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arXiv · 2604.14525

Quantifying Cross-Query Contradictions in Multi-Query LLM Reasoning

Rohit Kumar Salla·Ramya Manasa Amancherla·Manoj Saravanan
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Abstract

Large language models frequently produce mutually inconsistent answers when reasoning over multiple related queries. We study case-file logical consistency: maintaining a globally satisfiable belief state across interdependent queries. We introduce a benchmark of 390 multi-query reasoning instances with entailment/contradiction/unknown labels and propose set-level metrics including Case Satisfiability Rate, Contradiction Density and Revision Cost. Our solver-augmented approach extracts commitments, verifies global satisfiability and performs counterexample-guided repair. Across four reasoning domains, our method substantially reduces cross-query contradictions (SetCons: 0.56 to 0.94) while preserving per-query accuracy, demonstrating that global coherence is critical for robust multi-query reasoning.

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Rohit Kumar Salla, Ramya Manasa Amancherla, Manoj Saravanan. 2026-04-16. Quantifying Cross-Query Contradictions in Multi-Query LLM Reasoning. https://arxiv.org/abs/2604.14525

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