@inproceedings{sadhu-etal-2026-verdict,
title = "Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in {LLM} Compliance Systems",
author = "Sadhu, Saisab and
Sengupta, Aadit and
Sankarapu, Vinay kumar and
Seth, Pratinav",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.12/",
pages = "119--132",
ISBN = "979-8-89176-494-1",
abstract = "Large language model compliance systems are deployed on the assumption that a verdict depends on the regulatory rule it is given. We test this directly across five models and 20 regulatory and platform-policy domains: delete, swap, or negate the governing rule while holding the case fixed, and check whether the verdict changes (OCS) or the model{'}s internal representation of compliance shifts at all (ICS-delta). Neither moves much: models' verdicts are often invariant to substantial perturbations of the supplied rule, and the guard model, evaluated here under a custom-rule adaptation of its native taxonomy, is the least rule-sensitive and least accurate of the five, barely above chance (51{\%}, versus 90{--}92{\%} for general-purpose models). This reflects easy cases more than blanket neglect: on cases where deleting the rule changes a previously correct model prediction, models do track it closely. Neither better prompting nor direct intervention on the model{'}s internal representations closes this gap. Accuracy alone does not establish that a compliance verdict is grounded in the supplied rule."
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<abstract>Large language model compliance systems are deployed on the assumption that a verdict depends on the regulatory rule it is given. We test this directly across five models and 20 regulatory and platform-policy domains: delete, swap, or negate the governing rule while holding the case fixed, and check whether the verdict changes (OCS) or the model’s internal representation of compliance shifts at all (ICS-delta). Neither moves much: models’ verdicts are often invariant to substantial perturbations of the supplied rule, and the guard model, evaluated here under a custom-rule adaptation of its native taxonomy, is the least rule-sensitive and least accurate of the five, barely above chance (51%, versus 90–92% for general-purpose models). This reflects easy cases more than blanket neglect: on cases where deleting the rule changes a previously correct model prediction, models do track it closely. Neither better prompting nor direct intervention on the model’s internal representations closes this gap. Accuracy alone does not establish that a compliance verdict is grounded in the supplied rule.</abstract>
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%0 Conference Proceedings
%T Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems
%A Sadhu, Saisab
%A Sengupta, Aadit
%A Sankarapu, Vinay kumar
%A Seth, Pratinav
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F sadhu-etal-2026-verdict
%X Large language model compliance systems are deployed on the assumption that a verdict depends on the regulatory rule it is given. We test this directly across five models and 20 regulatory and platform-policy domains: delete, swap, or negate the governing rule while holding the case fixed, and check whether the verdict changes (OCS) or the model’s internal representation of compliance shifts at all (ICS-delta). Neither moves much: models’ verdicts are often invariant to substantial perturbations of the supplied rule, and the guard model, evaluated here under a custom-rule adaptation of its native taxonomy, is the least rule-sensitive and least accurate of the five, barely above chance (51%, versus 90–92% for general-purpose models). This reflects easy cases more than blanket neglect: on cases where deleting the rule changes a previously correct model prediction, models do track it closely. Neither better prompting nor direct intervention on the model’s internal representations closes this gap. Accuracy alone does not establish that a compliance verdict is grounded in the supplied rule.
%U https://aclanthology.org/2026.nllp-1.12/
%P 119-132
Markdown (Informal)
[Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems](https://aclanthology.org/2026.nllp-1.12/) (Sadhu et al., NLLP 2026)
ACL