Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution
Large language models produce chain-of-thought reasoning that appears logically sound but may not genuinely depend on its stated premises. This paper introduces interventional grounding audits, a black-box step-level test of premise dependency that substitutes a predicate in a single premise and checks for changes in the normalized conclusion of each reasoning step. Evaluated on ProntoQA with GPT-4o, the method achieves F1=0.806 for detecting proof-tree dependencies, significantly outperforming a self-consistency baseline (F1=0.343). Notably, 66% of correctly solved problems contain at least one step insensitive to a direct proof-tree dependency, revealing a 'right answer, wrong reasoning' signal.
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[Submitted on 11 Jul 2026]
Title:Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution
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Abstract:Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises. We introduce interventional grounding audits, a black-box, step-level test of premise dependency: we intervene on a single premise by substituting its target predicate with a fresh symbol, re-run the model, and check whether each reasoning step's normalized conclusion (canonical predicate form) changes. We evaluate on ProntoQA, a synthetic multi-hop deductive reasoning benchmark with gold proof trees, where step-level premise dependencies are known. Applied to 50 ProntoQA problems with GPT-4o, our method achieves F1 = 0.806 on detecting proof-tree dependencies (F1 = 0.885 on predicate-determining dependencies; Recall = 100%), significantly outperforming a self-consistency baseline (F1 = 0.343; 95% bootstrap CIs non-overlapping). We further identify that 66% of correctly-solved problems contain at least one aligned step insensitive to a direct proof-tree dependency under consistent substitution -- all involving entity-introduction premises, a documented blind spot of the consistent-substitution evaluator -- a "right answer, wrong reasoning" signal invisible to passive methods. All audit certificates, raw outputs, and reproduction scripts are available in a public GitHub repository, and we discuss scope limits beyond formal, parsable benchmarks.
Comments: Accepted at the ICLR 2026 Workshop on Logical Reasoning of Large Language Models (this https URL)
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Logic in Computer Science (cs.LO)
Cite as: arXiv:2607.13069 [cs.AI]
(or arXiv:2607.13069v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.13069
arXiv-issued DOI via DataCite
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From: Hironao Nakamura [view email] [v1] Sat, 11 Jul 2026 01:32:22 UTC (42 KB)
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