Explicit Trait Inference for Multi-Agent Coordination
Abstract
LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misaligned behaviors. We propose Explicit Trait Inference (ETI), a psychologically grounded method for improving coordination. ETI enables agents to infer and track partner characteristics along two established psychological dimensions--warmth (e.g., trust) and competence (e.g., skill)--from interaction histories to guide decisions. We evaluate ETI in controlled settings (economic games), where it reduces payoff loss by 45-77%, and in more realistic, complex multi-agent settings (MultiAgentBench), where it improves performance by 3-29% depending on the scenario and model, relative to a CoT baseline. Additional analysis shows that gains are closely linked to trait inference: ETI profiles predict agents' actions, and informative profiles drive improvements. These results highlight ETI as a lightweight and robust mechanism for improving coordination in diverse multi-agent settings, and provide the first systematic evidence that LLM agents can (i) reliably infer others' traits from interaction histories and (ii) leverage structured awareness of others' traits for coordination.
Cite
@article{arxiv.2604.19278,
title = {Explicit Trait Inference for Multi-Agent Coordination},
author = {Suhaib Abdurahman and Etsuko Ishii and Katerina Margatina and Divya Bhargavi and Monica Sunkara and Yi Zhang},
journal= {arXiv preprint arXiv:2604.19278},
year = {2026}
}
Comments
Accepted at ACL 2026 Main Conference