Indian Institute of Science Education and Research Thiruvananthapuram

Judgment prediction has mostly reasoned over case facts, yet real proceedings are shaped by exchanges among judge, petitioner and respondent. ArguGenCourt assigns three independent GPT-4o agents these roles and lets them converse in rounds, seeding prompts with courtroom utterances retrieved from the factually closest precedent document. On criminal petitioner arguments, local precedent retrieval in the multi-agent setting reaches a Legal-sim of 0.8921 against 0.8288 for fact-based conversation under the same retrieval; feeding the generated arguments into judgment prediction gives 0.55 accuracy versus 0.48 for zero-shot.

Judgment prediction has mostly reasoned over case facts, yet real proceedings are shaped by exchanges among judge, petitioner and respondent. ArguGenCourt assigns three independent GPT-4o agents these roles and lets them converse in rounds, seeding prompts with courtroom utterances retrieved from the factually closest precedent document. On criminal petitioner arguments, local precedent retrieval in the multi-agent setting reaches a Legal-sim of 0.8921 against 0.8288 for fact-based conversation under the same retrieval; feeding the generated arguments into judgment prediction gives 0.55 accuracy versus 0.48 for zero-shot.