@inproceedings{chen-etal-2026-ucve,
title = "{UCVE}: Graph Structured Retrieval for Compliance Validation",
author = "Chen, Emily and
Menachery, Michelle and
Nenavath, Rahul and
Kumar, Himanshu and
Iranitalab, Amirfarrokh",
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-1.48/",
pages = "609--627",
ISBN = "979-8-89176-494-1",
abstract = "Structured compliance validation requires determining whether every obligation in a predefined requirement space is satisfied, violated, or unaddressed by a given document. This can be described as a coverage problem that existing retrieval and classification systems, by treating requirements as independent, are not designed to solve. We introduce \textbf{{UCVE}}, a domain-agnostic framework that represents compliance requirement spaces as a {CRG} and performs retrieval via spectral reranking over its reinforcement learning-weighted Laplacian. Edges between requirement nodes are learned via reinforcement learning with a traversal reward grounded in violation detection, reshaping the graph{'}s community structure such that low-pass spectral filtering distinguishes relevant from irrelevant obligation clusters. Evaluating across three compliance domains, {UCVE} averages 4.3{\%} matched-$K$ recall lift over flat cosine retrieval and consistently outperforms strong retrieval and graph-based baselines."
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<abstract>Structured compliance validation requires determining whether every obligation in a predefined requirement space is satisfied, violated, or unaddressed by a given document. This can be described as a coverage problem that existing retrieval and classification systems, by treating requirements as independent, are not designed to solve. We introduce UCVE, a domain-agnostic framework that represents compliance requirement spaces as a CRG and performs retrieval via spectral reranking over its reinforcement learning-weighted Laplacian. Edges between requirement nodes are learned via reinforcement learning with a traversal reward grounded in violation detection, reshaping the graph’s community structure such that low-pass spectral filtering distinguishes relevant from irrelevant obligation clusters. Evaluating across three compliance domains, UCVE averages 4.3% matched-K recall lift over flat cosine retrieval and consistently outperforms strong retrieval and graph-based baselines.</abstract>
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%0 Conference Proceedings
%T UCVE: Graph Structured Retrieval for Compliance Validation
%A Chen, Emily
%A Menachery, Michelle
%A Nenavath, Rahul
%A Kumar, Himanshu
%A Iranitalab, Amirfarrokh
%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 chen-etal-2026-ucve
%X Structured compliance validation requires determining whether every obligation in a predefined requirement space is satisfied, violated, or unaddressed by a given document. This can be described as a coverage problem that existing retrieval and classification systems, by treating requirements as independent, are not designed to solve. We introduce UCVE, a domain-agnostic framework that represents compliance requirement spaces as a CRG and performs retrieval via spectral reranking over its reinforcement learning-weighted Laplacian. Edges between requirement nodes are learned via reinforcement learning with a traversal reward grounded in violation detection, reshaping the graph’s community structure such that low-pass spectral filtering distinguishes relevant from irrelevant obligation clusters. Evaluating across three compliance domains, UCVE averages 4.3% matched-K recall lift over flat cosine retrieval and consistently outperforms strong retrieval and graph-based baselines.
%U https://aclanthology.org/2026.nllp-1.48/
%P 609-627
Markdown (Informal)
[UCVE: Graph Structured Retrieval for Compliance Validation](https://aclanthology.org/2026.nllp-1.48/) (Chen et al., NLLP 2026)
ACL
- Emily Chen, Michelle Menachery, Rahul Nenavath, Himanshu Kumar, and Amirfarrokh Iranitalab. 2026. UCVE: Graph Structured Retrieval for Compliance Validation. In Proceedings of the Natural Legal Language Processing Workshop 2026, pages 609–627, Budapest, Hungary. Association for Computational Linguistics.