PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements

Raptopoulos Petros, Giorgos Filandrianos, Maria Lymperaiou, Giorgos Stamou

Keywords: Contracts, LLM/AI agents, retrieval-augmented generation, Language and Learning

TL;DR: Privately revealed to ACL ARR 2025 May, ACL ARR 2025 May Submission4483 Authors Introducing PAKTON: an open-source system for automated, collaborative contract review using RAG.

Abstract:

Contract review is a complex and time-intensive task that typically demands specialized legal expertise, rendering it largely inaccessible to non-experts. Moreover, legal interpretation is rarely straightforward—ambiguity is pervasive, and judgments often hinge on subjective assessments. Compounding these challenges, contracts are usually confidential, restricting their use with proprietary models and necessitating reliance on open-source alternatives. To address these challenges, we introduce PAKTON: a fully open-source, end-to-end, multi-agent framework with plug-and-play capabilities. PAKTON is designed to handle the complexities of contract analysis through collaborative agent workflows and a novel retrieval-augmented generation (RAG) component, enabling automated legal document review that is more accessible, adaptable, and privacy-preserving. Experiments demonstrate that PAKTON outperforms both general-purpose and pretrained models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications as evaluated through a human study and validated with automated metrics.
Arxiv: https://arxiv.org/abs/2506.00608

Submission Number: 117