mlsanityData to 7 Oct 2026

DualityCert: Verifier-Gated Language-Model Repair of Broken Duality Claims in Quantum Field Theory

arXiv 2607.23614 ↗First posted 26 Jul 2026cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

We present DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories. The verifier evaluates 't Hooft anomaly matching, superpotential R-charge consistency, central-charge matching, and a bounded chiral-ring proxy. A claim that passes receives a consistency certificate, which states that no tested inconsistency was found, not that the duality is proven. We use the verifier as a repair environment for language-model agents, which receive a deliberately broken claim and must edit it until it certifies. On a preregistered benchmark of 145 broken claims, with the analysis fixed before the first confirmatory model call, verifier-gated retry improves final repair success over a single attempt by +8.3 percentage points (pp) on deepseek-chat and +7.1 pp on qwen-plus (Holm-adjusted p<0.002). Under an equal budget of eleven attempts, the stop-first strategy portfolio underperforms independent verifier-filtered resampling by 10.3 percentage points on deepseek-chat but outperforms it by 14.7 points on qwen-plus, reversing the ordering of the two tested verifier-exploitation policies across the two confirmatory models. On qwen-plus, category-level verifier feedback is worth +8.7 pp over content-free retry, and interpretable obligation identities alone are worth +6.4 pp over structurally identical masked feedback. Neither effect is detected on deepseek-chat. Separately, a preregistered MiniMax-M2.5 extension again finds an iteration gain and independent verifier-filtered resampling outperforming the strategy portfolio. Which policy is better thus differs between the two models, while every winning policy uses the same cheap certificate. The verifier, benchmark, protocol, and all per-attempt records are released.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 38 references that are in the map. First posted on arXiv 26 Jul 2026.

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References · 18

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2023Let's Verify Step by Stepcited by 3,598
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  5. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 965
  6. 2023Teaching Large Language Models to Self-Debugcited by 963
  7. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 414
  8. 2025Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physicscited by 33
  9. 2020Quiver Mutations, Seiberg Duality and Machine Learningcited by 19
  10. 2025Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmarkcited by 16
  11. 2024HepLean: Digitalising high energy physicscited by 10
  12. 2023Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Dualitycited by 8
6 more
  1. 2026Learning to Disprove: Formal Counterexample Generation with Large Language Modelscited by 4
  2. 2024Generative AI for Brane Configurations and Coamoebacited by 2
  3. 2026PhysProver: Advancing Automatic Theorem Proving for Physicscited by 2
  4. 2026Formalization of QFTcited by 2
  5. 2026Axioms for physical reasoning: codifying the Seiberg--Witten solution in Leancited by 2
  6. 2026Grading the Unspoken: Evaluating Tacit Reasoning in Quantum Field Theory and String Theory with LLMscited by 1

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