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arXiv:2603.09185v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have enabled diverse retrieval methods. However, existing retrieval methods …
arXiv:2603.08938v1 Announce Type: new Abstract: The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as …
arXiv:2603.09890v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as a new paradigm for multi-agent systems. However, existing research on the behaviour of …
arXiv:2603.09203v1 Announce Type: new Abstract: Retrieval-augmented agents can query external evidence, yet their reliability in multi-step reasoning remains limited: noisy retrieval may derail multi-hop question …
arXiv:2603.09706v1 Announce Type: new Abstract: While safety alignment for Multimodal Large Language Models (MLLMs) has gained significant attention, current paradigms primarily target malicious intent or …
arXiv:2603.09192v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves factual grounding, yet most systems rely on flat chunk retrieval and provide limited control over multi-step …
arXiv:2603.09309v1 Announce Type: new Abstract: Verbalized confidence, in which LLMs report a numerical certainty score, is widely used to estimate uncertainty in black-box settings, yet …
arXiv:2603.09205v1 Announce Type: new Abstract: Large language models are routinely deployed on text that varies widely in emotional tone, yet their reasoning behavior is typically …
arXiv:2603.09151v1 Announce Type: new Abstract: Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers …
arXiv:2603.08877v1 Announce Type: new Abstract: Agentic Retrieval-Augmented Generation (RAG) systems combine iterative search, planning prompts, and retrieval backends, but deployed settings impose explicit budgets on …
arXiv:2603.09344v1 Announce Type: new Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution …
arXiv:2603.09476v1 Announce Type: new Abstract: Goal-conditioned systems assume goals are provided externally. We ask whether attentional priorities can emerge endogenously from an agent's internal cognitive …