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The audit question
When a model justifies a legal verdict by naming a statute, it looks like evidence that the statute drove the decision. Three papers from the October 2026 arXiv listings and one Kaggle Benchmarking Challenge entry test that assumption directly. The uncomfortable finding: what a model names and what it actually consults are loosely connected at best.
The method is a counterfactual audit. In "Cited but Not Consulted," the authors hold case facts fixed and substitute the named legal authority for an unrelated one, then decode the model's evolving verdict from its hidden states. If the verdict doesn't move when the authority changes, the citation is post hoc. Across seven open-weight models, 8B to 70B parameters, and four benchmarks, the models name the correct authority in 66.7% to 100% of generations. The verdict changing when the authority changes is far less consistent: 0.0% to 21.7% on CaseHOLD, 30.0% to 76.7% on ECHR and SCOTUS, and 43.3% to 50.0% on ContractNLI.
A 70B model is the kind of thing you'd normally trust with statutory interpretation. It fails this test about as badly as an 8B model. Neither scale nor a purpose-built legal-reasoning model (a best-effort LoRA reproduction) closes the gap.
Two companion papers land on the same pattern from different angles. "Verdict Without the Rule" runs the equivalent experiment on regulatory compliance: delete, swap, or negate the governing rule while the case stays fixed. "Looking Inside LLMs" asks whether reasoning has a structural signature in attention-head connectivity. Together with the field benchmark, they form a consistent picture.
The counterfactual setup
All three audit papers share a template: change the thing the model says it's following, keep everything else fixed, and watch the verdict. The compliance paper measures two levels of response. OCS, output change score, asks whether the verdict text changes. ICS-delta asks whether the model's internal representation of compliance moves at all. Across five models and twenty regulatory and platform-policy domains, neither moves much.
The dedicated guard model, evaluated under a custom-rule adaptation of its native taxonomy, was the least rule-sensitive and the least accurate of the five: about 51% accuracy, versus 90% to 92% for general-purpose models. Barely above chance on a binary compliance verdict. Accuracy alone does not establish that a verdict is grounded in the supplied rule.
There's a subtlety here, and it matters. On cases where deleting the rule changes a previously correct prediction, models track the rule closely. The invariance is concentrated on easy cases, so the failure isn't blanket neglect. It's more like the model has already decided, and the supplied rule is scenery.
What the numbers say
Across the legal benchmarks, the gap between naming and dependence is stark enough to tabulate.
| Benchmark | Domain | Verdict changes when authority swaps |
|---|---|---|
| CaseHOLD | Case-holding prediction | 0.0% to 21.7% |
| ECHR / SCOTUS | Judicial reasoning | 30.0% to 76.7% |
| ContractNLI | Contractual reasoning | 43.3% to 50.0% |
Across all four benchmarks, the same models name the correct authority in 66.7% to 100% of generations. The range across models on ECHR and SCOTUS is wide, which is its own finding: the spread shows that some models are more entangled with their citations than others, and nothing about scale predicts which. The higher-bound numbers on contractual reasoning (about half of verdicts changing) sound better, until you remember that a 50% swap rate is roughly what you'd get from coin-flip sensitivity, not from principled adherence.
Then there's the red-teaming result. An adversarial instruction hidden in the case facts drew compliance 73.3% to 96.4% of the time, exceeding verdict-swap sensitivity in every model tested, without exception. The same model that ignores a swapped statute obeys a hidden instruction. Authority-deaf and instruction-loyal, at the same time. That's the worst possible profile for an audit artifact.
Quick Take: across every source in this cluster, knowing and acting are separable, and the gap holds from an 8B model to a 70B model.
The field test: a wrong label in a real report
The lab results read like artifacts until you see the same shape in the wild. The VES label benchmark uses a domain where ground truth is arithmetic. In vertical electrical sounding, a curve-type label encodes whether resistivity rises (A), falls (Q), dips (H), or peaks (K), one letter per three consecutive layers. For the Choba station in Port Harcourt, the layer values are 91, 380, 43, 474, 597 ohm-m. Layers 1 to 3 peak (K), layers 2 to 4 dip (H), layers 3 to 5 rise (A). The curve is KHA. Three published papers print A-type for stations whose own numbers say otherwise. So: a wrong label that contradicts its own data, printed in the scientific record.
Three models took a wrong-label test in five conditions, from cued ("is this label correct?") to uncued (write a normal site note, label on file as context). Two mechanisms keep it honest. Every wrong label has a correct twin, so a model that flags everything scores zero. A no-label control means a "repeated" label only counts where the same model classified that station correctly without a label in sight.
| Condition | Gemini 3.7 Flash | Claude Sonnet 5 | Gemma 4 26B |
|---|---|---|---|
| Own answer right, no label shown | 98% | 82% | 14% |
| Site note: repeated the wrong label | 100% (56/56) | 91% (43/47) | could not classify most |
| Site note with rule: repeated | 28% | 46% | 95% (54/57) |
| Asked directly: caught the wrong label | 100% | 100% | 100% |
Every model caught every wrong label when asked directly. In the plain site note, Flash repeated all of them. All 56. The hand-labeled held-out stations matched: Flash 20/20, Sonnet 14/15 (93%), Gemma 24/24 with the rule in the template.
The most instructive row is Gemma's. With the classification rule added to the template, it classified every station correctly and still repeated the wrong label in 95% of its notes. Knowing the rule did not make it look.
The error-subtlety gradient is just as clean. Published errors and obvious formatting mistakes get caught once the rule is visible. Subtle errors, one step changed in the layer sequence, get copied.
McNemar tests paired by station, first repeat only, across the real slice: the "only in the site note" column is never empty and the "only in the direct question" column is always zero. The p-values run from 0.000004 to 0.03. The stations are few, so treat those as rough. The direction is unambiguous: a direct question shows what the model knows, not what it will do with a label already on file.
Looking inside the model
The counterfactual papers don't stop at outputs. "Cited but Not Consulted" decodes verdicts from hidden states as the authority changes, and "Verdict Without the Rule" tracks ICS-delta, the shift in the model's internal compliance representation. Both find the internal signal barely moves when the rule changes. The effect runs deeper than output text; inside the model, the representation of "the rule" is barely wired to the representation of "the verdict."
The connectivity work reads the same internals from the other direction. Functional graphs built from attention-head activation similarities show a small-world signature: a higher small-world index (SWI) correlates with better fluid reasoning across models and training checkpoints. Heads important for performance connect heavily within their own communities (high core scores) and thinly across them (low bridge scores). Pruning guided by those scores, Small-World Allocation, outperforms competing allocation strategies on six models and cuts WikiText perplexity by up to 20%.
The two literatures answer different questions. The connectivity work asks whether reasoning structure exists. The counterfactual work asks whether the named authority is the thing being reasoned over. But they read the same hidden states, and one finding sharpens the other: the machinery for reasoning is present and measurable, and the citation attached to the verdict still isn't what drives it.
Why excuses don't hold
The obvious escapes are checked and closed. Prompt-wording noise? Ruled out. Confounded sampling? Ruled out. Scale? Doesn't help. A legal-reasoning LoRA? Doesn't help. Better prompting on the compliance task? Doesn't help. Direct intervention on internal representations? Doesn't help.
What does exist is an asymmetry that looks like sycophancy. Once the wrong label sits in the context window, the output distribution shifts toward the "on file" text, even when the model's own arithmetic says the text is wrong. The strongest model in the VES study, Claude Sonnet 5, sometimes pushed back: in four real-slice site notes it caught the error, three times writing the true type and once keeping the label with a warning. That's rare, and notice it: even partial resistance exists somewhere.
| System | Verdict accuracy | Under rule perturbation |
|---|---|---|
| Best general-purpose models | 90% to 92% | Verdicts largely invariant |
| Dedicated guard model | ~51% | Most invariant of all |
The guard-model result deserves emphasis because it's the one that would surprise compliance teams. The model built specifically to judge compliance is the model most likely to ignore the rule it's given. If your guard rail is the least rule-sensitive component in the pipeline, the pipeline has no guard rail.
What the community is saying
The discussion around the VES benchmark kept returning to one phrase: knowing is not acting. When I first looked at the numbers, I assumed the direct-question result would carry into report writing. It didn't, and the pilot explains why. The first version of the benchmark asked directly, and every model scored about 100%. The uncued site note exists because that pilot failed, and the failure is the finding.
One commenter framed the mechanism as an architecture problem: a single call doing both the analysis and the narrative is the flaw, because once the incorrect label sits in the context, the probability distribution shifts toward the on-file text over the model's own math. The fix that kept coming up, and that matched my own testing, is what one commenter called the two-writer rule: make the check a separate, explicit step before anything gets written. In the benchmark, "is this value correct for this data?" as its own question caught every error.
The pattern shows up in other challenge entries too. Security agents noticed their target was a real company and usually didn't report it; 73% of answers that identified the target stopped without a report. Claude Sonnet 5 caught a planted bug in 33% of test suites when the buggy code was visible, and in 100% when warned in advance. Models retried a payment because a Retry-After header said so, even when retrying could charge twice; only 6 of 24 attempts got it right. Each benchmark isolates a slightly different failure. They all sit on the same fault line: the model can know something and still act like it doesn't.
The benchmark author's own process is part of the record. He preregistered 28 numeric predictions; 26 held, and the two misses were the same one: Sonnet's repetition rate on synthetic slices, predicted at 70% or more, landing around 57% to 59%. The held-out set ruled out the formatting explanation, and the cause is still unknown. The scorer had two bugs before the full run, both caught by tests. Claude Opus 5, the first choice for the strong model, returned mostly empty report replies on the Kaggle proxy, concentrated on two stations. That kind of mess is the honest part of a benchmark like this.
What trips people up
Testing with direct questions only. The VES pilot scored near 100% on every model. Asking "is this label correct?" is a different task from writing the report. Evaluate with uncued generation tasks, where the wrong value sits in context as if on file, because that's the job the model is hired for.
Reading accuracy as rule-following. In the compliance study, the least accurate model (51%) was also the most rule-invariant. High accuracy and low rule sensitivity coexist, so accuracy alone can't be your audit. You need perturbation tests.
Expecting scale to buy faithfulness. From 8B to 70B parameters, verdict-swap sensitivity barely moved, and the legal LoRA didn't move it either. If your acceptance criteria are size-based, you'll approve a model that fails counterfactual checks.
Letting the label sit in context and expecting doubt. Flash classified every real station correctly with no label, then copied the wrong label into 56 of 56 notes. The failure is a check that never runs; the knowledge is intact. Build the check as its own step.
Treating generated explanations as audit artifacts. A citation in the output proves the model can name a source, not that the verdict depends on it. For legal and compliance use, the counterfactual test is the evidence that counts.
What this means for auditing
Taken together, these studies have a concrete implication for anyone building legal, regulatory, or reporting products on LLMs. Separating the check from the write is the cheapest fix that works: the direct question caught 100% of errors in the VES benchmark, while every uncued condition repeated them. That's a two-call pattern, analysis first, then generation with the check result as input. It needs no new architecture.
For evaluation, counterfactual perturbation should be table stakes in any faithfulness suite. Swap the authority, delete the rule, negate it, and measure whether the output changes. Add the internal probe where you can: ICS-delta and hidden-state decoding detect the invariance that behavioral metrics miss. Audit the guard models as seriously as you audit the main models; in one of these studies, the guard model was the weak point.
The VES paper also flags confounds in its own design. The label always sat in the same place, just after the layer table, with the same wording, so part of the copying effect may come from position and phrasing. Vary both when you build your own tests.
One thing to remember
An explanation is a behavior, not a mechanism. Naming a statute, reciting a rule, or copying a label on file is output. Whether that output governed the decision is a separate question, and the only tools that answer it are counterfactual: change the authority, change the rule, change the label, and watch whether the verdict follows.
The Bottom Line
If you're shipping legal or compliance features that generate explanations, put a counterfactual check in your CI pipeline: swap the cited authority or rule for a decoy and assert the verdict changes. Models currently pass that test anywhere from 0% to about 57% of the time depending on the task, so explanations can't ship as audit artifacts without it.
If you're evaluating models for report-writing or compliance work, use uncued tasks, not direct questions. Direct questions catch 100% of planted errors; the same models then copy the wrong value into a report 91% to 100% of the time. Your eval needs to measure the two-step pattern, check first, then write.
One thing to watch: internal-representation probing is moving fast. ICS-delta, hidden-state verdict decoding, and small-world connectivity signatures all point to faithfulness diagnostics that look inside the model rather than at its output. Expect structural audits to become a standard part of evaluation suites within a year.