@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://preview.aclanthology.org/ingest-eacl/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)."
}