6059 papers
This paper addresses the challenge of agents with heterogeneous goals deciding when to cooperate or act alone by introducing a hierarchical learning framework that combines imitation and reinforcement learning, demonstrating superior performance over baselines and revealing that modeling teammates is most beneficial when their goals are less observable.
This paper introduces VASR, a multimodal reasoning framework for Context-Aware Visual Speech Recognition (CAVSR) that leverages an Audio-Visual Chain-of-Thought (AV-CoT) to explicitly ground acoustic signals with rich visual context like scenes and on-screen text, thereby overcoming single-modality dominance and achieving state-of-the-art performance.
This paper proposes a kinematics-aware latent world model that integrates vehicle kinematic information and geometry-aware supervision into the Recurrent State-Space Model (RSSM) to enhance spatial representation and long-horizon imagination fidelity, thereby achieving more data-efficient and stable autonomous driving policy learning compared to existing baselines.
This paper provides a comprehensive review of network-aware modeling, optimization, and control methods for Integrated Energy Systems, highlighting the critical role of network constraints in addressing operational challenges and outlining future research directions to achieve scalable, efficient, and low-carbon energy operations.
This paper introduces "trace inversion" models that can reconstruct detailed reasoning traces from only a target model's inputs, answers, and summaries, demonstrating that hiding reasoning chains fails to prevent the theft of reasoning capabilities and enabling significant performance gains for student models fine-tuned on these synthetic traces.
This paper introduces sketch-oriented databases, a categorical framework that unifies various graph-based paradigms and features through finite-limit sketches, while proposing localizers for lazy path inference and stuttering sketches to enable modular composition and scalable model growth.
AutoDataset is a lightweight, automated system that continuously monitors arXiv to detect, extract, and index newly released datasets from research papers, enabling real-time discovery and significantly improving search efficiency by up to 80%.
VisualDeltas is a lightweight, label-free preference-learning framework that leverages systematic visual quality perturbations to generate informative supervision signals, thereby improving multimodal model performance and generalization without relying on human annotations.
This paper establishes a worst-case to average-case reduction for a non-modular Short Integer Solution (SIS) problem over the integers, demonstrating that solving random instances of this problem efficiently allows for the polynomial-time approximation of the Shortest Independent Vectors Problem (SIVP) within a factor of .
This paper presents a small-scale laboratory study demonstrating that embedding lightweight interactive components into Large Language Model-generated scaffolding can shift learners from passive consumption to active engagement, thereby improving perceived attention and short-term learning outcomes.
LLM-FK is a novel multi-agent framework that overcomes the limitations of conventional heuristic and naive LLM methods in detecting foreign keys within large-scale complex databases by coordinating specialized agents to prune the search space, enhance reasoning with domain knowledge, and ensure global schema consistency, thereby achieving superior accuracy and scalability.
This paper presents a novel automated method combining the substitution technique with systematic backtracking and dynamic programming to prove that the bilinear complexity of $3 \times 3\mathbb{F}_2$ is at least 20, thereby improving the previous lower bound of 19.
This empirical study demonstrates that deployment-motivated prompting constraints significantly exacerbate citation hallucinations across four large language models, with no model achieving a citation existence rate above 47.5% and a substantial portion of unverifiable outputs being fabricated, thereby underscoring the critical need for post-hoc verification in academic and software engineering contexts.
This paper introduces BD-VITON, a new benchmark dataset featuring culturally diverse Bangladeshi garments with complex draping and layering challenges, and evaluates the performance of state-of-the-art virtual try-on models on this dataset to demonstrate significant improvements over zero-shot inference.
This paper proposes a TopRank-based coded caching strategy that optimizes delivery rates under non-uniform, unknown file demands by ranking files based on request count differences rather than estimating exact popularities, thereby achieving superior performance and sublinear regret in scenarios with limited users, small cache capacities, or noisy observation data.
This paper introduces MAviS, a domain-adaptive multimodal conversational assistant for avian species that leverages the newly created MAviS-Dataset and is evaluated on the MAviS-Bench to achieve state-of-the-art performance in fine-grained bird species understanding and multimodal question answering.
This paper proposes a modular, cortically inspired architecture for perceptual AI that leverages neuroscientific principles like predictive processing and specialized modules to overcome the interpretability and generalization limitations of current monolithic models, thereby enabling more transparent and human-aligned reasoning.
This paper proposes a training-centered approach that combines adversarial training with a lightweight feature-map smoothing block to generate saliency maps that are simultaneously sparse, input-stable, and output-stable, thereby enhancing their perceived trustworthiness and sufficiency.
This paper introduces Tursio, a secure, on-premises platform that empowers credit unions to query complex structured databases using natural language by leveraging Large Language Models to automatically generate semantic knowledge graphs and compliant query plans.
This study reveals that in factual verification tasks, users' trust and decision-making are primarily driven by the correctness and certainty framing of LLM rationales rather than their presentation format, highlighting the dual potential of well-designed rationales to either support decision-making or miscalibrate trust.