Quality follows upgrading
All Articles
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:2603.08933v1 Announce Type: new Abstract: The first 72 hours of a missing-child investigation are critical for successful recovery. However, law enforcement agencies often face fragmented, …
arXiv:2603.09127v1 Announce Type: new Abstract: Collective AI systems increasingly rely on multi-LLM deliberation, but their stability under repeated execution remains poorly characterized. We model five-agent …
arXiv:2603.09943v1 Announce Type: new Abstract: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading criteria, and clinical …
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:2603.09400v1 Announce Type: new Abstract: Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to …
arXiv:2603.09403v1 Announce Type: new Abstract: Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English datasets. …
arXiv:2603.09231v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate exceptional performance on general-purpose tasks. however, transferring them to complex engineering domains such as space …
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:2603.09416v1 Announce Type: new Abstract: Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training …
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:2603.09185v1 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 …