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arXiv:2603.02788v1 Announce Type: new Abstract: We present a framework for evaluating and benchmarking logical reasoning agents when assessment itself must be reproducible, auditable, and robust …
arXiv:2603.02798v1 Announce Type: new Abstract: As LLM-powered agents have been used for high-stakes decision-making, such as clinical diagnosis, it becomes critical to develop reliable verification …
arXiv:2603.02858v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance in analyzing and generating text, yet they struggle with explicit, transparent, and verifiable …
arXiv:2603.02874v1 Announce Type: new Abstract: Transformers excel at in-context retrieval but suffer from quadratic complexity with sequence length, while State Space Models (SSMs) offer efficient …
arXiv:2603.02908v1 Announce Type: new Abstract: In recent years, pre-trained large language models have achieved remarkable success across diverse tasks. Besides the pivotal role of self-supervised …
arXiv:2603.02939v1 Announce Type: new Abstract: Recent advancements in reinforcement fine-tuning have significantly improved the reasoning ability of large language models (LLMs). In particular, methods such …
arXiv:2603.02960v1 Announce Type: new Abstract: Large language models increasingly function as epistemic agents -- entities that can 1) autonomously pursue epistemic goals and 2) actively …
arXiv:2603.03002v1 Announce Type: new Abstract: Genuine spatial reasoning relies on the capacity to construct and manipulate coherent internal spatial representations, often conceptualized as mental models, …
arXiv:2603.03005v1 Announce Type: new Abstract: Multi-agent large language model frameworks are promising for complex multi step reasoning, yet existing systems remain weak for scientific and …
arXiv:2603.03018v1 Announce Type: new Abstract: Enterprise engineering organizations produce high-volume, heterogeneous telemetry from version control systems, CI/CD pipelines, issue trackers, and observability platforms. Large Language …
arXiv:2603.03072v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to assist scientists across diverse workflows. A key challenge is generating high-quality figures …
arXiv:2603.03078v1 Announce Type: new Abstract: Agentic Reinforcement Learning (Agentic RL) has shown remarkable potential in large language model-based (LLM) agents. These works can empower LLM …