ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning proposes a controller that dynamically manages deliberation steps in multi-agent LLM systems. The controller evaluates answer concentration, support diversity, contradictions, role behavior, verification history, and remaining budget to decide actions such as propose, challenge, verify, arbitrate, diversify, correct, or stop. In a held‑out evaluation on MedQA, CaseHOLD, and FinQA, ECMR‑Full improves pooled accuracy from 62.1% to 84.9%, lowers false‑consensus from 40.7% to 17.0%, and raises evidence‑support from 58.0% to 88.7%.
arXiv:2610.17869v1 Announce Type: new
Abstract: Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference. A fixed protocol cannot allocate computati...
By Yuan Liu, Haoyue Peng, Fanliang Hu, Lei Huang, Wenli Cai
The paper examines how large language model (LLM) agents can use trust estimates in repeated multi‑party, multi‑issue negotiations where one participant covertly tries to block agreement. By introducing a framework where agents maintain pairwise trust scores across successive games, the study compares trust‑aware and trust‑agnostic agents. Results show a trust–agreement gap: while trust‑aware agents quickly spot the adversary and trust can raise agreement rates by up to 23 percentage points, this benefit only materializes when agents are explicitly guided to incorporate trust into their decision‑making.
By Amit Sarker, Moumita Choudhury, Saaduddin Mahmud, Shlomo Zilberstein
The paper introduces TrustMI, a method to causally control how large language model assistants decide to trust their users. By creating 2,000 contrastive conversations that vary in ability, benevolence, and integrity, the authors learn steering matrices that adjust trust decisions along linear directions in model activations while keeping the model parameters frozen. Experiments across six instruction‑tuned models show that these steering changes reliably alter trust decisions and affect safety‑related behaviors such as compliance with harmful requests, prompt injections, and insider threats.
arXiv:2606. 14923v1 Announce Type: new Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates.
By Yujiao Chen
arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.
By Henrique Ferraz de Arruda, Carlos Gracia L\'azaro, Alberto Aleta, Yamir Moreno