Repairing LLM-Generated Specifications with Deterministic Diagnosis

Giovanni Vaccarino; Emanuele La Malfa; Dehai Min; Hanrong Zhang; Pier Luca Lanzi; Lu Cheng; Michael J. Wooldridge; Philip S. Yu; Sara Bernardini

arXiv · 2610.22568

Repairing LLM-Generated Specifications with Deterministic Diagnosis

Giovanni Vaccarino·Emanuele La Malfa·Dehai Min·Hanrong Zhang·Pier Luca Lanzi·Lu Cheng·Michael J. Wooldridge·Philip S. Yu·Sara Bernardini

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Abstract

Large language models (LLMs) are increasingly used as formalizers: given a reasoning problem expressed in natural language, they produce a formal specification that a symbolic solver can then solve, with guarantees relative to that specification. Many specifications are runnable yet semantically wrong, encoding incorrect constraints, domains, objectives, predicates, or goals, and solver-error feedback dries up once execution succeeds. Purely LLM-based diagnosis, lacking a formal ground truth, often fails to spot these errors, and its ungrounded critiques cause regressions in 11.8% of initially correct specifications in our experiments. We propose DRAGO, a repair framework that replaces this ungrounded critique with deterministic, formalism-specific diagnosis, and retains a rewrite only when the diagnosed error weight strictly decreases under a reference-free acceptance predicate. Across five benchmarks spanning logical reasoning (SMT-LIB2), constraint programming (MiniZinc), and planning (PDDL), DRAGO cuts regressions by nearly an order of magnitude, to 1.3%, while improving answer accuracy by 12.9 percentage points over zero-shot formalization and 8.7 percentage points over LLM-diagnostic repair, averaged across benchmarks. We also uncover two rendering defects in Planetarium that cause 50% (3,086 instances) of its floor-tile split to be inconsistent with the reference formalizations, and release a corrected split.

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BibTeX RIS

Giovanni Vaccarino, Emanuele La Malfa, Dehai Min, Hanrong Zhang, Pier Luca Lanzi, Lu Cheng, Michael J. Wooldridge, Philip S. Yu, Sara Bernardini. 2026-10-15. Repairing LLM-Generated Specifications with Deterministic Diagnosis. https://arxiv.org/abs/2610.22568

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