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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:2603.09435v1 Announce Type: new Abstract: The rapid rollout of AI in heterogeneous public and societal sectors has subsequently escalated the need for compliance with regulatory …
arXiv:2603.09209v1 Announce Type: new Abstract: We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify …
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:2603.09677v1 Announce Type: new Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the …
arXiv:2603.08964v1 Announce Type: new Abstract: Forward reachability analysis is a dominant approach for verifying reach-avoid specifications in neural feedback systems, i.e., dynamical systems controlled by …
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:2603.09909v1 Announce Type: new Abstract: While Multi-Agent Systems (MAS) show potential for complex clinical decision support, the field remains hindered by architectural fragmentation and the …
arXiv:2603.09486v1 Announce Type: new Abstract: The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool use, …
arXiv:2603.09043v1 Announce Type: new Abstract: Machine consciousness evaluations mostly see behavior. For language model agents that behavior is language and tool use. That lets an …
arXiv:2603.09341v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. However, …
arXiv:2603.09154v1 Announce Type: new Abstract: Large language models (LLMs) trained on internet-scale corpora can exhibit systematic biases that increase the probability of unwanted behavior. In …