# Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction

arXiv: https://arxiv.org/abs/2504.05358 · HTML: https://mlsanity.com/paper/2504.05358/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023)
- [A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges](https://mlsanity.com/paper/2204.04859/) (2022)
- [Pre-trained Language Models for the Legal Domain: A Case Study on Indian Law](https://mlsanity.com/paper/2209.06049/) (2022)
- [Overview of CAIL2018: Legal Judgment Prediction Competition](https://mlsanity.com/paper/1810.05851/) (2018)
- [Context-Aware Legal Citation Recommendation using Deep Learning](https://mlsanity.com/paper/2106.10776/) (2021)
- [Distribution of a Markov chain in reverse-time with cluster observations in the extremes of a finite time window](https://mlsanity.com/paper/2206.05607/) (2022)

## Built on by (most cited first, 9 of 9)

- [Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism](https://mlsanity.com/paper/2508.15030/) (2025)
- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework](https://mlsanity.com/paper/2506.18768/) (2025)
- [PD3: A Project Duplication Detection Framework via Adapted Multi-Agent Debate](https://mlsanity.com/paper/2505.17492/) (2025)
- [CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict](https://mlsanity.com/paper/2605.28369/) (2026)
- [LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction](https://mlsanity.com/paper/2603.11446/) (2026)
- [Is Monitoring Enough? Strategic Agent Selection For Stealthy Attack in Multi-Agent Discussions](https://mlsanity.com/paper/2603.21194/) (2026)
- [OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction](https://mlsanity.com/paper/2609.02158/) (2026)
- [Exploiting Target Knowledge from MLLMs for Robust Few-Shot Segmentation](https://mlsanity.com/paper/2609.28949/) (2026)

## Abstract (arXiv)

The use of AI in legal analysis and prediction (LegalAI) has gained widespread attention, with past research focusing on retrieval-based methods and fine-tuning large models. However, these approaches often require large datasets and underutilize the capabilities of modern large language models (LLMs). In this paper, inspired by the debate phase of real courtroom trials, we propose a novel legal judgment prediction model based on the Debate-Feedback architecture, which integrates LLM multi-agent debate and reliability evaluation models. Unlike traditional methods, our model achieves significant improvements in efficiency by minimizing the need for large historical datasets, thus offering a lightweight yet robust solution. Comparative experiments show that it outperforms several general-purpose and domain-specific legal models, offering a dynamic reasoning process and a promising direction for future LegalAI research.