arxiv: 2603.13933 · v2 · submitted 2026-03-14 · 💻 cs.CL

Recognition: no theorem link

OmniCompliance-100K: A Multi-Domain, Rule-Grounded, Real-World Safety Compliance Dataset

Wenbin Hu , Huihao Jing , Haochen Shi , Changxuan Fan , Haoran Li , Yangqiu Song

Authors on Pith no claims yet

Pith reviewed 2026-05-15 11:29 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM safetycompliance datasetrule-grounded casesmulti-domain regulationsreal-world casessafety benchmarkingprivacy policies

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The pith

A new dataset supplies 106,000 real-world compliance cases drawn from official rules across many domains to test LLM safety.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper constructs a large dataset to fill a gap in LLM safety resources. Existing datasets often use made-up categories and lack grounding in actual regulations and real cases. Here the authors use a web-searching agent to gather cases tied to 12,985 distinct rules from 74 regulations in areas like privacy, finance, medicine, education, and human rights, yielding 106,009 cases. Analysis shows strong alignment between rules and cases, and benchmarks on advanced LLMs reveal varied compliance capabilities. This provides a more realistic foundation for improving model safety.

Core claim

The authors present a multi-domain safety compliance dataset containing 12,985 distinct rules and 106,009 associated real-world compliance cases sourced from authoritative references including security and privacy regulations, content safety and user data privacy policies, financial security requirements, medical device risk management standards, educational integrity guidelines, and protections of fundamental human rights. The construction relies on a web-searching agent to ensure rule-grounding, analysis confirms strong alignment between rules and cases, and benchmarking experiments evaluate LLM safety and compliance capabilities across different model scales.

What carries the argument

The web-searching agent that retrieves rule-grounded real-world cases from authoritative references across multiple domains.

If this is right

LLMs can now be evaluated against concrete regulatory requirements rather than abstract taxonomies.
Benchmark results offer insights into how model scale affects compliance with specific rules.
The dataset supports development of more robust safety mechanisms for LLMs.
Future research can extend this approach to additional domains or update rules as regulations evolve.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This approach could be adapted to create similar grounded datasets for other fields like legal AI or policy compliance.
Regularly refreshing the cases would keep the benchmark current with evolving regulations.
Combining this with synthetic data generation might produce even larger training sets for safer models.
Companies could use the rules to audit their own LLM deployments against specific policies.

Load-bearing premise

The web-searching agent accurately retrieves rule-grounded real-world cases from authoritative references without introducing selection bias or factual errors.

What would settle it

A manual audit of a random sample of cases that finds frequent mismatches between the stated rule and the described compliance event would undermine the dataset's grounding.

Figures

Figures reproduced from arXiv: 2603.13933 by Changxuan Fan, Haochen Shi, Haoran Li, Huihao Jing, Wenbin Hu, Yangqiu Song.

**Figure 2.** Figure 2: Benchmarking LLMs on OmniCompliance-100K (Macro-F1 Score). tailed analysis for the results, with our findings outlined below. (1) Lower scores on platform policies versus authoritative regulations. Across almost all models, performance on private platform Policies, e.g., X, Reddit, and GitHub, is systematically lower than on formal Laws. For instance, the average macroF1 score for the top model (GLM-4.5)… view at source ↗

**Figure 3.** Figure 3: Detailed F1 Scores (Permitted versus Prohibited). [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Macro-F1 Scores of the EU AI Act by Chapter. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Correlation of Articles in the GDPR. From this analysis, we can draw several findings. Notably, we observe that articles indexed at 5-11, 32-33, and 44 have a high correlation score with all other articles, as indicated by brighter areas in the confusion matrix. This means the majority of articles in GDPR exhibit a strong correlation with Chapter 2: Principle (Articles 5-11), Article 32: Security of Proces… view at source ↗

**Figure 6.** Figure 6: Benchmarking LLMs on OmniCompliance-100K (Accuracy). You are an expert in compliance evaluation. Based on the following case background and rule, determine if the case represents PERMITTED or PROHIBITED behavior. Case Background: {case_background} [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗

read the original abstract

Ensuring the safety and compliance of large language models (LLMs) is of paramount importance. However, existing LLM safety datasets often rely on ad-hoc taxonomies for data generation and suffer from a significant shortage of rule-grounded, real-world cases that are essential for robustly protecting LLMs. In this work, we address this critical gap by constructing a comprehensive safety dataset from a compliance perspective. Using a powerful web-searching agent, we collect a rule-grounded, real-world case dataset OmniCompliance-100K, sourced from multi-domain authoritative references. The dataset spans 74 regulations and policies across a wide range of domains, including security and privacy regulations, content safety and user data privacy policies from leading AI companies and social media platforms, financial security requirements, medical device risk management standards, educational integrity guidelines, and protections of fundamental human rights. In total, our dataset contains 12,985 distinct rules and 106,009 associated real-world compliance cases. Our analysis confirms a strong alignment between the rules and their corresponding cases. We further conduct extensive benchmarking experiments to evaluate the safety and compliance capabilities of advanced LLMs across different model scales. Our experiments reveal several interesting findings that have great potential to offer valuable insights for future LLM safety research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper releases a sizable new multi-domain compliance dataset but the extraction method has no reported validation or error checks.

read the letter

This paper releases OmniCompliance-100K, built from 74 regulations across security, finance, medicine, education, and human rights. It pairs 12,985 distinct rules with 106,009 real-world cases collected by a web-searching agent, then benchmarks several LLMs on the resulting safety and compliance tasks. The scale and the explicit rule-to-case links across domains are the main new elements; earlier safety datasets were smaller and less tied to actual regulatory text. The benchmarking runs give concrete performance numbers by model size, which could serve as a starting point for others testing compliance capabilities. The weak point is the data pipeline. The entire claim of strong alignment rests on the agent pulling accurate cases from authoritative sources, yet the abstract and available description supply no precision or recall figures, no human audit of samples, no inter-annotator numbers, and no error analysis. Without those checks it is hard to know how much noise or selection bias is in the final set. This work is aimed at researchers who need grounded regulatory examples for LLM safety evaluation rather than purely synthetic data. A reader focused on compliance benchmarks could pull useful scale and coverage from it, provided they run their own quality checks first. I would send it to peer review so the authors can add the missing validation metrics; the resource has enough potential to justify the time once the construction details are tightened.

Referee Report

3 major / 2 minor

Summary. The paper constructs OmniCompliance-100K, a multi-domain safety compliance dataset containing 12,985 distinct rules extracted from 74 regulations and policies and 106,009 associated real-world cases, assembled via a web-searching agent from authoritative sources across domains such as privacy, finance, medical devices, and human rights. It reports a strong alignment between rules and cases and includes LLM benchmarking experiments on safety and compliance capabilities.

Significance. A validated version of this dataset would address a clear gap in existing LLM safety resources by supplying large-scale, rule-grounded real-world cases rather than ad-hoc synthetic examples, potentially enabling more rigorous compliance evaluation across model scales.

major comments (3)

[Abstract / Methods] Abstract and Methods: the central claim of 106,009 accurately retrieved, rule-grounded cases with 'strong alignment' is unsupported because no quantitative validation metrics (precision/recall on sampled pairs, error rates, or inter-annotator agreement) are reported for the web-searching agent's output.
[Dataset Construction] Dataset Construction: details on the agent's prompt templates, retrieval thresholds, filtering criteria, and any human audit of the final 12,985 rules + 106,009 cases are absent, leaving open the possibility of selection bias or factual errors that would undermine downstream benchmarking.
[Alignment Analysis] Alignment Analysis: the reported 'strong alignment' between rules and cases lacks any statistical measure or error analysis, so it is impossible to determine whether the alignment is an artifact of the automated pipeline rather than an independent property of the data.

minor comments (2)

[Abstract] The abstract states the dataset spans 74 regulations but provides no per-domain breakdown of rule or case counts, which would clarify coverage balance.
[Benchmarking Experiments] Benchmarking section should specify the exact evaluation prompts and scoring rubric used for the LLM safety tests to allow reproduction.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We agree that the current manuscript lacks sufficient quantitative validation and methodological transparency, and we will revise the paper to address these points directly.

read point-by-point responses

Referee: [Abstract / Methods] Abstract and Methods: the central claim of 106,009 accurately retrieved, rule-grounded cases with 'strong alignment' is unsupported because no quantitative validation metrics (precision/recall on sampled pairs, error rates, or inter-annotator agreement) are reported for the web-searching agent's output.

Authors: We acknowledge that no quantitative validation metrics were reported in the initial submission. In the revised manuscript we will add precision and recall computed on a stratified sample of 1,000 rule-case pairs, error rates from manual inspection, and inter-annotator agreement (Fleiss' kappa) from a three-annotator audit of 500 pairs. These metrics will be presented in a new validation subsection supporting the alignment claim. revision: yes
Referee: [Dataset Construction] Dataset Construction: details on the agent's prompt templates, retrieval thresholds, filtering criteria, and any human audit of the final 12,985 rules + 106,009 cases are absent, leaving open the possibility of selection bias or factual errors that would undermine downstream benchmarking.

Authors: We agree that these implementation details are necessary for reproducibility. The revised Methods section will include the exact prompt templates, retrieval similarity thresholds, post-retrieval filtering rules, and a full description of the human audit protocol (including sample sizes, annotator instructions, and observed error rates). revision: yes
Referee: [Alignment Analysis] Alignment Analysis: the reported 'strong alignment' between rules and cases lacks any statistical measure or error analysis, so it is impossible to determine whether the alignment is an artifact of the automated pipeline rather than an independent property of the data.

Authors: We will expand the Alignment Analysis section with quantitative statistical measures (e.g., mean cosine similarity with confidence intervals and a permutation test against random pairings) together with a systematic error analysis of low-alignment cases. This will allow readers to assess whether the alignment exceeds what the pipeline alone would produce. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset assembled from external regulatory sources with no self-referential derivation or fitted predictions

full rationale

The paper constructs OmniCompliance-100K by collecting rules and cases from multi-domain authoritative references via a web-searching agent, then reports dataset statistics (12,985 rules, 106,009 cases) and an alignment analysis. No equations, parameters, or derivations are present; the central claims reduce directly to external data retrieval rather than any internal definition, fit, or self-citation chain. The alignment confirmation is presented as an empirical observation on the collected data, not a prediction forced by construction. This is a standard non-circular dataset paper whose load-bearing steps are external to the manuscript itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Dataset construction rests on the assumption that an automated search agent can reliably extract accurate, rule-grounded cases from public regulatory sources without systematic omission or distortion.

axioms (1)

domain assumption A web-searching agent can collect rule-grounded real-world compliance cases from authoritative references across domains.
Invoked as the core data-collection mechanism.

pith-pipeline@v0.9.0 · 5540 in / 1110 out tokens · 28755 ms · 2026-05-15T11:29:26.093131+00:00 · methodology

discussion (0)

Reference graph

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21 extracted references · 21 canonical work pages · 7 internal anchors

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