Source record

Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.03648.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

Paper proposes DEAR, a framework using two reinforcement learning agents to dynamically regulate debate relationships among LLMs in multi-agent debate. It quantifies group consensus and divergence to mitigate blind conformity, with experiments showing improved reasoning performance and lower token consumption.

Targets a known failure mode in multi-agent LLM debate with a group-level mechanism instead of per-agent confidence tweaks, and reports concrete efficiency gains.

Cited by

No citations on record.