mlsanityData to 14 Oct 2026

Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness

arXiv 2610.12361 ↗First posted 8 Oct 2026cs.AI cs.CL cs.CYcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation also fits: Reasoning for agents, planning and code

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 11 of its 25 references that are in the map. First posted on arXiv 8 Oct 2026.

Builds on

11 of 25 references.

References · 11

  1. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  2. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 623
  3. 2025LegalDelta: Enhancing Legal Reasoning in LLMs via Reinforcement Learning with Chain-of-Thought Guided Information Gaincited by 12
  4. 2026When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agentscited by 10
  5. 2026Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structurescited by 6
  6. 2026Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Modelscited by 5
  7. 2026LegalCiteBench: Evaluating Citation Reliability in Legal Language Modelscited by 3
  8. 2026Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoningcited by 3
  9. 2026When Does a Language Model Commit? A Finite-Answer Theory of Pre-Verbalization Commitmentcited by 2
  10. 2026Who Checks the Citations? Benchmarking Legal Hallucination Detectioncited by 2
  11. 2026Citation Grounding Measures the Oracle: Graph Coverage Determines Reported LLM Hallucination Rates in Lawcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Thinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning
  2. 2026Legal Reasoning Is Not Lawyering: Rethinking Legal Benchmarks for Pro Se Access to Justice
  3. 2026Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain
  4. 2025When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
  5. 2025Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data
  6. 2025Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization
  7. 2026Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
  8. 2026Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning