@inproceedings{sojka-kowalczyk-2026-accurate,
    title = "Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication",
    author = "S{\'o}jka, Stanis{\l}aw  and
      Kowalczyk, Witold",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.102/",
    pages = "1469--1482",
    ISBN = "979-8-89176-394-4",
    abstract = "Legal texts often contain computational legal clauses{---}provisions whose understanding requires complex logic. While frontier Large Reasoning Models (LRMs) can describe such clauses, building production-ready systems is limited by reasoning errors and the high cost of inference. We propose Amortized Intelligence, a neuro-symbolic approach where we use an LLM once to translate a legal text into Deterministic Autonomous Contract Language (DACL): a typed graph intermediate representation. Adjudication then relies on deterministic graph executions with a visually auditable trace. In comparison against runtime LRM baselines (including GPT-5.2 and Gemini 3 Pro), our DACL-based Agent achieves near-perfect consistency and mitigates the ``reasoning cliff'' observed in probabilistic models. The system reduces compute costs by over 90{\%} in high-volume workflows while satisfying the strict auditability requirements of legal adjudication."
}