Paper

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate

Unreadunread

AI summary

arXiv:2606. 20621v1 Announce Type: new Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques.

Reading status

Priority

About this paper

Authors: Yang Feng, Ziwei Xu, Xia Hu, Fengxiang He
Released: 2026-06-23 (16d ago)
Topic: Related to current project
Relation to my work: Read next because PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, strong, text, under, eval, line, rate, position. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.20621

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.