# WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement

arXiv: https://arxiv.org/abs/2607.17745 · HTML: https://mlsanity.com/paper/2607.17745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements](https://mlsanity.com/paper/2505.22959/) (2025)
- [Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning](https://mlsanity.com/paper/2412.14588/) (2024)
- [Benchmarking LLMs for Environmental Review and Permitting](https://mlsanity.com/paper/2407.07321/) (2024)
- [Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain](https://mlsanity.com/paper/2501.06277/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are increasingly considered for environmental enforcement, but their ability to produce traceable enforcement decisions remains unclear. We introduce WuYu-EnvLE-Bench, a benchmark built from real enforcement cases, regulatory standards, and expert review. It contains 2,521 benchmark instances, 14 tasks, and 12 pollution-medium subdomains across pre-enforcement, in-enforcement, and post-enforcement workflows. Using Absolute Environmental Enforcement Score (AES) and Intelligent Enforcement Index (IEI), we evaluate open-source and closed-source LLMs across capability, response quality, and resource efficiency. Results show that LLMs perform well on rule-bounded tasks but remain unreliable in evidence-chain construction, contradiction detection, multi-source integration, and procedural judgment. Model scaling also shows diminishing returns: medium-sized models approach leading models in structured tasks, while larger models do not reliably overcome evidence-reasoning bottlenecks. WuYu-EnvLE-Bench highlights the need for evidence-grounded, rule-aware, and task-adaptive enforcement reasoning.