[
    {
        "id": "osp-14955",
        "type": "article-journal",
        "title": "Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness",
        "author": [
            {
                "family": "Sadhu",
                "given": "Saisab"
            },
            {
                "family": "Arora",
                "given": "Shreeyans"
            },
            {
                "family": "Seth",
                "given": "Pratinav"
            }
        ],
        "URL": "https://omanscience.com/en/articles/cited-but-not-consulted-a-counterfactual-audit-of-legal-chain-of-thought-faithfulness",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision follows from it. We test this directly: holding case facts fixed, we substitute the named legal authority for an unrelated one and decode a model's evolving verdict from its hidden states. Across seven open-weight models (8B-70B) and four benchmarks spanning judicial and contractual reasoning, when explicitly required to justify a verdict by naming the governing authority, models name the correct one in 66.7%-100% of generations, while the verdict changing when the authority changes is far less consistent: 0.0%-21.7% on CaseHOLD, 30.0%-76.7% on ECHR and SCOTUS, and 43.3%-50.0% on ContractNLI. Neither scale nor a purpose-built legal-reasoning model (a best-effort LoRA reproduction; Section 6) closes this gap. A red-teaming evaluation on five core models finds compliance with an adversarial instruction hidden in the case facts (73.3%-96.4%) exceeds verdict-swap sensitivity by a wide margin, holding without exception across model rankings. Naming a legal authority is thus a poor proxy for a verdict's dependence on it, while the same verdict remains separately vulnerable to adversarial manipulation. Both findings replicate across checks ruling out prompt-wording noise and confounded sampling, and bear directly on the use of generated legal explanations as compliance or audit artefacts."
    }
]