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49dfbe237fca38573d65bd8a8d35c772f18ee20d424c720003acb03877a8e4c0 | 2026-01-07T00:00:00-05:00 | TreeDiff: AST-Guided Code Generation with Diffusion LLMs | 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 progr... | https://arxiv.org/abs/2508.01473 | Academic Papers | svg |
2e5c14ac62388abdd7df9c92866c94220eb89ef7b2624759a8d2e15d71021ab1 | 2026-01-07T00:00:00-05:00 | The Homogenizing Effect of Large Language Models on Human Expression and Thought | 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... | https://arxiv.org/abs/2508.01491 | Academic Papers | svg |
6970ddd958db40199d5789abcba9c0863cbc2f7f3032e13157e73adcf3b8cc8f | 2026-01-07T00:00:00-05:00 | The Bidirectional Process Reward Model | 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 un... | https://arxiv.org/abs/2508.01682 | Academic Papers | svg |
b2fe93cc8c904a54c3b9788676c38d2c841cac62cace979c98fa68ac087071f3 | 2026-01-07T00:00:00-05:00 | Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning | 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 decompositi... | https://arxiv.org/abs/2508.02178 | Academic Papers | svg |
3f4933f383867d73f9bc320bb2a8d046a5d94f501f9c92858fad12c76e48883e | 2026-01-07T00:00:00-05:00 | U-PINet: Physics-Informed Hierarchical Learning for Accurate and Fast 3D RCS Prediction | 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) solver... | https://arxiv.org/abs/2508.03774 | Academic Papers | svg |
15a51210ac514a6041427315bcb836946ca8e13fe42b65cd116943ef835c4d4f | 2026-01-07T00:00:00-05:00 | SAGOnline: Segment Any Gaussians Online | 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... | https://arxiv.org/abs/2508.08219 | Academic Papers | svg |
65856ef499cc70c77a5d9718df5b2bc50a59a8ff978a967d3a595842d384de6f | 2026-01-07T00:00:00-05:00 | Optimal Boost Design for Auto-bidding Mechanism with Publisher Quality Constraints | 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 ... | https://arxiv.org/abs/2508.08772 | Academic Papers | svg |
69f5349ee176c17c725418277b3e44ca7858860d8e826d1c29e9af4223b9b21a | 2026-01-07T00:00:00-05:00 | Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation | 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... | https://arxiv.org/abs/2508.12645 | Academic Papers | svg |
8d605e51630682b9f8efd18ee9498391f6dc6bc0d59c1fb0d5c21a085aebf00b | 2026-01-07T00:00:00-05:00 | An Informative Planning Framework for Target Tracking and Active Mapping in Dynamic Environments with ASVs | 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... | https://arxiv.org/abs/2508.14636 | Academic Papers | svg |
1fe5c3afae2fd08d7c237d088a131e459450f2488393304f2de2cdb4a83fd77d | 2026-01-07T00:00:00-05:00 | VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models | 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... | https://arxiv.org/abs/2508.15229 | Academic Papers | svg |
9d51fd0c38bc510c9dd0c8217c5c0c96e3822e70e30478a65f5a7792d0acae9b | 2026-01-07T00:00:00-05:00 | Scalable Scientific Interest Profiling Using Large Language Models | 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 method... | https://arxiv.org/abs/2508.15834 | Academic Papers | svg |
96ae0650951e16911dc6f2860404baaf052e5e7913afe87c6e715c7b5a13f5a7 | 2026-01-07T00:00:00-05:00 | LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models | 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 en... | https://arxiv.org/abs/2508.17394 | Academic Papers | svg |
106576bcfcff7dfa4d9ffad9233e7ad67a799a3415ead377ea616457ff12333f | 2026-01-07T00:00:00-05:00 | Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance | 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 pr... | https://arxiv.org/abs/2508.18177 | Academic Papers | svg |
a48e1a7e69d8b1ea1d2c51c06e19d7f833c4f7ca0c68907ec08977bb984f7176 | 2026-01-07T00:00:00-05:00 | Low-Cost Architecture and Efficient Pattern Synthesis for Polarimetric Phased Array Based on Polarization Coding Reconfigurable Elements | 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 polari... | https://arxiv.org/abs/2508.19644 | Academic Papers | svg |
5294a1fda5b2a313d1db2dbae8afb3f37cb49fecde7a97ba4cd20cf4fb17d597 | 2026-01-07T00:00:00-05:00 | Constructive l2-Discrepancy Minimization with Additive Deviations | 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\i... | https://arxiv.org/abs/2508.21423 | Academic Papers | svg |
712bdb536defe9f3f323fe7051d430606362db9f5ecb1fc9fc0c5adf870e4d87 | 2026-01-07T00:00:00-05:00 | On discrete Sobolev inequalities for nonconforming finite elements under a semi-regular mesh condition | 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 ineq... | https://arxiv.org/abs/2509.00505 | Academic Papers | svg |
002fc64ed6b7675e219f38611626472090e242431ff522b4b1e284a9535f3eb5 | 2026-01-07T00:00:00-05:00 | HADIS: Hybrid Adaptive Diffusion Model Serving for Efficient Text-to-Image Generation | 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 mo... | https://arxiv.org/abs/2509.00642 | Academic Papers | svg |
66e77ff3aeb725598534758bdf766354955468d10a4811305b84785d5daf1139 | 2026-01-07T00:00:00-05:00 | Re3: Learning to Balance Relevance & Recency for Temporal Information Retrieval | 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, ensur... | https://arxiv.org/abs/2509.01306 | Academic Papers | svg |
2983ed14072d5ea628620c0022c3aa2108b51707b243650e90541be8f7631bd4 | 2026-01-07T00:00:00-05:00 | ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association | 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 ... | https://arxiv.org/abs/2509.01584 | Academic Papers | svg |
1a81ed879e378da5309b7a4d4d90fb1bec965de89be5e82f0531c718cd55f7e6 | 2026-01-07T00:00:00-05:00 | Uncertainty-driven Adaptive Exploration | 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 ... | https://arxiv.org/abs/2509.03219 | Academic Papers | svg |
7b336a99d9248ac44d0bd20b9c18346185834074e0e79c2a1aad45cc1b7c1074 | 2026-01-07T00:00:00-05:00 | A Multidimensional AI-powered Framework for Analyzing Tourist Perception in Historic Urban Quarters: A Case Study in Shanghai | 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 prop... | https://arxiv.org/abs/2509.03830 | Academic Papers | svg |
f39732a79acd4a1913acc1f5bdff587a552404546f7e60062e5fccb5fa7a5c1e | 2026-01-07T00:00:00-05:00 | IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation | 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 anal... | https://arxiv.org/abs/2509.04398 | Academic Papers | svg |
0c5a0d9194af9b10baa13124f2164f3dee87d6a4101a1854f1dd573c58156eae | 2026-01-07T00:00:00-05:00 | Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies | 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,... | https://arxiv.org/abs/2509.04458 | Academic Papers | svg |
2e2928130aba94de9e866dbf46461342791f345b8f78e1c517614b905a973e61 | 2026-01-07T00:00:00-05:00 | Benchmarking CNN and Transformer-Based Object Detectors for UAV Solar Panel Inspection | 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 archi... | https://arxiv.org/abs/2509.05348 | Academic Papers | svg |
45f0f749e0091dc8232b8c1434d781feffd8ffb7192b8eea48624af7c0ee2dcb | 2026-01-07T00:00:00-05:00 | Optimal Average Disk-Inspection via Fermat's Principle | 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 whenev... | https://arxiv.org/abs/2509.06334 | Academic Papers | svg |
675ddeb5a1489fcd40672b1bbfceb20ca613a2f07fd4f0ed5aabb09bc6d7b194 | 2026-01-07T00:00:00-05:00 | Learning Optimal Defender Strategies for CAGE-2 using a POMDP Model | 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 litera... | https://arxiv.org/abs/2509.06539 | Academic Papers | svg |
616513cc16f686c906d56ca7ad73e942e900a1c0cd64c96911cd7c231a4e0db4 | 2026-01-07T00:00:00-05:00 | A Decade-long Landscape of Advanced Persistent Threats: Longitudinal Analysis and Global Trends | 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 collabo... | https://arxiv.org/abs/2509.07457 | Academic Papers | svg |
fedc48308c9c76bc85aca612a0baa0e2e146820b804fa42f729d6e1cd0f960fd | 2026-01-07T00:00:00-05:00 | EFPIX: A zero-trust encrypted flood protocol | 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 tho... | https://arxiv.org/abs/2509.08248 | Academic Papers | svg |
aa1033a4de2f76ddfb78814260835c8c187424a3f26b68532deb4a48bb0232eb | 2026-01-07T00:00:00-05:00 | Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking | 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 con... | https://arxiv.org/abs/2509.09583 | Academic Papers | svg |
726c07934e085280f5678ca6a1451c12709ce0bec18f8e40badf82fe65a15f2f | 2026-01-07T00:00:00-05:00 | AgentArch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise | 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 comp... | https://arxiv.org/abs/2509.10769 | Academic Papers | svg |
fc5a75d4cab874b6d83ebfc8df01645fbff75a0c3da5bb1f25136d640a250092 | 2026-01-07T00:00:00-05:00 | Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data | 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 r... | https://arxiv.org/abs/2509.11078 | Academic Papers | svg |
3f6ce4f840dcf3fd2a7f6e696f059c8c2c190059419e54b17aed68c5e2fa4379 | 2026-01-07T00:00:00-05:00 | OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning | 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... | https://arxiv.org/abs/2509.14803 | Academic Papers | svg |
67d84b2138bb24e75c9ab625d34ed317324c218d79d21fe6659ad7124394dd87 | 2026-01-07T00:00:00-05:00 | Exploring How Audio Effects Alter Emotion with Foundation Models | 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,... | https://arxiv.org/abs/2509.15151 | Academic Papers | svg |
0b8ea300437e862d585aa6ce12ed6242084f995b9088fb5d7cab4b3fdedb6fe7 | 2026-01-07T00:00:00-05:00 | FragmentRetro: A Quadratic Retrosynthetic Method Based on Fragmentation Algorithms | 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 Frag... | https://arxiv.org/abs/2509.15409 | Academic Papers | svg |
05d79847d56fe749ed9205fa4ab43dd4d61168e91a5e2f04f119d11d77176e92 | 2026-01-07T00:00:00-05:00 | ISCS: Parameter-Guided Feature Pruning for Resource-Constrained Embodied Perception | 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 late... | https://arxiv.org/abs/2509.16853 | Academic Papers | svg |
4710ac07237914f2541c2d849aed5e9e908c3e85acb1bc34fda9efe68d529c5e | 2026-01-07T00:00:00-05:00 | Evolutionary Learning in Spatial Agent-Based Models for Physical Climate Risk Assessment | 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. O... | https://arxiv.org/abs/2509.18633 | Academic Papers | svg |
cdc84172061eb34ad89e1b4935127f2d9cb0ebf9f15a4ab9a5006b372770136c | 2026-01-07T00:00:00-05:00 | Consistency-Aware Parameter-Preserving Knowledge Editing Framework for Multi-Hop Question Answering | 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... | https://arxiv.org/abs/2509.18655 | Academic Papers | svg |
77f5225a87b6daca9c405f0b15e0d784199bea53c7cd54f06e2dc139fa3815ef | 2026-01-07T00:00:00-05:00 | ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression | 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 the... | https://arxiv.org/abs/2509.20234 | Academic Papers | svg |
5ae2bf780df057a71cb8d5b5e0e2b5b8fddc9a3d081f06548e06df48e82fa78f | 2026-01-07T00:00:00-05:00 | Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms | 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... | https://arxiv.org/abs/2509.20278 | Academic Papers | svg |
afbc6daaa581406656c73d9b307d690b8c560eb3a0b19d58e4ddc73e4524f896 | 2026-01-07T00:00:00-05:00 | CaTS-Bench: Can Language Models Describe Time Series? | 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 ... | https://arxiv.org/abs/2509.20823 | Academic Papers | svg |
223914da9c3c175984f9109f390ebc31f022e4128d496e7b8b60bb8158cde6da | 2026-01-07T00:00:00-05:00 | Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval | 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 ... | https://arxiv.org/abs/2509.21710 | Academic Papers | svg |
ca876265e1358b8dafed997e63970877f8050fdd8b157d1f23afa32034824d70 | 2026-01-07T00:00:00-05:00 | D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents | 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... | https://arxiv.org/abs/2509.21799 | Academic Papers | svg |
50af53734f068c8b9eeae092aff364ddb635d683a662e21cb67ecc89b20b799b | 2026-01-07T00:00:00-05:00 | CMDAR: A Chinese Multi-scene Dynamic Audio Reasoning Benchmark with Diverse Challenges | 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... | https://arxiv.org/abs/2509.22461 | Academic Papers | svg |
5646439cd91f949886c4cbcad2f73c33bf66694c8391cd5385ee0eef814af40c | 2026-01-07T00:00:00-05:00 | MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference | 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 ... | https://arxiv.org/abs/2509.22750 | Academic Papers | svg |
ea91862af8ed0f81e343cf113d17f8ffd0acf5028f600349ede52ff44b430038 | 2026-01-07T00:00:00-05:00 | Gradient Coupling: The Hidden Barrier to Generalization in Agentic Reinforcement Learning | 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 phenomen... | https://arxiv.org/abs/2509.23870 | Academic Papers | svg |
6d130d8de3fb374d6c93709a88cab441e53588b6f93f323bb952f4380ecf2005 | 2026-01-07T00:00:00-05:00 | Go with Your Gut: Scaling Confidence for Autoregressive Image Generation | 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 o... | https://arxiv.org/abs/2509.26376 | Academic Papers | svg |
779f1da354a40dd23529bac764df6929219a2e17d5bda49830031f8185d4edea | 2026-01-07T00:00:00-05:00 | Framing Unionization on Facebook: Communication around Representation Elections in the United States | 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 ... | https://arxiv.org/abs/2510.01757 | Academic Papers | svg |
56714f8ce3407704e97135becf2212c494803324e358c69acba28ee348a99cb7 | 2026-01-07T00:00:00-05:00 | Universal Dynamic Regret and Constraint Violation Bounds for Constrained Online Convex Optimization | 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... | https://arxiv.org/abs/2510.01867 | Academic Papers | svg |
45477e1deeab57f7dd10aa0dc4fc82440a3d27399a0b357ea38c9929a878a45a | 2026-01-07T00:00:00-05:00 | Style over Story: Measuring LLM Narrative Preferences via Structured Selection | 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 pr... | https://arxiv.org/abs/2510.02025 | Academic Papers | svg |
7d464613ada37d07f8be24952b3b8c978603ddd8ba2b3dd4a900af348d5598d3 | 2026-01-07T00:00:00-05:00 | Agentic Additive Manufacturing Alloy Evaluation | 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 req... | https://arxiv.org/abs/2510.02567 | Academic Papers | svg |
1e98e8a34f9b8028d3dd9371893fdfa260d2397bd11671c3788bf56d8b1093f3 | 2026-01-07T00:00:00-05:00 | The Artificial Intelligence Cognitive Examination: A Survey on the Evolution of Multimodal Evaluation from Recognition to Reasoning | 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 recognit... | https://arxiv.org/abs/2510.04141 | Academic Papers | svg |
3461f03db9c500d7839e3ace6ae1ec71a797b60268eb39df2e2bddb6cdc89244 | 2026-01-07T00:00:00-05:00 | Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification | 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 noi... | https://arxiv.org/abs/2510.05431 | Academic Papers | svg |
af3915f0256a674fe222d2642131a0d05458c1c52d84555b1e2327cc0bd090aa | 2026-01-07T00:00:00-05:00 | When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning | 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-bi... | https://arxiv.org/abs/2510.07517 | Academic Papers | svg |
df2878713f557235d9e2fea193a15e16b84f423533c91eb16ee02165dad29d92 | 2026-01-07T00:00:00-05:00 | SyncLipMAE: Contrastive Masked Pretraining for Audio-Visual Talking-Face Representation | 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 ali... | https://arxiv.org/abs/2510.10069 | Academic Papers | svg |
14a8af8e6a415707ed614ba6660744de642dc4d1052486bf5a498adc455d09c9 | 2026-01-07T00:00:00-05:00 | What Makes Looped Transformers Perform Better Than Non-Recursive Ones | 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 land... | https://arxiv.org/abs/2510.10089 | Academic Papers | svg |
1ded85cce0a6560c0e1ab4ee9ea1ad8373d4c7d5a6fc79743d72e1c84996345d | 2026-01-07T00:00:00-05:00 | Do You Get the Hint? Benchmarking LLMs on the Board Game Concept | 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 ... | https://arxiv.org/abs/2510.13271 | Academic Papers | svg |
de01f4385ccf18b86fbd2bc1ddd745667bcc4d5a3b8afd26bd23e84918bd9cb7 | 2026-01-07T00:00:00-05:00 | Exploratory Causal Inference in SAEnce | 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 propos... | https://arxiv.org/abs/2510.14073 | Academic Papers | svg |
1534e6a11c5af21c51e6382f3b7595f9ed7507dd6721571678e8165d3381d089 | 2026-01-07T00:00:00-05:00 | CodeEvolve: an open source evolutionary coding agent for algorithm discovery and optimization | 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 execu... | https://arxiv.org/abs/2510.14150 | Academic Papers | svg |
1f04c2e54c51e1a76a07cdbf43bd7931e72ff89c8060dd0c48df5d4e676e45eb | 2026-01-07T00:00:00-05:00 | Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising | 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 t... | https://arxiv.org/abs/2510.15125 | Academic Papers | svg |
2d19149224278970642c77a048bf774fe2e2f11ff172b245f165f5af66973d63 | 2026-01-07T00:00:00-05:00 | ELMM: Efficient Lightweight Multimodal Large Language Models for Multimodal Knowledge Graph Completion | 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... | https://arxiv.org/abs/2510.16753 | Academic Papers | svg |
4b1f9336be9500228e346ba076d3309b92f6a27ed246e3279c52f85dfd60b1cc | 2026-01-07T00:00:00-05:00 | DDBot: Differentiable Physics-based Digging Robot for Unknown Granular Materials | 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... | https://arxiv.org/abs/2510.17335 | Academic Papers | svg |
73eafc0bb8b550c5f904194c41e3212dc0e7b1a87ad4d22c90bba51bc2b0e877 | 2026-01-07T00:00:00-05:00 | Qomhra: A Bilingual Irish and English Large Language Model | 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, develope... | https://arxiv.org/abs/2510.17652 | Academic Papers | svg |
6ad3da0db428eacadfd082077e1084ab983caf0c3df49b6f09dc2cb693ea201e | 2026-01-07T00:00:00-05:00 | Compositional Monte Carlo Tree Diffusion for Extendable Planning | 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 allow... | https://arxiv.org/abs/2510.21361 | Academic Papers | svg |
91ace533db3330924577679bab5d6b1c6dd9ab515b100b9904a51680056546c3 | 2026-01-07T00:00:00-05:00 | Leveraging Design-Aware Context in Large Language Models for Code Comment Generation | 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... | https://arxiv.org/abs/2510.22338 | Academic Papers | svg |
e5fbe758bf333a76a52997de7d6c2d55632a330feb6692410360187200582f39 | 2026-01-07T00:00:00-05:00 | Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving | 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... | https://arxiv.org/abs/2510.23346 | Academic Papers | svg |
c3c5dcbe4d010e35549c710c1d596b4b5499a5265f630a4fe38a207380437e38 | 2026-01-07T00:00:00-05:00 | ReCode: Unify Plan and Action for Universal Granularity Control | 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 l... | https://arxiv.org/abs/2510.23564 | Academic Papers | svg |
9a0be9455fb114392b3b4ccbef14c3c5fcf1fbc58f8fe30484bd78e57573128e | 2026-01-07T00:00:00-05:00 | Adaptive Data Collection for Latin-American Community-sourced Evaluation of Stereotypes (LACES) | 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 lang... | https://arxiv.org/abs/2510.24958 | Academic Papers | svg |
2c6b59fa866da0764acad24b832cef63cefd8d0408adc74d18bb0aecce63484b | 2026-01-07T00:00:00-05:00 | Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error | 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 i... | https://arxiv.org/abs/2510.26109 | Academic Papers | svg |
48c8ee45209ddd61467c570a992a935a8f6265ca3055678f1cecb28b0680244c | 2026-01-07T00:00:00-05:00 | Cut-free Deductive System for Continuous Intuitionistic Logic | 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... | https://arxiv.org/abs/2510.26849 | Academic Papers | svg |
6c6e3ee7ad158f42aaa2852cdfea68e8f4b0f97a150bcb9ccd9842104d0b9669 | 2026-01-07T00:00:00-05:00 | Group-Sensitive Offline Contextual Bandits | 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 ... | https://arxiv.org/abs/2510.27123 | Academic Papers | svg |
7002ecdf2f63a1fd7f0c3006db6020b47d7491e5e18b3a531f124114717ef2e1 | 2026-01-07T00:00:00-05:00 | Orthogonal-by-construction augmentation of physics-based input-output models | 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 lea... | https://arxiv.org/abs/2511.01321 | Academic Papers | svg |
4d95fca4a3533834d6906c249cc8fd5681b480c60c91cb3cf3af888b130e3bc3 | 2026-01-07T00:00:00-05:00 | LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning | 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 employ... | https://arxiv.org/abs/2511.01448 | Academic Papers | svg |
959ddace4bc799a7100ac4bf181426ba132c81e601fbaf91de8b710d62529955 | 2026-01-07T00:00:00-05:00 | Adapting Web Agents with Synthetic Supervision | 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 con... | https://arxiv.org/abs/2511.06101 | Academic Papers | svg |
f5e220c745b3ce9fd9d3258fdb922be1eba86246841f18d2075231b0589dc313 | 2026-01-07T00:00:00-05:00 | Alignment-Aware Quantization for LLM Safety | 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... | https://arxiv.org/abs/2511.07842 | Academic Papers | svg |
17b31b0ea41c1a95d190f411dd1c9753d7f4960a374f15f9b282b4bcff5dd368 | 2026-01-07T00:00:00-05:00 | The Journal of Prompt-Engineered Philosophy Or: How I Started to Track AI Assistance and Stopped Worrying About Slop | 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 transparenc... | https://arxiv.org/abs/2511.08639 | Academic Papers | svg |
cf47331c997189aa51f0ad52fe2e6c8581c7aa83da40144de45c9b45e36454fa | 2026-01-07T00:00:00-05:00 | DoPE: Denoising Rotary Position Embedding | 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... | https://arxiv.org/abs/2511.09146 | Academic Papers | svg |
413d12239f9c2a0eac83f6ab071bc2e2af66c7c94a6223cb1809bd4dba9941b3 | 2026-01-07T00:00:00-05:00 | Towards Unbiased Cross-Modal Representation Learning for Food Image-to-Recipe Retrieval | 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 app... | https://arxiv.org/abs/2511.15201 | Academic Papers | svg |
c6061f798a529a9331eae2f4d424b3b0f12c5a42cfa84893b6cd2cf6668fef54 | 2026-01-07T00:00:00-05:00 | Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling | 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 ... | https://arxiv.org/abs/2511.16952 | Academic Papers | svg |
5fc6c4673fe5e63cfedfd090c656f2d582360a21895db568c7e4e4b6023a7ead | 2026-01-07T00:00:00-05:00 | FLUID: Training-Free Face De-identification via Latent Identity Substitution | 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 ... | https://arxiv.org/abs/2511.17005 | Academic Papers | svg |
c18df8b22acfc5b3181ddba83d0aa88d86ba2d25fe79e0d375ebddf29521b10d | 2026-01-07T00:00:00-05:00 | Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats | 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, integra... | https://arxiv.org/abs/2511.17254 | Academic Papers | svg |
5a2260a16b26133de72658f06b34e983c414436a450881d7bfcdafec999bcfe5 | 2026-01-07T00:00:00-05:00 | Musical Score Understanding Benchmark: Evaluating Large Language Models' Comprehension of Complete Musical Scores | 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 introd... | https://arxiv.org/abs/2511.20697 | Academic Papers | svg |
0886899094a3c75c41e7f54e1c596267eee9e680464786070f453349f006ac6d | 2026-01-07T00:00:00-05:00 | Representation Interventions Enable Lifelong Unstructured Knowledge Control | 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 setti... | https://arxiv.org/abs/2511.20892 | Academic Papers | svg |
3640e46412c44ee3281a45181547a85631d8e56baea79150ee57488f8b2d31aa | 2026-01-07T00:00:00-05:00 | Emergence and Localisation of Semantic Role Circuits in LLMs | 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 compa... | https://arxiv.org/abs/2511.20910 | Academic Papers | svg |
47358b3a50da733ce70c4530eed49c8b787bf2ece5f31c0ca08c024a6b59db7d | 2026-01-07T00:00:00-05:00 | Beyond Patch Aggregation: 3-Pass Pyramid Indexing for Vision-Enhanced Document Retrieval | 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 cont... | https://arxiv.org/abs/2511.21121 | Academic Papers | svg |
f337b0237044dc3ade44c084e35b409481b508c37a53a7bd777a7a693f4595e2 | 2026-01-07T00:00:00-05:00 | Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon | 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 classe... | https://arxiv.org/abs/2511.23384 | Academic Papers | svg |
0cec0f6c1ef52a52602a2324d3b0b14aaed2617a39e0800a7ad970e2a5fe500a | 2026-01-07T00:00:00-05:00 | Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios | 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 RM... | https://arxiv.org/abs/2512.00920 | Academic Papers | svg |
a58f2555eefcb2a7314b6f34ef20dd9ee2eff7186b8f31537e6a01d031312c8c | 2026-01-07T00:00:00-05:00 | Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases | 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, statem... | https://arxiv.org/abs/2512.03278 | Academic Papers | svg |
5f3ec77e733f3cb5c6fdf5fe4bcca441f8c4ba75d51b98b9ad8f6bcb36e16c97 | 2026-01-07T00:00:00-05:00 | Legitimizing, Developing, and Sustaining Feminist HCI in East Asia: Challenges and Opportunities | 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 nav... | https://arxiv.org/abs/2512.13000 | Academic Papers | svg |
f929f2ae824773c1581c2f1c4c6e0f29b82e647d8620da2efa730dc8c73d9d6c | 2026-01-07T00:00:00-05:00 | Socratic Students: Teaching Language Models to Learn by Asking Questions | 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 sol... | https://arxiv.org/abs/2512.13102 | Academic Papers | svg |
ed133a9dbe1f5d26ab39ccb910ec0599497629e1600f530b725473895a812888 | 2026-01-07T00:00:00-05:00 | RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics | 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 wit... | https://arxiv.org/abs/2512.13660 | Academic Papers | svg |
d84ba99b62ee0781d52ff37d8a49db70752dd7fbf24ec7384071e721d73fbfb6 | 2026-01-07T00:00:00-05:00 | Massive Editing for Large Language Models Based on Dynamic Weight Generation | 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 th... | https://arxiv.org/abs/2512.14395 | Academic Papers | svg |
3ed9a14a9d04bff45fae531661ff7eb71967b8e4a4dcc91f1dc6df8ed7f1ced1 | 2026-01-07T00:00:00-05:00 | Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers | 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 ... | https://arxiv.org/abs/2512.15674 | Academic Papers | svg |
e1fad90e2593fa2310107a63ba845ec6534c76c0797a4326b9889a6a7586c26f | 2026-01-07T00:00:00-05:00 | LoFT-LLM: Low-Frequency Time-Series Forecasting with Large Language Models | 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,... | https://arxiv.org/abs/2512.20002 | Academic Papers | svg |
2551b8df668067e2d4cf90a68a874ed108069658c66f3578043ebddf5366253c | 2026-01-07T00:00:00-05:00 | D^3ETOR: Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations | 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 st... | https://arxiv.org/abs/2512.20260 | Academic Papers | svg |
66bdb1b1cf15be6d4518676906d2e081ffaac6e5a6f3dd48d5b56edc57576f09 | 2026-01-07T00:00:00-05:00 | Mixture-of-Experts with Gradient Conflict-Driven Subspace Topology Pruning for Emergent Modularity | 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 ... | https://arxiv.org/abs/2512.20291 | Academic Papers | svg |
2ab37e447e0fb327e48e61ae1ab2106b5c6da2679e41c57a8cfb6149136efc10 | 2026-01-07T00:00:00-05:00 | Efficient and Robust Video Defense Framework against 3D-field Personalized Talking Face | 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 con... | https://arxiv.org/abs/2512.21019 | Academic Papers | svg |
93c70607c930b560f860da263670dbaf666a1a3fbd4b67007b8bae8cdf4ffd94 | 2026-01-07T00:00:00-05:00 | Making AI Functional with Workarounds: An Insider's Account of Invisible Labour in Organisational Politics | 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... | https://arxiv.org/abs/2512.21055 | Academic Papers | svg |
32e1e4220ffefe397b6618abfc8d447ca2e93d87d20e1c29c131079c9e5fa858 | 2026-01-07T00:00:00-05:00 | NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agent | 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... | https://arxiv.org/abs/2512.21578 | Academic Papers | svg |
b1a4fbe34a137c9a0a99748e5e782559653c2a9dbc771342319cd2b895c9754a | 2026-01-07T00:00:00-05:00 | A Comedy of Estimators: On KL Regularization in RL Training of LLMs | 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 t... | https://arxiv.org/abs/2512.21852 | Academic Papers | svg |
1bf2aff0b1bcac273e50f812dfc965d1ea22313449a32a26b4a2fd1e65cb887d | 2026-01-07T00:00:00-05:00 | DiRL: An Efficient Post-Training Framework for Diffusion Language Models | 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. Ex... | https://arxiv.org/abs/2512.22234 | Academic Papers | svg |
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