When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Ev
arXiv:2603.20620v1 Announce Type: new Abstract: Can we trust the reasoning traces that large reasoning models (LRMs) produce? We investigate whether these traces faithfully
arXiv:2605.29087v1 Announce Type: new Abstract: Reasoning models are evaluated on single-turn benchmarks but deployed in multi-turn dialogue, where users push back on correct
arXiv:2608.00422v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making
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:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit
arXiv:2605.01327v1 Announce Type: new Abstract: Existing reinforcement learning approaches for Large Language Models typically perform policy optimization at the granularity of
arXiv:2604.12184v1 Announce Type: new Abstract: TRUST Agents is a collaborative multi-agent framework for explainable fact verification and fake news detection. Rather than
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