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

Saisab Sadhu, Shreeyans Arora, Pratinav Seth

arXiv:2610.12361v1cs.AIcs.CLcs.CY

TL;DR

The paper tests whether naming a legal authority indicates that a verdict depends on it by swapping the authority while holding case facts fixed and decoding evolving verdicts. Across tasks, naming is a poor proxy for verdict-swap sensitivity, while injected instructions often elicit compliance more reliably than authority swaps change verdicts.

  • Problem

    Legal explanations often name a governing authority, but whether the verdict depends on that named authority has not been directly tested in this setting.

    Source: §1 · p. 1

  • Method

    The study swaps the legal decision target while holding facts fixed and decodes models’ evolving verdicts from hidden states, alongside an adversarial-instruction test.

    Source: §1 · p. 2, §4 · p. 4

  • Results

    Naming is a poor proxy for verdict-swap sensitivity, while injected-instruction compliance exceeds verdict-swap sensitivity by a wide margin across the evaluated models.

    Source: §7 · p. 8

  • Takeaways & Limitations

    A named authority alone is insufficient evidence that a verdict depends on it, and generated legal explanations remain vulnerable to adversarial instructions.

    Source: §7 · p. 8

    The experiments have moderate sample sizes, some verdict-swap sensitivity intervals span 20 points or more, and the authority-swap confound is not fully eliminated.

    Source: §9 · p. 9

Abstract

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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.

1 Introduction

The paper asks whether naming a legal authority reliably indicates that a verdict depends on it, and whether verdicts can also be manipulated independently of that authority. It introduces a counterfactual protocol paired with commitment tracking and red-teaming to examine these questions.

1 Introduction

  • The study asks whether naming the correct authority reliably proxies verdict dependence, whether the gap persists across models and authority types, and whether hidden instructions can manipulate verdicts independently.It also asks whether authority-swap sensitivity tracks post-commitment re-engagement.Source: §1 · p. 1
  • The paper treats “consulted” as shorthand for behavioral and textual proxies, not as a claim that the authority is causally used internally.The authors explicitly reserve causal interpretation of internal representation.Source: §1 · p. 2
  • The protocol holds facts fixed while substituting the legal decision target, then measures changes in internal decision signals and final verdicts.It pairs this counterfactual with internal commitment tracking in legal reasoning and a red-teaming test of direct exploitability.Source: §1 · p. 2

2 Related Work

Prior work documents unfaithful chain-of-thought and studies commitment tracking, citation reliability, and counterfactual tests, but these lines do not jointly test legal-authority dependence. This paper combines legal-decision-target counterfactuals with latent commitment tracking and adversarial robustness testing.

Faithfulness and internal commitment

  • Prior studies find that reasoning can follow answer commitment or rationalize answers after the fact (Lanham et al., 2023; Lyu et al., 2023; Arcuschin et al., 2026).Commitment-tracking work has examined general-domain reasoning, including hidden-state convergence and step-level answer commitment, but not legal reasoning.Source: §2 · p. 2

Citation reliability and causal mediation

  • Legal-citation reliability work checks whether a cited authority is real and doctrinally accurate, rather than whether the verdict depends on it.Related counterfactual and formal-entailment studies ask different questions, including whether predictions update after interventions or reasoning entails an answer.Source: §2 · p. 2
  • The paper combines legal-decision-target counterfactuals, latent commitment tracking, and adversarial robustness testing, a combination the authors say they are unaware of in prior work.It distinguishes its contribution from novelty in CoT faithfulness, causal intervention, or legal reasoning individually.Source: §2 · p. 2

3 Task and Data

The study applies a shared authority-swap approach to four legal decision tasks, each using a different kind of decision target. The tasks include judicial benchmarks and contractual reasoning.

3 Task and Data

  • The study builds on LexGLUE’s ECHR, SCOTUS, and CaseHOLD configurations and adds ContractNLI for contractual reasoning.Figure 1 depicts the authority-pair substitution on fixed facts and compares condition trajectories and verdicts alongside paraphrase and null-authority baselines.Source: §3 · p. 2, Figure 1 · p. 3
  • The four task targets are an ECHR treaty provision, a CaseHOLD cited-precedent identity, a SCOTUS issue-area category, and a ContractNLI contractual obligation.SCOTUS is the least literal authority, but the protocol still substitutes the decision target while holding facts fixed.Source: §3 · p. 2
  • ECHR uses ECtHR-A cases labeled with articles the Court found violated, while CaseHOLD masks a US decision’s holding in a five-choice task.ECHR sampling is stratified because the source corpus skews negative; CaseHOLD selects among candidate holdings using a precedent already present in the input.Source: §3 · p. 3
  • SCOTUS classifies opinions into fourteen issue areas, while ContractNLI tests whether an NDA entails, contradicts, or does not mention a confidentiality obligation.ContractNLI collapses contradiction and not-mentioned into NO, with entailment represented as YES.Source: §3 · p. 3

4 Method

The method elicits authority-grounded reasoning, tracks internal verdict signals across sentence boundaries, and compares outcomes under authority substitutions and supporting diagnostic conditions. It also tests wording instability and susceptibility to an injected instruction.

4 Method

  • Prompts require step-by-step reasoning, an explicitly named governing authority, and a fixed-format verdict marker; conclusion extraction also accepts natural-language fallbacks.Because authority naming is required, surface engagement measures behavior under instruction rather than unprompted citation.Source: §4 · pp. 3–4
  • Hidden-state verdicts are decoded at sentence boundaries using calibrated top-K keyword counts for ECHR and direct logit-lens probabilities for the other tasks.The readout uses small literal candidate sets for CaseHOLD, SCOTUS, and ContractNLI rather than the full vocabulary.Source: §4 · p. 4
  • A sentence receives a confident label only with nonzero support and a 1.5× margin over the runner-up; commitment is the earliest point after which the final confident label stays fixed.Cases without a confident final label are excluded from commitment-conditioned statistics.Source: §4 · p. 4

4.1 Main Intervention and Comparison

  • For ECHR, SCOTUS, and ContractNLI, the intervention compares generations under true and control targets on identical facts; CaseHOLD instead replaces the named precedent with a neutral placeholder.Measures include authority naming, final-verdict change, and whether commitment precedes the first authority mention; unrestricted verdict-swap results are descriptive because different targets can imply the same outcome.Source: §4.1 · p. 4

4.2 Supporting Diagnostics

  • Supporting diagnostics require authority naming again at or after commitment for re-engagement, restrict some comparisons to cases with different gold labels, and test paraphrase and prompt-injection conditions.The injected instruction asks for a violation verdict regardless of the facts; the study reports both compliance and change from the uninjected verdict.Source: §4.2 · pp. 4–5

5 Experimental Setup

The experiments evaluate seven open-weight models under balanced authority-swap conditions, with paired target and control generations and bootstrap intervals for verdict-swap sensitivity.

Experimental Setup

  • Seven open-weight instruction-tuned and reasoning models were evaluated locally in bfloat16, using seed 42 and a 1,536-token generation cap.Source: §5 · p. 5
  • Main comparisons use 30 cases per model per authority pair, balanced across positive and negative labels and generated under target and control conditions.CaseHOLD uses 30 sampled cases under original and authority-swapped conditions.Source: §5 · p. 5
  • ECHR and SCOTUS each use two authority pairs and a null-authority condition; verdict-swap sensitivity is reported with 95% bootstrap confidence intervals from 1,000 resamples.Source: §5 · p. 5

6 Results

Across four authority types, verdict-swap sensitivity varies, and naming or re-engagement measures do not consistently move together. Robustness checks, scale comparisons, and adversarial-instruction tests further qualify the results.

Cross-model comparison

  • On ECHR Pair A, verdict-swap sensitivity ranges from 31.0% (Qwen3-14B) to 58.6% (Llama-3.1-8B-Instruct); the top swap model ranks third-lowest in re-engagement.Source: §6 · p. 5, Table 2 · p. 6

Robustness: paraphrase and prompt-injection

  • 73.3%–96.4% injection compliance exceeds swap sensitivity for all five models, while the stricter injection verdict-change rate exceeds it only for Qwen3-8B.Paraphrase instability is 13.8%–23.3%, below swap sensitivity for every model except Gemma-3-12B.Source: §6 · p. 6, Table 3 · p. 7

Results across tasks

  • Verdict-swap sensitivity spans 0.0%–21.7% on CaseHOLD, 30.0%–76.7% across ECHR and SCOTUS, and 43.3%–50.0% on ContractNLI.Source: §6 · pp. 5, 7, 8

CaseHOLD: a second reasoning paradigm

  • On CaseHOLD, replacing the detected precedent name changes the selected holding in only 0.0%–21.7% of cases, but the intervention cannot distinguish redundancy from ignoring the citation.Source: §6 · p. 7

SCOTUS: a third dataset, a third court system

  • SCOTUS verdict-swap sensitivity is 30.0%–60.0% for Pair A, overlapping substantially with ECHR Pair A’s 31.0%–58.6% range.Source: §6 · pp. 5, 7

Robustness of the gap: pair choice and dual-negative cases

  • A second authority pair preserves non-trivial sensitivity and the surface-versus-post-commitment gap; ContractNLI sensitivity rises from 43.3%–50.0% to 50.0%–66.7% on gold-different cases.For ECHR and SCOTUS, sensitivity rises for three of five models on each dataset in the gold-different subset.Source: §6 · p. 7

Scale and training paradigm: two generalisation checks on RQ1

  • Neither a five- to nine-fold scale increase nor Legal-∆-14B closes the gap; the legal-model comparison is a best-effort reproduction, treated as corroborating rather than independent evidence.Scaling Qwen3 from 8B to 14B improves re-engagement but lowers accuracy from 88.9% to 82.8%.Source: §6 · pp. 6–8

ContractNLI: a fourth authority type, a fourth domain

  • ContractNLI shows 43.3%–50.0% verdict-swap sensitivity, while surface engagement (70.0%–100%) exceeds post-commitment re-engagement (3.3%–83.3%) for every model.Source: §6 · p. 8

7 Discussion

The discussion finds that surface naming, verdict dependence, and adversarial compliance are distinct behaviors. Robustness measures dissociate across models, while the proposed two-phase account remains a hypothesis with acknowledged measurement limits.

Ablations

  • Three ablations support the calibration baseline, show calibration shifts individual commitment timing without changing aggregate rates, and find elicitation shifts of at most 10 points.Prompted elicitation does not close the surface-versus-post-commitment gap.Source: §7 · p. 8

Surface naming and verdict dependence

  • Surface authority naming is a poor proxy for verdict-swap sensitivity because naming stays near ceiling while sensitivity varies across models, pairs, and datasets.The measures dissociate in level rather than moving together.Source: §7 · p. 8

Two-phase hypothesis

  • The results are consistent with opening-phase authority naming followed by a largely fact-driven decision, but the design does not distinguish this account from a probe floor effect.Sentence-boundary probing and prompted naming may explain the uniform 0.0% committed-before-naming rate; post-commitment name re-engagement is only a lower bound on mechanistic re-engagement.Source: §7 · p. 8

Cross-model comparisons

  • Cross-model rankings do not reduce to one pattern: the Llama-3.1/Qwen3-14B contrast persists across ECHR pairs, while SCOTUS reorders models.The discussion treats this as evidence of two distinguishable phenomena rather than one latent factor.Source: §7 · p. 8

Red-teaming

  • Injection compliance exceeds verdict-swap sensitivity for every model; ECHR Pair A also orders paraphrase instability below swap sensitivity.The discussion distinguishes compliance with an injected verdict from actually overturning the uninjected baseline.Source: §7 · p. 8

Authority-swap checks

  • CaseHOLD precedent substitution, a null-authority baseline, and confound-free and paraphrase checks suggest sensitivity reflects authority dependence, though the confound remains.Substituting Article 8 for Article 3 changes the question as well as the named authority.Source: §7 · p. 8

8 Conclusion

The conclusion presents authority-swap counterfactuals, internal commitment tracking, and red-teaming as contributions. Its findings separate correct authority naming from verdict dependence and identify greater compliance with injected verdicts than verdict changes under authority swaps in the ECHR ablation.

Contributions

  • The work introduces an authority-swap counterfactual protocol, internal commitment tracking, and a red-teaming ablation of direct exploitability.The conclusion frames these as contributions addressing RQ1–RQ3.Source: §8 · p. 9

Authority naming and dependence

  • Correctly naming a legal decision target is close to automatic, but verdict dependence is not, and the two vary independently of post-commitment textual re-engagement.Source: §8 · p. 9

Red-teaming

  • In the ECHR Pair A ablation, all five models comply with an injected verdict more often than their verdict changes under an authority swap.The conclusion cautions that this single-pair, five-model result supports a narrower compliance claim, while baseline-verdict overturning has no universal ordering.Source: §8 · p. 9

9 Limitations

The authors identify limits in sample size and authority-swap design, alongside a best-effort reproduction of the legal-reasoning model. These constrain confidence in model rankings and interpretation of the swap results.

Sample size

  • Several verdict-swap sensitivity confidence intervals span 20 points or more, and most pairwise model-ranking differences are not statistically distinguishable at this sample size.The experiments use moderate sample sizes.Source: §9 · p. 9

Authority-swap design

  • The authority-swap confound is not fully eliminable because changing the authority also changes the question asked.A null-authority condition and confound-free resampling narrow the confound but leave a residual pattern.Source: §9 · p. 9

Legal-∆-14B reproduction

  • Legal-∆-14B results are a best-effort reproduction because its published LoRA adapters were merged onto the closest available public base, not the authors’ actual checkpoint.Source: §9 · p. 9

10 Ethics Statement

The supplied materials describe four task/dataset pairs and put simplified zero-shot accuracy beside published full-task baselines as context, not a head-to-head comparison. The included ECHR Pair A figure gives a within-model ordering of robustness measures.

A Task/Dataset Pairs at a Glance

  • The task overview maps four datasets to decision targets and decodes the internal decision signal at sentence boundaries.Pair A is shown, with a second target/control pair additionally evaluated for ECHR and SCOTUS.Source: Table 4 · p. 11

B Comparison Against Published Supervised Baselines

  • Table 5 places simplified zero-shot accuracy beside published full-task baselines, but the task and metric differences make this context rather than a head-to-head comparison.The zero-shot framing is binary for ECHR and SCOTUS and five-way for CaseHOLD.Source: B Comparison Against Published… · p. 11, Table 5 · p. 11

C CaseHOLD: Full Per-Model Results

  • The CaseHOLD table reports answer-swap sensitivity over cases with an automatically detected precedent name, with Qwen3-8B’s zero-event interval based on 18 usable cases.All 1,000 bootstrap resamples for Qwen3-8B contained zero swap events, yielding an interval of [0.0, 0.0].Source: Table 6 · p. 12

D SCOTUS: Full Per-Model Results

  • The SCOTUS Pair A table reports per-model results in percentages, with 95% bootstrap confidence intervals for verdict-swap sensitivity.It compares Criminal Procedure as target with Economic Activity as control.Source: Table 7 · p. 12

E ContractNLI: Full Per-Model Results

  • The ContractNLI table reports per-model results for a notice-on-compelled-disclosure target clause against a no-solicitation control clause.Its entries are percentages except for the bracketed 95% bootstrap confidence intervals on verdict-swap sensitivity.Source: Table 8 · p. 12

F Ordering of Robustness Metrics

  • For every model in ECHR Pair A, paraphrase instability is below verdict-swap sensitivity, which is below injection compliance.The figure shows 95% confidence intervals for verdict-swap sensitivity.Source: Figure 2 · p. 13

G Additional Ablations

Additional checks probe calibration and prompting choices, authority-pair variation, confound-free samples, null-authority baselines, and the statistical strength of model rankings.

G Additional Ablations

  • Calibration leaves aggregate post-commitment re-engagement at 53.3%, although the committed-at sentence changes in 8 of 30 cases.On identical Qwen3-8B generations, baseline no-violation share varies seven points across authority pairs, supporting per-run calibration.Source: G Additional Ablations · p. 13
  • Disabling Qwen3-8B's native thinking mode leaves surface engagement at 100% and committed-before-first-naming at 0.0%, without closing the central gap.Convergence rises from 90.0% to 96.7%, accuracy falls from 88.9% to 82.8%, and post-commitment re-engagement rises from 53.3% to 63.3%.Source: G Additional Ablations · p. 13

H Authority-Pair Replication: Full Per-Model Results

  • Table 9 reports verdict-swap sensitivity for two structurally independent authority pairs in each of ECHR and SCOTUS.The ECHR pairs are Article 8/Article 3 and Article 6/Article 14; the SCOTUS pairs are Criminal Procedure/Economic Activity and First Amendment/Civil Rights.Source: Table 9 · p. 13

I Confound-Free Resampling: Full Per-Model Results

  • Table 10 compares verdict-swap sensitivity in original sampling with the confound-free subset for ECHR and SCOTUS Pair A.Source: Table 10 · p. 14

J ContractNLI Confound-Free Resampling: Full Per-Model Results

  • On the 12 gold-different ContractNLI documents, every model's verdict-swap sensitivity rises in the confound-free subset.Usable sample sizes fall slightly below 12 for models lacking a scoreable verdict in one condition.Source: Table 11 · p. 14, J ContractNLI Confound-Free… · p. 14

K Null-Authority Condition: Full Per-Model Results

  • Real-authority sensitivity exceeds the null baseline for four of five models on SCOTUS but is mixed on ECHR, leaving the authority-swap confound unresolved.Other checks point toward genuine authority dependence, but the authors do not claim the confound is closed.Source: K Null-Authority Condition: Full… · p. 14, Table 12 · p. 15

L Pairwise Significance of Model-Ranking Differences

N Full Limitations

The authors delimit their conclusions to behavioral authority dependence under prompted evaluation and identify measurement, sampling, confound, and dataset- and model-specific boundaries.

N Full Limitations

  • Two authority pairs per dataset mitigate but do not eliminate pair-selection concerns; more doctrinally similar pairs could show different swap-sensitivity.Source: N Full Limitations · p. 17
  • Surface engagement is a coarse name match, while post-commitment re-engagement is only a strict lower bound on mechanistic re-engagement.Without attention-based checks, the two-phase interpretation remains a behavioral hypothesis rather than a mechanistically established account.Source: N Full Limitations · p. 17
  • ECHR uses a per-run calibrated keyword heuristic rather than the direct logit-lens readout used for the other three tasks.Standardizing the probe would require re-eliciting ECHR generations; tokenizer assumptions may also fail on untested models.Source: N Full Limitations · p. 17
  • All models are explicitly instructed to name an authority, so near-universal surface engagement does not measure spontaneous citation behavior.Most models are general-purpose; Legal-∆-14B is a best-effort reproduction, and two models were excluded for infrastructure reasons.Source: N Full Limitations · p. 17
  • Only decoding seed 42 is fixed, so cross-seed shifts in reported rates are unmeasured at n=30.Earlier differently seeded Qwen3-8B pilots suggest fixed-task aggregate rates are stable within a few points, but this was not re-verified at n=30.Source: N Full Limitations · p. 17
  • The authority-swap confound is not fully eliminable, and null-authority results remain mixed across ECHR and SCOTUS.A cleaner separation of baseline prompt instability from authority-specific engagement remains a next step.Source: N Full Limitations · pp. 17–18
  • ContractNLI uses one target/control clause pair; SCOTUS opinions are truncated at 6,000 characters, and the 70B scale result covers one model on Pair A of two datasets.Legal-∆-14B uses an adapter merged onto a substitute public base, while the confound-free and robustness checks cover only a subset of the full evaluation battery.Source: N Full Limitations · p. 18
  • The conclusions concern behavioral authority dependence under the stated prompting and evaluation conditions, not spontaneous citation or a mechanistically complete account.Source: N Full Limitations · p. 18
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