Papers by John Seon Keun Yi

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Challenge:	Multi-agent debate is compute-intensive and requires long transcripts before answering questions.
Approach:	They propose a framework that distills multi-agent debate into a single LLM by combining debate structure learning with internalization via dynamic reward scheduling and length clipping.
Outcome:	The proposed model matches or exceeds explicit multi-agent debate performance using 93% fewer tokens across multiple models and benchmarks.