Prefix-Safe Bayesian Belief Tracking for LLM Reasoning Reliability:Separating Ca

arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet

arXiv:2605.01327v1 Announce Type: new Abstract: Existing reinforcement learning approaches for Large Language Models typically perform policy optimization at the granularity of

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

arXiv:2605.19376v1 Announce Type: new Abstract: How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising

arXiv:2605.15529v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward

arXiv:2603.19262v1 Announce Type: cross Abstract: Large language models (LLMs) that iteratively revise their outputs through mechanisms such as chain-of-thought reasoning,

arXiv:2608.11994v1 Announce Type: new Abstract: We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability

arXiv:2607.18279v1 Announce Type: cross Abstract: Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust,