@inproceedings{singh-etal-2026-compact,
title = "{COMPACT}: Building Compliance Paralegals via Clause Graph Reasoning over Contracts",
author = "Singh, Ayush and
Aggarwal, Dishank and
Bhagat, Pranav and
Khan, Ainulla and
Malik, Sameer and
Azad, Amar Prakash",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.377/",
pages = "8081--8112",
ISBN = "979-8-89176-380-7",
abstract = "Contract compliance verification requires reasoning about cross-clause dependencies where obligations, exceptions, and conditions interact across multiple provisions, yet existing legal NLP benchmarks like ContractNLI and CUAD focus exclusively on isolated single-clause tasks. We introduce COMPACT (COMpliance PAralegals via Clause graph reasoning over conTracts), a framework that models cross-clause dependencies through structured clause graphs. Our approach extracts deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. From these graphs, we introduce ACE (Assessing Compliance in Enterprise)- a benchmark containing 4,700 carefully constructed compliance scenarios derived from 633 real-world contracts covering 26 types of agreements. Each scenario requires multi-hop reasoning across multiple clauses, and undergoes independent LLM-based validation to ensure quality. Evaluation reveals that multi-clause reasoning poses a fundamental challenge for state-of-the-art models (34-57{\%} base accuracy), while training on ACE yields substantial improvements on compliance tasks (+22{--}43 {\%} points) and also enhances general legal reasoning performance on other benchmarks (PrivaCI-Bench, ContractNLI)."
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<abstract>Contract compliance verification requires reasoning about cross-clause dependencies where obligations, exceptions, and conditions interact across multiple provisions, yet existing legal NLP benchmarks like ContractNLI and CUAD focus exclusively on isolated single-clause tasks. We introduce COMPACT (COMpliance PAralegals via Clause graph reasoning over conTracts), a framework that models cross-clause dependencies through structured clause graphs. Our approach extracts deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. From these graphs, we introduce ACE (Assessing Compliance in Enterprise)- a benchmark containing 4,700 carefully constructed compliance scenarios derived from 633 real-world contracts covering 26 types of agreements. Each scenario requires multi-hop reasoning across multiple clauses, and undergoes independent LLM-based validation to ensure quality. Evaluation reveals that multi-clause reasoning poses a fundamental challenge for state-of-the-art models (34-57% base accuracy), while training on ACE yields substantial improvements on compliance tasks (+22–43 % points) and also enhances general legal reasoning performance on other benchmarks (PrivaCI-Bench, ContractNLI).</abstract>
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%0 Conference Proceedings
%T COMPACT: Building Compliance Paralegals via Clause Graph Reasoning over Contracts
%A Singh, Ayush
%A Aggarwal, Dishank
%A Bhagat, Pranav
%A Khan, Ainulla
%A Malik, Sameer
%A Azad, Amar Prakash
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F singh-etal-2026-compact
%X Contract compliance verification requires reasoning about cross-clause dependencies where obligations, exceptions, and conditions interact across multiple provisions, yet existing legal NLP benchmarks like ContractNLI and CUAD focus exclusively on isolated single-clause tasks. We introduce COMPACT (COMpliance PAralegals via Clause graph reasoning over conTracts), a framework that models cross-clause dependencies through structured clause graphs. Our approach extracts deontic-temporal entities from clauses and constructs typed relationship graphs capturing definitional dependencies, exception hierarchies, and temporal sequences. From these graphs, we introduce ACE (Assessing Compliance in Enterprise)- a benchmark containing 4,700 carefully constructed compliance scenarios derived from 633 real-world contracts covering 26 types of agreements. Each scenario requires multi-hop reasoning across multiple clauses, and undergoes independent LLM-based validation to ensure quality. Evaluation reveals that multi-clause reasoning poses a fundamental challenge for state-of-the-art models (34-57% base accuracy), while training on ACE yields substantial improvements on compliance tasks (+22–43 % points) and also enhances general legal reasoning performance on other benchmarks (PrivaCI-Bench, ContractNLI).
%U https://aclanthology.org/2026.eacl-long.377/
%P 8081-8112
Markdown (Informal)
[COMPACT: Building Compliance Paralegals via Clause Graph Reasoning over Contracts](https://aclanthology.org/2026.eacl-long.377/) (Singh et al., EACL 2026)
ACL