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ARXIV:2605.11738 · LLM AUDITING · SUBMITTED 13 MAY · 20:55 UTC · FRESHNESS STALE
ARXIV:2605.11738LLM AUDITINGSUBMITTED 13 MAY · 20:55 UTCFRESHNESS STALEZhong Li · Zihan Guo · Xiaohan Lu · Juntao Wang · Jie Song · Chao Shen · +2 at arXiv
A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency.
Opportunity summary
Pain A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency. We formulate this issue as \emph{optimization-modeling hallucination detection}, namely structural consistency auditing over the problem description, symbolic model,…
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference objective value is not a reliable test of correctness: an artifact may…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Together, these contributions turn optimization-modeling hallucination detection into a concrete empirical problem and suggest that modular, taxonomy-grounded auditing is a practical route to more…
LLM Auditing moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency.
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10.48550/arXiv.2605.11738A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency.
Abstract
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference objective value is not a reliable test of correctness: an artifact may agree numerically while still changing the underlying optimization semantics. We formulate this issue as \emph{optimization-modeling hallucination detection}, namely structural consistency auditing over the problem description, symbolic model, and solver implementation. We develop, to our knowledge, the first fine-grained hallucination taxonomy specifically for optimization modeling, spanning objective, variable, constraint, and implementation failures. We use this taxonomy to design OptArgus, a multi-agent detector with conductor routing, specialist auditors, and evidence consolidation. To evaluate this setting, we introduce a three-part benchmark suite with $484$ clean artifacts, $1266$ controlled injected artifacts, and $6292$ natural LLM-generated artifacts. Against a matched single-agent baseline, OptArgus produces fewer false alarms on clean artifacts, more accurate top-ranked localization on controlled single-error cases, and stronger detection on natural model outputs. Together, these contributions turn optimization-modeling hallucination detection into a concrete empirical problem and suggest that modular, taxonomy-grounded auditing is a practical route to more reliable optimization modeling.
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PROBLEM
A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency. We formulate this issue as \emph{optimization-modeling hallucination detection}, namely structural consistency auditing over the problem description,...
METHOD
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference objective value is not a reliable test of correctness: an artifact may agree numerically while still...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Together, these contributions turn optimization-modeling hallucination detection into a concrete empirical problem and suggest that modular, taxonomy-grounded auditing is a practical route to more reliabl...
WHY NOW
LLM Auditing moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency. We formulate this issue as \emph{optimization-modeling hallucination detection}, namely structural consistency auditing over the problem description, symbolic model, and solver implementation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference objective value is not a reliable test of correctness: an artifact may agree numerically while still changing the underlying optimization semantics. We formulate this issue as \emph{optimization-modeling hallucination detection}, namely structural consistency auditing over the problem description, symbolic model, and solver implementation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Together, these contributions turn optimization-modeling hallucination detection into a concrete empirical problem and suggest that modular, taxonomy-grounded auditing is a practical route to more reliable optimization modeling. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Auditing moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A multi-agent system to detect and audit hallucinations in LLM-generated optimization models by checking structural consistency.
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LLM Auditing
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