Title: A superpersuasive autonomous policy debating system
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Debate, debating, argument mining, argumentation, persuasion, superpersuasion, AI agents, agents, creative AI

Abstract: The capacity for complex, evidence-grounded, and strategically adaptive persuasion remains a formidable grand challenge for artificial intelligence. Prior work, like IBM Project Debater, focused on generating isolated persuasive speeches in highly simplified and shortened debate formats for lay audiences. We introduce a novel autonomous system capable of participating in and winning a full, unmodified two-team competitive policy debate. Our system employs a hierarchical architecture of specialized multi-agent workflows, where teams of LLM-powered agents collaborate and critique one another to perform discrete argumentative tasks. Each workflow utilizes iterative retrieval, synthesis, and self-correction using an existing massive corpus of policy debate evidence: OpenDebateEvidence, mirroring the creative and strategic processes of elite human debate teams at real debate tournaments. We demonstrate through a continuously running live spectacle debate performance that our agents can autonomously construct logically sound, evidence-backed cases and engage in multi-turn debates, generate full speech transcripts and intelligently cross-examine each other. In preliminary evaluations against human-authored cases, our system produces qualitatively superior argumentative components and consistently wins simulated rounds as adjudicated by an independent autonomous judge. We also find that expert human debate coaches consistently prefer the arguments, evidence, and cases constructed by our system vs human debaters. We open source all of our code to the public here: <redacted>

TL;DR: Basically a sequal to IBM Project Debater that's implemented using fully autonomous agents who debate against each other using competitive debate rules