Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning
The paper presents a novel approach to improve the reasoning capabilities of Large Language Models (LLMs) when generating structured outputs. It introduces a decomposed energy function that combines a quality scorer with analytical constraint penalties to verify outputs. The proposed method outperforms existing models on multiple benchmarks, demonstrating significant reductions in constraint violations and improved reasoning accuracy.
- ▪The study proposes a decomposed energy function for verifying structured outputs from LLMs.
- ▪A quality scorer, consisting of low-rank adapters, is used to rank candidates and assess uncertainty.
- ▪The method achieved a 53% reduction in constraint violations compared to existing models on the TravelPlanner benchmark.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18871 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.18871 (cs) [Submitted on 15 May 2026] Title:Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning Authors:Shireen Kudukkil Manchingal, Abhey Kalia, Fernanda Gonçalves, Shebin Rawther View a PDF of the paper titled Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning, by Shireen Kudukkil Manchingal and 3 other authors View PDF HTML (experimental) Abstract: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.
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