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arXiv:2603.15952v1 Announce Type: new Abstract: Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex …
arXiv:2603.15946v1 Announce Type: new Abstract: Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information …
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:2603.15909v1 Announce Type: new Abstract: This Monte Carlo simulation examines how prompt engineering strategies shape the quality of large language model (LLM)--generated personality assessment items …
arXiv:2603.15888v1 Announce Type: new Abstract: With AsgardBench we aim to evaluate visually grounded, high-level action sequence generation and interactive planning, focusing specifically on plan adaptation …
arXiv:2603.15885v1 Announce Type: new Abstract: Critical infrastructure increasingly incorporates embodied AI for monitoring, predictive maintenance, and decision support. However, AI systems designed to handle statistically …
arXiv:2603.15857v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, …
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:2603.15831v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in uncertain, sequential decision-making contexts. Yet it remains poorly understood …
arXiv:2603.15799v1 Announce Type: new Abstract: Prose2Policy (P2P) is a LLM-based practical tool that translates natural-language access control policies (NLACPs) into executable Rego code (the policy …
arXiv:2603.15723v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, …
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 …