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arXiv:2603.15976v1 Announce Type: new Abstract: While large language models have significantly accelerated scientific code generation, comprehensively evaluating the generated code remains a major challenge. Traditional …
arXiv:2603.15665v1 Announce Type: new Abstract: Starting from first principles and a linguistic perspective centered on part-of-speech (POS) and syntactic analysis, this paper explores and derives …
arXiv:2603.15642v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state …
arXiv:2603.15936v1 Announce Type: new Abstract: ClinicalTrials.gov (CT.gov) is the largest publicly accessible registry of clinical studies, yet its registry-oriented architecture and heterogeneous adverse event (AE) …
arXiv:2603.15929v1 Announce Type: new Abstract: We present a complete Lean 4 formalization of the equilibrium characterization in the Vlasov-Maxwell-Landau (VML) system, which describes the motion …
arXiv:2603.15848v1 Announce Type: new Abstract: The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P …
arXiv:2603.15661v1 Announce Type: new Abstract: Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable collaborative reasoning capabilities but introduce new attack surfaces, such as the …
arXiv:2603.16020v1 Announce Type: new Abstract: Artificial agents can achieve strong task performance while remaining opaque with respect to internal regulation, uncertainty management, and stability under …
arXiv:2603.15709v1 Announce Type: new Abstract: Decision-making in real applications is often affected by vagueness, incomplete information, heterogeneous data, and conflicting expert opinions. This survey reviews …
arXiv:2603.16110v1 Announce Type: new Abstract: Enterprise IT support is constrained by heterogeneous devices, evolving policies, and long-tail failure modes that are difficult to resolve centrally. …
arXiv:2603.16060v1 Announce Type: new Abstract: The dominant paradigm for improving mathematical reasoning in language models relies on Reinforcement Learning with verifiable rewards. Yet existing methods …
arXiv:2603.15677v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly central to clinician workflows, spanning clinical decision support, medical education, and patient communication. However, …