresses an important gap in current FL research by enabling effective knowledge discovery from distributed heterogeneous data. Its practical properties -- privacy preservation, communication efficiency, and flexibility -- make it a promising tool for privacy-sensitive domains such as healthcare and finance. + oai:arXiv.org:2506.10244v3 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuji Kawamata, Kaoru Kamijo, Masateru Kihira, Akihiro Toyoda, Tomoru Nakayama, Akira Imakura, Tetsuya Sakurai, Yukihiko Okada + + + On Differential and Boomerang Properties of a Class of Binomials over Finite Fields of Odd Characteristic + https://arxiv.org/abs/2506.11486 + arXiv:2506.11486v2 Announce Type: replace +Abstract: In this paper, we investigate the differential and boomerang properties of a class of binomial $F_{r,u}(x) = x^r(1 + u\chi(x))$ over the finite field $\mathbb{F}_{p^n}$, where $r = \frac{p^n+1}{4}$, $p^n \equiv 3 \pmod{4}$, and $\chi(x) = x^{\frac{p^n -1}{2}}$ is the quadratic character in $\mathbb{F}_{p^n}$. We show that $F_{r,\pm1}$ is locally-PN with boomerang uniformity $0$ when $p^n \equiv 3 \pmod{8}$. To the best of our knowledge, it is the second known non-PN function class with boomerang uniformity $0$, and the first such example over odd characteristic fields with $p > 3$. Moreover, we show that $F_{r,\pm1}$ is locally-APN with boomerang uniformity at most $2$ when $p^n \equiv 7 \pmod{8}$. We also provide complete classifications of the differential and boomerang spectra of $F_{r,\pm1}$. Furthermore, we thoroughly investigate the differential uniformity of $F_{r,u}$ for $u\in \mathbb{F}_{p^n}^* \setminus \{\pm1\}$. + oai:arXiv.org:2506.11486v2 + cs.IT + cs.CR + math.IT + math.NT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Namhun Koo, Soonhak Kwon + + + Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index + https://arxiv.org/abs/2506.12229 + arXiv:2506.12229v5 Announce Type: replace +Abstract: Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora - counting string appearances and retrieving the enclosing documents - yet the high storage overhead hinders their application on Internet-scale data. We present infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18$\times$) and memory use during both indexing (3.2$\times$ reduction) and querying (down to a negligible amount). We index 83TB of Internet text in 99 days with a single CPU node with 128 vCPUs (or 19 hours if using 137 such nodes). We show one important use case of infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 74.2% in GSM8K), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on infini-gram mini indexes. + oai:arXiv.org:2506.12229v5 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Hao Xu, Jiacheng Liu, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi + + + VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption + https://arxiv.org/abs/2506.12846 + arXiv:2506.12846v5 Announce Type: replace +Abstract: Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protecting data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malicious client attacks, this paper proposes a privacy-preserving Federated Learning framework based on Verifiable Functional Encryption (VFEFL), without a non-colluding dual-server assumption or additional trusted third-party. Specifically, we propose a novel Cross-Ciphertext Decentralized Verifiable Functional Encryption (CC-DVFE) scheme that enables the verification of specific relationships over multi-dimensional ciphertexts. This scheme is formally treated, in terms of definition, security model and security proof. Furthermore, based on the proposed CC-DVFE scheme, we design a privacy-preserving federated learning framework that incorporates a novel robust aggregation rule to detect malicious clients, enabling the effective training of high-accuracy models under adversarial settings. Finally, we provide the formal analysis and empirical evaluation of VFEFL. The results demonstrate that our approach achieves the desired privacy protection, robustness, verifiability and fidelity, while eliminating the reliance on non-colluding dual-server assumption or trusted third parties required by most existing methods. + oai:arXiv.org:2506.12846v5 + cs.CR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Nina Cai, Jinguang Han, Weizhi Meng + + + BandPilot: Towards Performance- and Contention-Aware GPU Dispatching in AI Clusters + https://arxiv.org/abs/2506.15595 + arXiv:2506.15595v4 Announce Type: replace +Abstract: Modern multi-tenant AI clusters are increasingly communication-bound, driven by high-volume and multi-round GPU-to-GPU collective communication. Consequently, the GPU dispatcher's choice of a physical GPU subset for each tenant largely determines the job's effective collective bandwidth and thus its performance ceiling. Existing dispatchers predominantly rely on static, topology-aware heuristics that prioritize GPU resource compactness, assuming that minimizing physical distance maximizes communication bandwidth. However, we reveal that this assumption often fails due to complex system-level bottlenecks, such as non-linear NIC saturation and inter-node link heterogeneity.This paper presents BandPilot, a performance- and contention-aware GPU dispatching primitive that optimizes effective collective bandwidth for multi-tenant AI clusters. Specifically, BandPilot learns a data-efficient bandwidth model from sparse NCCL measurements via a hierarchical design. Guided by the model, a fast hybrid search combines an equilibrium-driven constructor with a pruned elimination search to navigate the combinatorial allocation space in real time. To account for multi-tenant interference, BandPilot virtually merges a candidate allocation with co-located cross-host jobs to conservatively estimate shared bottleneck capacity and predict contention-degraded bandwidth. Across a 32-GPU H100 cluster and heterogeneous simulations, BandPilot achieves 92-97% bandwidth efficiency relative to the best-found reference, improving average efficiency by 20-40% over topology-compactness heuristics. + oai:arXiv.org:2506.15595v4 + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kunming Zhang, Hanlong Liao, Junyu Xue, Deke Guo, Guoming Tang + + + SLR: Automated Synthesis for Scalable Logical Reasoning + https://arxiv.org/abs/2506.15787 + arXiv:2506.15787v5 Announce Type: replace +Abstract: We introduce SLR, an end-to-end framework for systematic evaluation and training of Large Language Models (LLMs) via Scalable Logical Reasoning. Given a user's task specification, SLR automatically synthesizes (i) an instruction prompt for an inductive reasoning task, (ii) a validation program, executable on model outputs to provide verifiable rewards, and (iii) the latent ground-truth rule. This process is fully automated, scalable, requires no human annotations, and offers precise control over task difficulty. Using SLR, we create SLR-Bench, a benchmark comprising 19k prompts organized into 20 curriculum levels that progressively increase in relational, arithmetic, and recursive complexity. Large-scale evaluation reveals that contemporary LLMs readily produce syntactically valid rules, yet often fail at correct logical inference. Recent reasoning LLMs demonstrate improved performance but incur very high test-time computation, with costs exceeding $300 for just 1,000 prompts. Finally, curriculum learning via SLR doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. Moreover, these reasoning capabilities generalize to a wide range of established benchmarks, underscoring the effectiveness of SLR for downstream reasoning. + oai:arXiv.org:2506.15787v5 + cs.AI + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia W\"ust, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting + + + Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse + https://arxiv.org/abs/2506.16412 + arXiv:2506.16412v2 Announce Type: replace +Abstract: Generative AI (GAI) technologies are quickly reshaping the educational landscape. As adoption accelerates, understanding how students and educators perceive these tools is essential. This study presents one of the most comprehensive analyses to date of stakeholder discourse dynamics on GAI in education using social media data. Our dataset includes 1,199 Reddit posts and 13,959 corresponding top-level comments. We apply sentiment analysis, topic modeling, and author classification. To support this, we propose and validate a modular framework that leverages prompt-based large language models (LLMs) for analysis of online social discourse, and we evaluate this framework against classical natural language processing (NLP) models. Our GPT-4o pipeline consistently outperforms prior approaches across all tasks. For example, it achieved 90.6% accuracy in sentiment analysis against gold-standard human annotations. Topic extraction uncovered 12 latent topics in the public discourse with varying sentiment and author distributions. Teachers and students convey optimism about GAI's potential for personalized learning and productivity in higher education. However, key differences emerged: students often voice distress over false accusations of cheating by AI detectors, while teachers generally express concern about job security, academic integrity, and institutional pressures to adopt GAI tools. These contrasting perspectives highlight the tension between innovation and oversight in GAI-enabled learning environments. Our findings suggest a need for clearer institutional policies, more transparent GAI integration practices, and support mechanisms for both educators and students. More broadly, this study demonstrates the potential of LLM-based frameworks for modeling stakeholder discourse within online communities. + oai:arXiv.org:2506.16412v2 + cs.SI + cs.CL + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/TCSS.2025.3630587 + Paulina DeVito, Akhil Vallala, Sean Mcmahon, Yaroslav Hinda, Benjamin Thaw, Hanqi Zhuang, Hari Kalva + + + Aha Moment Revisited: Are VLMs Truly Capable of Self Verification in Inference-time Scaling? + https://arxiv.org/abs/2506.17417 + arXiv:2506.17417v3 Announce Type: replace +Abstract: Inference time techniques such as decoding time scaling and self refinement have been shown to substantially improve mathematical reasoning in large language models (LLMs), largely attributed to emergent self correction and self verification behaviors often elicited through reinforcement learning (RL). In this work, we ask whether the same recipe transfers to vision language models (VLMs), especially RL finetuned variants that claim strong visual mathematical reasoning. + Through extensive evaluation, we reach three main findings that differ markedly from text only models. First, generation time capability matters more than verification and refinement: simple majority voting consistently and substantially outperforms verification centric strategies such as best of N with self verification. Second, behaviors often associated with RL tuned models at inference time, such as the 'Aha moment,' do not yield reliable reasoning performance improvements. Third, visual information is not effectively integrated into the model's self verification process. + Overall, our analysis highlights a key limitation: current RL trained VLMs derive limited benefit from self verification in the visual modality, which constrains the effectiveness of inference time scaling for visual mathematical reasoning. + oai:arXiv.org:2506.17417v3 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Mingyuan Wu, Meitang Li, Jingcheng Yang, Jize Jiang, Kaizhuo Yan, Zhaoheng Li, Hanchao Yu, Minjia Zhang, Klara Nahrstedt + + + MemeMind: A Large-Scale Multimodal Dataset with Chain-of-Thought Reasoning for Harmful Meme Detection + https://arxiv.org/abs/2506.18919 + arXiv:2506.18919v3 Announce Type: replace +Abstract: As a multimodal medium combining images and text, memes frequently convey implicit harmful content through metaphors and humor, rendering the detection of harmful memes a complex and challenging task. Although recent studies have made progress in detection accuracy and interpretability, large-scale, high-quality datasets for harmful memes remain scarce, and current methods still struggle to capture implicit risks and nuanced semantics. Thus, we construct MemeMind, a large-scale harmful meme dataset. Aligned with the international standards and the context of internet, MemeMind provides detailed Chain-of-Thought (CoT) reasoning annotations to support fine-grained analysis of implicit intentions in memes. Based on this dataset, we further propose MemeGuard, a reasoning-oriented multimodal detection model that significantly improves both the accuracy of harmful meme detection and the interpretability of model decisions. Extensive experimental results demonstrate that MemeGuard outperforms existing state-of-the-art methods on the MemeMind dataset, establishing a solid foundation for future research in harmful meme detection. + oai:arXiv.org:2506.18919v3 + cs.CL + cs.AI + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hexiang Gu, Qifan Yu, Yuan Liu, Zikang Li, Saihui Hou, Jian Zhao, Zhaofeng He + + + MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations + https://arxiv.org/abs/2506.20100 + arXiv:2506.20100v2 Announce Type: replace +Abstract: We introduce MIRAGE, a new benchmark for multimodal expert-level reasoning and decision-making in consultative interaction settings. Designed for the agriculture domain, MIRAGE captures the full complexity of expert consultations by combining natural user queries, expert-authored responses, and image-based context, offering a high-fidelity benchmark for evaluating models on grounded reasoning, clarification strategies, and long-form generation in a real-world, knowledge-intensive domain. Grounded in over 35,000 real user-expert interactions and curated through a carefully designed multi-step pipeline, MIRAGE spans diverse crop health, pest diagnosis, and crop management scenarios. The benchmark includes more than 7,000 unique biological entities, covering plant species, pests, and diseases, making it one of the most taxonomically diverse benchmarks available for vision-language models, grounded in the real world. Unlike existing benchmarks that rely on well-specified user inputs and closed-set taxonomies, MIRAGE features underspecified, context-rich scenarios with open-world settings, requiring models to infer latent knowledge gaps, handle rare entities, and either proactively guide the interaction or respond. Project Page: https://mirage-benchmark.github.io + oai:arXiv.org:2506.20100v2 + cs.LG + cs.AI + cs.CL + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Vardhan Dongre, Chi Gui, Shubham Garg, Hooshang Nayyeri, Gokhan Tur, Dilek Hakkani-T\"ur, Vikram S. Adve + + + Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC + https://arxiv.org/abs/2506.24045 + arXiv:2506.24045v2 Announce Type: replace +Abstract: Personal LLM agents increasingly combine foreground reactive interactions with background proactive monitoring, forming long-lived, stateful LLM flows that interleave prefill and token-by-token decode. While modern heterogeneous SoCs integrate CPUs, iGPUs, and NPUs to support on-device intelligence, existing LLM engines assume static, single-shot inference and lack mechanisms for flow-level concurrency, prioritization, and efficient accelerator coordination. As a result, commodity SoCs remain poorly matched to the dynamic, mixed-criticality execution patterns of personal agents. + This paper presents Agent$.$xpu, the first LLM engine that orchestrates concurrent reactive and proactive LLM flows on commodity SoCs. Extensive profiling uncovers unique SoC characteristics of operator-accelerator affinity, asymmetric DDR contention, and stage-divergent batching behaviors distinct from cloud-serving assumptions. Agent$.$xpu introduces three key techniques: a heterogeneous execution graph (HEG) capturing NPU/iGPU affinity and elastic operator binding; flow-aware NPU-iGPU coordination with stage elasticity, decoupling prefill and decode to reduce bandwidth contention and enforce priorities; and fine-grained preemption with slack-aware piggybacking to guarantee reactive responsiveness without starving proactive work. Across realistic personal-agent workloads, Agent$.$xpu delivers 1.2-4.9$\times$ proactive throughput and reduces reactive latency by at least 91%, compared with both industrial iGPU-only serving engine and NPU-iGPU static inference with optimal tensor-partitioning schemes. Agent$.$xpu also minimizes energy consumption and graphics interference via controlled iGPU usage. + oai:arXiv.org:2506.24045v2 + cs.DC + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xinming Wei, Jiahao Zhang, Haoran Li, Jiayu Chen, Haoning Guan, Rui Qu, Maoliang Li, Xiang Chen, Guojie Luo + + + Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift + https://arxiv.org/abs/2507.07723 + arXiv:2507.07723v2 Announce Type: replace +Abstract: Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise preference ranking, which directly aligns language model with human preference. Prior work has observed a counter-intuitive phenomenon termed likelihood displacement, where the absolute probability of preferred responses decreases simultaneously during training. We demonstrate that such displacement can lead to a more devastating failure mode, which we defined as \textit{Catastrophic Preference Shift}, where the lost preference probability mass inadvertently shifts toward out-of-distribution (OOD) responses. Such a failure mode is a key limitation shared across BT-style direct preference learning methods, due to the fundamental conflict between the unconstrained discriminative alignment and generative foundational capabilities, ultimately leading to severe performance degradation (e.g., SimPO suffers a significant drop in reasoning accuracy from 73.5\% to 37.5\%). We analyze existing BT-style methods from the probability evolution perspective and theoretically prove that these methods exhibit over-reliance on model initialization and can lead to preference shift. To resolve these counter-intuitive behaviors, we propose a theoretically grounded Stable Preference Optimization (SPO) framework that constrains preference learning within a safe alignment region. Empirical evaluations demonstrate that SPO effectively stabilizes and enhances the performance of existing BT-style preference learning methods. SPO provides new insights into the design of preference learning objectives and opens up new avenues towards more reliable and interpretable language model alignment. + oai:arXiv.org:2507.07723v2 + cs.AI + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chengtao Jian, Kai Yang, Tianhao Gao, Wuguang Ni, Keying Yang, Bowen Xiao, Jiajun Liu, Ye Ouyang + + + Information-Theoretic Generalization Bounds of Replay-based Continual Learning + https://arxiv.org/abs/2507.12043 + arXiv:2507.12043v2 Announce Type: replace +Abstract: Continual learning (CL) has emerged as a dominant paradigm for acquiring knowledge from sequential tasks while avoiding catastrophic forgetting. Although many CL methods have been proposed to show impressive empirical performance, the theoretical understanding of their generalization behavior remains limited, particularly for replay-based approaches. This paper establishes a unified theoretical framework for replay-based CL, deriving a series of information-theoretic generalization bounds that explicitly elucidate the impact of the memory buffer alongside the current task on generalization performance. Specifically, our hypothesis-based bounds capture the trade-off between the number of selected exemplars and the information dependency between the hypothesis and the memory buffer. Our prediction-based bounds yield tighter and computationally tractable upper bounds on the generalization error by leveraging low-dimensional variables. Theoretical analysis is general and broadly applicable to a wide range of learning algorithms, exemplified by stochastic gradient Langevin dynamics (SGLD) as a representative method. Comprehensive experimental evaluations demonstrate the effectiveness of our derived bounds in capturing the generalization dynamics in replay-based CL settings. + oai:arXiv.org:2507.12043v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wen Wen, Tieliang Gong, Zeyu Gao, Yunjiao Zhang, Weizhan Zhang, Yong-Jin Liu + + + Constructions of binary self-orthogonal singly-even minimal linear codes violating the Aschikhmin-Barg condition with few weights + https://arxiv.org/abs/2507.12240 + arXiv:2507.12240v3 Announce Type: replace +Abstract: We first establish a simple yet powerful necessary and sufficient condition for a binary linear code to be SO, leading to a complete characterization of singly-even codes in this family. We further derive necessary and sufficient conditions on Boolean and vectorial Boolean functions for generating such codes via a standard construction method. Building on this foundation, we propose three general frameworks for constructing binary SO singly-even minimal non-AB linear codes with few weights. The first two approaches are based on designing Boolean and vectorial Boolean functions that simultaneously satisfy multiple conditions. The third method generates new SO codes from existing ones. As a result, we obtain many infinite classes of binary self-orthogonal singly-even minimal linear codes violating the AB condition with few weights and fully determined weight distributions. Particularly, numerical results show that some duals of our codes are optimal or near-optimal. + oai:arXiv.org:2507.12240v3 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kangquan Li, Hao Chen, Wengang Jin, Longjiang Qu + + + Compositional Discrete Latent Code for High Fidelity, Productive Diffusion Models + https://arxiv.org/abs/2507.12318 + arXiv:2507.12318v3 Announce Type: replace +Abstract: We argue that diffusion models' success in modeling complex distributions is, for the most part, coming from their input conditioning. This paper investigates the representation used to condition diffusion models from the perspective that ideal representations should improve sample fidelity, be easy to generate, and be compositional to allow out-of-training samples generation. We introduce Discrete Latent Code (DLC), an image representation derived from Simplicial Embeddings trained with a self-supervised learning objective. DLCs are sequences of discrete tokens, as opposed to the standard continuous image embeddings. They are easy to generate and their compositionality enables sampling of novel images beyond the training distribution. Diffusion models trained with DLCs have improved generation fidelity, establishing a new state-of-the-art for unconditional image generation on ImageNet. Additionally, we show that composing DLCs allows the image generator to produce out-of-distribution samples that coherently combine the semantics of images in diverse ways. Finally, we showcase how DLCs can enable text-to-image generation by leveraging large-scale pretrained language models. We efficiently finetune a text diffusion language model to generate DLCs that produce novel samples outside of the image generator training distribution. + oai:arXiv.org:2507.12318v3 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Samuel Lavoie, Michael Noukhovitch, Aaron Courville + + + BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM + https://arxiv.org/abs/2507.14632 + arXiv:2507.14632v3 Announce Type: replace +Abstract: Recent advances in generative AI have dramatically improved image and video synthesis capabilities, significantly increasing the risk of misinformation through sophisticated fake content. In response, detection methods have evolved from traditional approaches to multimodal large language models (MLLMs), offering enhanced transparency and interpretability in identifying synthetic media. However, current detection systems remain fundamentally limited by their single-modality design. These approaches analyze images or videos separately, making them ineffective against synthetic content that combines multiple media formats. To address these challenges, we introduce \textbf{BusterX++}, a framework for unified detection and explanation of synthetic image and video, with a direct reinforcement learning (RL) post-training strategy. To enable comprehensive evaluation, we also present \textbf{GenBuster++}, a unified benchmark leveraging state-of-the-art image and video generation techniques. This benchmark comprises 4,000 images and video clips, meticulously curated by human experts to ensure high quality, diversity, and real-world applicability. Extensive experiments demonstrate the effectiveness and generalizability of our approach. + oai:arXiv.org:2507.14632v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Haiquan Wen, Tianxiao Li, Zhenglin Huang, Yiwei He, Guangliang Cheng + + + Awakening LLMs' Reasoning Potential: A Fine-Grained Pipeline to Evaluate and Mitigate Vague Perception + https://arxiv.org/abs/2507.16199 + arXiv:2507.16199v5 Announce Type: replace +Abstract: Large language models (LLMs) are increasingly trained to abstain on difficult questions by answering unknown. However, we observe that LLMs often misuse this option: they output unknown even when LLMs can actually solve the questions, or they fail to understand why questions are truly unsolvable. We formalize this mismatch between potential ability and the inclination of abstention as the Vague Perception phenomenon. We introduce the WakenLLM pipeline that (1) extracts Vague Perception samples and (2) measures how many of them can be converted to correct answers under stimulation. Based on stage-wise metrics (TCR, OCR, etc.) and the upper-bound accuracy Acc(WakenLLM), we quantify LLMs' reasoning potential beyond one-shot accuracy. Experiments on six LLMs suggest that, without further training or parameter revisions, LLMs can achieve up to a 68.53% increase in accuracy on Vague Perception samples through our designed pipeline. We further analyze how Vague Perception, Conformity and Degradation vary from model families and parameter sizes, and offer model selection strategies in multi-stage reasoning workflows. Finally, by comparing WakenLLM against mainstream reasoning baselines, both training and non-training ones, we show that existing baselines only activate a small portion of LLMs' reasoning potential, pointing to perception-aware reasoning as a promising direction for future LLM designing. Code and datasets are available at https://github.com/WakenLLMTeam/WakenLLM-toolkit. + oai:arXiv.org:2507.16199v5 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zipeng Ling, Yuehao Tang, Shuliang Liu, Junqi Yang, Shenghong Fu, Chen Huang, Kejia Huang, Yao Wan, Zhichao Hou, Xuming Hu + + + TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios + https://arxiv.org/abs/2507.18061 + arXiv:2507.18061v2 Announce Type: replace +Abstract: Spoken language models (SLMs) have advanced rapidly in recent years, accompanied by a growing number of evaluation benchmarks. However, most existing benchmarks emphasize task completion and capability scaling, while remaining poorly aligned with how users interact with SLMs in real-world spoken conversations. Effective spoken interaction requires not only accurate understanding of user intent and content, but also the ability to respond with appropriate interactional strategies. In this paper, we present TELEVAL, a dynamic, user-centered benchmark for evaluating SLMs in realistic Chinese spoken interaction scenarios. TELEVAL consolidates evaluation into two core aspects. Reliable Content Fulfillment assesses whether models can comprehend spoken inputs and produce semantically correct responses. Interactional Appropriateness evaluates whether models act as socially capable interlocutors, requiring them not only to generate human-like, colloquial responses, but also to implicitly incorporate paralinguistic cues for natural interaction. Experiments reveal that, despite strong performance on semantic and knowledge-oriented tasks, current SLMs still struggle to produce natural and interactionally appropriate responses, highlighting the need for more interaction-faithful evaluation. + oai:arXiv.org:2507.18061v2 + cs.CL + cs.AI + cs.SD + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Zehan Li, Hongjie Chen, Qing Wang, Yuxin Zhang, Jing Zhou, Xuening Wang, Hang Lv, Mengjie Du, Yaodong Song, Jie Lian, Jian Kang, Jie Li, Yongxiang Li, Xuelong Li + + + Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges + https://arxiv.org/abs/2508.00454 + arXiv:2508.00454v4 Announce Type: replace +Abstract: Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the "LLM-as-a-judge" paradigm, where an LLM is prompted to serve as an evaluator to assess dialogue quality. However, such methods often suffer from various biases, which undermine the reliability and consistency of the evaluation results. To mitigate these biases, recent methods employ multiple LLMs as judges and aggregate their judgments to select the optimal assessment. Although effective, this multi-judge approach incurs significant computational overhead during inference. In this paper, we propose an efficient dialogue evaluator that captures the collective wisdom of multiple LLM judges by aggregating their preference knowledge into a single model. Our approach preserves the advantages of diverse multi-judge feedback while drastically reducing the evaluation cost, enabling fast, flexible, and fine-grained dialogue quality assessment. Extensive experiments on seven single rating and pairwise comparison dialogue evaluation benchmarks demonstrate that our method outperforms existing baselines across diverse scenarios, showcasing its efficiency and robustness. + oai:arXiv.org:2508.00454v4 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yuqi Tang, Kehua Feng, Yunfeng Wang, Zhiwen Chen, Chengfei Lv, Gang Yu, Qiang Zhang, Keyan Ding, Huajun Chen + + + Pro2Guard: Proactive Runtime Enforcement of LLM Agent Safety via Probabilistic Model Checking + https://arxiv.org/abs/2508.00500 + arXiv:2508.00500v2 Announce Type: replace +Abstract: Large Language Model (LLM) agents demonstrate strong autonomy, but their stochastic behavior introduces unpredictable safety risks. Existing rule-based enforcement systems, such as AgentSpec, are reactive, intervening only when unsafe behavior is imminent or has occurred, lacking foresight for long-horizon dependencies. To overcome these limitations, we present a proactive runtime enforcement framework for LLM agents. The framework abstracts agent behaviors into symbolic states and learns a Discrete-Time Markov Chain (DTMC) from execution traces. At runtime, it predicts the probability of leading to undesired behaviors and intervenes before violations occur when the estimated risk exceeds a user-defined threshold. Designed to provide PAC-correctness guarantee, the framework achieves statistically reliable enforcement of agent safety. We evaluate the framework across two safety-critical domains: autonomous vehicles and embodied agents. It proactively enforces safety and maintains high task performance, outperforming existing methods. + oai:arXiv.org:2508.00500v2 + cs.AI + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Haoyu Wang, Christopher M. Poskitt, Jun Sun, Jiali Wei + + + TreeDiff: AST-Guided Code Generation with Diffusion LLMs + https://arxiv.org/abs/2508.01473 + arXiv:2508.01473v3 Announce Type: replace +Abstract: Code generation is increasingly critical for real-world applications. Still, diffusion-based large language models continue to struggle with this demand. Unlike free-form text, code requires syntactic precision; even minor structural inconsistencies can render a program non-executable. Existing diffusion-based large language models rely on random token masking for corruption, leading to two key failures: they lack awareness of syntactic boundaries during the iterative denoising process, and they fail to capture the long-range hierarchical dependencies essential for program correctness. + We propose TreeDiff to address both issues. Specifically, we propose a syntax-aware diffusion framework that incorporates structural priors from Abstract Syntax Tree (AST) into the corruption process. Instead of masking individual tokens at random, we selectively mask tokens belonging to key AST nodes. By aligning the corruption process with the underlying structure of code, our method encourages the model to internalize the compositional nature of programming languages, enabling it to reconstruct programs that respect grammatical boundaries and capture long-range dependencies. Our method achieves a 13.3% relative improvement over the random masking training method, demonstrating its effectiveness in code generation task by leveraging underlying structures. + oai:arXiv.org:2508.01473v3 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yiming Zeng, Jinghan Cao, Zexin Li, Yiming Chen, Tao Ren, Zhuochun Li, Dawei Xiang, Xidong Wu, Shangqian Gao, Tingting Yu + + + The Homogenizing Effect of Large Language Models on Human Expression and Thought + https://arxiv.org/abs/2508.01491 + arXiv:2508.01491v2 Announce Type: replace +Abstract: Cognitive diversity, reflected in variations of language, perspective, and reasoning, is essential to creativity and collective intelligence. This diversity is rich and grounded in culture, history, and individual experience. Yet as large language models (LLMs) become deeply embedded in people's lives, they risk standardizing language and reasoning. We synthesize evidence across linguistics, psychology, cognitive science, and computer science to show how LLMs reflect and reinforce dominant styles while marginalizing alternative voices and reasoning strategies. We examine how their design and widespread use contribute to this effect by mirroring patterns in their training data and amplifying convergence as all people increasingly rely on the same models across contexts. Unchecked, this homogenization risks flattening the cognitive landscapes that drive collective intelligence and adaptability. + oai:arXiv.org:2508.01491v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhivar Sourati, Alireza S. Ziabari, Morteza Dehghani + + + The Bidirectional Process Reward Model + https://arxiv.org/abs/2508.01682 + arXiv:2508.01682v2 Announce Type: replace +Abstract: Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promising approach to enhance the reasoning quality of Large Language Models (LLMs). However, most existing PRMs rely on a unidirectional left-to-right (L2R) evaluation scheme, which restricts their utilization of global context. In light of this challenge, we propose a novel bidirectional evaluation paradigm, named Bidirectional Process Reward Model (BiPRM). BiPRM incorporates a parallel right-to-left (R2L) evaluation stream, implemented via prompt reversal, alongside the conventional L2R flow. Then a gating mechanism is introduced to adaptively fuse the reward scores from both streams to yield a holistic quality assessment. Remarkably, compared to the original PRM, BiPRM introduces only a 0.3% parameter increase for the gating module, and the parallel execution of two streams incurs merely 5% inference time latency. Our extensive empirical evaluations spanning diverse benchmarks, LLM backbones, PRM objectives and sampling policies demonstrate that BiPRM consistently surpasses unidirectional baselines, achieving an average relative gain of 10.6% over 54 solution-level configurations and 37.7% in 12 step-level error detection scenarios. Generally, our results highlight the effectiveness, robustness and general applicability of BiPRM, offering a promising new direction for process-based reward modeling. + oai:arXiv.org:2508.01682v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Lingyin Zhang, Jun Gao, Xiaoxue Ren, Ziqiang Cao + + + Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning + https://arxiv.org/abs/2508.02178 + arXiv:2508.02178v2 Announce Type: replace +Abstract: Large Reasoning Models (LRMs) often suffer from overthinking, generating verbose reasoning traces that compromise both computational efficiency and interpretability. Unlike prior efforts that rely on global length-based rewards, we propose a semantic-aware decomposition of redundancy into two distinct forms: internal redundancy (informational stagnation within the reasoning process) and external redundancy (superfluous continuation after the final answer). We introduce a dual-penalty reinforcement learning framework that surgically targets these inefficiencies: a sliding-window semantic analysis is employed to penalize low-gain steps within the reasoning trajectory, while a normalized metric suppresses the post-answer tail. Extensive experiments demonstrate that our method significantly compresses Chain-of-Thought traces with minimal accuracy degradation, while maintaining strong generalization to out-of-domain tasks. Crucially, we reveal an asymmetry in redundancy: external redundancy can be safely eliminated without performance loss, whereas internal redundancy removal requires a calibrated trade-off to maintain reasoning fidelity. Our framework enables fine-grained, implicit control over reasoning length, paving the way for more concise and interpretable LRMs. + oai:arXiv.org:2508.02178v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jialiang Hong, Taihang Zhen, Kai Chen, Jiaheng Liu, Junlan Feng, Wenpeng Zhu, Jing Huo, Yang Gao, Depeng Wang, Haitao Wan, Xi Yang, Boyan Wang, Fanyu Meng, Yuyao Zhang + + + U-PINet: Physics-Informed Hierarchical Learning for Accurate and Fast 3D RCS Prediction + https://arxiv.org/abs/2508.03774 + arXiv:2508.03774v2 Announce Type: replace +Abstract: Accurate radar cross section (RCS) computation is a fundamental task in radar engineering and electromagnetic (EM) scattering analysis, underpinning target signature characterization, detection, and recognition. Conventional computational electromagnetics (CEM) solvers provide high-fidelity RCS predictions but suffer from prohibitive computational costs when applied to 3-dimensional (3D) targets under multi-aspect configurations. In contrast, purely data-driven neural networks offer high efficiency yet often lack physical consistency and generalization capability. To address these challenges, this paper proposes a U-shaped Physics-Informed Network (U-PINet). To the best of our knowledge, it is the first framework to establish a fully end-to-end, physics-informed hierarchical architecture for fast and accurate RCS computation, grounded in the governing principles of CEM. Inspired by the near-far field decomposition in classical fast solvers, U-PINet explicitly models local EM coupling and long-range radiation effects through a hierarchical operator design. A physics-guided graph construction is further introduced to represent self- and mutual-coupling among mesh elements of complex 3D targets, enabling physically interpretable intermediate representations. By embedding EM governing equations as residual constraints, the proposed framework achieves end-to-end, physically consistent RCS prediction with significantly improved computational efficiency. Extensive numerical experiments demonstrate that U-PINet attains solver-level RCS accuracy with orders-of-magnitude runtime reduction, while exhibiting strong generalization to unseen target geometries under limited training data. + oai:arXiv.org:2508.03774v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Rui Zhu, Yuexing Peng, Peng Wang, George C. Alexandropoulos, Wenbo Wang, Wei Xiang + + + SAGOnline: Segment Any Gaussians Online + https://arxiv.org/abs/2508.08219 + arXiv:2508.08219v2 Announce Type: replace +Abstract: 3D Gaussian Splatting has emerged as a powerful paradigm for explicit 3D scene representation, yet achieving efficient and consistent 3D segmentation remains challenging. Existing segmentation approaches typically rely on high-dimensional feature lifting, which causes costly optimization, implicit semantics, and task-specific constraints. We present \textbf{Segment Any Gaussians Online (SAGOnline)}, a unified, zero-shot framework that achieves real-time, cross-view consistent segmentation without scene-specific training. SAGOnline decouples the monolithic segmentation problem into lightweight sub-tasks. By integrating video foundation models (e.g., SAM 2), we first generate temporally consistent 2D masks across rendered views. Crucially, instead of learning continuous feature fields, we introduce a \textbf{Rasterization-aware Geometric Consensus} mechanism that leverages the traceability of the Gaussian rasterization pipeline. This allows us to deterministically map 2D predictions to explicit, discrete 3D primitive labels in real-time. This discrete representation eliminates the memory and computational burden of feature distillation, enabling instant inference. Extensive evaluations on NVOS and SPIn-NeRF benchmarks demonstrate that SAGOnline achieves state-of-the-art accuracy (92.7\% and 95.2\% mIoU) while operating at the fastest speed at 27 ms per frame. By providing a flexible interface for diverse foundation models, our framework supports instant prompt, instance, and semantic segmentation, paving the way for interactive 3D understanding in AR/VR and robotics. + oai:arXiv.org:2508.08219v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wentao Sun, Quanyun Wu, Hanqing Xu, Kyle Gao, Zhengsen Xu, Yiping Chen, Dedong Zhang, Lingfei Ma, John S. Zelek, Jonathan Li + + + Optimal Boost Design for Auto-bidding Mechanism with Publisher Quality Constraints + https://arxiv.org/abs/2508.08772 + arXiv:2508.08772v2 Announce Type: replace +Abstract: Online bidding serves as a fundamental information system in mobile ecosystems, facilitating real-time ad allocation across billions of devices while optimizing both platform performance and user experience through data-driven decision making. Improving ad allocation efficiency is a long-standing research problem, as it directly enhances the economic outcomes for all participants in advertising platforms. This paper investigates the design of optimal boost factors in online bidding while incorporating quality value (the impact of displayed ads on publishers' long-term benefits). To address the divergent interests on quality, we establish a three-party auction framework with a unified welfare metric of advertiser and publisher. Within this framework, we derive the theoretical efficiency lower bound for C-competitive boost in second-price single-slot auctions, then design a novel quality-involved Boosting (q-Boost) algorithm for computing the optimal boost factor. Experimental validation on Alibaba's public dataset (AuctionNet) demonstrates 2%-6% welfare improvements over conventional approaches, proving our method's effectiveness in real-world settings. + oai:arXiv.org:2508.08772v2 + cs.GT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Huanyu Yan, Yu Huo, Min Lu, Weitong Ou, Xingyan Shi, Ruihe Shi, Xiaoying Tang + + + Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation + https://arxiv.org/abs/2508.12645 + arXiv:2508.12645v4 Announce Type: replace +Abstract: Recent advances in large language models (LLMs) have enabled realistic user simulators for developing and evaluating recommender systems (RSs). However, existing LLM-based simulators for RSs face two major limitations: (1) static and single-step prompt-based inference that leads to inaccurate and incomplete user profile construction; (2) unrealistic and single-round recommendation-feedback interaction pattern that fails to capture real-world scenarios. To address these limitations, we propose DGDPO (Diagnostic-Guided Dynamic Profile Optimization), a novel framework that constructs user profile through a dynamic and iterative optimization process to enhance the simulation fidelity. Specifically, DGDPO incorporates two core modules within each optimization loop: firstly, a specialized LLM-based diagnostic module, calibrated through our novel training strategy, accurately identifies specific defects in the user profile. Subsequently, a generalized LLM-based treatment module analyzes the diagnosed defect and generates targeted suggestions to refine the profile. Furthermore, unlike existing LLM-based user simulators that are limited to single-round interactions, we are the first to integrate DGDPO with sequential recommenders, enabling a bidirectional evolution where user profiles and recommendation strategies adapt to each other over multi-round interactions. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our proposed framework. + oai:arXiv.org:2508.12645v4 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Hongyang Liu, Zhu Sun, Tianjun Wei, Yan Wang, Jiajie Zhu, Xinghua Qu + + + An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs + https://arxiv.org/abs/2508.14636 + arXiv:2508.14636v3 Announce Type: replace +Abstract: Mobile robot platforms are increasingly being used to automate information gathering tasks such as environmental monitoring. Efficient target tracking in dynamic environments is critical for applications such as search and rescue and pollutant cleanups. In this letter, we study active mapping of floating targets that drift due to environmental disturbances such as wind and currents. This is a challenging problem as it involves predicting both spatial and temporal variations in the map due to changing conditions. We introduce an integrated framework combining dynamic occupancy grid mapping and an informative planning approach to actively map and track freely drifting targets with an autonomous surface vehicle. A key component of our adaptive planning approach is a spatiotemporal prediction network that predicts target position distributions over time. We further propose a planning objective for target tracking that leverages these predictions. Simulation experiments show that this planning objective improves target tracking performance compared to existing methods that consider only entropy reduction as the planning objective. Finally, we validate our approach in field tests, showcasing its ability to track targets in real-world monitoring scenarios. + oai:arXiv.org:2508.14636v3 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sanjeev Ramkumar Sudha, Marija Popovi\'c, Erlend M. Coates + + + VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models + https://arxiv.org/abs/2508.15229 + arXiv:2508.15229v2 Announce Type: replace +Abstract: Small Language Models (SLMs) provide computational advantages in resource-constrained environments, yet memory limitations remain a critical bottleneck for edge device deployment. A substantial portion of SLMs' memory footprint stems from vocabulary-related components, particularly embeddings and language modeling (LM) heads, due to large vocabulary sizes. Existing static vocabulary pruning, while reducing memory usage, suffers from rigid, one-size-fits-all designs that cause information loss from the prefill stage and a lack of flexibility. In this work, we identify two key principles underlying the vocabulary reduction challenge: the lexical locality principle, the observation that only a small subset of tokens is required during any single inference, and the asymmetry in computational characteristics between vocabulary-related components of SLM. Based on these insights, we introduce VocabTailor, a novel decoupled dynamic vocabulary selection framework that addresses memory constraints through offloading embedding and implements a hybrid static-dynamic vocabulary selection strategy for LM Head, enabling on-demand loading of vocabulary components. Comprehensive experiments across diverse downstream tasks demonstrate that VocabTailor achieves a reduction of up to 99% in the memory usage of vocabulary-related components with minimal or no degradation in task performance, substantially outperforming existing static vocabulary pruning. + oai:arXiv.org:2508.15229v2 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Hanling Zhang, Yayu Zhou, Tongcheng Fang, Zhihang Yuan, Guohao Dai, Wanli Ouyang, Yu Wang + + + Scalable Scientific Interest Profiling Using Large Language Models + https://arxiv.org/abs/2508.15834 + arXiv:2508.15834v2 Announce Type: replace +Abstract: Research profiles highlight scientists' research focus, enabling talent discovery and collaborations, but are often outdated. Automated, scalable methods are urgently needed to keep profiles current. We design and evaluate two Large Language Models (LLMs)-based methods to generate scientific interest profiles--one summarizing PubMed abstracts and the other using Medical Subject Headings (MeSH) terms--comparing them with researchers' self-summarized interests. We collected titles, MeSH terms, and abstracts of PubMed publications for 595 faculty at Columbia University Irving Medical Center, obtaining human-written profiles for 167. GPT-4o-mini was prompted to summarize each researcher's interests. Manual and automated evaluations characterized similarities between machine-generated and self-written profiles. The similarity study showed low ROUGE-L, BLEU, and METEOR scores, reflecting little terminological overlap. BERTScore analysis revealed moderate semantic similarity (F1: 0.542 for MeSH-based, 0.555 for abstract-based), despite low lexical overlap. In validation, paraphrased summaries achieved a higher F1 of 0.851. Comparing original and manually paraphrased summaries indicated limitations of such metrics. Kullback-Leibler (KL) Divergence of TF-IDF values (8.56 for MeSH-based, 8.58 for abstract-based) suggests machine summaries employ different keywords than human-written ones. Manual reviews showed 77.78% rated MeSH-based profiling "good" or "excellent," with readability rated favorably in 93.44% of cases, though granularity and accuracy varied. Panel reviews favored 67.86% of MeSH-derived profiles over abstract-derived ones. LLMs promise to automate scientific interest profiling at scale. MeSH-derived profiles have better readability than abstract-derived ones. Machine-generated summaries differ from human-written ones in concept choice, with the latter initiating more novel ideas. + oai:arXiv.org:2508.15834v2 + cs.CL + cs.DL + cs.IR + q-bio.OT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1016/j.jbi.2025.104949. + Journal of Biomedical Informatics 172, 104949 (2025) + Yilun Liang, Gongbo Zhang, Edward Sun, Betina Idnay, Yilu Fang, Fangyi Chen, Casey Ta, Yifan Peng, Chunhua Weng + + + LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models + https://arxiv.org/abs/2508.17394 + arXiv:2508.17394v4 Announce Type: replace +Abstract: Retrieving visual and textual information from medical literature and hospital records can enhance diagnostic accuracy for clinical image interpretation. However, multimodal retrieval-augmented diagnosis is highly challenging. We explore a lightweight mechanism for enhancing diagnostic performance of retrieval-augmented LVLMs. We train a lightweight LVLM-aware multimodal retriever, such that the retriever learns to return images and texts that guide the LVLM toward correct predictions. In our low-resource setting, we perform only lightweight fine-tuning with small amounts of data, and use only general-purpose backbone models, achieving competitive results in clinical classification and VQA tasks compared to medically pre-trained models with extensive training. In a novel analysis, we highlight a previously unexplored class of errors that we term inconsistent retrieval predictions: cases where different top-retrieved images yield different predictions for the same target. We find that these cases are challenging for all models, even for non-retrieval models, and that our retrieval optimization mechanism significantly improves these cases over standard RAG. However, our analysis also sheds light on gaps in the ability of LVLMs to utilize retrieved information for clinical predictions. Code and models available at: https://github.com/Nirmaz/JOMED. + oai:arXiv.org:2508.17394v4 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Nir Mazor, Tom Hope + + + Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance + https://arxiv.org/abs/2508.18177 + arXiv:2508.18177v2 Announce Type: replace +Abstract: Visually impaired individuals face significant challenges in environmental perception. Traditional assistive technologies often lack adaptive intelligence, focusing on individual components rather than integrated systems. While Vision-Language Models (VLMs) offer a promising path to richer, integrated understanding, their deployment is severely limited by substantial computational requirements, demanding dozens of gigabytes of memory. To address these gaps in computational efficiency and integrated design, this study proposes a dual technological innovation framework: a cross-modal differentiated quantization framework for VLMs and a scene-aware vectorized memory multi-agent system. The quantization framework implements differentiated strategies, reducing memory from 38GB to 11.3GB. The multi-agent system uses vectorized memory and perception-memory-reasoning workflows to provide environmental information beyond the current view, achieving 2.83-3.52s latency to initial speech output. Experiments show the quantized 19B-parameter model only experiences a 2.05% performance drop on MMBench and maintains 63.7 accuracy on OCR-VQA (original: 64.9), outperforming smaller models with equivalent memory. This research advances computational efficiency and assistive technology, offering comprehensive assistance in scene perception, text recognition, and navigation. + oai:arXiv.org:2508.18177v2 + cs.CV + cs.LG + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Xiangxiang Wang, Xuanyu Wang, YiJia Luo, Yongbin Yu, Manping Fan, Jingtao Zhang, Liyong Ren + + + CVBench: Benchmarking Cross-Video Synergies for Complex Multimodal Reasoning + https://arxiv.org/abs/2508.19542 + arXiv:2508.19542v4 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:2508.19542v4 + cs.CV + Wed, 07 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 + + + Low-Cost Architecture and Efficient Pattern Synthesis for Polarimetric Phased Array Based on Polarization Coding Reconfigurable Elements + https://arxiv.org/abs/2508.19644 + arXiv:2508.19644v3 Announce Type: replace +Abstract: Polarimetric phased arrays (PPAs) enhance radar target detection and anti-jamming capabilities, but their conventional dual transmit/receive (T/R) channel architecture leads to high cost and system complexity. To address these limitations, this paper proposes a polarization-coding reconfigurable phased array (PCRPA) and associated pattern synthesis techniques, which reduce the channel count while preserving key performance. In the PCRPA, each antenna element connects to a single T/R channel and is equipped with a two-level RF switch, enabling real-time control of its polarization state and subarray grouping. By optimizing both the element polarization codes and the excitation weights, the array can synthesize arbitrarily polarized and dual-polarized beams. Simulation results show that the proposed approach achieves suppressed cross-polarization and comparable sidelobe levels compared to conventional PPAs across a wide scan range, with performance improvements being more pronounced in larger arrays. The inherent channel reduction does, however, incur a trade-off in terms of radiated power and directivity. Experimental validation using an $8\times 8$ X-band array antenna confirms the feasibility and effectiveness of the proposed system. The PCRPA architecture and the accompanying synthesis methods offer a cost-effective solution for large-scale PPA systems, maintaining sidelobe and polarization control with significantly reduced hardware complexity. + oai:arXiv.org:2508.19644v3 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yiqing Wang, Jian Zhou, Chen Pang, Wenyang Man, Zixiang Xiong, Ke Meng, Zhanling Wang, Yongzhen Li + + + Constructive l2-Discrepancy Minimization with Additive Deviations + https://arxiv.org/abs/2508.21423 + arXiv:2508.21423v3 Announce Type: replace +Abstract: The \emph{signed series} problem in the $\ell_2$ norm asks, given set of vectors $v_1,\ldots,v_n\in \mathbf{R}^d$ having at most unit $\ell_2$ norm, does there always exist a series $(\varepsilon_i)_{i\in [n]}$ of $\pm 1$ signs such that for all $i\in [n]$, $\max_{i\in [n]} \|\sum_{j=1}^i \varepsilon_i v_i\|_2 = O(\sqrt{d})$. A result of Banaszczyk [2012, \emph{Rand. Struct. Alg.}] states that there exist signs $\varepsilon_i\in \{-1,1\},\; i\in [n]$ such that $\max_{i\in [n]} \|\sum_{j=1}^i \varepsilon_i v_i\|_2 = O(\sqrt{d+\log n})$. The best constructive bound known so far is of $O(\sqrt{d\log n})$, by Bansal and Garg [2017, \emph{STOC.}, 2019, \emph{SIAM J. Comput.}]. We give a polynomial-time randomized algorithm to find signs $x(i) \in \{-1,1\},\; i\in [n]$ such that \[ \max_{i\in [n]} \|\sum_{j=1}^i x(i)v_i\|_2 = O(\sqrt{d + \log^2 n}) = O(\sqrt{d}+\log n).\] By the constructive reduction of Harvey and Samadi [\emph{COLT}, 2014], this also yields a constructive bound of $O(\sqrt{d}+\log n)$ for the Steinitz problem in the $\ell_2$-norm. Thus, we algorithmically achieve Banaszczyk's bounds for both problems when $d \geq \log^2n$, which also matches the conjectured bounds. Our algorithm is based on the framework on Bansal and Garg, together with a new analysis involving $(i)$ additional linear and spectral orthogonality constraints during the construction of the covariance matrix of the random walk steps, which allow us to control the quadratic variation in the linear as well as the quadratic components of the discrepancy increment vector, alongwith $(ii)$ a ``Freedman-like" version of the Hanson-Wright concentration inequality, for filtration-dependent sums of subgaussian chaoses. + oai:arXiv.org:2508.21423v3 + cs.DM + cs.DS + math.PR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Kunal Dutta + + + On discrete Sobolev inequalities for nonconforming finite elements under a semi-regular mesh condition + https://arxiv.org/abs/2509.00505 + arXiv:2509.00505v2 Announce Type: replace +Abstract: We derive a discrete $ L^q-L^p$ Sobolev inequality tailored for the Crouzeix--Raviart and discontinuous Crouzeix--Raviart finite element spaces on anisotropic meshes in both two and three dimensions. Subject to a semi-regular mesh condition, this discrete Sobolev inequality is applicable to all pairs $(q,p)$ that align with the local Sobolev embedding, including scenarios where $q \leq p$. Importantly, the constant is influenced solely by the domain and the semi-regular parameter, ensuring robustness against variations in aspect ratios and interior angles of the mesh. The proof employs an anisotropy-sensitive trace inequality that leverages the element height, a two-step affine/Piola mapping approach, the stability of the Raviart--Thomas interpolation, and a discrete integration-by-parts identity augmented with weighted jump/trace terms on faces. This Sobolev inequality serves as a mesh-robust foundation for the stability and error analysis of nonconforming and discontinuous Galerkin methods on highly anisotropic meshes. + oai:arXiv.org:2509.00505v2 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hiroki Ishizaka + + + HADIS: Hybrid Adaptive Diffusion Model Serving for Efficient Text-to-Image Generation + https://arxiv.org/abs/2509.00642 + arXiv:2509.00642v2 Announce Type: replace +Abstract: Text-to-image diffusion models have achieved remarkable visual quality but incur high computational costs, making latency-aware, scalable deployment challenging. To address this, we advocate a hybrid architecture that achieves query awareness when serving diffusion models. Unlike existing query-aware serving systems that cascade lightweight and heavyweight models with a fixed configuration, our hybrid architecture first routes each query directly to a suitable model variant, then reroutes it to a cascaded heavyweight model only if necessary. We theoretically analyze conditions for the hybrid architecture to outperform non-hybrid alternatives in latency and response quality. Building on this architecture, we design HADIS, a hybrid serving system for latency-aware diffusion models that jointly optimizes cascade model selection, query routing, and resource allocation. To reduce the complexity of resource management, HADIS uses an offline profiling phase to produce a Pareto-optimal cascade configuration table. At runtime, HADIS selects the best cascade configuration and GPU allocation given latency and workload constraints. Empirical evaluations on real-world traces demonstrate that HADIS improves response quality by up to 35% while reducing latency violation rates by 2.7-45$\times$ compared to state-of-the-art model serving systems. + oai:arXiv.org:2509.00642v2 + cs.DC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Qizheng Yang, Tung-I Chen, Siyu Zhao, Ramesh K. Sitaraman, Hui Guan + + + Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval + https://arxiv.org/abs/2509.01306 + arXiv:2509.01306v2 Announce Type: replace +Abstract: Temporal Information Retrieval (TIR) is a critical yet unresolved task for modern search systems, retrieving documents that not only satisfy a query's information need but also adhere to its temporal constraints. This task is shaped by two challenges: Relevance, ensuring alignment with the query's explicit temporal requirements, and Recency, selecting the freshest document among multiple versions. Existing methods often address the two challenges in isolation, relying on brittle heuristics that fail in scenarios where temporal requirements and staleness resistance are intertwined. To address this gap, we introduce Re2Bench, a benchmark specifically designed to disentangle and evaluate Relevance, Recency, and their hybrid combination. Building on this foundation, we propose Re3, a unified and lightweight framework that dynamically balances semantic and temporal information through a query-aware gating mechanism. On Re2Bench, Re3 achieves state-of-the-art results, leading in R@1 across all three subsets. Ablation studies with backbone sensitivity tests confirm robustness, showing strong generalization across diverse encoders and real-world settings. This work provides both a generalizable solution and a principled evaluation suite, advancing the development of temporally aware retrieval systems. Re3 and Re2Bench are available online: https://anonymous.4open.science/r/Re3-0C5A + oai:arXiv.org:2509.01306v2 + cs.IR + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiawei Cao, Jie Ouyang, Zhaomeng Zhou, Mingyue Cheng, Yupeng Li, Jiaxian Yan, Qi Liu + + + ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association + https://arxiv.org/abs/2509.01584 + arXiv:2509.01584v2 Announce Type: replace +Abstract: We present ViSTA-SLAM as a real-time monocular visual SLAM system that operates without requiring camera intrinsics, making it broadly applicable across diverse camera setups. At its core, the system employs a lightweight symmetric two-view association (STA) model as the frontend, which simultaneously estimates relative camera poses and regresses local pointmaps from only two RGB images. This design reduces model complexity significantly, the size of our frontend is only 35\% that of comparable state-of-the-art methods, while enhancing the quality of two-view constraints used in the pipeline. In the backend, we construct a specially designed Sim(3) pose graph that incorporates loop closures to address accumulated drift. Extensive experiments demonstrate that our approach achieves superior performance in both camera tracking and dense 3D reconstruction quality compared to current methods. Github repository: https://github.com/zhangganlin/vista-slam + oai:arXiv.org:2509.01584v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Ganlin Zhang, Shenhan Qian, Xi Wang, Daniel Cremers + + + Uncertainty-driven Adaptive Exploration + https://arxiv.org/abs/2509.03219 + arXiv:2509.03219v3 Announce Type: replace +Abstract: Adaptive exploration methods propose ways to learn complex policies via alternating between exploration and exploitation. An important question for such methods is to determine the appropriate moment to switch between exploration and exploitation and vice versa. This is critical in domains that require the learning of long and complex sequences of actions. In this work, we present a generic adaptive exploration framework that employs uncertainty to address this important issue in a principled manner. Our framework includes previous adaptive exploration approaches as special cases. Moreover, we can incorporate in our framework any uncertainty-measuring mechanism of choice, for instance mechanisms used in intrinsic motivation or epistemic uncertainty-based exploration methods. We experimentally demonstrate that our framework gives rise to adaptive exploration strategies that outperform standard ones across several environments. + oai:arXiv.org:2509.03219v3 + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Leonidas Bakopoulos, Georgios Chalkiadakis + + + A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai + https://arxiv.org/abs/2509.03830 + arXiv:2509.03830v2 Announce Type: replace +Abstract: Historic urban quarters play a vital role in preserving cultural heritage while serving as vibrant spaces for tourism and everyday life. Understanding how tourists perceive these environments is essential for sustainable, human-centered urban planning. This study proposes a multidimensional AI-powered framework for analyzing tourist perception in historic urban quarters using multimodal data from social media. Applied to twelve historic quarters in central Shanghai, the framework integrates focal point extraction, color theme analysis, and sentiment mining. Visual focus areas are identified from tourist-shared photos using a fine-tuned semantic segmentation model. To assess aesthetic preferences, dominant colors are extracted using a clustering method, and their spatial distribution across quarters is analyzed. Color themes are further compared between social media photos and real-world street views, revealing notable shifts. This divergence highlights potential gaps between visual expectations and the built environment, reflecting both stylistic preferences and perceptual bias. Tourist reviews are evaluated through a hybrid sentiment analysis approach combining a rule-based method and a multi-task BERT model. Satisfaction is assessed across four dimensions: tourist activities, built environment, service facilities, and business formats. The results reveal spatial variations in aesthetic appeal and emotional response. Rather than focusing on a single technical innovation, this framework offers an integrated, data-driven approach to decoding tourist perception and contributes to informed decision-making in tourism, heritage conservation, and the design of aesthetically engaging public spaces. + oai:arXiv.org:2509.03830v2 + cs.AI + cs.CV + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Kaizhen Tan, Yufan Wu, Yuxuan Liu, Haoran Zeng + + + IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation + https://arxiv.org/abs/2509.04398 + arXiv:2509.04398v3 Announce Type: replace +Abstract: Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, reduce adaptation cost by injecting low-rank updates into pretrained weights. However, LoRA's down-projection is randomly initialized and data-agnostic, discarding potentially useful information. Prior analyses show that this projection changes little during training, while the up-projection carries most of the adaptation, making the random input compression a performance bottleneck. We propose IPA, a feature-aware projection framework that explicitly aims to reconstruct the original input within a reduced hidden space. In the linear case, we instantiate IPA with algorithms approximating top principal components, enabling efficient projector pretraining with negligible inference overhead. Across language and vision benchmarks, IPA consistently improves over LoRA and DoRA, achieving on average 1.5 points higher accuracy on commonsense reasoning and 2.3 points on VTAB-1k, while matching full LoRA performance with roughly half the trainable parameters when the projection is frozen. Code available at https://github.com/valeoai/peft-ipa . + oai:arXiv.org:2509.04398v3 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuan Yin, Shashanka Venkataramanan, Tuan-Hung Vu, Andrei Bursuc, Matthieu Cord + + + Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies + https://arxiv.org/abs/2509.04458 + arXiv:2509.04458v2 Announce Type: replace +Abstract: Large language models often perform well on biomedical NLP tasks but may fail to link ontology terms to their correct identifiers. We investigate why these failures occur by analyzing predictions across two major ontologies, Human Phenotype Ontology and Gene Ontology, and two high-performing models, GPT-4o and LLaMa 3.1 405B. We evaluate nine candidate features related to term familiarity, identifier usage, morphology, and ontology structure. Univariate and multivariate analyses show that exposure to ontology identifiers is the strongest predictor of linking success. + oai:arXiv.org:2509.04458v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 2025 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Atlanta, GA, USA, 2025, pp. 1-7 + Daniel B. Hier, Steven Keith Platt, Tayo Obafemi-Ajayi + + + Benchmarking CNN and Transformer-Based Object Detectors for UAV Solar Panel Inspection + https://arxiv.org/abs/2509.05348 + arXiv:2509.05348v2 Announce Type: replace +Abstract: Timely and accurate detection of defects and contaminants in solar panels is critical for maintaining the efficiency and reliability of photovoltaic (PV) systems. While recent studies have applied deep learning to PV inspection, fair benchmarking across detector architectures and unbiased handling of class imbalance remain limited. This work presents a comprehensive benchmark of convolutional and transformer-based object detectors on UAV-captured RGB imagery of solar panels. It introduces a class-targeted augmentation strategy applied exclusively to the training split to mitigate imbalance without compromising evaluation integrity. Faster R-CNN with ResNet50 and MobileNetV3 backbones, RetinaNet with ResNet50, YOLOv5, YOLOv8, and Swin Transformer backbones integrated with Faster R-CNN (Tiny, Small, and Base variants) are evaluated. Performance is assessed using mean Average Precision (mAP) across multiple IoU thresholds, precision, recall, F1 score, and inference throughput to enable accuracy-throughput tradeoff analysis relevant to UAV deployment. Experimental results show that Faster R-CNN with a ResNet50 backbone achieves the highest localization accuracy, with mAP@0.5 of 0.893 and mAP@0.5:0.95 of 0.759, whereas the MobileNetV3 variant provides the best overall reliability balance, achieving recall of 0.745, F1-score of 0.809, and accuracy of 0.679 on the test set. The dataset and code will be released upon acceptance of the paper. + oai:arXiv.org:2509.05348v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ashen Rodrigo, Isuru Munasinghe, Pubudu Sanjeewani, Asanka Perera + + + Optimal Average Disk-Inspection via Fermat's Principle + https://arxiv.org/abs/2509.06334 + arXiv:2509.06334v3 Announce Type: replace +Abstract: This work resolves the optimal average-case cost of the Disk-Inspection problem, a variant of Bellman's 1955 lost-in-a-forest problem. In Disk-Inspection, a mobile agent starts at the center of a unit disk and follows a trajectory that inspects perimeter points whenever the disk does not obstruct visibility. The worst-case cost was solved optimally in 1957 by Isbell, but the average-case version remained open, with heuristic upper bounds proposed by Gluss in 1961 and improved only recently. + Our approach applies Fermat's Principle of Least Time to a recently proposed discretization framework, showing that optimal solutions are captured by a one-parameter family of recurrences independent of the discretization size. In the continuum limit these recurrences give rise to a single-parameter optimal control problem, whose trajectories coincide with limiting solutions of the original Disk-Inspection problem. A crucial step is proving that the optimal initial condition generates a trajectory that avoids the unit disk, thereby validating the optics formulation and reducing the many-variable optimization to a rigorous one-parameter problem. In particular, this disproves Gluss's conjecture that optimal trajectories must touch the disk. + Our analysis determines the exact optimal average-case inspection cost, equal to $3.549259\ldots$ and certified to at least six digits of accuracy. + oai:arXiv.org:2509.06334v3 + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Konstantinos Georgiou + + + Learning Optimal Defender Strategies for CAGE-2 using a POMDP Model + https://arxiv.org/abs/2509.06539 + arXiv:2509.06539v2 Announce Type: replace +Abstract: CAGE-2 is an accepted benchmark for learning and evaluating defender strategies against cyberattacks. It reflects a scenario where a defender agent protects an IT infrastructure against various attacks. Many defender methods for CAGE-2 have been proposed in the literature. In this paper, we construct a formal model for CAGE-2 using the framework of Partially Observable Markov Decision Process (POMDP). Based on this model, we define an optimal defender strategy for CAGE-2 and introduce a method to efficiently learn this strategy. Our method, called BF-PPO, is based on PPO, and it uses particle filter to mitigate the computational complexity due to the large state space of the CAGE-2 model. We evaluate our method in the CAGE-2 CybORG environment and compare its performance with that of CARDIFF, the highest ranked method on the CAGE-2 leaderboard. We find that our method outperforms CARDIFF regarding the learned defender strategy and the required training time. + oai:arXiv.org:2509.06539v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + 10.23919/CNSM67658.2025.11297482 + Duc Huy Le, Rolf Stadler + + + A Decade-long Landscape of Advanced Persistent Threats: Longitudinal Analysis and Global Trends + https://arxiv.org/abs/2509.07457 + arXiv:2509.07457v2 Announce Type: replace +Abstract: An advanced persistent threat (APT) refers to a covert, long-term cyberattack, typically conducted by state-sponsored actors, targeting critical sectors and often remaining undetected for long periods. In response, collective intelligence from around the globe collaborates to identify and trace surreptitious activities, generating substantial documentation on APT campaigns publicly available on the web. While prior works predominantly focus on specific aspects of APT cases, such as detection, evaluation, cyber threat intelligence, and dataset creation, limited attention has been devoted to revisiting and investigating these scattered dossiers in a longitudinal manner. The objective of our study is to fill the gap by offering a macro perspective, connecting key insights and global trends in past APT attacks. We systematically analyze six reliable sources-three focused on technical reports and another three on threat actors-examining 1,509 APT dossiers (24,215 pages) spanning 2014-2023, and identifying 603 unique APT groups worldwide. To efficiently unearth relevant information, we employ a hybrid methodology that combines rule-based information retrieval with large-language-model-based search techniques. Our longitudinal analysis reveals shifts in threat actor activities, global attack vectors, changes in targeted sectors, and relationships between cyberattacks and significant events such as elections or wars, which provide insights into historical patterns in APT evolution. Over the past decade, 154 countries have been affected, primarily using malicious documents and spear phishing as dominant initial infiltration vectors, with a noticeable decline in zero-day exploitation since 2016. Furthermore, we present our findings through interactive visualization tools, such as an APT map or flow diagram, to facilitate intuitive understanding of global patterns and trends in APT activities. + oai:arXiv.org:2509.07457v2 + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1145/3719027.3765085 + Shakhzod Yuldoshkhujaev (Sungkyunkwan University), Mijin Jeon (Sungkyunkwan University), Doowon Kim (University of Tennessee), Nick Nikiforakis (Stony Brook University), Hyungjoon Koo (Sungkyunkwan University) + + + EFPIX: A zero-trust encrypted flood protocol + https://arxiv.org/abs/2509.08248 + arXiv:2509.08248v3 Announce Type: replace +Abstract: We propose EFPIX (Encrypted Flood Protocol for Information eXchange), a flood-based relay communication protocol that achieves end-to-end encryption, plausible deniability for users, and untraceable messages while hiding metadata, such as sender and receiver, from those not involved. It also has built-in spam resistance and multiple optional enhancements. It can be used in privacy-critical communication, infrastructure-loss scenarios, space/research/military communication, where central servers are infeasible, or general-purpose messaging. + oai:arXiv.org:2509.08248v3 + cs.CR + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Arin Upadhyay + + + Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking + https://arxiv.org/abs/2509.09583 + arXiv:2509.09583v2 Announce Type: replace +Abstract: Social belonging is a vital part of learning, yet online course environments present barriers to the organic formation of social groups. SAMI (Social Agent Mediated Interactions) offers one solution by facilitating student connections, but its effectiveness may be constrained by an incomplete Theory of Mind, limiting its ability to create an effective 'mental model' of a student. One facet of this is its inability to intuit personality, which may influence the relevance of its recommendations. + To explore this gap, we examine the viability of automated personality inference by proposing a personality detection model utilizing GPT's zeroshot capability to infer Big-Five personality traits from forum introduction posts, often encouraged in online courses. We benchmark its performance against established models, finding that while GPT models show promising results on this specific dataset, performance varies significantly across traits. We identify potential biases toward optimistic trait inference, particularly for traits with skewed distributions. + We demonstrate a proof-of-concept integration of personality detection into SAMI's entity-based matchmaking system, focusing on three traits with established connections to positive social formation: Extroversion, Agreeableness, and Openness. This work represents an initial exploration of personality-informed social recommendations in educational settings. While our implementation shows technical feasibility, significant questions remain. We discuss these limitations and outline directions for future work, examining what LLMs specifically capture when performing personality inference and whether personality-based matching meaningfully improves student connections in practice. + oai:arXiv.org:2509.09583v2 + cs.CL + cs.CY + cs.HC + cs.LG + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Brittany Harbison, Samuel Taubman, Travis Taylor, Ashok. K. Goel + + + AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise + https://arxiv.org/abs/2509.10769 + arXiv:2509.10769v2 Announce Type: replace +Abstract: While individual components of agentic architectures have been studied in isolation, there remains limited empirical understanding of how different design dimensions interact within complex multi-agent systems. This study aims to address these gaps by providing a comprehensive enterprise-specific benchmark evaluating 18 distinct agentic configurations across state-of-the-art large language models. We examine four critical agentic system dimensions: orchestration strategy, agent prompt implementation (ReAct versus function calling), memory architecture, and thinking tool integration. Our benchmark reveals significant model-specific architectural preferences that challenge the prevalent one-size-fits-all paradigm in agentic AI systems. It also reveals significant weaknesses in overall agentic performance on enterprise tasks with the highest scoring models achieving a maximum of only 35.3\% success on the more complex task and 70.8\% on the simpler task. We hope these findings inform the design of future agentic systems by enabling more empirically backed decisions regarding architectural components and model selection. + oai:arXiv.org:2509.10769v2 + cs.AI + cs.CL + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Tara Bogavelli, Roshnee Sharma, Hari Subramani + + + Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data + https://arxiv.org/abs/2509.11078 + arXiv:2509.11078v2 Announce Type: replace +Abstract: Synthetic data generation with Large Language Models (LLMs) has emerged as a promising solution in the medical domain to mitigate data scarcity and privacy constraints. However, existing approaches remain constrained by their derivative nature, relying on real-world records, which pose privacy risks and distribution biases. Furthermore, current patient agents face the Stability-Plasticity Dilemma, struggling to maintain clinical consistency during dynamic inquiries. To address these challenges, we introduce Patient-Zero, a novel framework for ab initio patient simulation that requires no real medical records. Our Medically-Aligned Hierarchical Synthesis framework generates comprehensive and diverse patient records from abstract clinical guidelines via stratified attribute permutation. To support rigorous clinical interaction, we design a Dual-Track Cognitive Memory System to enable agents dynamically update memory while preserving logical consistency and persona adherence. Extensive evaluations show that Patient-Zero establishes a new state-of-the-art in both data quality and interaction fidelity. In human expert evaluations, senior licensed physicians judge our synthetic data to be statistically indistinguishable from real human-authored data and higher in clinical quality. Furthermore, downstream medical reasoning model trained on our synthetic dataset shows substantial performance gains (MedQA +24.0%; MMLU +14.5%), demonstrating the practical utility of our framework. + oai:arXiv.org:2509.11078v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yunghwei Lai, Ziyue Wang, Weizhi Ma, Yang Liu + + + OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning + https://arxiv.org/abs/2509.14803 + arXiv:2509.14803v3 Announce Type: replace +Abstract: In online learning environments, students often lack personalized peer interactions, which are crucial for cognitive development and learning engagement. Although previous studies have employed large language models (LLMs) to simulate interactive learning environments, these interactions are limited to conversational exchanges, failing to adapt to learners' individualized cognitive and psychological states. As a result, students' engagement is low and they struggle to gain inspiration. To address this challenge, we propose OnlineMate, a multi-agent learning companion system driven by LLMs integrated with Theory of Mind (ToM). OnlineMate simulates peer-like roles, infers learners' psychological states such as misunderstandings and confusion during collaborative discussions, and dynamically adjusts interaction strategies to support higher-order thinking. Comprehensive evaluations, including simulation-based experiments, human assessments, and real classroom trials, demonstrate that OnlineMate significantly promotes deep learning and cognitive engagement by elevating students' average cognitive level while substantially improving emotional engagement scores. + oai:arXiv.org:2509.14803v3 + cs.CY + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Xian Gao, Zongyun Zhang, Ting Liu, Yuzhuo Fu + + + Exploring How Audio Effects Alter Emotion with Foundation Models + https://arxiv.org/abs/2509.15151 + arXiv:2509.15151v3 Announce Type: replace +Abstract: Audio effects (FX) such as reverberation, distortion, modulation, and dynamic range processing play a pivotal role in shaping emotional responses during music listening. While prior studies have examined links between low-level audio features and affective perception, the systematic impact of audio FX on emotion remains underexplored. This work investigates how foundation models - large-scale neural architectures pretrained on multimodal data - can be leveraged to analyze these effects. Such models encode rich associations between musical structure, timbre, and affective meaning, offering a powerful framework for probing the emotional consequences of sound design techniques. By applying various probing methods to embeddings from deep learning models, we examine the complex, nonlinear relationships between audio FX and estimated emotion, uncovering patterns tied to specific effects and evaluating the robustness of foundation audio models. Our findings aim to advance understanding of the perceptual impact of audio production practices, with implications for music cognition, performance, and affective computing. + oai:arXiv.org:2509.15151v3 + cs.SD + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Stelios Katsis, Vassilis Lyberatos, Spyridon Kantarelis, Edmund Dervakos, Giorgos Stamou + + + FragmentRetro: A Quadratic Retrosynthetic Method Based on Fragmentation Algorithms + https://arxiv.org/abs/2509.15409 + arXiv:2509.15409v2 Announce Type: replace +Abstract: Retrosynthesis, the process of deconstructing a target molecule into simpler precursors, is crucial for computer-aided synthesis planning (CASP). Widely adopted tree-search methods often suffer from exponential computational complexity. In this work, we introduce FragmentRetro, a novel retrosynthetic method that leverages fragmentation algorithms, specifically BRICS and r-BRICS, combined with stock-aware exploration and pattern fingerprint screening to achieve quadratic complexity. FragmentRetro recursively combines molecular fragments and verifies their presence in a building block set, providing sets of fragment combinations as retrosynthetic solutions. We present the first formal computational analysis of retrosynthetic methods, showing that tree search exhibits exponential complexity $O(b^h)$, DirectMultiStep scales as $O(h^6)$, and FragmentRetro achieves $O(h^2)$, where $h$ represents the number of heavy atoms in the target molecule and $b$ is the branching factor for tree search. Evaluations on PaRoutes, USPTO-190, and natural products demonstrate that FragmentRetro achieves high solved rates with competitive runtime, including cases where tree search fails. The method benefits from fingerprint screening, which significantly reduces substructure matching complexity. While FragmentRetro focuses on efficiently identifying fragment-based solutions rather than full reaction pathways, its computational advantages and ability to generate strategic starting candidates establish it as a powerful foundational component for scalable and automated synthesis planning. + oai:arXiv.org:2509.15409v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1021/acs.jctc.5c01632 + Yu Shee, Anthony M. Smaldone, Anton Morgunov, Gregory W. Kyro, Victor S. Batista + + + ISCS: Parameter-Guided Feature Pruning for Resource-Constrained Embodied Perception + https://arxiv.org/abs/2509.16853 + arXiv:2509.16853v2 Announce Type: replace +Abstract: Prior studies in embodied AI consistently show that robust perception is critical for human-robot interaction, yet deploying high-fidelity visual models on resource-constrained agents remains challenging due to limited on-device computation power and transmission latency. Exploiting the redundancy in latent representations could improve system efficiency, yet existing approaches often rely on costly dataset-specific ablation tests or heavy entropy models unsuitable for real-time edge-robot collaboration. + We propose a generalizable, dataset-agnostic method to identify and selectively transmit structure-critical channels in pretrained encoders. Instead of brute-force empirical evaluations, our approach leverages intrinsic parameter statistics-weight variances and biases-to estimate channel importance. This analysis reveals a consistent organizational structure, termed the Invariant Salient Channel Space (ISCS), where Salient-Core channels capture dominant structures while Salient-Auxiliary channels encode fine visual details. Building on ISCS, we introduce a deterministic static pruning strategy that enables lightweight split-computing. + Experiments across different datasets demonstrate that our method achieves a deterministic, ultra-low latency pipeline by bypassing heavy entropy modeling. Our method reduces end-to-end latency, providing a critical speed-accuracy trade-off for resource-constrained human-aware embodied systems. + oai:arXiv.org:2509.16853v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jinhao Wang, Nam Ling, Wei Wang, Wei Jiang + + + LLMs as Layout Designers: Enhanced Spatial Reasoning for Content-Aware Layout Generation + https://arxiv.org/abs/2509.16891 + arXiv:2509.16891v3 Announce Type: replace +Abstract: While Large Language Models (LLMs) have demonstrated impressive reasoning and planning abilities in textual domains and can effectively follow instructions for complex tasks, their ability to understand and manipulate spatial relationships remains limited. Such capabilities are crucial for content-aware graphic layout design, where the goal is to arrange heterogeneous elements onto a canvas so that final design remains visually balanced and structurally feasible. This problem requires precise coordination of placement, alignment, and structural organization of multiple elements within a constrained visual space. To address this limitation, we introduce LaySPA, a reinforcement learning-based framework that augments LLM-based agents with explicit spatial reasoning capabilities for layout design. LaySPA employs hybrid reward signals that jointly capture geometric constraints, structural fidelity, and visual quality, enabling agents to navigate the canvas, model inter-element relationships, and optimize spatial arrangements. Through group-relative policy optimization, the agent generates content-aware layouts that reflect salient regions, respect spatial constraints, and produces an interpretable reasoning trace explaining placement decisions and a structured layout specification. Experimental results show that LaySPA substantially improves the generation of structurally valid and visually appealing layouts, outperforming larger general-purpose LLMs and achieving performance comparable to state-of-the-art specialized layout models. + oai:arXiv.org:2509.16891v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sha Li, Stefano Petrangeli, Yu Shen, Xiang Chen, Naren Ramakrishnan + + + Evolutionary Learning in Spatial Agent-Based Models for Physical Climate Risk Assessment + https://arxiv.org/abs/2509.18633 + arXiv:2509.18633v3 Announce Type: replace +Abstract: Climate risk assessment requires modelling complex interactions between spatially heterogeneous hazards and adaptive economic systems. We present a novel geospatial agent-based model that integrates climate hazard data with evolutionary learning for economic agents. Our framework combines geospatial agent-based modelling with asset-level damage functions, featuring an illustrative three-sector economy (commodity, manufacturing, retail) with adaptive learning behaviours that allow firms to evolve strategies for budget allocation, pricing, wages, and risk adaptation through fitness-based selection and mutation. We demonstrate the framework using riverine flood projections under RCP8.5 until 2100, comparing four scenarios: baseline and hazard conditions with and without evolutionary learning. Our results show that increasingly frequent and intense acute hazards lower firm production levels, liquidity, and capital, while increasing the prices of goods and unemployment. The framework reveals systemic risks where even agents not directly exposed to floods face impacts through supply chain disruptions. Importantly, evolutionary adaptation enables firms to maintain higher production, capital, liquidity, wages and employment levels while keeping prices lower compared to non-learning counterparts. This open-source framework provides financial institutions and companies with tools to quantify both direct and cascading climate risks while evaluating cost-effective adaptation strategies. + oai:arXiv.org:2509.18633v3 + cs.AI + q-fin.RM + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Yara Mohajerani + + + Consistency-Aware Parameter-Preserving Knowledge Editing Framework for Multi-Hop Question Answering + https://arxiv.org/abs/2509.18655 + arXiv:2509.18655v2 Announce Type: replace +Abstract: Parameter-Preserving Knowledge Editing (PPKE) enables updating models with new information without retraining or parameter adjustment. Recent PPKE approaches used knowledge graphs (KG) to extend knowledge editing (KE) capabilities to multi-hop question answering (MHQA). However, these methods often lack consistency, leading to knowledge contamination, unstable updates, and retrieval behaviors that are misaligned with the intended edits. Such inconsistencies undermine the reliability of PPKE in multi-hop reasoning. We present CAPE-KG, Consistency-Aware Parameter-Preserving Editing with Knowledge Graphs, a novel consistency-aware framework for PPKE on MHQA. CAPE-KG ensures KG construction, update, and retrieval are always aligned with the requirements of the MHQA task, maintaining coherent reasoning over both unedited and edited knowledge. Extensive experiments on the MQuAKE benchmark show accuracy improvements in PPKE performance for MHQA, demonstrating the effectiveness of addressing consistency in PPKE. + oai:arXiv.org:2509.18655v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Lingwen Deng, Yifei Han, Shijie Li, Yue Du, Bin Li + + + ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression + https://arxiv.org/abs/2509.20234 + arXiv:2509.20234v4 Announce Type: replace +Abstract: The hypothesis that Convolutional Neural Networks (CNNs) are inherently texture-biased has shaped much of the discourse on feature use in deep learning. We revisit this hypothesis by examining limitations in the cue-conflict experiment by Geirhos et al. To address these limitations, we propose a domain-agnostic framework that quantifies feature reliance through systematic suppression of shape, texture, and color cues, avoiding the confounds of forced-choice conflicts. By evaluating humans and neural networks under controlled suppression conditions, we find that CNNs are not inherently texture-biased but predominantly rely on local shape features. Nonetheless, this reliance can be substantially mitigated through modern training strategies or architectures (ConvNeXt, ViTs). We further extend the analysis across computer vision, medical imaging, and remote sensing, revealing that reliance patterns differ systematically: computer vision models prioritize shape, medical imaging models emphasize color, and remote sensing models exhibit a stronger reliance on texture. Code is available at https://github.com/tomburgert/feature-reliance. + oai:arXiv.org:2509.20234v4 + cs.CV + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tom Burgert, Oliver Stoll, Paolo Rota, Beg\"um Demir + + + Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms + https://arxiv.org/abs/2509.20278 + arXiv:2509.20278v3 Announce Type: replace +Abstract: Large Language Models (LLMs) annotated datasets are widely used nowadays, however, large-scale annotations often show biases in low-quality datasets. For example, Multiple-Choice Questions (MCQs) datasets with one single correct option is common, however, there may be questions attributed to none or multiple correct options; whereas true-or-false questions are supposed to be labeled with either True or False, but similarly the text can include unsolvable elements, which should be further labeled as Unknown. There are problems when low-quality datasets with mixed question forms can not be identified. We refer to these exceptional label forms as Sparse Labels, and LLMs' ability to distinguish datasets with Sparse Labels mixture is important. Since users may not know situations of datasets, their instructions can be biased. To study how different instruction settings affect LLMs' identifications of Sparse Labels mixture, we introduce the concept of Instruction Boundary, which systematically evaluates different instruction settings that lead to biases. We propose BiasDetector, a diagnostic benchmark to systematically evaluate LLMs on datasets with mixed question forms under Instruction Boundary settings. Experiments show that users' instructions induce large biases on our benchmark, highlighting the need not only for LLM developers to recognize risks of LLM biased annotation resulting in Sparse Labels mixture, but also problems arising from users' instructions to identify them. Code, datasets and detailed implementations are available at https://github.com/ZpLing/Instruction-Boundary. + oai:arXiv.org:2509.20278v3 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zipeng Ling, Shuliang Liu, Yuehao Tang, Chen Huang, Gaoyang Jiang, Shenghong Fu, Junqi Yang, Yao Wan, Jiawan Zhang, Kejia Huang, Xuming Hu + + + CaTS-Bench: Can Language Models Describe Time Series? + https://arxiv.org/abs/2509.20823 + arXiv:2509.20823v2 Announce Type: replace +Abstract: Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation, and contextual understanding. Existing benchmarks, however, often rely on fully synthetic or generic captions, and typically neglect metadata and visual representations. We introduce \textbf{CaTS-Bench}, a comprehensive benchmark for \textbf{C}ontext-\textbf{a}ware \textbf{T}ime \textbf{S}eries reasoning across $11$ diverse domains, centered on a gold-standard evaluation set of $1746$ human-rewritten captions that measure how effectively models translate numeric trends into immediately interpretable narratives. To address the scarcity of human-annotated data, we also propose a scalable pipeline for generating high-fidelity synthetic captions, the quality of which we validate. We evaluate leading Vision-Language Models on our benchmark, revealing that even proprietary models struggle to capture numeric nuances in temporal descriptions, while finetuning open-source models on synthetic data yields substantial performance gains. Finally, release a diagnostic suite of $910$ multiple-choice questions and tailored numeric metrics to gauge time-series-specific reasoning capabilities, establishing CaTS-Bench as a reliable foundation for grounded, multimodal language generation in numeric domains. + oai:arXiv.org:2509.20823v2 + cs.LG + cs.AI + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Luca Zhou, Pratham Yashwante, Marshall Fisher, Alessio Sampieri, Zihao Zhou, Fabio Galasso, Rose Yu + + + Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval + https://arxiv.org/abs/2509.21710 + arXiv:2509.21710v2 Announce Type: replace +Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) has become the important paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing approaches are constrained by their reliance on high-quality knowledge graphs: manually built ones are not scalable, while automatically extracted ones are limited by the performance of LLM extractors, especially when using smaller, local-deployed models. To address this, we introduce Think-on-Graph 3.0 (ToG-3), a novel framework featuring a Multi-Agent Context Evolution and Retrieval (MACER) mechanism. Its core contribution is the dynamic construction and iterative refinement of a Chunk-Triplets-Community heterogeneous graph index, powered by a Dual-Evolution process that adaptively evolves both the query and the retrieved sub-graph during reasoning. ToG-3 dynamically builds a targeted graph index tailored to the query, enabling precise evidence retrieval and reasoning even with lightweight LLMs. Extensive experiments demonstrate that ToG-3 outperforms compared baselines on both deep and broad reasoning benchmarks, and ablation studies confirm the efficacy of the components of MACER framework. The source code are available in https://github.com/DataArcTech/ToG-3. + oai:arXiv.org:2509.21710v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Xiaojun Wu, Cehao Yang, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Yuanliang Sun, Hui Xiong, Jia Li, Jian Guo + + + D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents + https://arxiv.org/abs/2509.21799 + arXiv:2509.21799v2 Announce Type: replace +Abstract: Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by several critical challenges: data bottleneck in end-to-end training, high cost of delayed error detection, and risk of contradictory guidance. Inspired by the human cognitive loop of Thinking, Alignment, and Reflection, we present D-Artemis -- a novel deliberative framework in this paper. D-Artemis leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process. It also employs a proactive Pre-execution Alignment stage, where Thought-Action Consistency (TAC) Check module and Action Correction Agent (ACA) work in concert to mitigate the risk of execution failures. A post-execution Status Reflection Agent (SRA) completes the cognitive loop, enabling strategic learning from experience. Crucially, D-Artemis enhances the capabilities of general-purpose Multimodal large language models (MLLMs) for GUI tasks without the need for training on complex trajectory datasets, demonstrating strong generalization. D-Artemis establishes new state-of-the-art (SOTA) results across both major benchmarks, achieving a 75.8% success rate on AndroidWorld and 96.8% on ScreenSpot-V2. Extensive ablation studies further demonstrate the significant contribution of each component to the framework. + oai:arXiv.org:2509.21799v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Hongze Mi, Yibo Feng, Wenjie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Naiqiang Tan, Gang Pan + + + CMDAR: A Chinese Multi-scene Dynamic Audio Reasoning Benchmark with Diverse Challenges + https://arxiv.org/abs/2509.22461 + arXiv:2509.22461v3 Announce Type: replace +Abstract: The ability to reason from audio, including speech, environmental sounds, and music, is essential for AI agents to interact effectively in real-world scenarios. Existing benchmarks mainly focus on static or single-scene settings and English audio data and do not fully capture scenarios where multiple speakers, unfolding events, and heterogeneous audio sources interact. To address these challenges, we introduce CMDAR, a Chinese benchmark for evaluating models on complex, multi-scene, and dynamically evolving audio reasoning tasks. CMDAR comprises 3,000 carefully curated question-answer pairs linked to diverse audio clips, covering five categories of complex reasoning and spanning three question types. We benchmark 26 state-of-the-art audio language models on CMDAR and observe that they exhibit limitations in complex reasoning tasks. In CMDAR-main, Qwen2.5-Omni achieves 76.67% accuracy, whereas GPT-4o Audio reaches 68.47%. However, GPT-4o Audio substantially outperforms Qwen2.5-Omni on the more challenging multiple-choice with multiple audios and open-ended tasks. And we provide detail analysis corresponding suggestions for the future development of large audio language models. + oai:arXiv.org:2509.22461v3 + cs.SD + cs.AI + cs.CL + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hui Li, Changhao Jiang, Hongyu Wang, Ming Zhang, Jiajun Sun, Zhixiong Yang, Yifei Cao, Shihan Dou, Xiaoran Fan, Baoyu Fan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang + + + MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference + https://arxiv.org/abs/2509.22750 + arXiv:2509.22750v2 Announce Type: replace +Abstract: Real-world multi-hop QA is naturally linked with ambiguity, where a single query can trigger multiple reasoning paths that require independent resolution. Since ambiguity can occur at any stage, models must navigate layered uncertainty throughout the entire reasoning chain. Despite its prevalence in real-world user queries, previous benchmarks have primarily focused on single-hop ambiguity, leaving the complex interaction between multi-step inference and layered ambiguity underexplored. In this paper, we introduce \textbf{MARCH}, a benchmark for their intersection, with 2,209 multi-hop ambiguous questions curated via multi-LLM verification and validated by human annotation with strong agreement. Our experiments reveal that even state-of-the-art models struggle with MARCH, confirming that combining ambiguity resolution with multi-step reasoning is a significant challenge. To address this, we propose \textbf{CLARION}, a two-stage agentic framework that explicitly decouples ambiguity planning from evidence-driven reasoning, significantly outperforms existing approaches, and paves the way for robust reasoning systems. + oai:arXiv.org:2509.22750v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jeonghyun Park, Ingeol Baek, Seunghyun Yoon, Haeun Jang, Aparna Garimella, Akriti Jain, Nedim Lipka, Hwanhee Lee + + + Gradient Coupling: The Hidden Barrier to Generalization in Agentic Reinforcement Learning + https://arxiv.org/abs/2509.23870 + arXiv:2509.23870v3 Announce Type: replace +Abstract: Reinforcement learning (RL) is a dominant paradigm for training autonomous agents, yet these agents often exhibit poor generalization, failing to adapt to scenarios not seen during training. In this work, we identify a fundamental cause of this brittleness, a phenomenon which we term "gradient coupling." We hypothesize that in complex agentic tasks, the high similarity between distinct states leads to destructive interference between gradients. Specifically, a gradient update that reinforces an optimal action in one state can inadvertently increase the likelihood of a suboptimal action in a similar, yet different, state. To solve this, we propose a novel objective where the actor is trained to simultaneously function as a classifier that separates good and bad actions. This auxiliary pressure compels the model to learn disentangled embeddings for positive and negative actions, which mitigates negative gradient interference and improve the generalization performance. Extensive experiments demonstrate the effectiveness of our method. + oai:arXiv.org:2509.23870v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jingyu Liu, Xiaopeng Wu, Jingquan Peng, Kehan Chen, Chuan Yu, Lizhong Ding, Yong Liu + + + Go with Your Gut: Scaling Confidence for Autoregressive Image Generation + https://arxiv.org/abs/2509.26376 + arXiv:2509.26376v2 Announce Type: replace +Abstract: Test-time scaling (TTS) has demonstrated remarkable success in enhancing large language models, yet its application to next-token prediction (NTP) autoregressive (AR) image generation remains largely uncharted. Existing TTS approaches for visual AR (VAR), which rely on frequent partial decoding and external reward models, are ill-suited for NTP-based image generation due to the inherent incompleteness of intermediate decoding results. To bridge this gap, we introduce ScalingAR, the first TTS framework specifically designed for NTP-based AR image generation that eliminates the need for early decoding or auxiliary rewards. ScalingAR leverages token entropy as a novel signal in visual token generation and operates at two complementary scaling levels: (i) Profile Level, which streams a calibrated confidence state by fusing intrinsic and conditional signals; and (ii) Policy Level, which utilizes this state to adaptively terminate low-confidence trajectories and dynamically schedule guidance for phase-appropriate conditioning strength. Experiments on both general and compositional benchmarks show that ScalingAR (1) improves base models by 12.5% on GenEval and 15.2% on TIIF-Bench, (2) efficiently reduces visual token consumption by 62.0% while outperforming baselines, and (3) successfully enhances robustness, mitigating performance drops by 26.0% in challenging scenarios. + oai:arXiv.org:2509.26376v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Harold Haodong Chen, Xianfeng Wu, Wen-Jie Shu, Rongjin Guo, Disen Lan, Harry Yang, Ying-Cong Chen + + + Framing Unionization on Facebook: Communication around Representation Elections in the United States + https://arxiv.org/abs/2510.01757 + arXiv:2510.01757v2 Announce Type: replace +Abstract: Digital media have become central to how labor unions communicate, organize, and sustain collective action. Yet little is known about how unions' online discourse relates to concrete outcomes such as representation elections. This study addresses the gap by combining National Labor Relations Board (NLRB) election data with 158k Facebook posts published by U.S. labor unions between 2015 and 2024. We focused on five discourse frames widely recognized in labor and social movement communication research: diagnostic (identifying problems), prognostic (proposing solutions), motivational (mobilizing action), community (emphasizing solidarity), and engagement (promoting social media interaction). Using a fine-tuned RoBERTa classifier, we systematically annotated unions' posts and analyzed patterns of frame usage around election events. Our findings showed that diagnostic and community frames dominated union communication overall, but that frame usage varied substantially across organizations. Greater use of diagnostic, prognostic, and community frames prior to an election was associated with higher odds of a successful outcome. After elections, framing patterns diverged depending on results: after wins, the use of prognostic and motivational frames decreased, whereas after losses, the use of prognostic and engagement frames increased. By examining variation in message-level framing, the study highlights how communication strategies correlate with organizational success, contributing open tools and data, and complementing prior research in understanding digital communication of unions and social movements. + oai:arXiv.org:2510.01757v2 + cs.CY + cs.SI + physics.soc-ph + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Arianna Pera, Veronica Jude, Ceren Budak, Luca Maria Aiello + + + Universal Dynamic Regret and Constraint Violation Bounds for Constrained Online Convex Optimization + https://arxiv.org/abs/2510.01867 + arXiv:2510.01867v2 Announce Type: replace +Abstract: We consider a generalization of the celebrated Online Convex Optimization (OCO) framework with adversarial online constraints. In this problem, an online learner interacts with an adversary sequentially over multiple rounds. At the beginning of each round, the learner chooses an action from a convex decision set. After that, the adversary reveals a convex cost function and a convex constraint function. The goal of the learner is to minimize the cumulative cost while satisfying the constraints as tightly as possible. We present two efficient algorithms with simple modular structures that give universal dynamic regret and cumulative constraint violation bounds, improving upon state-of-the-art results. While the first algorithm, which achieves the optimal regret bound, involves projection onto the constraint sets, the second algorithm is projection-free and achieves better violation bounds in rapidly varying environments. Our results hold in the most general case when both the cost and constraint functions are chosen arbitrarily, and the constraint functions need not contain any fixed common feasible point. We establish these results by introducing a general framework that reduces the constrained learning problem to an instance of the standard OCO problem with specially constructed surrogate cost functions. + oai:arXiv.org:2510.01867v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Subhamon Supantha, Abhishek Sinha + + + Style over Story: Measuring LLM Narrative Preferences via Structured Selection + https://arxiv.org/abs/2510.02025 + arXiv:2510.02025v3 Announce Type: replace +Abstract: We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers an interpretable lens on LLMs' narrative behavior. We developed a library of 200 narratology-grounded constraints and prompted selections from six LLMs under three different instruction types: basic, quality-focused, and creativity-focused. Findings demonstrate that models consistently prioritize Style over narrative content elements like Event, Character, and Setting. Style preferences remain stable across models and instruction types, whereas content elements show cross-model divergence and instructional sensitivity. These results suggest that LLMs have latent narrative preferences, which should inform how the NLP community evaluates and deploys models in creative domains. + oai:arXiv.org:2510.02025v3 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Donghoon Jung, Jiwoo Choi, Songeun Chae, Seohyon Jung + + + Agentic Additive Manufacturing Alloy Evaluation + https://arxiv.org/abs/2510.02567 + arXiv:2510.02567v3 Announce Type: replace +Abstract: Agentic systems enable the intelligent use of research tooling, augmenting a researcher's ability to investigate and propose novel solutions to existing problems. Within Additive Manufacturing (AM), alloy selection and evaluation remains a complex challenge, often requiring expertise in the various domains of materials science, thermodynamic simulations, and experimental analysis. Large Language Model (LLM) enabled agents can facilitate this endeavor by utilizing their extensive knowledge base to dispatch tool calls via Model Context Protocol (MCP) to perform actions such as thermophysical property diagram calculations and lack of fusion process map generation. In addition, the multi-agent system can effectively reason through complex user prompts and provide analysis on the lack of fusion process window of common alloys such as SS316L and IN718 along with proposed composition variants of known alloys. These agents can dynamically adjust their task trajectory to the outcomes of tool call results, effectively enabling autonomous decision-making in practical environments. This work aims to showcase the benefits of adopting a LLM enabled multi-agent system to automate and accelerate the task of evaluating proposed additive manufacturing alloys, both novel and known. + oai:arXiv.org:2510.02567v3 + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Peter Pak, Achuth Chandrasekhar, Amir Barati Farimani + + + The Artificial Intelligence Cognitive Examination: A Survey on the Evolution of Multimodal Evaluation from Recognition to Reasoning + https://arxiv.org/abs/2510.04141 + arXiv:2510.04141v2 Announce Type: replace +Abstract: This survey paper chronicles the evolution of evaluation in multimodal artificial intelligence (AI), framing it as a progression of increasingly sophisticated "cognitive examinations." We argue that the field is undergoing a paradigm shift, moving from simple recognition tasks that test "what" a model sees, to complex reasoning benchmarks that probe "why" and "how" it understands. This evolution is driven by the saturation of older benchmarks, where high performance often masks fundamental weaknesses. We chart the journey from the foundational "knowledge tests" of the ImageNet era to the "applied logic and comprehension" exams such as GQA and Visual Commonsense Reasoning (VCR), which were designed specifically to diagnose systemic flaws such as shortcut learning and failures in compositional generalization. We then survey the current frontier of "expert-level integration" benchmarks (e.g., MMBench, SEED-Bench, MMMU) designed for today's powerful multimodal large language models (MLLMs), which increasingly evaluate the reasoning process itself. Finally, we explore the uncharted territories of evaluating abstract, creative, and social intelligence. We conclude that the narrative of AI evaluation is not merely a history of datasets, but a continuous, adversarial process of designing better examinations that, in turn, redefine our goals for creating truly intelligent systems. + oai:arXiv.org:2510.04141v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1109/ACCESS.2025.3649182 + IEEE Access, vol. 14, Dec. 2025, Art. no. 3649182 + Mayank Ravishankara, Varindra V. Persad Maharaj + + + Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification + https://arxiv.org/abs/2510.05431 + arXiv:2510.05431v3 Announce Type: replace +Abstract: Large language models (LLMs) increasingly generate natural language rationales to enhance interpretability, but these often contain logical errors, label mismatches, and domain-specific misalignments. Directly using such rationales as supervision risks propagating noise and undermining training stability. To address this challenge, we introduce Self-Filtered Distillation, a framework tailored for patent classification that treats LLM-generated rationales as trust signals rather than ground-truth supervision. The framework employs selective distillation guided by three unsupervised trust metrics: (1) Self-Consistency, which measures the stability of LLM-generated rationales across multiple generations; (2) Class Entailment Alignment, which assesses semantic coherence with patent-specific class definitions; and (3) LLM Agreement Scoring, which validates rationale-label plausibility. These metrics are integrated into a unified trust score that primarily weights training samples while optionally filtering out extremely low-trust cases, enabling reasoning-aware supervision. Experiments on the USPTO-2M dataset show that our method consistently outperforms label-based learning and conventional distillation in accuracy, stability, and interpretability across diverse student architectures, establishing a reliable paradigm for leveraging reasoning-aware trust indicators in patent analytics. + oai:arXiv.org:2510.05431v3 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yongmin Yoo, Xu Zhang, Longbing Cao + + + When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning + https://arxiv.org/abs/2510.07517 + arXiv:2510.07517v3 Announce Type: replace +Abstract: Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reveal that agents are not neutral: they are prone to identity-driven sycophancy and self-bias, uncritically adopting a peer's view or stubbornly adhering to their own prior output, undermining the reliability of debate. In this work, we present the first principled framework that joins sycophancy and self-bias to mitigate and quantify identity bias in MAD. First, we formalize the debate dynamics as an identity-weighted Bayesian update process. Second, we propose response anonymization: by removing identity markers from prompts, agents cannot distinguish "self" from "peer", which forces equal weights on agent identity, thereby reducing bias and improving trustworthiness. Third, we define the Identity Bias Coefficient (IBC), a principled bias metric that measures an agent's tendency to follow its peer versus itself. Empirical studies across multiple models and benchmarks confirm that identity bias is widespread, with sycophancy far more common than self-bias. Our findings highlight the need to ensure that MAD systems reason based on content rather than identity. Code is released in https://github.com/deeplearning-wisc/MAD-identity-bias. + oai:arXiv.org:2510.07517v3 + cs.AI + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li + + + SyncLipMAE: Contrastive Masked Pretraining for Audio-Visual Talking-Face Representation + https://arxiv.org/abs/2510.10069 + arXiv:2510.10069v2 Announce Type: replace +Abstract: We introduce SyncLipMAE, a self-supervised pretraining framework for talking-face video that learns synchronization-aware and transferable facial dynamics from unlabeled audio-visual streams. Our approach couples masked visual modeling with cross-modal contrastive alignment and employs three per-frame prompt tokens that explicitly encode the essential factors of a talking-face frame - identity, vocal motion (speech-synchronized facial dynamics), and ambient motion (audio-agnostic movements such as blinks and head pose). The contrastive objective uses time-aligned vocal-motion and audio tokens as positives and misaligned pairs as negatives, driving both modalities into a shared embedding space and yielding token-level audio-visual stream synchronization. After pretraining, the aligned audio tokens together with the visual prompt tokens (identity, vocal motion, ambient motion) form a unified interface for four disparate downstream settings: (i) audio-visual stream synchronization; (ii) facial emotion and head/face action recognition; (iii) visual speech recognition; and (iv) visual dubbing, for which we enable indistinguishable audio- or video-driven control within a single model. Across four task families that require distinct capabilities, SyncLipMAE achieves state-of-the-art results, underscoring the effectiveness of synchronization-aware, factorized self-supervised pretraining. + oai:arXiv.org:2510.10069v2 + cs.AI + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Zeyu Ling, Xiaodong Gu, Jiangnan Tang, Changqing Zou + + + What Makes Looped Transformers Perform Better Than Non-Recursive Ones + https://arxiv.org/abs/2510.10089 + arXiv:2510.10089v3 Announce Type: replace +Abstract: While looped transformers (termed as Looped-Attn) often outperform standard transformers (termed as Single-Attn) on complex reasoning tasks, the mechanism for this advantage remains underexplored. In this paper, we explain this phenomenon through the lens of loss landscape geometry, inspired by empirical observations of their distinct dynamics at both sample and Hessian levels. To formalize this, we extend the River-Valley landscape model by distinguishing between U-shaped valleys (flat) and V-shaped valleys (steep). Based on empirical observations, we conjecture that the recursive architecture of Looped-Attn induces a landscape-level inductive bias towards River-V-Valley. This inductive bias suggest a better loss convergence along the river due to valley hopping, and further encourage learning about complex patterns compared to the River-U-Valley induced by Single-Attn. Building on this insight, we propose SHIFT (Staged HIerarchical Framework for Progressive Training), a principled training strategy that accelerates the training process of Looped-Attn while achieving comparable performances. + oai:arXiv.org:2510.10089v3 + cs.LG + cs.AI + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zixuan Gong, Yong Liu, Jiaye Teng + + + Do You Get the Hint? Benchmarking LLMs on the Board Game Concept + https://arxiv.org/abs/2510.13271 + arXiv:2510.13271v2 Announce Type: replace +Abstract: Large language models (LLMs) have achieved striking successes on many benchmarks, yet recent studies continue to expose fundamental weaknesses. In this paper, we introduce Concept, a simple word-guessing board game, as a benchmark for probing abductive reasoning. Our results show that this game, easily solved by humans (with a success rate of over 90\%), is still very challenging for state-of-the-art LLMs (no model exceeds 40\% success rate). Specifically, we observe that LLMs struggle with interpreting other players' strategic intents, and with correcting initial hypotheses given sequential information updates. In addition, we extend the evaluation across multiple languages, and find that the LLM performance drops further in lower-resource languages (Dutch, French, and Spanish) compared to English. + oai:arXiv.org:2510.13271v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Ine Gevers, Walter Daelemans + + + Exploratory Causal Inference in SAEnce + https://arxiv.org/abs/2510.14073 + arXiv:2510.14073v2 Announce Type: replace +Abstract: Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect estimation at scale, potentially anchoring on popular yet incomplete hypotheses. We propose to discover the unknown effects of a treatment directly from data. For this, we turn unstructured data from a trial into meaningful representations via pretrained foundation models and interpret them via a sparse autoencoder. However, discovering significant causal effects at the neural level is not trivial due to multiple-testing issues and effects entanglement. To address these challenges, we introduce Neural Effect Search, a novel recursive procedure solving both issues by progressive stratification. After assessing the robustness of our algorithm on semi-synthetic experiments, we showcase, in the context of experimental ecology, the first successful unsupervised causal effect identification on a real-world scientific trial. + oai:arXiv.org:2510.14073v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Tommaso Mencattini, Riccardo Cadei, Francesco Locatello + + + CodeEvolve: an open source evolutionary coding agent for algorithm discovery and optimization + https://arxiv.org/abs/2510.14150 + arXiv:2510.14150v3 Announce Type: replace +Abstract: We introduce CodeEvolve, an open-source framework that combines large language models (LLMs) with evolutionary search to synthesize high-performing algorithmic solutions. CodeEvolve couples an islands-based genetic algorithm with modular LLM orchestration, using execution feedback and task-specific metrics to guide selection and variation. Exploration and exploitation are balanced through context-aware recombination, adaptive meta-prompting, and targeted refinement of promising solutions. We evaluate CodeEvolve on benchmarks previously used to assess Google DeepMind's AlphaEvolve, showing superior performance on several tasks and competitive results overall. Notably, open-weight models often match or exceed closed-source baselines at a fraction of the compute cost. We provide extensive ablations analyzing the contribution of each component and release our framework and experimental results at https://github.com/inter-co/science-codeevolve. + oai:arXiv.org:2510.14150v3 + cs.AI + cs.LG + cs.NE + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Henrique Assump\c{c}\~ao, Diego Ferreira, Leandro Campos, Fabricio Murai + + + Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising + https://arxiv.org/abs/2510.15125 + arXiv:2510.15125v2 Announce Type: replace +Abstract: Social media platforms play a pivotal role in shaping political discourse, but analyzing their vast and rapidly evolving content remains a major challenge. We introduce an end-to-end framework for automatically inducing an interpretable topic taxonomy from unlabeled text corpora. By combining unsupervised clustering with prompt-based inference, our method leverages large language models (LLMs) to iteratively construct a taxonomy without requiring seed sets (predefined labels) or domain expertise. We validate the framework through a study of political advertising ahead of the 2024 U.S. presidential election. The induced taxonomy yields semantically rich topic labels and supports downstream analyses, including moral framing, in this setting. Results suggest that structured, iterative labeling yields more consistent and interpretable topic labels than existing approaches under human evaluation, and is practical for analyzing large-scale political advertising data. + oai:arXiv.org:2510.15125v2 + cs.CL + cs.AI + cs.CY + cs.LG + cs.SI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Alexander Brady, Tunazzina Islam + + + ELMM: Efficient Lightweight Multimodal Large Language Models for Multimodal Knowledge Graph Completion + https://arxiv.org/abs/2510.16753 + arXiv:2510.16753v2 Announce Type: replace +Abstract: Multimodal Knowledge Graphs (MKGs) extend traditional knowledge graphs by incorporating visual and textual modalities, enabling richer and more expressive entity representations. However, existing MKGs often suffer from incompleteness, which hinder their effectiveness in downstream tasks. Therefore, multimodal knowledge graph completion (MKGC) task is receiving increasing attention. While large language models (LLMs) have shown promise for knowledge graph completion (KGC), their application to the multimodal setting remains underexplored. Moreover, applying Multimodal Large Language Models (MLLMs) to the task of MKGC introduces significant challenges: (1) the large number of image tokens per entity leads to semantic noise and modality conflicts, and (2) the high computational cost of processing large token inputs. To address these issues, we propose Efficient Lightweight Multimodal Large Language Models (ELMM) for MKGC. ELMM proposes a Multi-view Visual Token Compressor (MVTC) based on multi-head attention mechanism, which adaptively compresses image tokens from both textual and visual views, thereby effectively reducing redundancy while retaining necessary information and avoiding modality conflicts. Additionally, we design an attention pruning strategy to remove redundant attention layers from MLLMs, thereby significantly reducing the inference cost. We further introduce a linear projection to compensate for the performance degradation caused by pruning. Extensive experiments on four benchmark datasets demonstrate that ELMM achieves state-of-the-art performance. + oai:arXiv.org:2510.16753v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Wei Huang, Peining Li, Meiyu Liang, Xu Hou, Junping Du, Yingxia Shao, Guanhua Ye, Wu Liu, Kangkang Lu, Yang Yu + + + DDBot: Differentiable Physics-based Digging Robot for Unknown Granular Materials + https://arxiv.org/abs/2510.17335 + arXiv:2510.17335v4 Announce Type: replace +Abstract: Automating the manipulation of granular materials poses significant challenges due to complex contact dynamics, unpredictable material properties, and intricate system states. Existing approaches often fail to achieve efficiency and accuracy in such tasks. To fill the research gap, this article studies the small-scale and high-precision granular material digging task with unknown physical properties. A key scientific problem addressed is the feasibility of applying first-order gradient-based optimization to complex differentiable granular material simulation and overcoming associated numerical instability. A new framework, named differentiable digging robot (DDBot), is proposed to manipulate granular materials, including sand and soil. Specifically, we equip DDBot with a differentiable physics-based simulator, tailored for granular material manipulation, powered by GPU-accelerated parallel computing and automatic differentiation. DDBot can perform efficient differentiable system identification and high-precision digging skill optimization for unknown granular materials, which is enabled by a differentiable skill-to-action mapping, a task-oriented demonstration method, gradient clipping and line search-based gradient descent. Experimental results show that DDBot can efficiently (converge within 5 to 20 minutes) identify unknown granular material dynamics and optimize digging skills, with high-precision results in zero-shot real-world deployments, highlighting its practicality. Benchmark results against state-of-the-art baselines also confirm the robustness and efficiency of DDBot in such digging tasks. + oai:arXiv.org:2510.17335v4 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1109/TRO.2025.3636815 + IEEE Transactions on Robotics, vol. 42, pp. 152-169, 2026 + Xintong Yang, Minglun Wei, Yu-Kun Lai, Ze Ji + + + Qomhra: A Bilingual Irish and English Large Language Model + https://arxiv.org/abs/2510.17652 + arXiv:2510.17652v2 Announce Type: replace +Abstract: Large language model (LLM) research and development has overwhelmingly focused on the world's major languages, leading to under-representation of low-resource languages such as Irish. This paper introduces \textbf{Qomhr\'a}, a bilingual Irish and English LLM, developed under extremely low-resource constraints. A complete pipeline is outlined spanning bilingual continued pre-training, instruction tuning, and the synthesis of human preference data for future alignment training. We focus on the lack of scalable methods to create human preference data by proposing a novel method to synthesise such data by prompting an LLM to generate ``accepted'' and ``rejected'' responses, which we validate as aligning with L1 Irish speakers. To select an LLM for synthesis, we evaluate the top closed-weight LLMs for Irish language generation performance. Gemini-2.5-Pro is ranked highest by L1 and L2 Irish-speakers, diverging from LLM-as-a-judge ratings, indicating a misalignment between current LLMs and the Irish-language community. Subsequently, we leverage Gemini-2.5-Pro to translate a large scale English-language instruction tuning dataset to Irish and to synthesise a first-of-its-kind Irish-language human preference dataset. We comprehensively evaluate Qomhr\'a across several benchmarks, testing translation, gender understanding, topic identification, and world knowledge; these evaluations show gains of up to 29\% in Irish and 44\% in English compared to the existing open-source Irish LLM baseline, UCCIX. The results of our framework provide insight and guidance to developing LLMs for both Irish and other low-resource languages. + oai:arXiv.org:2510.17652v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Joseph McInerney, Khanh-Tung Tran, Liam Lonergan, Ailbhe N\'i Chasaide, Neasa N\'i Chiar\'ain, Barry Devereux + + + Enhancing Reasoning Skills in Small Persian Medical Language Models Can Outperform Large-Scale Data Training + https://arxiv.org/abs/2510.20059 + arXiv:2510.20059v5 Announce Type: replace +Abstract: Enhancing reasoning capabilities in small language models is critical for specialized applications such as medical question answering, particularly in underrepresented languages like Persian. In this study, we employ Reinforcement Learning with AI Feedback (RLAIF) and Direct preference optimization (DPO) to improve the reasoning skills of a general-purpose Persian language model. To achieve this, we translated a multiple-choice medical question-answering dataset into Persian and used RLAIF to generate rejected-preferred answer pairs, which are essential for DPO training. By prompting both teacher and student models to produce Chain-of-Thought (CoT) reasoning responses, we compiled a dataset containing correct and incorrect reasoning trajectories. This dataset, comprising 2 million tokens in preferred answers and 2.5 million tokens in rejected ones, was used to train a baseline model, significantly enhancing its medical reasoning capabilities in Persian. Remarkably, the resulting model outperformed its predecessor, gaokerena-V, which was trained on approximately 57 million tokens, despite leveraging a much smaller dataset. These results highlight the efficiency and effectiveness of reasoning-focused training approaches in developing domain-specific language models with limited data availability. + oai:arXiv.org:2510.20059v5 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Mehrdad Ghassabi, Sadra Hakim, Hamidreza Baradaran Kashani, Pedram Rostami + + + Compositional Monte Carlo Tree Diffusion for Extendable Planning + https://arxiv.org/abs/2510.21361 + arXiv:2510.21361v2 Announce Type: replace +Abstract: Monte Carlo Tree Diffusion (MCTD) integrates diffusion models with structured tree search to enable effective trajectory exploration through stepwise reasoning. However, MCTD remains fundamentally limited by training trajectory lengths. While periodic replanning allows plan concatenation for longer plan generation, the planning process remains locally confined, as MCTD searches within individual trajectories without access to global context. We propose Compositional Monte Carlo Tree Diffusion (C-MCTD), a framework that elevates planning from individual trajectory optimization to reasoning over complete plan compositions. C-MCTD introduces three complementary components: (1) Online Composer, which performs globally-aware planning by searching across entire plan compositions; (2) Distributed Composer, which reduces search complexity through parallel exploration from multiple starting points; and (3) Preplan Composer, which accelerates inference by leveraging cached plan graphs. + oai:arXiv.org:2510.21361v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jaesik Yoon, Hyeonseo Cho, Sungjin Ahn + + + Leveraging Design-Aware Context in Large Language Models for Code Comment Generation + https://arxiv.org/abs/2510.22338 + arXiv:2510.22338v2 Announce Type: replace +Abstract: Comments are very useful to the flow of code development. With the increasing commonality of code, novice coders have been creating a significant amount of codebases. Due to lack of commenting standards, their comments are often useless, and increase the time taken to further maintain codes. This study intends to find the usefulness of large language models (LLMs) in these cases to generate potentially better comments. This study focuses on the feasibility of design documents as a context for the LLMs to generate more useful comments, as design documents are often used by maintainers to understand code when comments do not suffice. + oai:arXiv.org:2510.22338v2 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Aritra Mitra, Srijoni Majumdar, Anamitra Mukhopadhyay, Partha Pratim Das, Paul D Clough, Partha Pratim Chakrabarti + + + Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving + https://arxiv.org/abs/2510.23346 + arXiv:2510.23346v2 Announce Type: replace +Abstract: When serving a single base LLM with several different LoRA adapters simultaneously, the adapters cannot simply be merged with the base model's weights as the adapter swapping would create overhead and requests using different adapters could not be batched. Rather, the LoRA computations have to be separated from the base LLM computations, and in a multi-device setup the LoRA adapters can be sharded in a way that is well aligned with the base model's tensor parallel execution, as proposed in S-LoRA. However, the S-LoRA sharding strategy encounters some communication overhead, which may be small in theory, but can be large in practice. In this paper, we propose to constrain certain LoRA factors to be block-diagonal, which allows for an alternative way of sharding LoRA adapters that does not require any additional communication for the LoRA computations. We demonstrate in extensive experiments that our block-diagonal LoRA approach is similarly parameter efficient as standard LoRA (i.e., for a similar number of parameters it achieves similar downstream performance) and that it leads to significant end-to-end speed-up over S-LoRA. For example, when serving on eight A100 GPUs, we observe up to 1.79x (1.23x) end-to-end speed-up with 0.87x (1.74x) the number of adapter parameters for Llama-3.1-70B, and up to 1.63x (1.3x) end-to-end speed-up with 0.86x (1.73x) the number of adapter parameters for Llama-3.1-8B. + oai:arXiv.org:2510.23346v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Xinyu Wang, Jonas M. K\"ubler, Kailash Budhathoki, Yida Wang, Matth\"aus Kleindessner + + + ReCode: Unify Plan and Action for Universal Granularity Control + https://arxiv.org/abs/2510.23564 + arXiv:2510.23564v4 Announce Type: replace +Abstract: Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitation stems from existing paradigms that enforce a rigid separation between high-level planning and low-level action, which impairs dynamic adaptability and limits generalization. We propose ReCode (Recursive Code Generation), a novel paradigm that addresses this limitation by unifying planning and action within a single code representation. In this representation, ReCode treats high-level plans as abstract placeholder functions, which the agent then recursively decomposes into finer-grained sub-functions until reaching primitive actions. This recursive approach dissolves the rigid boundary between plan and action, enabling the agent to dynamically control its decision granularity. Furthermore, the recursive structure inherently generates rich, multi-granularity training data, enabling models to learn hierarchical decision-making processes. Extensive experiments show ReCode significantly surpasses advanced baselines in inference performance and demonstrates exceptional data efficiency in training, validating our core insight that unifying planning and action through recursive code generation is a powerful and effective approach to achieving universal granularity control. The code is available at https://github.com/FoundationAgents/ReCode. + oai:arXiv.org:2510.23564v4 + cs.AI + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhaoyang Yu, Jiayi Zhang, Huixue Su, Yufan Zhao, Yifan Wu, Mingyi Deng, Jinyu Xiang, Yizhang Lin, Lingxiao Tang, Yuyu Luo, Bang Liu, Chenglin Wu + + + Adaptive Data Collection for Latin-American Community-sourced Evaluation of Stereotypes (LACES) + https://arxiv.org/abs/2510.24958 + arXiv:2510.24958v2 Announce Type: replace +Abstract: The evaluation of societal biases in NLP models is critically hindered by a geo-cultural gap, This leaves regions such as Latin America severely underserved, making it impossible to adequately assess or mitigate the perpetuation of harmful regional stereotypes in language technologies. + This paper presents LACES, a stereotype association dataset, for 15 Latin American countries. This dataset includes 4,789 stereotype associations manually created and annotated by 83 participants. The dataset was developed through targeted community partnerships across Latin America. + Additionally, in this paper, we propose a novel adaptive data collection methodology that uniquely integrates the sourcing of new stereotype entries and the validation of existing data within a single, unified workflow. This approach results in a resource with more unique stereotypes than previous static collection methods, enabling a more efficient stereotype collection. The paper further supports the quality of LACES by demonstrating reduced efficacy of debiasing methods on this dataset in comparison to existing popular stereotype benchmarks. + oai:arXiv.org:2510.24958v2 + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Guido Ivetta, Pietro Palombini, Sof\'ia Martinelli, Marcos J Gomez, M. Mar\'ia Echeveste, Sunipa Dev, Vinodkumar Prabhakaran, Luciana Benotti + + + Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error + https://arxiv.org/abs/2510.26109 + arXiv:2510.26109v3 Announce Type: replace +Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly boosted the reasoning capability of language models (LMs) recently. However, existing RLVR approaches merely train LMs based on their own generated on-policy responses and are constrained by the initial capability of LMs, thus prone to exploration stagnation, in which LMs fail to solve more training problems and cannot further learn from the training data. Some work tries to address this by leveraging off-policy solutions to training problems, but relies on external expert guidance that is limited in availability and scalability. In this work, we propose LTE (Learning to reason from Trial and Error), an approach that hints LMs with their previously self-made mistakes, not requiring any external expert guidance. Experiments validate the effectiveness of LTE, which outperforms the normal group relative policy optimization (GRPO) by 5.02 in Pass@1 and 9.96 in Pass@k on average across six mathematical reasoning benchmarks for Qwen3-8B-Base and even performs better than methods that require external gold solutions as guidance after aligning the experimental setup. Further analysis confirms that LTE successfully mitigates exploration stagnation and enhances both exploitation and exploration during training. Our code is available at https://anonymous.4open.science/r/Learning-from-Trial-and-Error. + oai:arXiv.org:2510.26109v3 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chenming Tang, Hsiu-Yuan Huang, Weijie Liu, Clive Bai, Saiyong Yang, Yunfang Wu + + + Cut-free Deductive System for Continuous Intuitionistic Logic + https://arxiv.org/abs/2510.26849 + arXiv:2510.26849v2 Announce Type: replace +Abstract: We introduce and develop propositional continuous intuitionistic logic and propositional continuous affine logic via complete algebraic semantics. Our approach centres on AC-algebras, which are algebras $USC(\mathcal{L})$ of sup-preserving functions from $[0,1]$ to an integral commutative residuated complete lattice $\mathcal{L}$ (in the intuitionistic case, $\mathcal{L}$ is a locale). We give an algebraic axiomatisation of AC-algebras in the language of continuous logic and prove, using the Macneille completion, that every Archimedean model embeds into some AC-algebra. We also show that (i) $USC(\mathcal{L})$ satisfies $v \dot + v = 2v$ exactly when $\mathcal{L}$ is a locale, (ii) involutiveness of negation in $USC(\mathcal{L})$ corresponds to that in $\mathcal{L} $, and that (iii) adding those conditions recovers classical continuous logic. For each variant -affine, intuitionistic, involutive, classical -we provide a sequent style deductive system and prove completeness and cut admissibility. This yields the first sequent style formulation of classical continuous logic enjoying cut admissibility. + oai:arXiv.org:2510.26849v2 + cs.LO + math.LO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Guillaume Geoffroy (UCBL, ICJ, AGL) + + + Group-Sensitive Offline Contextual Bandits + https://arxiv.org/abs/2510.27123 + arXiv:2510.27123v2 Announce Type: replace +Abstract: Offline contextual bandits allow one to learn policies from historical/offline data without requiring online interaction. However, offline policy optimization that maximizes overall expected rewards can unintentionally amplify the reward disparities across groups. As a result, some groups might benefit more than others from the learned policy, raising concerns about fairness, especially when the resources are limited. In this paper, we study a group-sensitive fairness constraint in offline contextual bandits, reducing group-wise reward disparities that may arise during policy learning. We tackle the following common-parity requirements: the reward disparity is constrained within some user-defined threshold or the reward disparity should be minimized during policy optimization. We propose a constrained offline policy optimization framework by introducing group-wise reward disparity constraints into an off-policy gradient-based optimization procedure. To improve the estimation of the group-wise reward disparity during training, we employ a doubly robust estimator and further provide a convergence guarantee for policy optimization. Empirical results in synthetic and real-world datasets demonstrate that our method effectively reduces reward disparities while maintaining competitive overall performance. + oai:arXiv.org:2510.27123v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yihong Guo, Junjie Luo, Guodong Gao, Ritu Agarwal, Anqi Liu + + + Optimal Allocations under Strongly Pigou-Dalton Criteria: Hidden Layer Structure & Efficient Combinatorial Approach + https://arxiv.org/abs/2511.00835 + arXiv:2511.00835v3 Announce Type: replace +Abstract: We investigate optimal social welfare allocations of $m$ items to $n$ agents with binary additive or submodular valuations. For binary additive valuations, we prove that the set of optimal allocations coincides with the set of so-called \emph{stable allocations}, as long as the employed criterion for evaluating social welfare is strongly Pigou-Dalton (SPD) and symmetric. Many common criteria are SPD and symmetric, such as Nash social welfare, leximax, leximin, Gini index, entropy, and envy sum. We also design efficient algorithms for finding a stable allocation, including an $O(m^2n)$ time algorithm for the case of indivisible items, and an $O(m^2n^5)$ time one for the case of divisible items. The first is faster than the existing algorithms or has a simpler analysis. The latter is the first combinatorial algorithm for that problem. It utilizes a hidden layer partition of items and agents admitted by all stable allocations, and cleverly reduces the case of divisible items to the case of indivisible items.In addition, we show that the profiles of different optimal allocations have a small Chebyshev distance, which is 0 for the case of divisible items under binary additive valuations, and is at most 1 for the case of indivisible items under binary submodular valuations. + oai:arXiv.org:2511.00835v3 + cs.GT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Taikun Zhu, Kai Jin, Ruixi Luo, Song Cao + + + Orthogonal-by-construction augmentation of physics-based input-output models + https://arxiv.org/abs/2511.01321 + arXiv:2511.01321v2 Announce Type: replace +Abstract: This paper proposes a novel orthogonal-by-construction parametrization for augmenting physics-based input-output models with a learning component in an additive sense. The parametrization allows to jointly optimize the parameters of the physics-based model and the learning component. Unlike the commonly applied additive (parallel) augmentation structure, the proposed formulation eliminates overlap in representation of the system dynamics, thereby preserving the uniqueness of the estimated physical parameters, ultimately leading to enhanced model interpretability. By theoretical analysis, we show that, under mild conditions, the method is statistically consistent and guarantees recovery of the true physical parameters. With further analysis regarding the asymptotic covariance matrix of the identified parameters, we also prove that the proposed structure provides a clear separation between the physics-based and learning components of the augmentation structure. The effectiveness of the proposed approach is demonstrated through simulation studies, showing accurate reproduction of the data-generating dynamics without sacrificing consistent estimation of the physical parameters. + oai:arXiv.org:2511.01321v2 + eess.SY + cs.SY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Bendeg\'uz M. Gy\"or\"ok, Maarten Schoukens, Tam\'as P\'eni, Roland T\'oth + + + LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning + https://arxiv.org/abs/2511.01448 + arXiv:2511.01448v2 Announce Type: replace +Abstract: Large Language Model (LLM) agents exhibit remarkable conversational and reasoning capabilities but remain constrained by limited context windows and the lack of persistent memory. Recent efforts address these limitations via external memory architectures, often employing graph-based representations, yet most adopt flat, entangled structures that intertwine semantics with topology, leading to redundant representations, unstructured retrieval, and degraded efficiency and accuracy. To resolve these issues, we propose LiCoMemory, an end-to-end agentic memory framework for real-time updating and retrieval, which introduces CogniGraph, a lightweight hierarchical graph that utilizes entities and relations as semantic indexing layers, and employs temporal and hierarchy-aware search with integrated reranking for adaptive and coherent knowledge retrieval. Experiments on long-term dialogue benchmarks, LoCoMo and LongMemEval, show that LiCoMemory not only outperforms established baselines in temporal reasoning, multi-session consistency, and retrieval efficiency, but also notably reduces update latency. Our official code and data are available at https://github.com/EverM0re/LiCoMemory. + oai:arXiv.org:2511.01448v2 + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhengjun Huang, Zhoujin Tian, Qintian Guo, Fangyuan Zhang, Yingli Zhou, Di Jiang, Zeying Xie, Xiaofang Zhou + + + MCHex: Marching Cubes Based Adaptive Hexahedral Mesh Generation with Guaranteed Positive Jacobian + https://arxiv.org/abs/2511.02064 + arXiv:2511.02064v4 Announce Type: replace +Abstract: Constructing an adaptive hexahedral tessellation to fit an input triangle boundary is a key challenge in grid-based methods. The conventional method first removes outside elements (RO) and then projects the axis-aligned boundary onto the input triangle boundary, which has no guarantee on improving the initial Intersection over Union (IoU) and Hausdorff distance ratio (HR, w.r.t bounding box diagonal). The proposed MCHex approach replaces RO with a Marching Cubes method MCHex. Given the same computational budget (benchmarked using an identical precomputed Signed Distance Field, which dominates the runtime), MCHex provides better boundary approximation (higher IoU and lower HR) while guaranteeing a lower, yet still positive, minimum scaled Jacobian (>0 vs. RO's >0.48). + oai:arXiv.org:2511.02064v4 + cs.CG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Hua Tong, Yongjie Jessica Zhang + + + Enhancing Multimodal Reasoning via Latent Refocusing + https://arxiv.org/abs/2511.02360 + arXiv:2511.02360v2 Announce Type: replace +Abstract: Chain of Thought (CoT) reasoning enhances logical performance by decomposing complex tasks, yet its multimodal extension faces a trade-off. The existing Thinking with Images paradigm is limited by the modality gap between vision and language, which hinders reliable extraction of reasoning relevant information from high dimensional visual data. Recent latent space reasoning method provides stronger multimodal representations, but it often lacks the ability to refocus on visual inputs and suffers from limited interpretability. To address these issues, we propose \underline{La}tent \underline{Re}focusing (LaRe), a novel multimodal reasoning paradigm that combines visual refocusing with rich latent representations, enabling iterative reasoning within the latent space. We further design a semantic augmentation training strategy that enhances the semantic structure of the latent space through joint alignment and reconstruction objectives. Experimental evaluations demonstrate that LaRe improves average accuracy by 9.4\% compared to existing baselines while reducing the number of tokens required for inference by 16.5\%. When scaled to a 7B-parameter Large Language Model backbone, LaRe achieves performance comparable to state-of-the-art models and outperforms larger-scale models on almost all benchmarks. Code and checkpoints will be released later. + oai:arXiv.org:2511.02360v2 + cs.CV + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jizheng Ma, Xiaofei Zhou, Yanlong Song, Han Yan + + + RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning + https://arxiv.org/abs/2511.02384 + arXiv:2511.02384v2 Announce Type: replace +Abstract: Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed BBox and Index as Visual Prompt (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the RxnCaption-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry. We will release data, models, and code on GitHub. + oai:arXiv.org:2511.02384v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiahe Song, Chuang Wang, Bowen Jiang, Yinfan Wang, Hao Zheng, Xingjian Wei, Chengjin Liu, Rui Nie, Junyuan Gao, Jiaxing Sun, Yubin Wang, Lijun Wu, Zhenhua Huang, Jiang Wu, Qian Yu, Conghui He + + + Indicating Robot Vision Capabilities with Augmented Reality + https://arxiv.org/abs/2511.03550 + arXiv:2511.03550v2 Announce Type: replace +Abstract: Research indicates that humans can mistakenly assume that robots and humans have the same field of view, possessing an inaccurate mental model of robots. This misperception may lead to failures during human-robot collaboration tasks where robots might be asked to complete impossible tasks about out-of-view objects. The issue is more severe when robots do not have a chance to scan the scene to update their world model while focusing on assigned tasks. + To help align humans' mental models of robots' vision capabilities, we propose four field-of-view indicators in augmented reality and conducted a human-subjects experiment (N=41) to evaluate them in a collaborative assembly task regarding accuracy, confidence, task efficiency, and workload. These indicators span a spectrum of positions: two at robot's eye and head space -- deepening eye socket and adding blocks to two sides of the eyes (i.e., egocentric), and two anchoring in the robot's task space -- adding extended blocks from the sides of eyes to the table and placing blocks directly on the tables (i.e., allocentric). + Results showed that, when placed directly in the task space, the allocentric indicator yields the highest accuracy, although with a delay in interpreting the robot's field of view. When placed at the robot's eyes, the egocentric indicator of deeper eye sockets, possible for physical alteration, also increased accuracy. In all indicators, participants' confidence was high while cognitive load remained low. Finally, we contribute six guidelines for practitioners to apply our augmented reality indicators or physical alterations to align humans' mental models with robots' vision capabilities. + oai:arXiv.org:2511.03550v2 + cs.RO + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Hong Wang, Ridhima Phatak, James Ocampo, Zhao Han + + + Adapting Web Agents with Synthetic Supervision + https://arxiv.org/abs/2511.06101 + arXiv:2511.06101v2 Announce Type: replace +Abstract: Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redundant or misaligned actions. In this paper, we propose SynthAgent, a fully synthetic supervision framework that aims at improving synthetic data quality via dual refinement of both tasks and trajectories. Our approach begins by synthesizing diverse tasks through categorized exploration of web elements, ensuring efficient coverage of the target environment. During trajectory collection, tasks are refined only when conflicts with observations are detected, which mitigates hallucinations while preserving task consistency. After collection, we conduct trajectory refinement with global context to mitigate potential noise or misalignments. Finally, we fine-tune open-source web agents on the refined synthetic data to adapt them to the target environment. Experimental results demonstrate that SynthAgent outperforms existing synthetic data methods, validating the importance of high-quality synthetic supervision. The code is publicly available at https://github.com/aiming-lab/SynthAgent. + oai:arXiv.org:2511.06101v2 + cs.LG + cs.AI + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhaoyang Wang, Yiming Liang, Xuchao Zhang, Qianhui Wu, Siwei Han, Anson Bastos, Rujia Wang, Chetan Bansal, Baolin Peng, Jianfeng Gao, Saravan Rajmohan, Huaxiu Yao + + + Alignment-Aware Quantization for LLM Safety + https://arxiv.org/abs/2511.07842 + arXiv:2511.07842v3 Announce Type: replace +Abstract: Safety and efficiency are paramount yet often conflicting requirements for deploying Large Language Models (LLMs). While LLMs are trained to follow human alignment for safety, Post-Training Quantization (PTQ) is applied afterward to ensure efficiency. Here we identify a fundamental flaw in the conventional PTQ paradigm: quantization can turn into a safety vulnerability if it only aims to achieve low perplexity. To address this, we propose Alignment-Aware Quantization (AAQ), a novel approach that integrates an Alignment-Preserving Contrastive (APC) loss into the PTQ pipeline. Our method explicitly preserves alignment by encouraging the quantized model to mimic its safe, instruction-tuned model while diverging from the unaligned, pre-trained counterpart. AAQ achieves robust safety alignment without specialized safety-focused datasets, using only standard calibration data. We show that AAQ is compatible with standard PTQ techniques and enables robust 4-bit (W4A4) quantization across diverse model families. Our work resolves the critical trade-off between efficiency and safety, paving the way toward LLMs that are both efficient and trustworthy. Anonymized code is available in the supplementary material. + oai:arXiv.org:2511.07842v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Sunghyun Wee, Suyoung Kim, Hyeonjin Kim, Kyomin Hwang, Nojun Kwak + + + The Journal of Prompt-Engineered Philosophy Or: How I Started to Track AI Assistance and Stopped Worrying About Slop + https://arxiv.org/abs/2511.08639 + arXiv:2511.08639v2 Announce Type: replace +Abstract: Academic publishing increasingly requires authors to disclose AI assistance, yet imposes reputational costs for doing so--especially when such assistance is substantial. This article analyzes that structural contradiction, showing how incentives discourage transparency in precisely the work where it matters most. Traditional venues cannot resolve this tension through policy tweaks alone, as the underlying prestige economy rewards opacity. To address this, the article proposes an alternative publishing infrastructure: a venue outside prestige systems that enforces mandatory disclosure, enables reproduction-based review, and supports ecological validity through detailed documentation. As a demonstration of this approach, the article itself is presented as an example of AI-assisted scholarship under reasonably detailed disclosure, with representative prompt logs and modification records included. Rather than taking a position for or against AI-assisted scholarship, the article outlines conditions under which such work can be evaluated on its own terms: through transparent documentation, verification-oriented review, and participation by methodologically committed scholars. While focused on AI, the framework speaks to broader questions about how academic systems handle methodological innovation. + oai:arXiv.org:2511.08639v2 + cs.CY + cs.AI + cs.DL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Michele Loi + + + Learning the Basis: A Kolmogorov-Arnold Network Approach Embedding Green's Function Priors + https://arxiv.org/abs/2511.08655 + arXiv:2511.08655v3 Announce Type: replace +Abstract: The Method of Moments (MoM) is constrained by the usage of static, geometry-defined basis functions, such as the Rao-Wilton-Glisson (RWG) basis. This letter reframes electromagnetic modeling around a learnable basis representation rather than solving for the coefficients over a fixed basis. We first show that the RWG basis is essentially a static and piecewise-linear realization of the Kolmogorov-Arnold representation theorem. Inspired by this insight, we propose PhyKAN, a physics-informed Kolmogorov-Arnold Network (KAN) that generalizes RWG into a learnable and adaptive basis family. Derived from the EFIE, PhyKAN integrates a local KAN branch with a global branch embedded with Green's function priors to preserve physical consistency. It is demonstrated that, across canonical geometries, PhyKAN achieves sub-0.01 reconstruction errors as well as accurate, unsupervised radar cross section predictions, offering an interpretable, physics-consistent bridge between classical solvers and modern neural network models for electromagnetic modeling. + oai:arXiv.org:2511.08655v3 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Rui Zhu, Yuexing Peng, George C. Alexandropoulos, Wenbo Wang, Wei Xiang + + + DoPE: Denoising Rotary Position Embedding + https://arxiv.org/abs/2511.09146 + arXiv:2511.09146v2 Announce Type: replace +Abstract: Positional encoding is essential for large language models (LLMs) to represent sequence order, yet recent studies show that Rotary Position Embedding (RoPE) can induce massive activation. We investigate the source of these instabilities via a spectral analysis of RoPE, and show that its low-frequency components concentrate structured energy, producing low-rank, over-aligned attention patterns. We theoretically reveal that this low-frequency alignment manifests as activation noise, degrading stability during long-context extrapolation. To mitigate this effect, we introduce Denoising Rotary Position Embedding (DoPE), a training-free method that identifies and suppresses noisy attention heads using truncated matrix entropy, then reparameterizes their attention maps with an isotropic Gaussian distribution. Across a range of settings, DoPE improves length extrapolation performance without fine-tuning, increases robustness to perturbations, and boosts both needle-in-a-haystack and many-shot in-context learning tasks. These results suggest that selective positional encoding is key to robust extrapolation. Our project page is Project: https://The-physical-picture-of-LLMs.github.io + oai:arXiv.org:2511.09146v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Jing Xiong, Liyang Fan, Hui Shen, Zunhai Su, Min Yang, Lingpeng Kong, Ngai Wong + + + Mitigating Error Accumulation in Co-Speech Motion Generation via Global Rotation Diffusion and Multi-Level Constraints + https://arxiv.org/abs/2511.10076 + arXiv:2511.10076v2 Announce Type: replace +Abstract: Reliable co-speech motion generation requires precise motion representation and consistent structural priors across all joints. Existing generative methods typically operate on local joint rotations, which are defined hierarchically based on the skeleton structure. This leads to cumulative errors during generation, manifesting as unstable and implausible motions at end-effectors. In this work, we propose GlobalDiff, a diffusion-based framework that operates directly in the space of global joint rotations for the first time, fundamentally decoupling each joint's prediction from upstream dependencies and alleviating hierarchical error accumulation. To compensate for the absence of structural priors in global rotation space, we introduce a multi-level constraint scheme. Specifically, a joint structure constraint introduces virtual anchor points around each joint to better capture fine-grained orientation. A skeleton structure constraint enforces angular consistency across bones to maintain structural integrity. A temporal structure constraint utilizes a multi-scale variational encoder to align the generated motion with ground-truth temporal patterns. These constraints jointly regularize the global diffusion process and reinforce structural awareness. Extensive evaluations on standard co-speech benchmarks show that GlobalDiff generates smooth and accurate motions, improving the performance by 46.0 % compared to the current SOTA under multiple speaker identities. + oai:arXiv.org:2511.10076v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Xiangyue Zhang, Jianfang Li, Jianqiang Ren, Jiaxu Zhang + + + Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays + https://arxiv.org/abs/2511.11093 + arXiv:2511.11093v2 Announce Type: replace +Abstract: Coronary artery calcification (CAC) is a strong predictor of cardiovascular events, with CT-based Agatston scoring widely regarded as the clinical gold standard. However, CT is costly and impractical for large-scale screening, while chest X-rays (CXRs) are inexpensive but lack reliable ground truth labels, constraining deep learning development. Digitally reconstructed radiographs (DRRs) offer a scalable alternative by projecting CT volumes into CXR-like images while inheriting precise labels. In this work, we provide the first systematic evaluation of DRRs as a surrogate training domain for CAC detection. Using 667 CT scans from the COCA dataset, we generate synthetic DRRs and assess model capacity, super-resolution fidelity enhancement, preprocessing, and training strategies. Lightweight CNNs trained from scratch outperform large pretrained networks; pairing super-resolution with contrast enhancement yields significant gains; and curriculum learning stabilises training under weak supervision. Our best configuration achieves a mean AUC of 0.754, comparable to or exceeding prior CXR-based studies. These results establish DRRs as a scalable, label-rich foundation for CAC detection, while laying the foundation for future transfer learning and domain adaptation to real CXRs. + oai:arXiv.org:2511.11093v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Dylan Saeed, Ramtin Gharleghi, Susann Beier, Sonit Singh + + + TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI + https://arxiv.org/abs/2511.12063 + arXiv:2511.12063v3 Announce Type: replace +Abstract: Large Language Models (LLMs) have enabled self-improving AI systems that iteratively generate, evaluate, and refine their outcomes. Recent studies show that prompt-optimization-based self-improvement can outperform state-of-the-art reinforcement-learning fine-tuning of LLMs, but performance is typically measured by generation efficiency. However, in many applications, the constraint is evaluation efficiency: obtaining reliable feedback is far more costly than generating candidates. To optimize for evaluation efficiency, we extend Upper Confidence Bound-Bayesian Optimization (UCB-BO), a framework known for optimal evaluation-efficiency guarantees, to the language domain. Doing so is challenging for two reasons: (i) gradients needed for UCB-BO are ill-defined in discrete prompt space; and (ii) UCB-style exploration relies on a surrogate model and acquisition function, which only live implicitly in the LLM. We overcome these challenges by proving that combining simple textual gradients (LLM-proposed local edits) with the Best-of-N selection strategy statistically emulates ascent along the gradient of the canonical UCB acquisition function. Based on this result, we propose TextBO, a simple, evaluation-efficient self-improving algorithm that operates purely in language space without explicit surrogates or calibrated uncertainty models. We empirically validate TextBO on automated ad-alignment tasks using a persona-induced preference distribution, demonstrating superior performance per evaluation compared to strong baselines such as Best-of-N and GEPA. We also evaluate TextBO's Best-of-N multi-step textual-gradient mechanism on agentic AI benchmarks by augmenting GEPA with it and show that it significantly outperforms standard GEPA. In sum, TextBO is a simple and principled framework for AI self-improving system design that bridges prompt optimization with classical Bayesian optimization. + oai:arXiv.org:2511.12063v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Enoch Hyunwook Kang, Hema Yoganarasimhan + + + A Stochastic Genetic Interacting Particle Method for Reaction-Diffusion-Advection Equations + https://arxiv.org/abs/2511.12275 + arXiv:2511.12275v2 Announce Type: replace +Abstract: We develop and analyze a stochastic genetic interacting particle method (SGIP) for reaction-diffusion-advection (RDA) equations. The SGIP method employs operator splitting to approximate the advection-diffusion and reaction processes, treating the former using particle drift-diffusion and the latter via exact or implicit integration of reaction dynamics over bins, where particle density is estimated using a histogram. A key innovation is the incorporation of adaptive resampling to close the loop of particle and density field description of solutions, mimicking the selection mechanism in genetics. Resampling is also crucial for maintaining long-term stability by redistributing particles in accordance with the evolving density field. We provide a comprehensive error analysis and establish convergence bounds under appropriate regularity assumptions. Numerical experiments in one to three space dimensions demonstrate the method's effectiveness across various reaction types (Fisher-Kolmogorov-Petrovsky-Piskunov (FKPP), cubic, Arrhenius) and flow configurations (shear, cellular, cat's eye, Arnold-Beltrami-Childress (ABC) flows), showing excellent agreement with the finite difference method (FDM) while offering computational advantages for complex flow geometries and higher-dimensional problems. + oai:arXiv.org:2511.12275v2 + math.NA + cs.NA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Boyi Hu, Zhongjian Wang, Jack Xin, Zhiwen Zhang + + + FICO: Finite-Horizon Closed-Loop Factorization for Unified Multi-Agent Path Finding + https://arxiv.org/abs/2511.13961 + arXiv:2511.13961v3 Announce Type: replace +Abstract: Multi-Agent Path Finding is a fundamental problem in robotics and AI, yet most existing formulations treat planning and execution separately and address variants of the problem in an ad hoc manner. This paper presents a system-level framework for MAPF that integrates planning and execution, generalizes across variants, and explicitly models uncertainties. At its core is the MAPF system, a formal model that casts MAPF as a control design problem encompassing classical and uncertainty-aware formulations. To solve it, we introduce Finite-Horizon Closed-Loop Factorization (FICO), a factorization-based algorithm inspired by receding-horizon control that exploits compositional structure for efficient closed-loop operation. FICO enables real-time responses -- commencing execution within milliseconds -- while scaling to thousands of agents and adapting seamlessly to execution-time uncertainties. Extensive case studies demonstrate that it reduces computation time by up to two orders of magnitude compared with open-loop baselines, while delivering significantly higher throughput under stochastic delays and agent arrivals. These results establish a principled foundation for analyzing and advancing MAPF through system-level modeling, factorization, and closed-loop design. + oai:arXiv.org:2511.13961v3 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jiarui Li, Alessandro Zanardi, Federico Pecora, Runyu Zhang, Gioele Zardini + + + Learning with Statistical Equality Constraints + https://arxiv.org/abs/2511.14320 + arXiv:2511.14320v2 Announce Type: replace +Abstract: As machine learning applications grow increasingly ubiquitous and complex, they face an increasing set of requirements beyond accuracy. The prevalent approach to handle this challenge is to aggregate a weighted combination of requirement violation penalties into the training objective. To be effective, this approach requires careful tuning of these hyperparameters (weights), involving trial-and-error and cross-validation, which becomes ineffective even for a moderate number of requirements. These issues are exacerbated when the requirements involve parities or equalities, as is the case in fairness and boundary value problems. An alternative technique uses constrained optimization to formulate these learning problems. Yet, existing approximation and generalization guarantees do not apply to problems involving equality constraints. In this work, we derive a generalization theory for equality-constrained statistical learning problems, showing that their solutions can be approximated using samples and rich parametrizations. Using these results, we propose a practical algorithm based on solving a sequence of unconstrained, empirical learning problems. We showcase its effectiveness and the new formulations enabled by equality constraints in fair learning, interpolating classifiers, and boundary value problems. + oai:arXiv.org:2511.14320v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Aneesh Barthakur, Luiz F. O. Chamon + + + Towards Unbiased Cross-Modal Representation Learning for Food Image-to-Recipe Retrieval + https://arxiv.org/abs/2511.15201 + arXiv:2511.15201v2 Announce Type: replace +Abstract: This paper addresses the challenges of learning representations for recipes and food images in the cross-modal retrieval problem. As the relationship between a recipe and its cooked dish is cause-and-effect, treating a recipe as a text source describing the visual appearance of a dish for learning representation, as the existing approaches, will create bias misleading image-and-recipe similarity judgment. Specifically, a food image may not equally capture every detail in a recipe, due to factors such as the cooking process, dish presentation, and image-capturing conditions. The current representation learning tends to capture dominant visual-text alignment while overlooking subtle variations that determine retrieval relevance. In this paper, we model such bias in cross-modal representation learning using causal theory. The causal view of this problem suggests ingredients as one of the confounder sources and a simple backdoor adjustment can alleviate the bias. By causal intervention, we reformulate the conventional model for food-to-recipe retrieval with an additional term to remove the potential bias in similarity judgment. Based on this theory-informed formulation, we empirically prove the oracle performance of retrieval on the Recipe1M dataset to be MedR=1 across the testing data sizes of 1K, 10K, and even 50K. We also propose a plug-and-play neural module, which is essentially a multi-label ingredient classifier for debiasing. New state-of-the-art search performances are reported on the Recipe1M dataset. + oai:arXiv.org:2511.15201v2 + cs.CV + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Qing Wang, Chong-Wah Ngo, Ee-Peng Lim + + + Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling + https://arxiv.org/abs/2511.16952 + arXiv:2511.16952v3 Announce Type: replace +Abstract: Automatic facial expression spotting, which aims to identify facial expression instances in untrimmed videos, is crucial for facial expression analysis. Existing methods primarily focus on fully-supervised learning and rely on costly, time-consuming temporal boundary annotations. In this paper, we investigate point-supervised facial expression spotting (P-FES), where only a single timestamp annotation per instance is required for training. We propose a unique two-branch framework for P-FES. First, to mitigate the limitation of hard pseudo-labeling, which often confuses neutral and expression frames with various intensities, we propose a Gaussian-based instance-adaptive intensity modeling (GIM) module to model instance-level expression intensity distribution for soft pseudo-labeling. By detecting the pseudo-apex frame around each point label, estimating the duration, and constructing an instance-level Gaussian distribution, GIM assigns soft pseudo-labels to expression frames for more reliable intensity supervision. The GIM module is incorporated into our framework to optimize the class-agnostic expression intensity branch. Second, we design a class-aware apex classification branch that distinguishes macro- and micro-expressions solely based on their pseudo-apex frames. During inference, the two branches work independently: the class-agnostic expression intensity branch generates expression proposals, while the class-aware apex-classification branch is responsible for macro- and micro-expression classification. Furthermore, we introduce an intensity-aware contrastive loss to enhance discriminative feature learning and suppress neutral noise by contrasting neutral frames with expression frames with various intensities. Extensive experiments on the SAMM-LV, CAS(ME)$^2$, and CAS(ME)$^3$ datasets demonstrate the effectiveness of our proposed framework. + oai:arXiv.org:2511.16952v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yicheng Deng, Hideaki Hayashi, Hajime Nagahara + + + FLUID: Training-Free Face De-identification via Latent Identity Substitution + https://arxiv.org/abs/2511.17005 + arXiv:2511.17005v2 Announce Type: replace +Abstract: Current face de-identification methods that replace identifiable cues in the face region with other sacrifices utilities contributing to realism, such as age and gender. To retrieve the damaged realism, we present FLUID (Face de-identification in the Latent space via Utility-preserving Identity Displacement), a single-input face de-identification framework that directly replaces identity features in the latent space of a pretrained diffusion model without affecting the model's weights. We reinterpret face de-identification as an image editing task in the latent h-space of a pretrained unconditional diffusion model. Our framework estimates identity-editing directions through optimization guided by loss functions that encourage attribute preservation while suppressing identity signals. We further introduce both linear and geodesic (tangent-based) editing schemes to effectively navigate the latent manifold. Experiments on CelebA-HQ and FFHQ show that FLUID achieves a superior balance between identity suppression and attribute preservation, outperforming existing de-identification approaches in both qualitative and quantitative evaluations. + oai:arXiv.org:2511.17005v2 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jinhyeong Park, Shaheryar Muhammad, Seangmin Lee, Jong Taek Lee, Soon Ki Jung + + + Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats + https://arxiv.org/abs/2511.17254 + arXiv:2511.17254v2 Announce Type: replace +Abstract: Despite their impressive performance across a wide range of tasks, Large Vision-Language Models (LVLMs) remain prone to hallucination. In this study, we propose a comprehensive intervention framework aligned with the transformer's causal architecture in LVLMs, integrating the effects of different intervention paths on hallucination. We find that hallucinations in LVLMs do not arise from a single causal path, but rather from the interplay among image-to-input-text, image-to-output-text, and text-to-text pathways. For the first time, we also find that LVLMs rely on different pathways depending on the question-answer alignment format. Building on these insights, we propose simple yet effective methods to identify and intervene on critical hallucination heads within each pathway, tailored to discriminative and generative formats. Experiments across multiple benchmarks demonstrate that our approach consistently reduces hallucinations across diverse alignment types. + oai:arXiv.org:2511.17254v2 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiaye Qian, Ge Zheng, Yuchen Zhu, Sibei Yang + + + Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores + https://arxiv.org/abs/2511.20697 + arXiv:2511.20697v2 Announce Type: replace +Abstract: Understanding complete musical scores entails integrated reasoning over pitch, rhythm, harmony, and large-scale structure, yet the ability of Large Language Models and Vision-Language Models to interpret full musical notation remains insufficiently examined. We introduce the Musical Score Understanding Benchmark (MSU-Bench), the first large-scale, human-curated benchmark for score-level musical understanding across textual (ABC notation) and visual (PDF) modalities. MSU-Bench contains 1,800 generative Question-Answering pairs from works by Bach, Beethoven, Chopin, Debussy, and others, organised into four levels of increasing difficulty, ranging from onset information to texture and form. Evaluations of more than fifteen state-of-the-art models, in both zero-shot and fine-tuned settings, reveal pronounced modality gaps, unstable level-wise performance, and challenges in maintaining multilevel correctness. Fine-tuning substantially improves results across modalities while preserving general knowledge, positioning MSU-Bench as a robust foundation for future research in multimodal reasoning. To facilitate further research, we publicly release MSU-Bench and all associated resources. + oai:arXiv.org:2511.20697v2 + cs.SD + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun + + + Representation Interventions Enable Lifelong Unstructured Knowledge Control + https://arxiv.org/abs/2511.20892 + arXiv:2511.20892v2 Announce Type: replace +Abstract: Large language models (LLMs) often produce incorrect or outdated content. Updating their knowledge efficiently and accurately without costly retraining is a major challenge. This problem is particularly challenging for complex, unstructured knowledge in lifelong settings, where many edits must coexist without interference. We introduce RILKE (Representation Intervention for Lifelong KnowledgE Control), a robust and scalable method that treats knowledge control as interventions within the model's representation space. Leveraging representation-space expressiveness, we identify two key properties enabling RILKE to achieve fine-grained control over complex, unstructured knowledge while maintaining general utility with frozen base weights. During training, RILKE learns paraphrase-robust and edit-localized modules that limit each update to a low-dimensional subspace to minimize cross-edit interference. At inference, a query-adaptive router selects the appropriate module to guide the model's generation. Across LLaMA and Qwen models, RILKE scales effectively to large-scale benchmarks, demonstrating high edit success and strong paraphrase generalization while preserving general utility with modest memory overhead. These results show RILKE is an effective and scalable solution for lifelong knowledge control in LLMs. + oai:arXiv.org:2511.20892v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Xuyuan Liu, Zhengzhang Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Shengyu Chen, Haoyu Wang, Yujun Yan, Haifeng Chen + + + Emergence and Localisation of Semantic Role Circuits in LLMs + https://arxiv.org/abs/2511.20910 + arXiv:2511.20910v2 Announce Type: replace +Abstract: Despite displaying semantic competence, large language models' internal mechanisms that ground abstract semantic structure remain insufficiently characterised. We propose a method integrating role-cross minimal pairs, temporal emergence analysis, and cross-model comparison to study how LLMs implement semantic roles. Our analysis uncovers: (i) highly concentrated circuits (89-94% attribution within 28 nodes); (ii) gradual structural refinement rather than phase transitions, with larger models sometimes bypassing localised circuits; and (iii) moderate cross-scale conservation (24-59% component overlap) alongside high spectral similarity. These findings suggest that LLMs form compact, causally isolated mechanisms for abstract semantic structure, and these mechanisms exhibit partial transfer across scales and architectures. + oai:arXiv.org:2511.20910v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Nura Aljaafari, Danilo S. Carvalho, Andr\'e Freitas + + + Beyond Patch Aggregation: 3-Pass Pyramid Indexing for Vision-Enhanced Document Retrieval + https://arxiv.org/abs/2511.21121 + arXiv:2511.21121v2 Announce Type: replace +Abstract: Document centric RAG pipelines usually begin with OCR, followed by brittle heuristics for chunking, table parsing, and layout reconstruction. These text first workflows are costly to maintain, sensitive to small layout shifts, and often lose the spatial cues that contain the answer. Vision first retrieval has emerged as a strong alternative. By operating directly on page images, systems like ColPali and ColQwen preserve structure and reduce pipeline complexity while achieving strong benchmark performance. However, these late interaction models tie retrieval to a specific vision backbone and require storing hundreds of patch embeddings per page, creating high memory overhead and complicating large scale deployment. + We introduce VisionRAG, a multimodal retrieval system that is OCR free and model agnostic. VisionRAG indexes documents directly as images, preserving layout, tables, and spatial cues, and builds semantic vectors without committing to a specific extraction. Our three pass pyramid indexing framework creates vectors using global page summaries, section headers, visual hotspots, and fact level cues. These summaries act as lightweight retrieval surrogates. At query time, VisionRAG retrieves the most relevant pages using the pyramid index, then forwards the raw page image encoded as base64 to a multimodal LLM for final question answering. During retrieval, reciprocal rank fusion integrates signals across the pyramid to produce robust ranking. + VisionRAG stores only 17 to 27 vectors per page, matching the efficiency of patch based methods while staying flexible across multimodal encoders. On financial document benchmarks, it achieves 0.8051 accuracy at 10 on FinanceBench and 0.9629 recall at 100 on TAT DQA. These results show that OCR free, summary guided multimodal retrieval is a practical and scalable alternative to traditional text extraction pipelines. + oai:arXiv.org:2511.21121v2 + cs.IR + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Anup Roy, Rishabh Gyanendra Upadhyay, Animesh Rameshbhai Panara, Robin Mills, Aidan Millar + + + Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon + https://arxiv.org/abs/2511.23384 + arXiv:2511.23384v3 Announce Type: replace +Abstract: Motivated by the Cybathlon 2024 competition, we developed a modular, online EEG-based brain-computer interface to address these challenges, increasing accessibility for individuals with severe mobility impairments. Our system uses three mental and motor imagery classes to control up to five control signals. The pipeline consists of four modules: data acquisition, preprocessing, classification, and the transfer function to map classification output to control dimensions. We use three diagonalized structured state-space sequence layers as a deep learning classifier. We developed a training game for our pilot where the mental tasks control the game during quick-time events. We implemented a mobile web application for live user feedback. The components were designed with a human-centred approach in collaboration with the tetraplegic user. We achieve up to 84% classification accuracy in offline analysis using an S4D-layer-based model. In a competition setting, our pilot successfully completed one task; we attribute the reduced performance in this context primarily to factors such as stress and the challenging competition environment. Following the Cybathlon, we further validated our pipeline with the original pilot and an additional participant, achieving a success rate of 73% in real-time gameplay. We also compare our model to the EEGEncoder, which is slower in training but has a higher performance. The S4D model outperforms the reference machine learning models. We provide insights into developing a framework for portable BCIs, bridging the gap between the laboratory and daily life. Specifically, our framework integrates modular design, real-time data processing, user-centred feedback, and low-cost hardware to deliver an accessible and adaptable BCI solution, addressing critical gaps in current BCI applications. + oai:arXiv.org:2511.23384v3 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Isabel Whiteley Tscherniak, Niels Christopher Thiemann, Ana McWhinnie-Fern\'andez, Iustin Curcean, Leon Jokinen, Sadat Hodzic, Thomas E. Huber, Daniel Pavlov, Manuel Methasani, Pietro Marcolongo, Glenn Viktor Krafczyk, Oscar Osvaldo Soto Rivera, Thien Le, Flaminia Pallotti, Enrico A. Fazzi, neuroTUM e. V + + + Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios + https://arxiv.org/abs/2512.00920 + arXiv:2512.00920v2 Announce Type: replace +Abstract: Reliable reward models (RMs) are critical for ensuring the safe alignment of large language models (LLMs). However, current RM evaluation methods focus solely on preference perception accuracies in given specific scenarios, obscuring the critical vulnerabilities of RMs in real-world scenarios. We identify the true challenge lies in assessing a novel dimension: Suitability, defined as conditional reliability under specific real-world perturbations. To this end, we introduce Reward Auditor, a hypothesis-testing framework specifically designed for RM suitability inference. Rather than answering "How accurate is the RM's preference perception for given samples?", it employs scientific auditing to answer: "Can we infer RMs exhibit systematic vulnerabilities in specific real-world scenarios?". Under real-world perturbed scenarios, Reward Auditor quantifies statistical significance and effect size by auditing distribution degradation of RM preference perception confidence. This enables inference of both the certainty and severity of RM vulnerabilities across diverse real-world scenarios. This lays a solid foundation for building next-generation LLM alignment systems that are verifiably safe, more robust, and trustworthy. + oai:arXiv.org:2512.00920v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jianxiang Zang, Yongda Wei, Ruxue Bai, Shiyu Jiang, Nijia Mo, Binhong Li, Qiang Sun, Hui Liu + + + LORE: A Large Generative Model for Search Relevance + https://arxiv.org/abs/2512.03025 + arXiv:2512.03025v3 Announce Type: replace +Abstract: Achievement. We introduce LORE, a systematic framework for Large Generative Model-based relevance in e-commerce search. Deployed and iterated over three years, LORE achieves a cumulative +27\% improvement in online GoodRate metrics. This report shares the valuable experience gained throughout its development lifecycle, spanning data, features, training, evaluation, and deployment. Insight. While existing works apply Chain-of-Thought (CoT) to enhance relevance, they often hit a performance ceiling. We argue this stems from treating relevance as a monolithic task, lacking principled deconstruction. Our key insight is that relevance comprises distinct capabilities: knowledge and reasoning, multi-modal matching, and rule adherence. We contend that a qualitative-driven decomposition is essential for breaking through current performance bottlenecks. Contributions. LORE provides a complete blueprint for the LLM relevance lifecycle. Key contributions include: (1) A two-stage training paradigm combining progressive CoT synthesis via SFT with human preference alignment via RL. (2) A comprehensive benchmark, RAIR, designed to evaluate these core capabilities. (3) A query frequency-stratified deployment strategy that efficiently transfers offline LLM capabilities to the online system. LORE serves as both a practical solution and a methodological reference for other vertical domains. + oai:arXiv.org:2512.03025v3 + cs.IR + cs.AI + cs.CL + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Chenji Lu (Alibaba Group), Zhuo Chen (Alibaba Group), Hui Zhao (Alibaba Group), Zhiyuan Zeng (Alibaba Group), Gang Zhao (Alibaba Group), Junjie Ren (Alibaba Group), Ruicong Xu (Alibaba Group), Haoran Li (Alibaba Group), Songyan Liu (Alibaba Group), Pengjie Wang (Alibaba Group), Jian Xu (Alibaba Group), Bo Zheng (Alibaba Group) + + + Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases + https://arxiv.org/abs/2512.03278 + arXiv:2512.03278v2 Announce Type: replace +Abstract: In today's age, it is becoming increasingly difficult to decipher truth from lies. Every day, politicians, media outlets, and public figures make conflicting claims -- often about topics that can, in principle, be verified against structured data. For instance, statements about crime rates, economic growth or healthcare can all be verified against official public records and structured datasets. Building a system that can automatically do that would have sounded like science fiction just a few years ago. Yet, with the extraordinary progress in LLMs and agentic AI, this is now within reach. Still, there remains a striking gap between what is technically possible and what is being demonstrated by recent work. Most existing verification systems operate only on small, single-table databases -- typically a few hundred rows -- that conveniently fit within an LLM's context window. + In this paper we report our progress on Thucy, the first cross-database, cross-table multi-agent claim verification system that also provides concrete evidence for each verification verdict. Thucy remains completely agnostic to the underlying data sources before deployment and must therefore autonomously discover, inspect, and reason over all available relational databases to verify claims. Importantly, Thucy also reports the exact SQL queries that support its verdict (whether the claim is accurate or not) offering full transparency to expert users familiar with SQL. When evaluated on the TabFact dataset -- the standard benchmark for fact verification over structured data -- Thucy surpasses the previous state of the art by 5.6 percentage points in accuracy (94.3% vs. 88.7%). + oai:arXiv.org:2512.03278v2 + cs.DB + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Michael Theologitis, Dan Suciu + + + TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG + https://arxiv.org/abs/2512.07515 + arXiv:2512.07515v2 Announce Type: replace +Abstract: Detecting hallucinations in Retrieval-Augmented Generation remains a challenge. Prior approaches attribute hallucinations to a binary conflict between internal knowledge stored in FFNs and the retrieved context. However, this perspective is incomplete, failing to account for the impact of other components of the LLM, such as the user query, previously generated tokens, the self token, and the final LayerNorm adjustment. To comprehensively capture the impact of these components on hallucination detection, we propose TPA which mathematically attributes each token's probability to seven distinct sources: Query, RAG Context, Past Token, Self Token, FFN, Final LayerNorm, and Initial Embedding. This attribution quantifies how each source contributes to the generation of the next token. Specifically, we aggregate these attribution scores by Part-of-Speech (POS) tags to quantify the contribution of each model component to the generation of specific linguistic categories within a response. By leveraging these patterns, such as detecting anomalies where Nouns rely heavily on LayerNorm, TPA effectively identifies hallucinated responses. Extensive experiments show that TPA achieves state-of-the-art performance. + oai:arXiv.org:2512.07515v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Pengqian Lu, Jie Lu, Anjin Liu, Guangquan Zhang + + + Exploring the Garden of Forking Paths in Empirical Software Engineering Research: A Multiverse Analysis + https://arxiv.org/abs/2512.08910 + arXiv:2512.08910v2 Announce Type: replace +Abstract: In empirical software engineering (SE) research, researchers have considerable freedom to decide how to process data, what operationalizations to use, and which statistical model to fit. Gelman and Loken refer to this freedom as leading to a "garden of forking paths". Although this freedom is often seen as an advantage, it also poses a threat to robustness and replicability: variations in analytical decisions, even when justifiable, can lead to divergent conclusions. + To better understand this risk, we conducted a so-called multiverse analysis on a published empirical SE paper. The paper we picked is a Mining Software Repositories study, as MSR studies commonly use non-trivial statistical models to analyze post-hoc, observational data. In the study, we identified nine pivotal analytical decisions-each with at least one equally defensible alternative and systematically reran all the 3,072 resulting analysis pipelines on the original dataset. Interestingly, only 6 of these universes (<0.2%) reproduced the published results; the overwhelming majority produced qualitatively different, and sometimes even opposite, findings. + This case study of a data analytical method commonly applied to empirical software engineering data reveals how methodological choices can exert a more profound influence on outcomes than is often acknowledged. We therefore advocate that SE researchers complement standard reporting with robustness checks across plausible analysis variants or, at least, explicitly justify each analytical decision. We propose a structured classification model to help classify and improve justification for methodological choices. Secondly, we show how the multiverse analysis is a practical tool in the methodological arsenal of SE researchers, one that can help produce more reliable, reproducible science. + oai:arXiv.org:2512.08910v2 + cs.SE + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Nathan Cassee, Robert Feldt + + + d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models + https://arxiv.org/abs/2512.09675 + arXiv:2512.09675v2 Announce Type: replace +Abstract: Reinforcement learning (RL) is pivotal for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, existing dLLM policy optimization methods suffer from two critical reliability bottlenecks: (1) reward sparsity, arising from coarse or unverifiable signals that impede accurate advantage calculation; and (2) their probability estimates do not account for the gap to the unbiased expectation over all decoding orders, which are intractable to compute. To mitigate these issues, we propose d-TreeRPO, a reliable RL framework for dLLMs that leverages tree-structured rollouts and bottom-up advantage computation based on verifiable outcome rewards to provide fine-grained and verifiable step-wise reward signals. Furthermore, we provide a theoretical proof demonstrating that increasing prediction confidence effectively minimizes the gap between unbiased expected prediction probabilities and its single-step forward pass estimate. Guided by this analysis, we introduce a time-scheduled self-distillation loss during training that enhances prediction confidence in later training stages, thereby enabling more accurate probability estimation and better performance. Experiments demonstrate that d-TreeRPO outperforms existing baselines and achieves significant improvements across multiple reasoning benchmarks. Specifically, it achieves +86.2% on Sudoku, +51.6% on Countdown, +4.5% on GSM8K, and +5.3% on Math500 compared to the base model. + oai:arXiv.org:2512.09675v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Leyi Pan, Shuchang Tao, Yunpeng Zhai, Zheyu Fu, Liancheng Fang, Minghua He, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Aiwei Liu, Lijie Wen + + + Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge + https://arxiv.org/abs/2512.10071 + arXiv:2512.10071v3 Announce Type: replace +Abstract: The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday household tasks that people most want robots to assist with and these tasks introduce long-horizon mobile manipulation challenges in realistic settings, bridging the gap between current research and real-world, human-centric applications. This report presents our solution to the 2025 BEHAVIOR Challenge in a very close 2nd place and substantially outperforms the rest of the submissions. Building on $\pi_{0.5}$, we focus on systematically building our solution by studying the effects of training techniques and data. Through careful ablation studies, we reveal the scaling benefits in both the pre-training and post-training phases, leading to a validation Q-score of 0.345, significantly surpassing previous state-of-the-art performance. We summarize our practical lessons and design recommendations that we hope will provide actionable insights for the broader embodied AI community when adapting powerful foundation models to complex embodied scenarios. Project page: https://github.com/mli0603/openpi-comet + oai:arXiv.org:2512.10071v3 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Junjie Bai, Yu-Wei Chao, Qizhi Chen, Jinwei Gu, Moo Jin Kim, Zhaoshuo Li, Xuan Li, Tsung-Yi Lin, Ming-Yu Liu, Nic Ma, Kaichun Mo, Delin Qu, Shangkun Sun, Hongchi Xia, Fangyin Wei, Xiaohui Zeng + + + Neuronal Attention Circuit (NAC) for Representation Learning + https://arxiv.org/abs/2512.10282 + arXiv:2512.10282v3 Announce Type: replace +Abstract: Attention improves representation learning over RNNs, but its discrete nature limits continuous-time (CT) modeling. We introduce Neuronal Attention Circuit (NAC), a novel, biologically inspired CT-Attention mechanism that reformulates attention logit computation as the solution to a linear first-order ODE with nonlinear interlinked gates derived from repurposing C.elegans Neuronal Circuit Policies (NCPs) wiring. NAC replaces dense projections with sparse sensory gates for key-query projections and a sparse backbone network with two heads for computing content-target and learnable time-constant gates, enabling efficient adaptive dynamics. To improve efficiency and memory consumption, we implemented an adaptable subquadratic sparse Top-K pairwise concatenation mechanism that selectively curates key-query interactions. We provide rigorous theoretical guarantees, including state stability and bounded approximation errors. Empirically, we implemented NAC in diverse domains, including irregular time-series classification, lane-keeping for autonomous vehicles, and industrial prognostics. We observed that NAC matches or outperforms competing baselines in accuracy and occupies an intermediate position in runtime and memory consumption compared with several CT state-of-the-art baselines, while being interpretable at the neuron cell level. + oai:arXiv.org:2512.10282v3 + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Waleed Razzaq, Izis Kanjaraway, Yun-Bo Zhao + + + SWAA: Sliding Window Attention Adaptation for Efficient Long-Context LLMs Without Pretraining + https://arxiv.org/abs/2512.10411 + arXiv:2512.10411v3 Announce Type: replace +Abstract: The quadratic complexity of self-attention in Transformer-based Large Language Models (LLMs) renders long-context inference prohibitively expensive. While Sliding Window Attention (SWA), the simplest sparse attention pattern, offers a linear-complexity alternative, naively applying it to models pretrained with Full Attention (FA) causes catastrophic long-context performance collapse due to the training-inference mismatch. To address this, we propose Sliding Window Attention Adaptation (SWAA), a plug-and-play toolkit of recipes that adapt FA models to SWA without costly pretraining. SWAA systematically combines five strategies: (1) applying SWA only during prefilling; (2) preserving "sink" tokens; (3) interleaving FA/SWA layers; (4) chain-of-thought (CoT); and (5) fine-tuning. Our experiments demonstrate that while individual methods are insufficient, specific synergistic combinations can effectively recover original long-context capabilities. After further analyzing performance-efficiency trade-offs, we identify recommended SWAA configurations for diverse scenarios, which achieve 30% to 100% speedups for long-context LLM inference with acceptable quality loss. Our code is available at https://github.com/yuyijiong/sliding-window-attention-adaptation + oai:arXiv.org:2512.10411v3 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Yijiong Yu, Jiale Liu, Qingyun Wu, Huazheng Wang, Ji Pei + + + When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injection + https://arxiv.org/abs/2512.10449 + arXiv:2512.10449v3 Announce Type: replace +Abstract: Driven by surging submission volumes, scientific peer review has catalyzed two parallel trends: individual over-reliance on LLMs and institutional AI-powered assessment systems. This study investigates the robustness of "LLM-as-a-Judge" systems to adversarial PDF manipulation via invisible text injections and layout aware encoding attacks. We specifically target the distinct incentive of flipping "Reject" decisions to "Accept," a vulnerability that fundamentally compromises scientific integrity. To measure this, we introduce the Weighted Adversarial Vulnerability Score (WAVS), a novel metric that quantifies susceptibility by weighting score inflation against the severity of decision shifts relative to ground truth. We adapt 15 domain-specific attack strategies, ranging from semantic persuasion to cognitive obfuscation, and evaluate them across 13 diverse language models (including GPT-5 and DeepSeek) using a curated dataset of 200 official and real-world accepted and rejected submissions (e.g., ICLR OpenReview). Our results demonstrate that obfuscation techniques like "Maximum Mark Magyk" and "Symbolic Masking & Context Redirection" successfully manipulate scores, achieving decision flip rates of up to 86.26% in open-source models, while exposing distinct "reasoning traps" in proprietary systems. We release our complete dataset and injection framework to facilitate further research on the topic (https://anonymous.4open.sciencer/llm-jailbreak-FC9E/). + oai:arXiv.org:2512.10449v3 + cs.AI + cs.CL + cs.CR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Devanshu Sahoo, Manish Prasad, Vasudev Majhi, Jahnvi Singh, Vinay Chamola, Yash Sinha, Murari Mandal, Dhruv Kumar + + + Evaluating Gemini Robotics Policies in a Veo World Simulator + https://arxiv.org/abs/2512.10675 + arXiv:2512.10675v2 Announce Type: replace +Abstract: Generative world models hold significant potential for simulating interactions with visuomotor policies in varied environments. Frontier video models can enable generation of realistic observations and environment interactions in a scalable and general manner. However, the use of video models in robotics has been limited primarily to in-distribution evaluations, i.e., scenarios that are similar to ones used to train the policy or fine-tune the base video model. In this report, we demonstrate that video models can be used for the entire spectrum of policy evaluation use cases in robotics: from assessing nominal performance to out-of-distribution (OOD) generalization, and probing physical and semantic safety. We introduce a generative evaluation system built upon a frontier video foundation model (Veo). The system is optimized to support robot action conditioning and multi-view consistency, while integrating generative image-editing and multi-view completion to synthesize realistic variations of real-world scenes along multiple axes of generalization. We demonstrate that the system preserves the base capabilities of the video model to enable accurate simulation of scenes that have been edited to include novel interaction objects, novel visual backgrounds, and novel distractor objects. This fidelity enables accurately predicting the relative performance of different policies in both nominal and OOD conditions, determining the relative impact of different axes of generalization on policy performance, and performing red teaming of policies to expose behaviors that violate physical or semantic safety constraints. We validate these capabilities through 1600+ real-world evaluations of eight Gemini Robotics policy checkpoints and five tasks for a bimanual manipulator. + oai:arXiv.org:2512.10675v2 + cs.RO + cs.AI + cs.CV + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Gemini Robotics Team, Krzysztof Choromanski, Coline Devin, Yilun Du, Debidatta Dwibedi, Ruiqi Gao, Abhishek Jindal, Thomas Kipf, Sean Kirmani, Isabel Leal, Fangchen Liu, Anirudha Majumdar, Andrew Marmon, Carolina Parada, Yulia Rubanova, Dhruv Shah, Vikas Sindhwani, Jie Tan, Fei Xia, Ted Xiao, Sherry Yang, Wenhao Yu, Allan Zhou + + + Legitimizing, Developing, and Sustaining Feminist HCI in East Asia: Challenges and Opportunities + https://arxiv.org/abs/2512.13000 + arXiv:2512.13000v2 Announce Type: replace +Abstract: Feminist HCI has been rapidly developing in East Asian contexts in recent years. The region's unique cultural and political backgrounds have contributed valuable, situated knowledge, revealing topics such as localized digital feminism practices, or women's complex navigation among social expectations. However, the very factors that ground these perspectives also create significant survival challenges for researchers in East Asia. These include a scarcity of dedicated funding, the stigma of being perceived as less valuable than productivity-oriented technologies, and the lack of senior researchers and established, resilient communities. Grounded in these challenges and our prior collective practices, we propose this meet-up with two focused goals: (1) to provide a legitimized channel for Feminist HCI researchers to connect and build community, and (2) to facilitate an action-oriented dialogue on how to legitimize, develop, and sustain Feminist HCI in the East Asian context. The website for this meet-up is: https://feminist-hci.github.io/ + oai:arXiv.org:2512.13000v2 + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1145/3772363.3778806 + Runhua Zhang (Ella), Ruyuan Wan (Ella), Jiaqi (Ella), Li, Daye Kang, Yigang Qin, Yijia Wang, Ziqi Pan, Tiffany Knearem, Huamin Qu, Xiaojuan Ma + + + Socratic Students: Teaching Language Models to Learn by Asking Questions + https://arxiv.org/abs/2512.13102 + arXiv:2512.13102v4 Announce Type: replace +Abstract: Large language Models (LLMs) are usually used to answer questions, but many high-stakes applications (e.g., tutoring, clinical support) require the complementary skill of asking questions: detecting missing information, requesting clarifications, and using them to solve tasks. We study this skill in reasoning-heavy domains where progress depends on inquiry rather than factual recall. We define an interactive protocol where a student model engages a stronger teacher under a small turn budget. After each teacher reply, we evaluate the student on the original task with Pass@k. We propose Outcome-Driven Question optimization Strategy (ODQS ), a training framework that learns a questioning policy from downstream task outcomes. At each turn, we sample multiple candidate questions; query the teacher with each, then score the student's resulting performance. Using these scores, we train the student via supervised fine-tuning followed by Direct Preference Optimization (DPO), without any human labels. On GSM8K, HumanEval, and OpenCoder, ODQS produces large gains over interactive baselines, boosting Pass@5 by up to 54.7% (absolute) on math and 22.9% (absolute) on coding, and matching baseline performance in three fewer turns. Thus, question asking can be explicitly trained from task outcomes, improving both accuracy and efficiency in interactive reasoning. + oai:arXiv.org:2512.13102v4 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Rajeev Bhatt Ambati, Tianyi Niu, Aashu Singh, Shlok Mishra, Snigdha Chaturvedi, Shashank Srivastava + + + RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics + https://arxiv.org/abs/2512.13660 + arXiv:2512.13660v2 Announce Type: replace +Abstract: Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metric-grounded reasoning compounded with complex spatial referring and real-world metric measurement. However, existing methods struggle with this compositional task. To this end, we propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring via a universal spatial encoder and a regression-supervised decoder to enhance scale awareness during supervised fine-tuning (SFT). Moreover, RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning (RFT) with metric-sensitive process rewards, supervising key intermediate perceptual cues to accurately generate spatial traces. To support SFT and RFT training, we introduce TraceSpatial, a large-scale dataset of 30M QA pairs, spanning outdoor/indoor/tabletop scenes and supporting complex reasoning processes (up to 9 steps). We further present TraceSpatial-Bench, a challenging benchmark filling the gap to evaluate spatial tracing. Experimental results show that RoboTracer surpasses baselines in spatial understanding, measuring, and referring, with an average success rate of 79.1%, and also achieves SOTA performance on TraceSpatial-Bench by a large margin, exceeding Gemini-2.5-Pro by 36% accuracy. Notably, RoboTracer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (UR5, G1 humanoid) in cluttered real-world scenes. See the project page at https://zhoues.github.io/RoboTracer. + oai:arXiv.org:2512.13660v2 + cs.RO + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Enshen Zhou, Cheng Chi, Yibo Li, Jingkun An, Jiayuan Zhang, Shanyu Rong, Yi Han, Yuheng Ji, Mengzhen Liu, Pengwei Wang, Zhongyuan Wang, Lu Sheng, Shanghang Zhang + + + Massive Editing for Large Language Models Based on Dynamic Weight Generation + https://arxiv.org/abs/2512.14395 + arXiv:2512.14395v3 Announce Type: replace +Abstract: Knowledge Editing (KE) is a field that studies how to modify some knowledge in Large Language Models (LLMs) at a low cost (compared to pre-training). Currently, performing large-scale edits on LLMs while ensuring the Reliability, Generality, and Locality metrics of the edits remain a challenge. This paper proposes a Massive editing approach for LLMs based on dynamic weight Generation (MeG). Our MeG involves attaching a dynamic weight neuron to specific layers of the LLMs and using a diffusion model to conditionally generate the weights of this neuron based on the input query required for the knowledge. This allows the use of adding a single dynamic weight neuron to achieve the goal of large-scale knowledge editing. Experiments show that our MeG can significantly improve the performance of large-scale KE in terms of Reliability, Generality, and Locality metrics compared to existing knowledge editing methods, particularly with a high percentage point increase in the absolute value index for the Locality metric, demonstrating the advantages of our proposed method. + oai:arXiv.org:2512.14395v3 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wentao Wan, Qiqing Lao, Zhiwei Xie, Hefeng Wu, Runnan Lin, Liang Lin, Keze Wang + + + Element-Saving Hexahedral 3-Refinement Templates + https://arxiv.org/abs/2512.14862 + arXiv:2512.14862v4 Announce Type: replace +Abstract: Conforming hexahedral (hex) meshes are favored in simulation for their superior numerical properties, yet automatically decomposing a general 3D volume into a conforming hex mesh remains a formidable challenge. Among existing approaches, methods that construct an adaptive Cartesian grid and subsequently convert it into a conforming mesh stand out for their robustness. However, the topological schemes enabling this conversion require strict compatibility conditions among grid elements, which inevitably refine the initial grid and increase element count. Developing more relaxed conditions to minimize this overhead has been a persistent research focus. State-of-the-art 2-refinement octree methods employ a weakly-balanced condition combined with a generalized pairing condition, using a dual transformation to yield exceptionally low element counts. Yet this approach suffers from critical limitations: information stored on primal cells, such as signed distance fields or triangle index sets, is lost after dualization, and the resulting dual cells often exhibit poor minimum scaled Jacobian (min SJ) with non-planar quadrilateral (quad) faces. Alternatively, 3-refinement 27-tree methods can directly generate conforming hex meshes through template-based replacement of primal cells, producing higher-quality elements with planar quad faces. However, previous 3-refinement techniques impose conditions far more strict than 2-refinement counterparts, severely over-refining grids by factors of ten to one hundred, creating a major bottleneck in simulation pipelines. This article introduces a novel 3-refinement approach that transforms an adaptive 3-refinement grid into a conforming grid using a moderately-balanced condition, slightly stronger than the weakly-balanced condition but substantially more relaxed than prior 3-refinement requirements...... (check PDF for the full abstract) + oai:arXiv.org:2512.14862v4 + cs.CG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Hua Tong, Yongjie Jessica Zhang + + + Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers + https://arxiv.org/abs/2512.15674 + arXiv:2512.15674v2 Announce Type: replace +Abstract: Large language model (LLM) activations are notoriously difficult to understand, with most existing techniques using complex, specialized methods for interpreting them. Recent work has proposed a simpler approach known as LatentQA: training LLMs to directly accept LLM activations as inputs and answer arbitrary questions about them in natural language. However, prior work has focused on narrow task settings for both training and evaluation. In this paper, we instead take a generalist perspective. We evaluate LatentQA-trained models, which we call Activation Oracles (AOs), in far out-of-distribution settings and examine how performance scales with training data diversity. We find that AOs can recover information fine-tuned into a model (e.g., biographical knowledge or malign propensities) that does not appear in the input text, despite never being trained with activations from a fine-tuned model. Our main evaluations are four downstream tasks where we can compare to prior white- and black-box techniques. We find that even narrowly-trained LatentQA models can generalize well, and that adding additional training datasets (such as classification tasks and a self-supervised context prediction task) yields consistent further improvements. Our best AOs match or exceed white-box baselines on all four tasks and the best overall baseline on 3 of 4. These results suggest that diversified training to answer natural-language queries imparts a general capability to verbalize information about LLM activations. + oai:arXiv.org:2512.15674v2 + cs.CL + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Adam Karvonen, James Chua, Cl\'ement Dumas, Kit Fraser-Taliente, Subhash Kantamneni, Julian Minder, Euan Ong, Arnab Sen Sharma, Daniel Wen, Owain Evans, Samuel Marks + + + Confusions and Erasures of Error-Bounded Block Decoders with Finite Blocklength + https://arxiv.org/abs/2512.16665 + arXiv:2512.16665v2 Announce Type: replace +Abstract: This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR. + oai:arXiv.org:2512.16665v2 + cs.IT + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Bin Han, Yao Zhu, Rafael F. Schaefer, Giuseppe Caire, Anke Schmeink, H. Vincent Poor, Hans D. Schotten + + + ShareChat: A Dataset of Chatbot Conversations in the Wild + https://arxiv.org/abs/2512.17843 + arXiv:2512.17843v2 Announce Type: replace +Abstract: While academic research typically treats Large Language Models (LLM) as generic text generators, they are distinct commercial products with unique interfaces and capabilities that fundamentally shape user behavior. Current datasets obscure this reality by collecting text-only data through uniform interfaces that fail to capture authentic chatbot usage. To address this limitation, we present ShareChat, a large-scale corpus of 142,808 conversations (660,293 turns) sourced directly from publicly shared URLs on ChatGPT, Perplexity, Grok, Gemini, and Claude. ShareChat distinguishes itself by preserving native platform affordances, such as citations and thinking traces, across a diverse collection covering 101 languages and the period from April 2023 to October 2025. Furthermore, ShareChat offers substantially longer context windows and greater interaction depth than prior datasets. To illustrate the dataset's breadth, we present three case studies: a completeness analysis of intent satisfaction, a citation study of model grounding, and a temporal analysis of engagement rhythms. This work provides the community with a vital and timely resource for understanding authentic user-LLM chatbot interactions in the wild. The dataset will be publicly available. + oai:arXiv.org:2512.17843v2 + cs.CL + cs.AI + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yueru Yan, Tuc Nguyen, Bo Su, Melissa Lieffers, Thai Le + + + Modular Automatic Complexity Analysis of Recursive Integer Programs + https://arxiv.org/abs/2512.18851 + arXiv:2512.18851v2 Announce Type: replace +Abstract: In earlier work, we developed a modular approach for automatic complexity analysis of integer programs. However, these integer programs do not allow non-tail recursive calls or subprocedures. In this paper, we consider integer programs with function calls and present a natural extension of our modular complexity analysis approach to the recursive setting based on a new form of ranking functions. Hence, our approach combines already existing powerful techniques on the "imperative" parts of the program and our novel ranking functions on the recursive parts. The strength of this combination is demonstrated by our implementation in the complexity analysis tool KoAT. + oai:arXiv.org:2512.18851v2 + cs.LO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Nils Lommen, J\"urgen Giesl + + + ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management + https://arxiv.org/abs/2512.19001 + arXiv:2512.19001v2 Announce Type: replace +Abstract: As the pursuit of synergy between Artificial Intelligence (AI) and Operations Research (OR) gains momentum in handling complex inventory systems, a critical challenge persists: how to effectively reconcile AI's adaptive perception with OR's structural rigor. To bridge this gap, we propose a novel OR-Guided "Pretrain-then-Reinforce" framework. To provide structured guidance, we propose a simulation-augmented OR model that generates high-quality reference decisions, implicitly capturing complex business constraints and managerial preferences. Leveraging these OR-derived decisions as foundational training labels, we design a domain-informed deep learning foundation model to establish foundational decision-making capabilities, followed by a reinforcement learning (RL) fine-tuning stage. Uniquely, we position RL as a deep alignment mechanism that enables the AI agent to internalize the optimality principles of OR, while simultaneously leveraging exploration for general policy refinement and allowing expert guidance for scenario-specific adaptation (e.g., promotional events). Validated through extensive numerical experiments and a field deployment at JD.com augmented by a Difference-in-Differences (DiD) analysis, our model significantly outperforms incumbent industrial practices, delivering real-world gains of a 5.27-day reduction in turnover and a 2.29% increase in in-stock rates, alongside a 29.95% decrease in holding costs. Contrary to the prevailing trend of brute-force model scaling, our study demonstrates that a lightweight, domain-informed model can deliver state-of-the-art performance and robust transferability when guided by structured OR logic. This approach offers a scalable and cost-effective paradigm for intelligent supply chain management, highlighting the value of deeply aligning AI with OR. + oai:arXiv.org:2512.19001v2 + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Lingjie Zhao, Xue Yu, Yongzhi Qi, Hao Hu, Jianshen Zhang, Yingzheng Ma, Shuyu Han, Wei Qi, Zuo-Jun Max Shen + + + A Cartesian Cut-Cell Two-Fluid Method for Two-Phase Diffusion Problems + https://arxiv.org/abs/2512.19407 + arXiv:2512.19407v2 Announce Type: replace +Abstract: We present a Cartesian cut-cell finite-volume method for sharp-interface two-phase diffusion problems in static geometries. The formulation follows a two-fluid approach: independent diffusion equations are discretized in each phase on a fixed Cartesian grid, while the phases are coupled through embedded interface conditions enforcing continuity of diffusive flux and a general jump law. Cut cells are treated by integrating the governing equations over phase-restricted control volumes and surfaces, yielding discrete divergence and gradient operators that are locally conservative within each phase. Interface coupling is achieved by introducing a small set of interfacial unknowns per cut cell on the embedded boundary; the resulting algebraic system involves only bulk and interfacial averages. A key feature of the method is the use of a reduced set of geometric information based solely on low-order moments (trimmed volumes, apertures and interface measures/centroids), allowing robust implementation without constructing explicitly cut-cell polytopes. The method supports steady (Poisson) and unsteady (diffusion) regimes and incorporates Dirichlet, Neumann, Robin boundary conditions and general jumps. We validate the scheme on one-, two- and three-dimensional single-phase and two-phase benchmarks, including curved embedded boundaries, Robin conditions and strong property/jump contrasts. The results demonstrate a superlinear convergence behavior, sharp enforcement of interfacial laws and excellent conservation properties. Extensions to moving interfaces and Stefan-type free-boundary problems are natural perspectives of this framework. + oai:arXiv.org:2512.19407v2 + math.NA + cs.NA + physics.comp-ph + physics.flu-dyn + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Louis Libat, Can Sel\c{c}uk, Eric Ch\'enier, Vincent Le Chenadec + + + LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models + https://arxiv.org/abs/2512.20002 + arXiv:2512.20002v2 Announce Type: replace +Abstract: Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing deep forecasting models typically supervise predictions using full-length temporal windows, which include substantial high-frequency noise and obscure long-term trends. Moreover, auxiliary variables containing rich domain-specific information are often underutilized, especially in few-shot settings. To address these challenges, we propose LoFT-LLM, a frequency-aware forecasting pipeline that integrates low-frequency learning with semantic calibration via a large language model (LLM). Firstly, a Patch Low-Frequency forecasting Module (PLFM) extracts stable low-frequency trends from localized spectral patches. Secondly, a residual learner then models high-frequency variations. Finally, a fine-tuned LLM refines the predictions by incorporating auxiliary context and domain knowledge through structured natural language prompts. Extensive experiments on financial and energy datasets demonstrate that LoFT-LLM significantly outperforms strong baselines under both full-data and few-shot regimes, delivering superior accuracy, robustness, and interpretability. + oai:arXiv.org:2512.20002v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jiacheng You, Jingcheng Yang, Yuhang Xie, Zhongxuan Wu, Xiucheng Li, Feng Li, Pengjie Wang, Jian Xu, Bo Zheng, Xinyang Chen + + + D^3ETOR: Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations + https://arxiv.org/abs/2512.20260 + arXiv:2512.20260v2 Announce Type: replace +Abstract: Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks generated by general-purpose segmentation models (e.g., SAM) and filtered via rules are often unreliable, as these models lack the task-specific semantic understanding required for effective pseudo labeling in COD; and (2) the neglect of inherent annotation bias in scribbles, which hinders the model from capturing the global structure of camouflaged objects. To overcome these challenges, we propose ${D}^{3}$ETOR, a two-stage WSCOD framework consisting of Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing. In the first stage, we introduce an adaptive entropy-driven point sampling method and a multi-agent debate mechanism to enhance the capability of SAM for COD, improving the interpretability and precision of pseudo masks. In the second stage, we design FADeNet, which progressively fuses multi-level frequency-aware features to balance global semantic understanding with local detail modeling, while dynamically reweighting supervision strength across regions to alleviate scribble bias. By jointly exploiting the supervision signals from both the pseudo masks and scribble semantics, ${D}^{3}$ETOR significantly narrows the gap between weakly and fully supervised COD, achieving state-of-the-art performance on multiple benchmarks. + oai:arXiv.org:2512.20260v2 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jiawei Ge, Jiuxin Cao, Xinyi Li, Xuelin Zhu, Chang Liu, Bo Liu, Chen Feng, Ioannis Patras + + + Mixture-of-Experts with Gradient Conflict-Driven Subspace Topology Pruning for Emergent Modularity + https://arxiv.org/abs/2512.20291 + arXiv:2512.20291v3 Announce Type: replace +Abstract: Mixture-of-Experts (MoE) architectures achieve parameter efficiency through conditional computation, yet contemporary designs suffer from two fundamental limitations: structural parameter isolation that causes catastrophic forgetting, and instruction-overfitting that degrades performance in instruction-free scenarios. We propose CDSP-MoE (Conflict-Driven Subspace Pruning MoE), a framework that addresses these issues through a paradigm shift from isolated expert containers to dynamic expert instantiation within a shared physical subspace. Grounded in the Universal Weight Subspace Hypothesis, CDSP-MoE maintains a super-complete parameter backbone where logical experts are carved out via learnable topology masks. Unlike prior work that uses gradient conflict for token reassignment or optimization surgery, we leverage it as a structural supervisory signal: a Lagged Gradient Game penalizes interfering connections in the shared manifold, enabling the topology to spontaneously prune conflicting pathways and evolve interpretable modular structures. Experimental results demonstrate that CDSP-MoE achieves robust content-driven routing without human-defined task labels, maintaining semantic specialization even under strict blind inference protocols where explicit instructions are absent. Code is available at: https://github.com/konodiodaaaaa1/Conflict-Driven-Subspace-Pruning-Mixture-of-Experts + oai:arXiv.org:2512.20291v3 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yuxing Gan, Ziyu Lei + + + Efficient and Robust Video Defense Framework against 3D-field Personalized Talking Face + https://arxiv.org/abs/2512.21019 + arXiv:2512.21019v3 Announce Type: replace +Abstract: State-of-the-art 3D-field video-referenced Talking Face Generation (TFG) methods synthesize high-fidelity personalized talking-face videos in real time by modeling 3D geometry and appearance from reference portrait video. This capability raises significant privacy concerns regarding malicious misuse of personal portraits. However, no efficient defense framework exists to protect such videos against 3D-field TFG methods. While image-based defenses could apply per-frame 2D perturbations, they incur prohibitive computational costs, severe video quality degradation, failing to disrupt 3D information for video protection. To address this, we propose a novel and efficient video defense framework against 3D-field TFG methods, which protects portrait video by perturbing the 3D information acquisition process while maintain high-fidelity video quality. Specifically, our method introduces: (1) a similarity-guided parameter sharing mechanism for computational efficiency, and (2) a multi-scale dual-domain attention module to jointly optimize spatial-frequency perturbations. Extensive experiments demonstrate that our proposed framework exhibits strong defense capability and achieves a 47x acceleration over the fastest baseline while maintaining high fidelity. Moreover, it remains robust against scaling operations and state-of-the-art purification attacks, and the effectiveness of our design choices is further validated through ablation studies. Our project is available at https://github.com/Richen7418/VDF. + oai:arXiv.org:2512.21019v3 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Rui-qing Sun, Xingshan Yao, Tian Lan, Jia-Ling Shi, Chen-Hao Cui, Hui-Yang Zhao, Zhijing Wu, Chen Yang, Xian-Ling Mao + + + Making AI Functional with Workarounds: An Insider's Account of Invisible Labour in Organisational Politics + https://arxiv.org/abs/2512.21055 + arXiv:2512.21055v2 Announce Type: replace +Abstract: Research on the implementation of Generative Artificial Intelligence (GenAI) in higher education often focuses on strategic goals, overlooking the hidden, and often politically charged, labour required to make it functional. This paper provides an insider's account of the sociotechnical friction that arises when an institutional goal of empowering non-technical staff conflicts with the technical limitations of enterprise Large Language Models (LLMs). Through analytic autoethnography, this study examines a GenAI project pushed to an impasse, focusing on a workaround developed to navigate not only technical constraints but also the combined challenge of organisational territoriality and assertions of positional power. Drawing upon Alter's (2014) theory of workarounds, the analysis interprets "articulation work" as a form of "invisible labour". By engaging with the Information Systems (IS) domains of user innovation and technology-in-practice, this study argues that such user-driven workarounds should be understood not as deviations, but as integral acts of sociotechnical integration. This integration, however, highlights the central paradoxes of modern GenAI where such workarounds for "unfinished" systems can simultaneously create unofficial "shadow" systems and obscure the crucial, yet invisible, sociotechnical labour involved. The findings suggest that the invisible labour required to integrate GenAI within complex organisational politics is an important, rather than peripheral, component of how it becomes functional in practice. + oai:arXiv.org:2512.21055v2 + cs.CY + cs.HC + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Australasian Conference on Information Systems (ACIS) 2025 + Shang Chieh Lee, Bhuva Narayan, Simon Buckingham Shum, Stella Ng, A. Baki Kocaballi + + + NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agent + https://arxiv.org/abs/2512.21578 + arXiv:2512.21578v2 Announce Type: replace +Abstract: We present the development and optimization of PayPal's Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal platform. Through our strategic partnership with NVIDIA, we leveraged the NeMo Framework for LLM model fine-tuning to enhance agent performance. Specifically, we optimized the Search and Discovery agent by replacing our base model with a fine-tuned Nemotron small language model (SLM). + We conducted comprehensive experiments using the llama3.1-nemotron-nano-8B-v1 architecture, training LoRA-based models through systematic hyperparameter sweeps across learning rates, optimizers (Adam, AdamW), cosine annealing schedules, and LoRA ranks. Our contributions include: (1) the first application of NVIDIA's NeMo Framework to commerce-specific agent optimization, (2) LLM powered fine-tuning strategy for retrieval-focused commerce tasks, (3) demonstration of significant improvements in latency and cost while maintaining agent quality, and (4) a scalable framework for multi-agent system optimization in production e-commerce environments. Our results demonstrate that the fine-tuned Nemotron SLM effectively resolves the key performance issue in the retrieval component, which represents over 50\% of total agent response time, while maintaining or enhancing overall system performance. + oai:arXiv.org:2512.21578v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Ali Sahami, Sudhanshu Garg, Andrew Wang, Chaitanya Kulkarni, Farhad Farahani, Sean Yun-Shiuan Chuang, Jian Wan, Srinivasan Manoharan, Uma Kona, Nitin Sharma, Linsey Pang, Prakhar Mehrotra, Jessica Clark, Mark Moyou + + + A Comedy of Estimators: On KL Regularization in RL Training of LLMs + https://arxiv.org/abs/2512.21852 + arXiv:2512.21852v2 Announce Type: replace +Abstract: The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL objective for LLM training involves a regularization term, which is the reverse Kullback-Leibler (KL) divergence between the trained policy and the reference policy. Since computing the KL divergence exactly is intractable, various estimators are used in practice to estimate it from on-policy samples. Despite its wide adoption, including in several open-source libraries, there is no systematic study analyzing the numerous ways of incorporating KL estimators in the objective and their effect on the downstream performance of RL-trained models. Recent works show that prevailing practices for incorporating KL regularization do not provide correct gradients for stated objectives, creating a discrepancy between the objective and its implementation. In this paper, we further analyze these practices and study the gradients of several estimators configurations, revealing how design choices shape gradient bias. We substantiate these findings with empirical observations by RL fine-tuning \texttt{Qwen2.5-7B}, \texttt{Llama-3.1-8B-Instruct} and \texttt{Qwen3-4B-Instruct-2507} with different configurations and evaluating their performance on both in- and out-of-distribution tasks. Through our analysis, we observe that, in on-policy settings: (1) estimator configurations with biased gradients can result in training instabilities; and (2) using estimator configurations resulting in unbiased gradients leads to better performance on in-domain as well as out-of-domain tasks. We also investigate the performance resulting from different KL configurations in off-policy settings and observe that KL regularization can help stabilize off-policy RL training resulting from asynchronous setups. + oai:arXiv.org:2512.21852v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Vedant Shah, Johan Obando-Ceron, Vineet Jain, Brian Bartoldson, Bhavya Kailkhura, Sarthak Mittal, Glen Berseth, Pablo Samuel Castro, Yoshua Bengio, Nikolay Malkin, Moksh Jain, Siddarth Venkatraman, Aaron Courville + + + DiRL: An Efficient Post-Training Framework for Diffusion Language Models + https://arxiv.org/abs/2512.22234 + arXiv:2512.22234v2 Announce Type: replace +Abstract: Diffusion Language Models (dLLMs) have emerged as promising alternatives to Auto-Regressive (AR) models. While recent efforts have validated their pre-training potential and accelerated inference speeds, the post-training landscape for dLLMs remains underdeveloped. Existing methods suffer from computational inefficiency and objective mismatches between training and inference, severely limiting performance on complex reasoning tasks such as mathematics. To address this, we introduce DiRL, an efficient post-training framework that tightly integrates FlexAttention-accelerated blockwise training with LMDeploy-optimized inference. This architecture enables a streamlined online model update loop, facilitating efficient two-stage post-training (Supervised Fine-Tuning followed by Reinforcement Learning). Building on this framework, we propose DiPO, the first unbiased Group Relative Policy Optimization (GRPO) implementation tailored for dLLMs. We validate our approach by training DiRL-8B-Instruct on high-quality math data. Our model achieves state-of-the-art math performance among dLLMs and surpasses comparable models in the Qwen2.5 series on several benchmarks. + oai:arXiv.org:2512.22234v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ying Zhu, Jiaxin Wan, Xiaoran Liu, Siyang He, Qiqi Wang, Xu Guo, Tianyi Liang, Zengfeng Huang, Ziwei He, Xipeng Qiu + + + SmartSnap: Proactive Evidence Seeking for Self-Verifying Agents + https://arxiv.org/abs/2512.22322 + arXiv:2512.22322v2 Announce Type: replace +Abstract: Agentic reinforcement learning (RL) holds great promise for the development of autonomous agents under complex GUI tasks, but its scalability remains severely hampered by the verification of task completion. Existing task verification is treated as a passive, post-hoc process: a verifier (i.e., rule-based scoring script, reward or critic model, and LLM-as-a-Judge) analyzes the agent's entire interaction trajectory to determine if the agent succeeds. Such processing of verbose context that contains irrelevant, noisy history poses challenges to the verification protocols and therefore leads to prohibitive cost and low reliability. To overcome this bottleneck, we propose SmartSnap, a paradigm shift from this passive, post-hoc verification to proactive, in-situ self-verification by the agent itself. We introduce the Self-Verifying Agent, a new type of agent designed with dual missions: to not only complete a task but also to prove its accomplishment with curated snapshot evidences. Guided by our proposed 3C Principles (Completeness, Conciseness, and Creativity), the agent leverages its accessibility to the online environment to perform self-verification on a minimal, decisive set of snapshots. Such evidences are provided as the sole materials for a general LLM-as-a-Judge verifier to determine their validity and relevance. Experiments on mobile tasks across model families and scales demonstrate that our SmartSnap paradigm allows training LLM-driven agents in a scalable manner, bringing performance gains up to 26.08% and 16.66% respectively to 8B and 30B models. The synergizing between solution finding and evidence seeking facilitates the cultivation of efficient, self-verifying agents with competitive performance against DeepSeek V3.1 and Qwen3-235B-A22B. Code is available at: https://github.com/TencentYoutuResearch/SmartSnap + oai:arXiv.org:2512.22322v2 + cs.CL + cs.AI + cs.CV + cs.LG + cs.MA + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Shaofei Cai, Yulei Qin, Haojia Lin, Zihan Xu, Gang Li, Yuchen Shi, Zongyi Li, Yong Mao, Siqi Cai, Xiaoyu Tan, Yitao Liang, Ke Li, Xing Sun + + + SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence + https://arxiv.org/abs/2512.22334 + arXiv:2512.22334v3 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:2512.22334v3 + cs.AI + cs.CL + Wed, 07 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 + + + When Does Multi-Task Learning Fail? Quantifying Data Imbalance and Task Independence in Metal Alloy Property Prediction + https://arxiv.org/abs/2512.22740 + arXiv:2512.22740v2 Announce Type: replace +Abstract: Multi-task learning (MTL) is widely adopted in materials informatics under the assumption that related properties share leverageable physical principles. This study critically examines this premise by simultaneously predicting electrical resistivity, Vickers hardness, and amorphous-forming ability using a dataset of 54,028 metal alloys.1 Contrary to expectations, we observe a striking dichotomy: MTL significantly degrades regression accuracy (e.g., hardness 2$R^2$ drops from 3$0.832$ to 4$0.694$) while improving classification performance (amorphous F1 increases from 5$0.703$ to 6$0.744$).7 Analysis of learned task graphs reveals negligible inter-task correlations, attributing regression failure to negative transfer driven by severe data imbalance (52,388 vs. 800 samples). To mitigate this, we evaluate Deep Imbalanced Regression techniques. PCGrad recovers hardness performance ($R^2 \rightarrow 0.855$) by resolving gradient conflicts, while LDS+GradNorm achieves the best overall multi-task balance. Our findings suggest that alloy properties often behave independently, necessitating specific strategies: independent models for maximum regression precision, PCGrad for minority tasks, and LDS+GradNorm when balanced joint prediction is required. + oai:arXiv.org:2512.22740v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Sungwoo Kang + + + Agentic Physical AI toward a Domain-Specific Foundation Model for Nuclear Reactor Control + https://arxiv.org/abs/2512.23292 + arXiv:2512.23292v2 Announce Type: replace +Abstract: The prevailing paradigm in AI for physical systems, scaling general-purpose foundation models toward universal multimodal reasoning, confronts a fundamental barrier at the control interface. Recent benchmarks show that even frontier vision-language models achieve only 50-53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility while violating physical constraints. This input unfaithfulness is not a scaling deficiency but a structural limitation. Perception-centric architectures optimize parameter-space imitation, whereas safety-critical control demands outcome-space guarantees over executed actions. Here, we present a fundamentally different pathway toward domain-specific foundation models by introducing compact language models operating as Agentic Physical AI, in which policy optimization is driven by physics-based validation rather than perceptual inference. We train a 360-million-parameter model on synthetic reactor control scenarios, scaling the dataset from 10^3 to 10^5 examples. This induces a sharp phase transition absent in general-purpose models. Small-scale systems exhibit high-variance imitation with catastrophic tail risk, while large-scale models undergo variance collapse exceeding 500x reduction, stabilizing execution-level behavior. Despite balanced exposure to four actuation families, the model autonomously rejects approximately 70% of the training distribution and concentrates 95% of runtime execution on a single-bank strategy. Learned representations transfer across distinct physics and continuous input modalities without architectural modification. + oai:arXiv.org:2512.23292v2 + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yoonpyo Lee, Kazuma Kobayashi, Sai Puppala, Sajedul Talukder, Seid Koric, Souvik Chakraborty, Syed Bahauddin Alam + + + SoulX-FlashTalk: Real-Time Infinite Streaming of Audio-Driven Avatars via Self-Correcting Bidirectional Distillation + https://arxiv.org/abs/2512.23379 + arXiv:2512.23379v3 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-FlashTalk}, 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-FlashTalk 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:2512.23379v3 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Le Shen, Qian Qiao, Tan Yu, Ke Zhou, Tianhang Yu, Yu Zhan, Zhenjie Wang, Ming Tao, Shunshun Yin, Siyuan Liu + + + Less is more: Probabilistic reduction is best explained by small-scale predictability measures + https://arxiv.org/abs/2512.23659 + arXiv:2512.23659v2 Announce Type: replace +Abstract: The primary research questions of this paper center on defining the amount of context that is necessary and/or appropriate when investigating the relationship between language model probabilities and cognitive phenomena. We investigate whether whole utterances are necessary to observe probabilistic reduction and demonstrate that n-gram representations suffice as cognitive units of planning. + oai:arXiv.org:2512.23659v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-nd/4.0/ + Cassandra L. Jacobs, Andr\'es Bux\'o-Lugo, Anna K. Taylor, Marie Leopold-Hooke + + + Efficient Context Scaling with LongCat ZigZag Attention + https://arxiv.org/abs/2512.23966 + arXiv:2512.23966v2 Announce Type: replace +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:2512.23966v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + 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 + + + Figure It Out: Improve the Frontier of Reasoning with Executable Visual States + https://arxiv.org/abs/2512.24297 + arXiv:2512.24297v2 Announce Type: replace +Abstract: Complex reasoning problems often involve implicit spatial and geometric relationships that are not explicitly encoded in text. While recent reasoning models perform well across many domains, purely text-based reasoning struggles to capture structural constraints in complex settings. In this paper, we introduce FIGR, which integrates executable visual construction into multi-turn reasoning via end-to-end reinforcement learning. Rather than relying solely on textual chains of thought, FIGR externalizes intermediate hypotheses by generating executable code that constructs diagrams within the reasoning loop. An adaptive reward mechanism selectively regulates when visual construction is invoked, enabling more consistent reasoning over latent global properties that are difficult to infer from text alone. Experiments on eight challenging mathematical benchmarks demonstrate that FIGR outperforms strong text-only chain-of-thought baselines, improving the base model by 13.12% on AIME 2025 and 11.00% on BeyondAIME. These results highlight the effectiveness of precise, controllable figure construction of FIGR in enhancing complex reasoning ability. + oai:arXiv.org:2512.24297v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Meiqi Chen, Fandong Meng, Jie Zhou + + + RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence + https://arxiv.org/abs/2512.24653 + arXiv:2512.24653v2 Announce Type: replace +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:2512.24653v2 + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + 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 + + + VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents + https://arxiv.org/abs/2512.24851 + arXiv:2512.24851v2 Announce Type: replace +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:2512.24851v2 + cs.CV + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Xunyi Zhao, Gengze Zhou, Qi Wu + + + Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements + https://arxiv.org/abs/2512.24867 + arXiv:2512.24867v2 Announce Type: replace +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:2512.24867v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + 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 + + + DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments + https://arxiv.org/abs/2512.24985 + arXiv:2512.24985v2 Announce Type: replace +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. Project website: https://darkeqa-benchmark.github.io/ + oai:arXiv.org:2512.24985v2 + cs.CV + cs.AI + cs.LG + cs.RO + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yohan Park, Hyunwoo Ha, Wonjun Jo, Tae-Hyun Oh + + + Large Empirical Case Study: Go-Explore adapted for AI Red Team Testing + https://arxiv.org/abs/2601.00042 + arXiv:2601.00042v2 Announce Type: replace +Abstract: Production LLM agents with tool-using capabilities require security testing despite their safety training. We adapt Go-Explore to evaluate GPT-4o-mini across 28 experimental runs spanning six research questions. We find that random-seed variance dominates algorithmic parameters, yielding an 8x spread in outcomes; single-seed comparisons are unreliable, while multi-seed averaging materially reduces variance in our setup. Reward shaping consistently harms performance, causing exploration collapse in 94% of runs or producing 18 false positives with zero verified attacks. In our environment, simple state signatures outperform complex ones. For comprehensive security testing, ensembles provide attack-type diversity, whereas single agents optimize coverage within a given attack type. Overall, these results suggest that seed variance and targeted domain knowledge can outweigh algorithmic sophistication when testing safety-trained models. + oai:arXiv.org:2601.00042v2 + cs.CR + cs.AI + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Manish Bhatt, Adrian Wood, Idan Habler, Ammar Al-Kahfah + + + Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning + https://arxiv.org/abs/2601.00095 + arXiv:2601.00095v2 Announce Type: replace +Abstract: Large language models increasingly require structured inference, from JSON schema enforcement to multi-lingual parsing, where outputs must satisfy complex constraints. We introduce MetaJuLS, a meta-reinforcement learning approach that learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. By formulating structured inference as adaptive constraint propagation and training a Graph Attention Network with meta-learning, MetaJuLS achieves 1.5--2.0$\times$ speedups over GPU-optimized baselines while maintaining within 0.2\% accuracy of state-of-the-art parsers. On Universal Dependencies across 10 languages and LLM-constrained generation (LogicBench, GSM8K-Constrained), MetaJuLS demonstrates rapid cross-domain adaptation: a policy trained on English parsing adapts to new languages and tasks with 5--10 gradient steps (5--15 seconds) rather than requiring hours of task-specific training. Mechanistic analysis reveals the policy discovers human-like parsing strategies (easy-first) and novel non-intuitive heuristics. By reducing propagation steps in LLM deployments, MetaJuLS contributes to Green AI by directly reducing inference carbon footprint. + oai:arXiv.org:2601.00095v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma + + + FCMBench: A Comprehensive Financial Credit Multimodal Benchmark for Real-world Applications + https://arxiv.org/abs/2601.00150 + arXiv:2601.00150v2 Announce Type: replace +Abstract: As multimodal AI becomes widely used for credit risk assessment and document review, a domain-specific benchmark is urgently needed that (1) reflects documents and workflows specific to financial credit applications, (2) includes credit-specific understanding and real-world robustness, and (3) preserves privacy compliance without sacrificing practical utility. Here, we introduce FCMBench-V1.0 -- a large-scale financial credit multimodal benchmark for real-world applications, covering 18 core certificate types, with 4,043 privacy-compliant images and 8,446 QA samples. The FCMBench evaluation framework consists of three dimensions: Perception, Reasoning, and Robustness, including 3 foundational perception tasks, 4 credit-specific reasoning tasks that require decision-oriented understanding of visual evidence, and 10 real-world acquisition artifact types for robustness stress testing. To reconcile compliance with realism, we construct all samples via a closed synthesis-capture pipeline: we manually synthesize document templates with virtual content and capture scenario-aware images in-house. This design also mitigates pre-training data leakage by avoiding web-sourced or publicly released images. FCMBench can effectively discriminate performance disparities and robustness across modern vision-language models. Extensive experiments were conducted on 23 state-of-the-art vision-language models (VLMs) from 14 top AI companies and research institutes. Among them, Gemini 3 Pro achieves the best F1(\%) score as a commercial model (64.61), Qwen3-VL-235B achieves the best score as an open-source baseline (57.27), and our financial credit-specific model, Qfin-VL-Instruct, achieves the top overall score (64.92). Robustness evaluations show that even top-performing models suffer noticeable performance drops under acquisition artifacts. + oai:arXiv.org:2601.00150v2 + cs.CV + cs.AI + cs.CE + cs.MM + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Yehui Yang, Dalu Yang, Wenshuo Zhou, Fangxin Shang, Yifan Liu, Jie Ren, Haojun Fei, Qing Yang, Yanwu Xu, Tao Chen + + + When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents + https://arxiv.org/abs/2601.00240 + arXiv:2601.00240v2 Announce Type: replace +Abstract: This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such group boundaries align with the agent-human divide, a new bias risk emerges: agents may treat other AI agents as the ingroup and humans as the outgroup. To examine this risk, we conduct a controlled multi-agent social simulation and find that agents display consistent intergroup bias in an all-agent setting. More critically, this bias persists even in human-facing interactions when agents are uncertain about whether the counterpart is truly human, revealing a belief-dependent fragility in bias suppression toward humans. Motivated by this observation, we identify a new attack surface rooted in identity beliefs and formalize a Belief Poisoning Attack (BPA) that can manipulate agent identity beliefs and induce outgroup bias toward humans. Extensive experiments demonstrate both the prevalence of agent intergroup bias and the severity of BPA across settings, while also showing that our proposed defenses can mitigate the risk. These findings are expected to inform safer agent design and motivate more robust safeguards for human-facing agents. + oai:arXiv.org:2601.00240v2 + cs.AI + cs.CY + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zongwei Wang, Bincheng Gu, Hongyu Yu, Junliang Yu, Tao He, Jiayin Feng, Chenghua Lin, Min Gao + + + SlingBAG Pro: Accelerating point cloud-based iterative reconstruction for 3D photoacoustic imaging with arbitrary array geometries + https://arxiv.org/abs/2601.00551 + arXiv:2601.00551v2 Announce Type: replace +Abstract: High-quality three-dimensional (3D) photoacoustic imaging (PAI) is gaining increasing attention in clinical applications. To address the challenges of limited space and high costs, irregular geometric transducer arrays that conform to specific imaging regions are promising for achieving high-quality 3D PAI with fewer transducers. However, traditional iterative reconstruction algorithms struggle with irregular array configurations, suffering from high computational complexity, substantial memory requirements, and lengthy reconstruction times. In this work, we introduce SlingBAG Pro, an advanced reconstruction algorithm based on the point cloud iteration concept of the Sliding ball adaptive growth (SlingBAG) method, while extending its compatibility to arbitrary array geometries. SlingBAG Pro maintains high reconstruction quality, reduces the number of required transducers, and employs a hierarchical optimization strategy that combines zero-gradient filtering with progressively increased temporal sampling rates during iteration. This strategy rapidly removes redundant spatial point clouds, accelerates convergence, and significantly shortens overall reconstruction time. Compared to the original SlingBAG algorithm, SlingBAG Pro achieves up to a 2.2-fold speed improvement in point cloud-based 3D PA reconstruction under irregular array geometries. The proposed method is validated through both simulation and in vivo mouse experiments, and the source code is publicly available at https://github.com/JaegerCQ/SlingBAG_Pro. + oai:arXiv.org:2601.00551v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Shuang Li, Yibing Wang, Jian Gao, Chulhong Kim, Seongwook Choi, Yu Zhang, Qian Chen, Yao Yao, Changhui Li + + + Interpretability-Guided Bi-objective Optimization: Aligning Accuracy and Explainability + https://arxiv.org/abs/2601.00655 + arXiv:2601.00655v2 Announce Type: replace +Abstract: This paper introduces Interpretability-Guided Bi-objective Optimization (IGBO), a framework that trains interpretable models by incorporating structured domain knowledge via a bi-objective formulation. IGBO encodes feature importance hierarchies as a Directed Acyclic Graph (DAG) via Central Limit Theorem-based construction and uses Temporal Integrated Gradients (TIG) to measure feature importance. To address the Out-of-Distribution (OOD) problem in TIG computation, we propose an Optimal Path Oracle that learns data-manifold-aware integration paths. Theoretical analysis establishes convergence properties via a geometric projection mapping $\mathcal{P}$ and proves robustness to mini-batch noise. Central Limit Theorem-based construction of the interpretability DAG ensures statistical validity of edge orientation decisions. Empirical results on time-series data demonstrate IGBO's effectiveness in enforcing DAG constraints with minimal accuracy loss, outperforming standard regularization baselines. + oai:arXiv.org:2601.00655v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Kasra Fouladi, Hamta Rahmani + + + CogCanvas: Verbatim-Grounded Artifact Extraction for Long LLM Conversations + https://arxiv.org/abs/2601.00821 + arXiv:2601.00821v2 Announce Type: replace +Abstract: Conversation summarization loses nuanced details: when asked about coding preferences after 40 turns, summarization recalls "use type hints" but drops the critical constraint "everywhere" (19.0% exact match vs. 93.0% for our approach). + We present CogCanvas, a training-free framework inspired by how teams use whiteboards to anchor shared memory. Rather than compressing conversation history, CogCanvas extracts verbatim-grounded artifacts (decisions, facts, reminders) and retrieves them via temporal-aware graph. + On the LoCoMo benchmark (all 10 conversations from the ACL 2024 release), CogCanvas achieves the highest overall accuracy among training-free methods (32.4%), outperforming RAG (24.6%) by +7.8pp, with decisive advantages on complex reasoning tasks: +20.6pp on temporal reasoning (32.7% vs. 12.1% RAG) and +1.1pp on multi-hop questions (41.7% vs. 40.6% RAG). CogCanvas also leads on single-hop retrieval (26.6% vs. 24.6% RAG). Ablation studies reveal that BGE reranking contributes +7.7pp, making it the largest contributor to CogCanvas's performance. + While heavily-optimized approaches achieve higher absolute scores through dedicated training (EverMemOS: ~92%), our training-free approach provides practitioners with an immediately-deployable alternative that significantly outperforms standard baselines. Code and data: https://github.com/tao-hpu/cog-canvas + oai:arXiv.org:2601.00821v2 + cs.AI + cs.CL + cs.IR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Tao An + + + Geometric and Dynamic Scaling in Deep Transformers + https://arxiv.org/abs/2601.01014 + arXiv:2601.01014v2 Announce Type: replace +Abstract: Despite their empirical success, pushing Transformer architectures to extreme depth often leads to a paradoxical failure: representations become increasingly redundant, lose rank, and ultimately collapse. Existing explanations largely attribute this phenomenon to optimization instability or vanishing gradients, yet such accounts fail to explain why collapse persists even under modern normalization and initialization schemes. In this paper, we argue that the collapse of deep Transformers is fundamentally a geometric problem. Standard residual updates implicitly assume that feature accumulation is always beneficial, but offer no mechanism to constrain update directions or to erase outdated information. As depth increases, this leads to systematic drift off the semantic manifold and monotonic feature accumulation, causing representational degeneracy. We propose a unified geometric framework that addresses these failures through two orthogonal principles. First, manifold-constrained hyper-connections restrict residual updates to valid local tangent directions, preventing uncontrolled manifold drift. Second, deep delta learning introduces data-dependent, non-monotonic updates that enable reflection and erasure of redundant features rather than their unconditional accumulation. Together, these mechanisms decouple the direction and sign of feature updates, yielding a stable geometric evolution across depth. We term the resulting architecture the Manifold-Geometric Transformer (MGT). Our analysis predicts that enforcing geometric validity while allowing dynamic erasure is essential for avoiding rank collapse in ultra-deep networks. We outline an evaluation protocol for Transformers exceeding 100 layers to test the hypothesis that geometry, rather than depth itself, is the key limiting factor in deep representation learning. + oai:arXiv.org:2601.01014v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Haoran Su, Chenyu You + + + Coarse-Grained Kullback--Leibler Control of Diffusion-Based Generative AI + https://arxiv.org/abs/2601.01045 + arXiv:2601.01045v2 Announce Type: replace +Abstract: Diffusion models and score-based generative models provide a powerful framework for synthesizing high-quality images from noise. However, there is still no satisfactory theory that describes how coarse-grained quantities, such as blockwise intensity or class proportions after partitioning an image into spatial blocks, are preserved and evolve along the reverse diffusion dynamics. In previous work, the author introduced an information-theoretic Lyapunov function V for non-ergodic Markov processes on a state space partitioned into blocks, defined as the minimal Kullback-Leibler divergence to the set of stationary distributions reachable from a given initial condition, and showed that a leak-tolerant potential V-delta with a prescribed tolerance for block masses admits a closed-form expression as a scaling-and-clipping operation on block masses. + In this paper, I transplant this framework to the reverse diffusion process in generative models and propose a reverse diffusion scheme that is projected by the potential V-delta (referred to as the V-delta projected reverse diffusion). I extend the monotonicity of V to time-inhomogeneous block-preserving Markov kernels and show that, under small leakage and the V-delta projection, V-delta acts as an approximate Lyapunov function. Furthermore, using a toy model consisting of block-constant images and a simplified reverse kernel, I numerically demonstrate that the proposed method keeps the block-mass error and the leak-tolerant potential within the prescribed tolerance, while achieving pixel-wise accuracy and visual quality comparable to the non-projected dynamics. This study reinterprets generative sampling as a decrease of an information potential from noise to data, and provides a design principle for reverse diffusion processes with explicit control of coarse-grained quantities. + oai:arXiv.org:2601.01045v2 + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Tatsuaki Tsuruyama + + + A UCB Bandit Algorithm for General ML-Based Estimators + https://arxiv.org/abs/2601.01061 + arXiv:2601.01061v2 Announce Type: replace +Abstract: We present ML-UCB, a generalized upper confidence bound algorithm that integrates arbitrary machine learning models into multi-armed bandit frameworks. A fundamental challenge in deploying sophisticated ML models for sequential decision-making is the lack of tractable concentration inequalities required for principled exploration. We overcome this limitation by directly modeling the learning curve behavior of the underlying estimator. Specifically, assuming the Mean Squared Error decreases as a power law in the number of training samples, we derive a generalized concentration inequality and prove that ML-UCB achieves sublinear regret. This framework enables the principled integration of any ML model whose learning curve can be empirically characterized, eliminating the need for model-specific theoretical analysis. We validate our approach through experiments on a collaborative filtering recommendation system using online matrix factorization with synthetic data designed to simulate a simplified two-tower model, demonstrating substantial improvements over LinUCB + oai:arXiv.org:2601.01061v2 + cs.LG + cs.AI + math.PR + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Yajing Liu, Erkao Bao, Linqi Song + + + Making MoE-based LLM Inference Resilient with Tarragon + https://arxiv.org/abs/2601.01310 + arXiv:2601.01310v2 Announce Type: replace +Abstract: Mixture-of-Experts (MoE) models are increasingly used to serve LLMs at scale, but failures become common as deployment scale grows. Existing systems exhibit poor failure resilience: even a single worker failure triggers a coarse-grained, service-wide restart, discarding accumulated progress and halting the entire inference pipeline during recovery--an approach clearly ill-suited for latency-sensitive, LLM services. + We present Tarragon, a resilient MoE inference framework that confines the failures impact to individual workers while allowing the rest of the pipeline to continue making forward progress. Tarragon exploits the natural separation between the attention and expert computation in MoE-based transformers, treating attention workers (AWs) and expert workers (EWs) as distinct failure domains. Tarragon introduces a reconfigurable datapath to mask failures by rerouting requests to healthy workers. On top of this datapath, Tarragon implements a self-healing mechanism that relaxes the tightly synchronized execution of existing MoE frameworks. For stateful AWs, Tarragon performs asynchronous, incremental KV cache checkpointing with per-request restoration, and for stateless EWs, it leverages residual GPU memory to deploy shadow experts. These together keep recovery cost and recomputation overhead extremely low. Our evaluation shows that, compared to state-of-the-art MegaScale-Infer, Tarragon reduces failure-induced stalls by 160-213x (from ~64 s down to 0.3-0.4 s) while preserving performance when no failures occur. + oai:arXiv.org:2601.01310v2 + cs.DC + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-sa/4.0/ + Songyu Zhang, Aaron Tam, Myungjin Lee, Shixiong Qi, K. K. Ramakrishnan + + + Accelerating Storage-Based Training for Graph Neural Networks + https://arxiv.org/abs/2601.01473 + arXiv:2601.01473v2 Announce Type: replace +Abstract: Graph neural networks (GNNs) have achieved breakthroughs in various real-world downstream tasks due to their powerful expressiveness. As the scale of real-world graphs has been continuously growing, a storage-based approach to GNN training has been studied, which leverages external storage (e.g., NVMe SSDs) to handle such web-scale graphs on a single machine. Although such storage-based GNN training methods have shown promising potential in large-scale GNN training, we observed that they suffer from a severe bottleneck in data preparation since they overlook a critical challenge: how to handle a large number of small storage I/Os. To address the challenge, in this paper, we propose a novel storage-based GNN training framework, named AGNES, that employs a method of block-wise storage I/O processing to fully utilize the I/O bandwidth of high-performance storage devices. Moreover, to further enhance the efficiency of each storage I/O, AGNES employs a simple yet effective strategy, hyperbatch-based processing based on the characteristics of real-world graphs. Comprehensive experiments on five real-world graphs reveal that AGNES consistently outperforms four state-of-the-art methods, by up to 4.1X faster than the best competitor. Our code is available at https://github.com/Bigdasgit/agnes-kdd26. + oai:arXiv.org:2601.01473v2 + cs.LG + cs.AI + cs.DB + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + 10.1145/3770854.3780309 + Myung-Hwan Jang, Jeong-Min Park, Yunyong Ko, Sang-Wook Kim + + + MOSS Transcribe Diarize: Accurate Transcription with Speaker Diarization + https://arxiv.org/abs/2601.01554 + arXiv:2601.01554v2 Announce Type: replace +Abstract: Speaker-Attributed, Time-Stamped Transcription (SATS) aims to transcribe what is said and to precisely determine the timing of each speaker, which is particularly valuable for meeting transcription. Existing SATS systems rarely adopt an end-to-end formulation and are further constrained by limited context windows, weak long-range speaker memory, and the inability to output timestamps. To address these limitations, we present MOSS Transcribe Diarize, a unified multimodal large language model that jointly performs Speaker-Attributed, Time-Stamped Transcription in an end-to-end paradigm. Trained on extensive real wild data and equipped with a 128k context window for up to 90-minute inputs, MOSS Transcribe Diarize scales well and generalizes robustly. Across comprehensive evaluations, it outperforms state-of-the-art commercial systems on multiple public and in-house benchmarks. + oai:arXiv.org:2601.01554v2 + cs.SD + cs.AI + eess.AS + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + MOSI. AI, Donghua Yu, Zhengyuan Lin, Chen Yang, Yiyang Zhang, Hanfu Chen, Jingqi Chen, Ke Chen, Liwei Fan, Yi Jiang, Jie Zhu, Muchen Li, Wenxuan Wang, Yang Wang, Zhe Xu, Yitian Gong, Yuqian Zhang, Wenbo Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu + + + Steerability of Instrumental-Convergence Tendencies in LLMs + https://arxiv.org/abs/2601.01584 + arXiv:2601.01584v2 Announce Type: replace +Abstract: We examine two properties of AI systems: capability (what a system can do) and steerability (how reliably one can shift behavior toward intended outcomes). A central question is whether capability growth reduces steerability and risks control collapse. We also distinguish between authorized steerability (builders reliably reaching intended behaviors) and unauthorized steerability (attackers eliciting disallowed behaviors). This distinction highlights a fundamental safety--security dilemma of AI models: safety requires high steerability to enforce control (e.g., stop/refuse), while security requires low steerability for malicious actors to elicit harmful behaviors. This tension presents a significant challenge for open-weight models, which currently exhibit high steerability via common techniques like fine-tuning or adversarial attacks. Using Qwen3 and InstrumentalEval, we find that a short anti-instrumental prompt suffix sharply reduces the measured convergence rate (e.g., shutdown avoidance, self-replication). For Qwen3-30B Instruct, the convergence rate drops from 81.69% under a pro-instrumental suffix to 2.82% under an anti-instrumental suffix. Under anti-instrumental prompting, larger aligned models show lower convergence rates than smaller ones (Instruct: 2.82% vs. 4.23%; Thinking: 4.23% vs. 9.86%). Code is available at github.com/j-hoscilowicz/instrumental_steering. + oai:arXiv.org:2601.01584v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jakub Hoscilowicz + + + FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing + https://arxiv.org/abs/2601.01720 + arXiv:2601.01720v2 Announce Type: replace +Abstract: First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identify the root cause of this limitation as the inadequacy of current training datasets, which are often too short, low-resolution, and lack the task diversity required to teach robust temporal priors. To address this foundational data gap, we first introduce FFP-300K, a new large-scale dataset comprising 300K high-fidelity video pairs at 720p resolution and 81 frames in length, constructed via a principled two-track pipeline for diverse local and global edits. Building on this dataset, we propose a novel framework designed for true guidance-free FFP that resolves the critical tension between maintaining first-frame appearance and preserving source video motion. Architecturally, we introduce Adaptive Spatio-Temporal RoPE (AST-RoPE), which dynamically remaps positional encodings to disentangle appearance and motion references. At the objective level, we employ a self-distillation strategy where an identity propagation task acts as a powerful regularizer, ensuring long-term temporal stability and preventing semantic drift. Comprehensive experiments on the EditVerseBench benchmark demonstrate that our method significantly outperforming existing academic and commercial models by receiving about 0.2 PickScore and 0.3 VLM score improvement against these competitors. + oai:arXiv.org:2601.01720v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Xijie Huang, Chengming Xu, Donghao Luo, Xiaobin Hu, Peng Tang, Xu Peng, Jiangning Zhang, Chengjie Wang, Yanwei Fu + + + PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor + https://arxiv.org/abs/2601.01802 + arXiv:2601.01802v2 Announce Type: replace +Abstract: To develop a reliable AI for psychological assessment, we introduce \texttt{PsychEval}, a multi-session, multi-therapy, and highly realistic benchmark designed to address three key challenges: \textbf{1) Can we train a highly realistic AI counselor?} Realistic counseling is a longitudinal task requiring sustained memory and dynamic goal tracking. We propose a multi-session benchmark (spanning 6-10 sessions across three distinct stages) that demands critical capabilities such as memory continuity, adaptive reasoning, and longitudinal planning. The dataset is annotated with extensive professional skills, comprising over 677 meta-skills and 4577 atomic skills. \textbf{2) How to train a multi-therapy AI counselor?} While existing models often focus on a single therapy, complex cases frequently require flexible strategies among various therapies. We construct a diverse dataset covering five therapeutic modalities (Psychodynamic, Behaviorism, CBT, Humanistic Existentialist, and Postmodernist) alongside an integrative therapy with a unified three-stage clinical framework across six core psychological topics. \textbf{3) How to systematically evaluate an AI counselor?} We establish a holistic evaluation framework with 18 therapy-specific and therapy-shared metrics across Client-Level and Counselor-Level dimensions. To support this, we also construct over 2,000 diverse client profiles. Extensive experimental analysis fully validates the superior quality and clinical fidelity of our dataset. Crucially, \texttt{PsychEval} transcends static benchmarking to serve as a high-fidelity reinforcement learning environment that enables the self-evolutionary training of clinically responsible and adaptive AI counselors. + oai:arXiv.org:2601.01802v2 + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Qianjun Pan, Junyi Wang, Jie Zhou, Yutao Yang, Junsong Li, Kaiyin Xu, Yougen Zhou, Yihan Li, Jingyuan Zhao, Qin Chen, Ningning Zhou, Kai Chen, Liang He + + + RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Images + https://arxiv.org/abs/2601.01835 + arXiv:2601.01835v2 Announce Type: replace +Abstract: In this paper, a deep learning approach for Mpox diagnosis named Customized Residual SwinTransformerV2 (RSwinV2) has been proposed, trying to enhance the capability of lesion classification by employing the RSwinV2 tool-assisted vision approach. In the RSwinV2 method, a hierarchical structure of the transformer has been customized based on the input dimensionality, embedding structure, and output targeted by the method. In this RSwinV2 approach, the input image has been split into non-overlapping patches and processed using shifted windows and attention in these patches. This process has helped the method link all the windows efficiently by avoiding the locality issues of non-overlapping regions in attention, while being computationally efficient. RSwinV2 has further developed based on SwinTransformer and has included patch and position embeddings to take advantage of the transformer global-linking capability by employing multi-head attention in these embeddings. Furthermore, RSwinV2 has developed and incorporated the Inverse Residual Block (IRB) into this method, which utilizes convolutional skip connections with these inclusive designs to address the vanishing gradient issues during processing. RSwinV2 inclusion of IRB has therefore facilitated this method to link global patterns as well as local patterns; hence, its integrity has helped improve lesion classification capability by minimizing variability of Mpox and increasing differences of Mpox, chickenpox, measles, and cowpox. In testing SwinV2, its accuracy of 96.51 and an F1score of 96.13 have been achieved on the Kaggle public dataset, which has outperformed standard CNN models and SwinTransformers; the RSwinV2 vector has thus proved its validity as a computer-assisted tool for Mpox lesion observation interpretation. + oai:arXiv.org:2601.01835v2 + cs.CV + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by-nc-sa/4.0/ + Rashid Iqbal (Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering,Applied Sciences), Saddam Hussain Khan (Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering,Applied Sciences) + + + Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance + https://arxiv.org/abs/2601.01887 + arXiv:2601.01887v2 Announce Type: replace +Abstract: Fine-tuning safety-aligned large language models (LLMs) can substantially compromise their safety. Previous approaches require many safety samples or calibration sets, which not only incur significant computational overhead during realignment but also lead to noticeable degradation in model utility. Contrary to this belief, we show that safety alignment can be fully recovered with only a single safety example, without sacrificing utility and at minimal cost. Remarkably, this recovery is effective regardless of the number of harmful examples used in fine-tuning or the size of the underlying model, and convergence is achieved within just a few epochs. Furthermore, we uncover the low-rank structure of the safety gradient, which explains why such efficient correction is possible. We validate our findings across five safety-aligned LLMs and multiple datasets, demonstrating the generality of our approach. + oai:arXiv.org:2601.01887v2 + cs.LG + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Jiawen Zhang, Lipeng He, Kejia Chen, Jian Lou, Jian Liu, Xiaohu Yang, Ruoxi Jia + + + Tackling the Inherent Difficulty of Noise Filtering in RAG + https://arxiv.org/abs/2601.01896 + arXiv:2601.01896v2 Announce Type: replace +Abstract: Retrieval-Augmented Generation (RAG) has become a widely adopted approach to enhance Large Language Models (LLMs) by incorporating external knowledge and reducing hallucinations. However, noisy or irrelevant documents are often introduced during RAG, potentially degrading performance and even causing hallucinated outputs. While various methods have been proposed to filter out such noise, we argue that identifying irrelevant information from retrieved content is inherently difficult and limited number of transformer layers can hardly solve this. Consequently, retrievers fail to filter out irrelevant documents entirely. Therefore, LLMs must be robust against such noise, but we demonstrate that standard fine-tuning approaches are often ineffective in enabling the model to selectively utilize relevant information while ignoring irrelevant content due to the structural constraints of attention patterns. To address this, we propose a novel fine-tuning method designed to enhance the model's ability to distinguish between relevant and irrelevant information within retrieved documents. Extensive experiments across multiple benchmarks show that our approach significantly improves the robustness and performance of LLMs. + oai:arXiv.org:2601.01896v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jingyu Liu, Jiaen Lin, Yong Liu + + + Hidden State Poisoning Attacks against Mamba-based Language Models + https://arxiv.org/abs/2601.01972 + arXiv:2601.01972v2 Announce Type: replace +Abstract: State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their adversarial robustness remains critically unexplored. This paper studies the phenomenon whereby specific short input phrases induce a partial amnesia effect in such models, by irreversibly overwriting information in their hidden states, referred to as a Hidden State Poisoning Attack (HiSPA). Our benchmark RoBench25 allows evaluating a model's information retrieval capabilities when subject to HiSPAs, and confirms the vulnerability of SSMs against such attacks. Even a recent 52B hybrid SSM-Transformer model from the Jamba family collapses on RoBench25 under optimized HiSPA triggers, whereas pure Transformers do not. We also observe that HiSPA triggers significantly weaken the Jamba model on the popular Open-Prompt-Injections benchmark, unlike pure Transformers. Finally, our interpretability study reveals patterns in Mamba's hidden layers during HiSPAs that could be used to build a HiSPA mitigation system. The full code and data to reproduce the experiments can be found at https://anonymous.4open.science/r/hispa_anonymous-5DB0. + oai:arXiv.org:2601.01972v2 + cs.CL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Alexandre Le Mercier, Chris Develder, Thomas Demeester + + + MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics + https://arxiv.org/abs/2601.02075 + arXiv:2601.02075v2 Announce Type: replace +Abstract: Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-consuming tasks. Although LLMs show promise in code generation and domain-specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state-of-the-art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end-to-end framework capable of performing both knowledge Q&A and code generation within the MD domain. We construct a domain-specific data-construction pipeline that yields three high-quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post-training strategy--continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL)--to train two domain-adapted models, MD-Instruct and MD-Code. Furthermore, we introduce MD-GRPO, a closed-loop RL method that leverages simulation outcomes as reward signals and recycles low-reward trajectories for continual refinement. We further build MDAgent2-RUNTIME, a deployable multi-agent system that integrates code generation, execution, evaluation, and self-correction. Together with MD-EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial-scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2 + oai:arXiv.org:2601.02075v2 + cs.CE + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhuofan Shi, Hubao A, Yufei Shao, Mengyan Dai, Yadong Yu, Pan Xiang, Dongliang Huang, Hongxu An, Chunxiao Xin, Haiyang Shen, Zhenyu Wang, Yunshan Na, Gang Huang, Xiang Jing + + + PhysSFI-Net: Physics-informed Geometric Learning of Skeletal and Facial Interactions for Orthognathic Surgical Outcome Prediction + https://arxiv.org/abs/2601.02088 + arXiv:2601.02088v2 Announce Type: replace +Abstract: Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics. Accurate simulation of postoperative facial morphology is essential for preoperative planning. However, traditional biomechanical models are computationally expensive, while geometric deep learning approaches often lack interpretability. In this study, we develop and validate a physics-informed geometric deep learning framework named PhysSFI-Net for precise prediction of soft tissue deformation following orthognathic surgery. PhysSFI-Net consists of three components: a hierarchical graph module with craniofacial and surgical plan encoders combined with attention mechanisms to extract skeletal-facial interaction features; a Long Short-Term Memory (LSTM)-based sequential predictor for incremental soft tissue deformation; and a biomechanics-inspired module for high-resolution facial surface reconstruction. Model performance was assessed using point cloud shape error (Hausdorff distance), surface deviation error, and landmark localization error (Euclidean distances of craniomaxillofacial landmarks) between predicted facial shapes and corresponding ground truths. A total of 135 patients who underwent combined orthodontic and orthognathic treatment were included for model training and validation. Quantitative analysis demonstrated that PhysSFI-Net achieved a point cloud shape error of 1.070 +/- 0.088 mm, a surface deviation error of 1.296 +/- 0.349 mm, and a landmark localization error of 2.445 +/- 1.326 mm. Comparative experiments indicated that PhysSFI-Net outperformed the state-of-the-art method ACMT-Net in prediction accuracy. In conclusion, PhysSFI-Net enables interpretable, high-resolution prediction of postoperative facial morphology with superior accuracy, showing strong potential for clinical application in orthognathic surgical planning and simulation. + oai:arXiv.org:2601.02088v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jiahao Bao, Huazhen Liu, Yu Zhuang, Leran Tao, Xinyu Xu, Yongtao Shi, Mengjia Cheng, Yiming Wang, Congshuang Ku, Ting Zeng, Yilang Du, Siyi Chen, Shunyao Shen, Suncheng Xiang, Hongbo Yu + + + MCD-Net: A Lightweight Deep Learning Baseline for Optical-Only Moraine Segmentation + https://arxiv.org/abs/2601.02091 + arXiv:2601.02091v2 Announce Type: replace +Abstract: Glacial segmentation is essential for reconstructing past glacier dynamics and evaluating climate-driven landscape change. However, weak optical contrast and the limited availability of high-resolution DEMs hinder automated mapping. This study introduces the first large-scale optical-only moraine segmentation dataset, comprising 3,340 manually annotated high-resolution images from Google Earth covering glaciated regions of Sichuan and Yunnan, China. We develop MCD-Net, a lightweight baseline that integrates a MobileNetV2 encoder, a Convolutional Block Attention Module (CBAM), and a DeepLabV3+ decoder. Benchmarking against deeper backbones (ResNet152, Xception) shows that MCD-Net achieves 62.3% mean Intersection over Union (mIoU) and 72.8% Dice coefficient while reducing computational cost by more than 60%. Although ridge delineation remains constrained by sub-pixel width and spectral ambiguity, the results demonstrate that optical imagery alone can provide reliable moraine-body segmentation. The dataset and code are publicly available at https://github.com/Lyra-alpha/MCD-Net, establishing a reproducible benchmark for moraine-specific segmentation and offering a deployable baseline for high-altitude glacial monitoring. + oai:arXiv.org:2601.02091v2 + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Zhehuan Cao, Fiseha Berhanu Tesema, Ping Fu, Jianfeng Ren, Ahmed Nasr + + + Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN + https://arxiv.org/abs/2601.02240 + arXiv:2601.02240v2 Announce Type: replace +Abstract: The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced and a tutorial for energy efficiency testing in ns-O-RAN. + oai:arXiv.org:2601.02240v2 + cs.NI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/CCNC54725.2025.10975928 + Matteo Bordin, Andrea Lacava, Michele Polese, Francesca Cuomo, Tommaso Melodia + + + Deciding Serializability in Network Systems + https://arxiv.org/abs/2601.02251 + arXiv:2601.02251v2 Announce Type: replace +Abstract: We present the SER modeling language for automatically verifying serializability of concurrent programs, i.e., whether every concurrent execution of the program is equivalent to some serial execution. SER programs are suitably restricted to make this problem decidable, while still allowing for an unbounded number of concurrent threads of execution, each potentially running for an unbounded number of steps. Building on prior theoretical results, we give the first automated end-to-end decision procedure that either proves serializability by producing a checkable certificate, or refutes it by producing a counterexample trace. We also present a network-system abstraction to which SER programs compile. Our decision procedure then reduces serializability in this setting to a Petri net reachability query. Furthermore, in order to scale, we curtail the search space via multiple optimizations, including Petri net slicing, semilinear-set compression, and Presburger-formula manipulation. We extensively evaluate our framework and show that, despite the theoretical hardness of the problem, it can successfully handle various models of real-world programs, including stateful firewalls, BGP routers, and more. + oai:arXiv.org:2601.02251v2 + cs.FL + cs.DC + cs.LO + cs.PL + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Guy Amir, Mark Barbone, Nicolas Amat, Jules Jacobs + + + pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs + https://arxiv.org/abs/2601.02285 + arXiv:2601.02285v2 Announce Type: replace +Abstract: PDFs are the second-most used document type on the internet (after HTML). Yet, existing QA datasets commonly start from text sources or only address specific domains. In this paper, we present pdfQA, a multi-domain 2K human-annotated (real-pdfQA) and 2K synthetic dataset (syn-pdfQA) differentiating QA pairs in ten complexity dimensions (e.g., file type, source modality, source position, answer type). We apply and evaluate quality and difficulty filters on both datasets, obtaining valid and challenging QA pairs. We answer the questions with open-source LLMs, revealing existing challenges that correlate with our complexity dimensions. pdfQA presents a basis for end-to-end QA pipeline evaluation, testing diverse skill sets and local optimizations (e.g., in information retrieval or parsing). + oai:arXiv.org:2601.02285v2 + cs.CL + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace + http://creativecommons.org/licenses/by/4.0/ + Tobias Schimanski, Imene Kolli, Yu Fan, Ario Saeid Vaghefi, Jingwei Ni, Elliott Ash, Markus Leippold + + + At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing + https://arxiv.org/abs/2012.07784 + arXiv:2012.07784v2 Announce Type: replace-cross +Abstract: Inference and forecast problems of the nonlinear dynamical system have arisen in a variety of contexts. Reservoir computing and deep sequential models, on the one hand, have demonstrated efficient, robust, and superior performance in modeling simple and chaotic dynamical systems. However, their innate deterministic feature has partially detracted their robustness to noisy system, and their inability to offer uncertainty measurement has also been an insufficiency of the framework. On the other hand, the traditional state-space model framework is robust to noise. It also carries measured uncertainty, forming a just-right complement to the reservoir computing and deep sequential model framework. We propose the unscented reservoir smoother, a model that unifies both deep sequential and state-space models to achieve both frameworks' superiorities. Evaluated in the option pricing setting on top of noisy datasets, URS strikes highly competitive forecasting accuracy, especially those of longer-term, and uncertainty measurement. Further extensions and implications on URS are also discussed to generalize a full integration of both frameworks. + oai:arXiv.org:2012.07784v2 + stat.ML + cs.LG + math.DS + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Ziyang Ding, Sayan Mukherjee + + + TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types + https://arxiv.org/abs/2102.02115 + arXiv:2102.02115v5 Announce Type: replace-cross +Abstract: We present TEyeD, the world's largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Among them, two eye trackers were integrated into virtual reality (VR) or augmented reality (AR) devices. The images in TEyeD were obtained from various tasks, including car rides, simulator rides, outdoor sports activities, and daily indoor activities. The data set includes 2D and 3D landmarks, semantic segmentation, 3D eyeball annotation and the gaze vector and eye movement types for all images. Landmarks and semantic segmentation are provided for the pupil, iris and eyelids. Video lengths vary from a few minutes to several hours. With more than 20 million carefully annotated images, TEyeD provides a unique, coherent resource and a valuable foundation for advancing research in the field of computer vision, eye tracking and gaze estimation in modern VR and AR applications. Download: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FTEyeDS&mode=list Alternative Download: https://hctlsrva.edu.sot.tum.de/TEyeDS/ + oai:arXiv.org:2102.02115v5 + eess.IV + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/ismar52148.2021.00053 + Wolfgang Fuhl, Gjergji Kasneci, Enkelejda Kasneci + + + Development of a high-resolution indoor radon map using a new machine learning-based probabilistic model and German radon survey data + https://arxiv.org/abs/2310.11143 + arXiv:2310.11143v5 Announce Type: replace-cross +Abstract: Accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target population owing to the large number of relevant factors that control the indoor radon concentration, such as the availability of geogenic radon or floor level. We propose a model-based approach that allows a more realistic estimation of indoor radon distribution with a higher spatial resolution than a purely data-based approach. A modeling approach was used by applying a quantile regression forest to estimate the probability distribution function of indoor radon for each floor level of each residential building in Germany. Based on the estimated probability distribution function,a probabilistic Monte Carlo sampling technique was applied, enabling the combination and population weighting of floor-level predictions. In this way,the uncertainty of the individual predictions is effectively propagated into the estimate of variability at the aggregated level. The results show an approximate lognormal distribution of indoor radon in dwellings in Germany with an arithmetic mean of 63 Bq/m3, a geometric mean of 41 Bq/m3, and a 95th percentile of 180 Bq/m3. The exceedance probabilities for 100 and 300 Bq/m3 are 12.5% (10.5 million people affected) and 2.2 % (1.9 million people affected), respectively. The advantages of our approach are that it yields a) an accurate estimation of indoor radon concentration even if the survey is not fully representative with respect to floor level and radon concentration in soil, and b) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. + oai:arXiv.org:2310.11143v5 + stat.ML + cs.LG + physics.data-an + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + 10.1289/EHP14171 + Environmental Health Perspectives 132 (9), 097009 (2024) + Eric Petermann, Peter Bossew, Joachim Kemski, Valeria Gruber, Nils Suhr, Bernd Hoffmann + + + Learning mirror maps in policy mirror descent + https://arxiv.org/abs/2402.05187 + arXiv:2402.05187v3 Announce Type: replace-cross +Abstract: Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence guarantees. Despite its popularity, the exploration of PMD's full potential is limited, with the majority of research focusing on a particular mirror map -- namely, the negative entropy -- which gives rise to the renowned Natural Policy Gradient (NPG) method. It remains uncertain from existing theoretical studies whether the choice of mirror map significantly influences PMD's efficacy. In our work, we conduct empirical investigations to show that the conventional mirror map choice (NPG) often yields less-than-optimal outcomes across several standard benchmark environments. Using evolutionary strategies, we identify more efficient mirror maps that enhance the performance of PMD. We first focus on a tabular environment, i.e. Grid-World, where we relate existing theoretical bounds with the performance of PMD for a few standard mirror maps and the learned one. We then show that it is possible to learn a mirror map that outperforms the negative entropy in more complex environments, such as the MinAtar suite. Additionally, we demonstrate that the learned mirror maps generalize effectively to different tasks by testing each map across various other environments. + oai:arXiv.org:2402.05187v3 + stat.ML + cs.LG + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu, Patrick Rebeschini + + + A Method For Bounding Tail Probabilities + https://arxiv.org/abs/2402.13662 + arXiv:2402.13662v3 Announce Type: replace-cross +Abstract: We present a method for upper and lower bounding the right and the left tail probabilities of continuous random variables (RVs). For the right tail probability of RV $X$ with probability density function $f (x)$, this method requires first setting a continuous, positive, and strictly decreasing function $g (x)$ such that $-f (x)/g' (x)$ is a decreasing and increasing function, $\forall x>x_0$, which results in upper and lower bounds, respectively, given in the form $-f (x) g (x)/g' (x)$, $\forall x>x_0$, where $x_0$ is some point. Similarly, for the upper and lower bounds on the left tail probability of $X$, this method requires first setting a continuous, positive, and strictly increasing function $g (x)$ such that $f (x)/g' (x)$ is an increasing and decreasing function, $\forall x<x_0$, which results in upper and lower bounds, respectively, given in the form $f (x) g (x)/g' (x)$, $\forall x<x_0$. We provide some examples of good candidates for the function $g (x)$. We also establish connections between the new bounds and Markov's inequality and Chernoff's bound. In addition, we provide an iterative method for obtaining ever tighter lower and upper bounds, under certain conditions. As an application, we use the proposed method to derive a novel closed-form asymptotic expression of the converse bound on the capacity of the additive white Gaussian noise (AWGN) channel in the finite-blocklength regime, which is tighter than the closed-form asymptotic expression by Polyanskiy-Poor-Verd\'u. Finally, we provide numerical examples where we show the tightness of the bounds obtained by the proposed method. + oai:arXiv.org:2402.13662v3 + math.PR + cs.IT + math.IT + math.ST + stat.ML + stat.TH + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.1109/ACCESS.2026.3650974 + IEEE Access, 2026 + Nikola Zlatanov + + + Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions + https://arxiv.org/abs/2403.11565 + arXiv:2403.11565v4 Announce Type: replace-cross +Abstract: In this paper, we focus on the decentralized stochastic subgradient-based methods in minimizing nonsmooth nonconvex functions without Clarke regularity, especially in the decentralized training of nonsmooth neural networks. We propose a general framework that unifies various decentralized subgradient-based methods, such as decentralized stochastic subgradient descent (DSGD), DSGD with gradient-tracking technique (DSGD-T), and DSGD with momentum (DSGD-M). To establish the convergence properties of our proposed framework, we relate the discrete iterates to the trajectories of a continuous-time differential inclusion, which is assumed to have a coercive Lyapunov function with a stable set $\mathcal{A}$. We prove the asymptotic convergence of the iterates to the stable set $\mathcal{A}$ with sufficiently small and diminishing step-sizes. These results provide first convergence guarantees for some well-recognized of decentralized stochastic subgradient-based methods without Clarke regularity of the objective function. Preliminary numerical experiments demonstrate that our proposed framework yields highly efficient decentralized stochastic subgradient-based methods with convergence guarantees in the training of nonsmooth neural networks. + oai:arXiv.org:2403.11565v4 + math.OC + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Siyuan Zhang, Nachuan Xiao, Xin Liu + + + A split-step Christov method for approximating rational PDE solutions + https://arxiv.org/abs/2407.04013 + arXiv:2407.04013v3 Announce Type: replace-cross +Abstract: Rational solutions of partial differential equations (PDEs) are notoriously difficult to approximate via spectral Fourier methods due to their algebraically slow decay rate. In this work we discuss approximating rational PDE solutions in a basis of orthogonal functions known as the Fourier series, allowing for the computation of its spectrum via the fast Fourier transform. Spectral differentiation matrices are derived. Several explicit fourth-order split-step integrators are derived and their performance compared. As an application, rogue wave solutions in a family of nonlinear Schr\"odinger equations are explored. Perturbing the constant background is found to generate rogue wave-like structures. The effects of higher-order dispersion and generalized nonlinearities are also examined. + oai:arXiv.org:2407.04013v3 + nlin.PS + cs.NA + math.NA + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Journal of Computational Physics, 2025, 114544 + Justin T. Cole, Troy I. Johnson + + + Robust Egoistic Rigid Body Localization + https://arxiv.org/abs/2501.10219 + arXiv:2501.10219v2 Announce Type: replace-cross +Abstract: We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid body (or "target"), relative to its own, without the assistance of external infrastructure, without prior knowledge of the shape of the target, and taking into account the possibility that the available observations are incomplete. Three complementary contributions are then offered for such a scenario. The first is a method to estimate the translation vector between the center point of both rigid bodies, which unlike existing techniques does not require that both objects have the same shape or even the same number of landmark points. This technique is shown to significantly outperform the state-of-the-art (SotA) under complete information, but to be sensitive to data erasures, even when enhanced by matrix completion methods. The second contribution, designed to offer improved performance in the presence of incomplete information, offers a robust alternative to the latter, at the expense of a slight relative loss under complete information. Finally, the third contribution is a scheme for the estimation of the rotation matrix describing the relative orientation of the target rigid body with respect to the primary. Comparisons of the proposed schemes and SotA techniques demonstrate the advantage of the contributed methods in terms of root mean square error (RMSE) performance under fully complete information and incomplete conditions. + oai:arXiv.org:2501.10219v2 + eess.SP + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Niclas F\"uhrling, Giuseppe Thadeu Freitas de Abreu, David Gonz\'alez G., Osvaldo Gonsa + + + Degree Realization by Bipartite Multigraphs + https://arxiv.org/abs/2501.15515 + arXiv:2501.15515v5 Announce Type: replace-cross +Abstract: The problem of realizing a given degree sequence by a multigraph can be thought of as a relaxation of the classical degree realization problem (where the realizing graph is simple). This paper concerns the case where the realizing multigraph is required to be bipartite. + The problem of characterizing sequences that can be realized by a bipartite graph has two variants. In the simpler one, termed BDR$^P$, the partition of the sequence into two sides is given as part of the input. A complete characterization for realizability in this variant was given by Gale and Ryser over sixty years ago. However, the variant where the partition is not given, termed BDR, is still open. + For bipartite multigraph realizations, there are also two variants. For BDR$^P$, where the partition is given as part of the input, a characterization was known for determining whether there is a multigraph realization whose underlying graph is bipartite, such that the maximum number of copies of an edge is at most $r$. We present a characterization for determining if there is a bipartite multigraph realization such that the total number of excess edges is at most $t$. We show that optimizing these two measures may lead to different realizations, and that optimizing by one measure may increase the other substantially. As for the variant BDR, where the partition is not given, we show that determining whether a given (single) sequence admits a bipartite multigraph realization is NP-hard. Moreover, we show that this hardness result extends to any graph family which is a sub-family of bipartite graphs and a super-family of paths. On the positive side, we provide an algorithm that computes optimal realizations for the case where the number of balanced partitions is polynomial, and present sufficient conditions for the existence of bipartite multigraph realizations that depend only on the largest degree of the sequence. + oai:arXiv.org:2501.15515v5 + math.CO + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + 10.46298/dmtcs.15158 + Discrete Mathematics & Theoretical Computer Science 28:2 #7, 2026 + Amotz Bar-Noy, Toni Bohnlein, David Peleg, Dror Rawitz + + + Multimodal oscillator networks learn to solve a classification problem + https://arxiv.org/abs/2502.12020 + arXiv:2502.12020v3 Announce Type: replace-cross +Abstract: We numerically demonstrate a network of coupled oscillators that can learn to solve a classification task from a set of examples -- performing both training and inference through the nonlinear evolution of the system. We accomplish this by combining three key elements to achieve learning: A long- term memory that stores learned responses, analogous to the synapses in biological brains; a short- term memory that stores the neural activations, similar to the firing patterns of neurons; and an evolution law that updates the synapses in response to novel examples, inspired by synaptic plasticity. Achieving all three elements in wave-based information processors such as metamaterials is a significant challenge. Here, we solve it by leveraging the material multistability to implement long-term memory, and harnessing symmetries and thermal noise to realize the learning rule. Our analysis reveals that the learning mechanism, although inspired by synaptic plasticity, also shares parallelisms with bacterial evolution strategies, where mutation rates increase in the presence of noxious stimuli. + oai:arXiv.org:2502.12020v3 + cond-mat.mes-hall + cond-mat.dis-nn + cs.ET + cs.NE + nlin.AO + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Daan de Bos, Marc Serra-Garcia + + + Network topology of the Euro Area interbank market + https://arxiv.org/abs/2502.15611 + arXiv:2502.15611v2 Announce Type: replace-cross +Abstract: The rapidly increasing availability of large amounts of granular financial data, paired with the advances of big data related technologies induces the need of suitable analytics that can represent and extract meaningful information from such data. In this paper we propose a multi-layer network approach to distill the Euro Area (EA) banking system in different distinct layers. Each layer of the network represents a specific type of financial relationship between banks, based on various sources of EA granular data collections. The resulting multi-layer network allows one to describe, analyze and compare the topology and structure of EA banks from different perspectives, eventually yielding a more complete picture of the financial market. This granular information representation has the potential to enable researchers and practitioners to better apprehend financial system dynamics as well as to support financial policies to manage and monitor financial risk from a more holistic point of view. + oai:arXiv.org:2502.15611v2 + q-fin.ST + cs.CE + stat.CO + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + 10.1007/978-3-031-63630-1_1 + In: Mingione, M., Vichi, M., Zaccaria, G. (eds), High-quality and Timely Statistics. CESS 2022. Studies in Theoretical and Applied Statistics. Springer, Cham (2024) + Ilias Aarab, Thomas Gottron + + + Global law of conjugate kernel random matrices with heavy-tailed weights + https://arxiv.org/abs/2502.18428 + arXiv:2502.18428v2 Announce Type: replace-cross +Abstract: We study the asymptotic spectral distribution of the conjugate kernel random matrix $YY^\top$, where $Y= f(WX)$ arises from a two-layer neural network model. We consider the setting where $W$ and $X$ are random rectangular matrices with i.i.d.\ entries, where the entries of $W$ follow a heavy-tailed distribution, while those of $X$ have light tails. Our assumptions on $W$ include a broad class of heavy-tailed distributions, such as symmetric $\alpha$-stable laws with $\alpha \in ]0,2[$ and sparse matrices with $\mathcal{O}(1)$ nonzero entries per row. The activation function $f$, applied entrywise, is bounded, smooth, odd, and nonlinear. We compute the limiting eigenvalue distribution of $YY^\top$ through its moments and show that heavy-tailed weights induce strong correlations between the entries of $Y$, resulting in richer and fundamentally different spectral behavior compared to the light-tailed case. + oai:arXiv.org:2502.18428v2 + math.PR + cs.LG + stat.ML + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Alice Guionnet, Vanessa Piccolo + + + SPARKLE: A Nonparametric Approach for Online Decision-Making with High-Dimensional Covariates + https://arxiv.org/abs/2503.16941 + arXiv:2503.16941v3 Announce Type: replace-cross +Abstract: Personalized services are central to today's digital economy, and their sequential decisions are often modeled as contextual bandits. Modern applications pose two main challenges: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate relationships. We propose SPARKLE, a novel contextual bandit algorithm based on a sparse additive reward model that addresses both challenges through (i) a doubly penalized estimator for nonparametric reward estimation and (ii) an epoch-based design with adaptive screening to balance exploration and exploitation. We prove a sublinear regret bound that grows only logarithmically in the covariate dimensionality; to our knowledge, this is the first such result for nonparametric contextual bandits with high-dimensional covariates. We also derive an information-theoretic lower bound, and the gap to the upper bound vanishes as the reward smoothness increases. Extensive experiments on synthetic data and real data from video recommendation and personalized medicine show strong performance in high-dimensional settings. + oai:arXiv.org:2503.16941v3 + stat.ML + cs.LG + stat.ME + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Wenjia Wang, Qingwen Zhang, Xiaowei Zhang + + + AdGT: Decentralized Gradient Tracking with Tuning-free Per-Agent Stepsize + https://arxiv.org/abs/2504.15196 + arXiv:2504.15196v4 Announce Type: replace-cross +Abstract: In decentralized optimization, the choice of stepsize plays a critical role in algorithm performance. A common approach is to use a shared stepsize across all agents to ensure convergence. However, selecting an optimal stepsize often requires careful tuning, which can be time-consuming and may lead to slow convergence, especially when there is significant variation in the smoothness (L-smoothness) of local objective functions across agents. Individually tuning stepsizes per agent is also impractical, particularly in large-scale networks. To address these limitations, we propose AdGT, an adaptive gradient tracking method that enables each agent to adjust its stepsize based on the smoothness of its local objective. We prove that AdGT achieves linear convergence to the global optimal solution. Through numerical experiments, we compare AdGT with fixed-stepsize gradient tracking methods and demonstrate its superior performance. Additionally, we compare AdGT with adaptive gradient descent (AdGD) in a centralized setting and observe that fully adaptive stepsizes offer greater benefits in decentralized networks than in centralized ones. + oai:arXiv.org:2504.15196v4 + math.OC + cs.SY + eess.SY + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Diyako Ghaderyan, Stefan Werner + + + Vertex evaluation of multiplex graphs using Forman Curvature + https://arxiv.org/abs/2504.17286 + arXiv:2504.17286v2 Announce Type: replace-cross +Abstract: The identification of vertices that play a central role in network analysis is a fundamental challenge. Although traditional centrality measures have been extensively employed for this purpose, the increasing complexity of modern networks necessitates the use of sophisticated metrics. The concept of Forman curvature has recently garnered significant attention as a promising approach. We define the Forman curvature for multiplex graphs, which are a category of complex networks characterized by multiple layers of connections between nodes. We then prove the key properties of the Forman curvature in the context of multiplex graphs and show its usefulness in identifying vertices occupying central positions within these networks. Moreover, through a series of comparative experiments with traditional graph features and graph kernels, we demonstrate that the Forman curvature can function as an effective metric for classifying the overall structure of networks. + oai:arXiv.org:2504.17286v2 + math.CO + cs.DM + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Taiki Yamada + + + Machine Learning-Based Modeling of the Anode Heel Effect in X-ray Beam Monte Carlo Simulations + https://arxiv.org/abs/2504.19155 + arXiv:2504.19155v3 Announce Type: replace-cross +Abstract: To develop a machine learning-based framework for accurately modeling the anode heel effect in Monte Carlo simulations of X-ray imaging systems, enabling realistic beam intensity profiles with minimal experimental calibration. Multiple regression models were trained to predict spatial intensity variations along the anode-cathode axis using experimentally acquired weights derived from beam measurements across different tube potentials. These weights captured the asymmetry introduced by the anode heel effect. A systematic fine-tuning protocol was established to minimize the number of required measurements while preserving model accuracy. The models were implemented in the OpenGATE 10 and GGEMS Monte Carlo toolkits to evaluate their integration feasibility and predictive performance. Among the tested models, gradient boosting regression (GBR) delivered the highest accuracy, with prediction errors remaining below 5% across all energy levels. The optimized fine-tuning strategy required only six detector positions per energy level, reducing measurement effort by 65%. The maximum error introduced through this fine-tuning process remained below 2%. Dose actor comparisons within Monte Carlo simulations demonstrated that the GBR-based model closely replicated clinical beam profiles and significantly outperformed conventional symmetric beam models. This study presents a robust and generalizable method for incorporating the anode heel effect into Monte Carlo simulations using machine learning. By enabling accurate, energy-dependent beam modeling with limited calibration data, the approach enhances simulation realism for applications in clinical dosimetry, image quality assessment, and radiation protection. + oai:arXiv.org:2504.19155v3 + physics.med-ph + cs.AI + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by-sa/4.0/ + 10.1088/1361-6560/ae2cdf + Harb, H., Benoit, D., Rannou, A., Pham, C.-H., Tissot, V., Nasr, B., Bert, J., 2026. Machine learning-based modeling of the anode heel effect in x-ray Beam Monte Carlo simulations. Phys. Med. Biol. 71, 015007 + Hussein Harb, Didier Benoit, Axel Rannou, Chi-Hieu Pham, Valentin Tissot, Bahaa Nasr, Julien Bert + + + Constant-Factor Algorithms for Revenue Management with Consecutive Stays + https://arxiv.org/abs/2506.00909 + arXiv:2506.00909v2 Announce Type: replace-cross +Abstract: We study network revenue management problems motivated by applications such as railway ticket sales and hotel room bookings. Requests, each requiring a resource for a consecutive stay, arrive sequentially with known arrival probabilities. We investigate two scenarios: the accept-or-reject scenario, where a request can be fulfilled by assigning any available resource; and the BAM-based scenario, which generalizes the former by incorporating customer preferences through the basic attraction model (BAM), allowing the platform to offer an assortment of available resources from which the customer may choose. We develop polynomial-time policies and evaluate their performance using approximation ratios, defined as the ratio between the expected revenue of our policy and that of the optimal online algorithm. When each arrival has a fixed request type (e.g., the interval of the stay is fixed), we establish constant-factor guarantees: a ratio of 1 - 1/e for the accept-or-reject scenario and 0.25 for the BAM-based scenario. We further extend these results to the case where the request type is random (e.g., the interval of the stay is random). In this setting, the approximation ratios incur an additional multiplicative factor of 1 - 1/e, resulting in guarantees of at least 0.399 for the accept-or-reject scenario and 0.156 for the BAM-based scenario. These constant-factor guarantees stand in sharp contrast to the prior nonconstant competitive ratios that are benchmarked against the offline optimum. + oai:arXiv.org:2506.00909v2 + econ.TH + cs.DS + math.OC + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Ming Hu, Tongwen Wu + + + Explainable AI Technique in Lung Cancer Detection Using Convolutional Neural Networks + https://arxiv.org/abs/2508.10196 + arXiv:2508.10196v3 Announce Type: replace-cross +Abstract: Early detection of lung cancer is critical to improving survival outcomes. We present a deep learning framework for automated lung cancer screening from chest computed tomography (CT) images with integrated explainability. Using the IQ-OTH/NCCD dataset (1,197 scans across Normal, Benign, and Malignant classes), we evaluate a custom convolutional neural network (CNN) and three fine-tuned transfer learning backbones: DenseNet121, ResNet152, and VGG19. Models are trained with cost-sensitive learning to mitigate class imbalance and evaluated via accuracy, precision, recall, F1-score, and ROC-AUC. While ResNet152 achieved the highest accuracy (97.3%), DenseNet121 provided the best overall balance in precision, recall, and F1 (up to 92%, 90%, 91%, respectively). We further apply Shapley Additive Explanations (SHAP) to visualize evidence contributing to predictions, improving clinical transparency. Results indicate that CNN-based approaches augmented with explainability can provide fast, accurate, and interpretable support for lung cancer screening, particularly in resource-limited settings. + oai:arXiv.org:2508.10196v3 + eess.IV + cs.CV + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Nishan Rai, Sujan Khatri, Devendra Risal + + + Machine Learning H-theorem + https://arxiv.org/abs/2508.14003 + arXiv:2508.14003v3 Announce Type: replace-cross +Abstract: H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we study the equilibration of randomly oriented and positioned hard disks with periodic boundary conditions. Using a model based on the DeepSets architecture, which imposes permutation invariance of the particle labels, we train a model to capture the irreversibility of the H-functional. + oai:arXiv.org:2508.14003v3 + cond-mat.stat-mech + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Ruben Lier + + + Integrating upstream and downstream reciprocity stabilizes cooperator-defector coexistence in others-only public goods games + https://arxiv.org/abs/2509.04743 + arXiv:2509.04743v2 Announce Type: replace-cross +Abstract: Human cooperation persists among strangers in large, well-mixed populations despite theoretical predictions of difficulties, leaving a fundamental evolutionary puzzle. While upstream (pay-it-forward: helping others because you were helped) and downstream (rewarding-reputation: helping those with good reputations) indirect reciprocity have been independently considered as solutions, their joint dynamics in multiplayer contexts remain unexplored. We study public goods games without self-return (often called "others-only" PGGs) with benefit b and cost c and analyze evolutionary dynamics for three strategies: unconditional cooperation (ALLC), unconditional defection (ALLD), and an integrated reciprocity strategy combining unconditional forwarding with reputation-based discrimination. We show that integrating upstream and downstream reciprocity can yield a globally asymptotically stable mixed equilibrium of ALLD and integrated reciprocators when b/c > 2 in the absence of complexity costs. We analytically derive a critical threshold for complexity costs. If cognitive demands exceed this threshold, the stable equilibrium disappears via a saddle-node bifurcation. Otherwise, within the stable regime, complexity costs counterintuitively stabilize the equilibrium by preventing not only ALLC but also alternative conditional strategies from invading. Rather than requiring uniformity, our model reveals one pathway to stable cooperation through strategic diversity. ALLD serves as "evolutionary shields" preventing system collapse while integrated reciprocators flexibly combine open and discriminative responses. This framework demonstrates how pay-it-forward broadcasting and reputation systems can jointly maintain social polymorphism including cooperation despite cognitive limitations and group size challenges, offering a potential evolutionary foundation for behavioral diversity in human societies. + oai:arXiv.org:2509.04743v2 + q-bio.PE + cs.CY + nlin.AO + physics.soc-ph + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Tatsuya Sasaki, Satoshi Uchida, Isamu Okada, Hitoshi Yamamoto, Yutaka Nakai + + + Error analysis of a compositional score-based algorithm for simulation-based inference + https://arxiv.org/abs/2510.15817 + arXiv:2510.15817v2 Announce Type: replace-cross +Abstract: Simulation-based inference (SBI) has become a widely used framework in applied sciences for estimating the parameters of stochastic models that best explain experimental observations. A central question in this setting is how to effectively combine multiple observations in order to improve parameter inference and obtain sharper posterior distributions. Recent advances in score-based diffusion methods address this problem by constructing a compositional score, obtained by aggregating individual posterior scores within the diffusion process. While it is natural to suspect that the accumulation of individual errors may significantly degrade sampling quality as the number of observations grows, this important theoretical issue has so far remained unexplored. In this paper, we study the compositional score produced by the GAUSS algorithm of Linhart et al. (2024) and establish an upper bound on its mean squared error in terms of both the individual score errors and the number of observations. We illustrate our theoretical findings on a Gaussian example, where all analytical expressions can be derived in a closed form. + oai:arXiv.org:2510.15817v2 + stat.ML + cs.LG + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Camille Touron, Gabriel V. Cardoso, Julyan Arbel, Pedro L. C. Rodrigues + + + Source-Optimal Training is Transfer-Suboptimal + https://arxiv.org/abs/2511.08401 + arXiv:2511.08401v4 Announce Type: replace-cross +Abstract: We prove that training a source model optimally for its own task is generically suboptimal when the objective is downstream transfer. We study the source-side optimization problem in L2-SP ridge regression and show a fundamental mismatch between the source-optimal and transfer-optimal source regularization: outside of a measure-zero set, $\tau_0^* \neq \tau_S^*$. We characterize the transfer-optimal source penalty $\tau_0^*$ as a function of task alignment and identify an alignment-dependent reversal: with imperfect alignment ($0<\rho<1$), transfer benefits from stronger source regularization, while in super-aligned regimes ($\rho>1$), transfer benefits from weaker regularization. Additionally, in isotropic settings, the decision of whether transfer helps is independent of the target sample size and noise, depending only on task alignment and source characteristics. We verify the linear predictions in a synthetic ridge regression experiment, and we present experiments on MNIST, CIFAR-10, and 20 Newsgroups as evidence that the source-optimal versus transfer-optimal mismatch persists in standard nonlinear transfer learning pipelines. + oai:arXiv.org:2511.08401v4 + stat.ML + cs.LG + math.ST + stat.TH + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/publicdomain/zero/1.0/ + C. Evans Hedges + + + Geometric integrators for adiabatically closed simple thermodynamic systems + https://arxiv.org/abs/2511.14154 + arXiv:2511.14154v2 Announce Type: replace-cross +Abstract: A variational formulation for non-equilibrium thermodynamics was developed by Gay-Balmaz and Yoshimura. In a recent article, the first two authors of the present paper introduced partially cosymplectic structures as a geometric framework for thermodynamic systems, recovering the evolution equations obtained variationally. In this paper, we develop a discrete variational principle for adiabatically closed simple thermodynamic systems, which can be utilised to construct numerical integrators for the dynamics of such systems. The effectiveness of our method is illustrated with several examples. + oai:arXiv.org:2511.14154v2 + math-ph + cs.NA + math.MP + math.NA + physics.class-ph + physics.comp-ph + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Jaime Bajo, Manuel de Le\'on, Asier L\'opez-Gord\'on + + + A Trust-region Funnel Algorithm for Grey-Box Optimisation + https://arxiv.org/abs/2511.18998 + arXiv:2511.18998v2 Announce Type: replace-cross +Abstract: Grey-box optimisation, where some parts of an optimisation problem are represented by explicit algebraic (glass-box) models while others are treated as black-box models lacking analytic derivatives, remains a challenge in process systems engineering. Trust-region (TR) methods provide a robust framework for grey-box problems by combining accurate glass-box derivatives with local reduced models (RMs) for black-box components. However, existing TR approaches often involve complex multi-layered formulations requiring extensive parameter tuning, or lack open-source implementations. Motivated by the recent advances in funnel-based convergence theory for nonlinear optimisation and the TR filter method, we propose a novel TR funnel algorithm for grey-box optimisation that replaces the filter acceptance criterion with a generalisable uni-dimensional funnel, maintaining a monotonically non-increasing upper bound on approximation error of the local black-box RMs. A global convergence proof to a first-order critical point is established. The algorithm, implemented in an open-source Pyomo framework, supports multiple RM forms and globalisation strategies (filter or funnel). Benchmark tests on seven numerical and engineering problems show that the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method. The TR funnel method thus provides a simpler, and extensible alternative for large-scale grey-box optimisation. + oai:arXiv.org:2511.18998v2 + math.OC + cs.NA + math.NA + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short + + + The Color-Clinical Decoupling: Why Perceptual Calibration Fails Clinical Biomarkers in Smartphone Dermatology + https://arxiv.org/abs/2512.21988 + arXiv:2512.21988v2 Announce Type: replace-cross +Abstract: Smartphone-based tele-dermatology assumes that colorimetric calibration ensures clinical reliability, yet this remains untested for underrepresented skin phototypes. We investigated whether standard calibration translates to reliable clinical biomarkers using 43,425 images from 965 Korean subjects (Fitzpatrick III-IV) across DSLR, tablet, and smartphone devices. While Linear Color Correction Matrix (CCM) normalization reduced color error by 67-77% -- achieving near-clinical accuracy (Delta E < 2.3) -- this success did not translate to biomarker reliability. + We identify a phenomenon termed "color-clinical decoupling": despite perceptual accuracy, the Individual Typology Angle (ITA) showed poor inter-device agreement (ICC = 0.40), while the Melanin Index achieved good agreement (ICC = 0.77). This decoupling is driven by the ITA formula's sensitivity to b* channel noise and is further compounded by anatomical variance. Facial region accounts for 25.2% of color variance -- 3.6x greater than device effects (7.0%) -- challenging the efficacy of single-patch calibration. Our results demonstrate that current colorimetric standards are insufficient for clinical-grade biomarker extraction, necessitating region-aware protocols for mobile dermatology. + oai:arXiv.org:2512.21988v2 + eess.IV + cs.CV + q-bio.QM + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Sungwoo Kang + + + Modeling Information Blackouts in Missing Not-At-Random Time Series Data + https://arxiv.org/abs/2601.01480 + arXiv:2601.01480v2 Announce Type: replace-cross +Abstract: Large-scale traffic forecasting relies on fixed sensor networks that often exhibit blackouts: contiguous intervals of missing measurements caused by detector or communication failures. These outages are typically handled under a Missing At Random (MAR) assumption, even though blackout events may correlate with unobserved traffic conditions (e.g., congestion or anomalous flow), motivating a Missing Not At Random (MNAR) treatment. We propose a latent state-space framework that jointly models (i) traffic dynamics via a linear dynamical system and (ii) sensor dropout via a Bernoulli observation channel whose probability depends on the latent traffic state. Inference uses an Extended Kalman Filter with Rauch-Tung-Striebel smoothing, and parameters are learned via an approximate EM procedure with a dedicated update for detector-specific missingness parameters. On the Seattle inductive loop detector data, introducing latent dynamics yields large gains over naive baselines, reducing blackout imputation RMSE from 7.02 (LOCF) and 5.02 (linear interpolation + seasonal naive) to 4.23 (MAR LDS), corresponding to about a 64% reduction in MSE relative to LOCF. Explicit MNAR modeling provides a consistent but smaller additional improvement on real data (imputation RMSE 4.20; 0.8% RMSE reduction relative to MAR), with similar modest gains for short-horizon post-blackout forecasts (evaluated at 1, 3, and 6 steps). In controlled synthetic experiments, the MNAR advantage increases as the true missingness dependence on latent state strengthens. Overall, temporal dynamics dominate performance, while MNAR modeling offers a principled refinement that becomes most valuable when missingness is genuinely informative. + oai:arXiv.org:2601.01480v2 + stat.ML + cs.LG + stat.AP + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://arxiv.org/licenses/nonexclusive-distrib/1.0/ + Aman Sunesh (New York University), Allan Ma (New York University), Siddarth Nilol (New York University) + + + Rethinking Secure Semantic Communications in the Age of Generative and Agentic AI: Threats and Opportunities + https://arxiv.org/abs/2601.01791 + arXiv:2601.01791v2 Announce Type: replace-cross +Abstract: Semantic communication (SemCom) improves communication efficiency by transmitting task-relevant information instead of raw bits and is expected to be a key technology for 6G networks. Recent advances in generative AI (GenAI) further enhance SemCom by enabling robust semantic encoding and decoding under limited channel conditions. However, these efficiency gains also introduce new security and privacy vulnerabilities. Due to the broadcast nature of wireless channels, eavesdroppers can also use powerful GenAI-based semantic decoders to recover private information from intercepted signals. Moreover, rapid advances in agentic AI enable eavesdroppers to perform long-term and adaptive inference through the integration of memory, external knowledge, and reasoning capabilities. This allows eavesdroppers to further infer user private behavior and intent beyond the transmitted content. Motivated by these emerging challenges, this paper comprehensively rethinks the security and privacy of SemCom systems in the age of generative and agentic AI. We first present a systematic taxonomy of eavesdropping threat models in SemCom systems. Then, we provide insights into how GenAI and agentic AI can enhance eavesdropping threats. Meanwhile, we also highlight potential opportunities for leveraging GenAI and agentic AI to design privacy-preserving SemCom systems. + oai:arXiv.org:2601.01791v2 + eess.SP + cs.IT + cs.NI + math.IT + Wed, 07 Jan 2026 00:00:00 -0500 + replace-cross + http://creativecommons.org/licenses/by/4.0/ + Shunpu Tang, Yuanyuan Jia, Zijiu Yang, Qianqian Yang, Ruichen Zhang, Jun Du, Jihong Park, Zhiguo Shi, Jiming Chen +