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arXiv:2603.00680v1 Announce Type: new Abstract: Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing …
arXiv:2603.00691v1 Announce Type: new Abstract: The world is undergoing a major demographic shift as older adults become a rapidly growing share of the population, creating …
arXiv:2603.00730v1 Announce Type: new Abstract: Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems. In many real-world scenarios, tasks often have …
arXiv:2603.00801v1 Announce Type: new Abstract: Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources. However, these sources can …
arXiv:2603.00808v1 Announce Type: new Abstract: A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and …
arXiv:2603.00873v1 Announce Type: new Abstract: With the increasing demand for step-wise, cross-modal, and knowledge-grounded reasoning, multimodal large language models (MLLMs) are evolving beyond the traditional …
arXiv:2603.00876v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical …
arXiv:2603.00977v1 Announce Type: new Abstract: Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally limited in long-horizon …
arXiv:2603.00991v1 Announce Type: new Abstract: AI agents that interact with the real world through tool calls pose fundamental safety challenges: agents might leak private information, …
arXiv:2603.00993v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized AI-generated content evaluation, with the LLM-as-a-Judge paradigm becoming increasingly popular. However, current single-LLM evaluation …
arXiv:2603.01055v1 Announce Type: new Abstract: We present MMCOMET, the first multimodal commonsense knowledge graph (MMKG) that integrates physical, social, and eventive knowledge. MMCOMET extends the …
arXiv:2603.01092v1 Announce Type: new Abstract: Large language models are adept at synthesizing and recombining familiar material, yet they often fail at a specific kind of …