AI RESEARCH
Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning
arXiv CS.AI
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ArXi:2605.18871v1 Announce Type: cross When Large Language Models produce structured outputs such as travel plans, code solutions, or multi-step proofs, individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions. We propose a decomposed energy function that combines a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.