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Research questionHow can we quantify and reduce divergent, nonsensical reasoning in large language models?LLMs can generate multiple incompatible reasoning chains from the same prompt, including branches that become implausible or nonsensical. This makes it difficult to distinguish productive exploration from reasoning instability and to reduce the latter.
AI
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph ComplexityThe source examines reasoning uncertainty in four LLMs across five datasets. It represents potential reasoning branches as directed acyclic graphs, uses graph complexity to approximate uncertainty, and studies reinforcement learning that treats reduced uncertainty as a reward.research paper · Sep 4, 2026
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