@inproceedings{abdurahman-etal-2026-explicit,
title = "Explicit Trait Inference for Multi-Agent Coordination",
author = "Abdurahman, Suhaib and
Ishii, Etsuko and
Margatina, Katerina and
Bhargavi, Divya and
Sunkara, Monica and
Zhang, Yi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.77/",
pages = "1670--1704",
ISBN = "979-8-89176-390-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Explicit Trait Inference for Multi-Agent Coordination
%A Abdurahman, Suhaib
%A Ishii, Etsuko
%A Margatina, Katerina
%A Bhargavi, Divya
%A Sunkara, Monica
%A Zhang, Yi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F abdurahman-etal-2026-explicit
%X 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.
%U https://aclanthology.org/2026.acl-long.77/
%P 1670-1704
Markdown (Informal)
[Explicit Trait Inference for Multi-Agent Coordination](https://aclanthology.org/2026.acl-long.77/) (Abdurahman et al., ACL 2026)
ACL
- Suhaib Abdurahman, Etsuko Ishii, Katerina Margatina, Divya Bhargavi, Monica Sunkara, and Yi Zhang. 2026. Explicit Trait Inference for Multi-Agent Coordination. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1670–1704, San Diego, California, United States. Association for Computational Linguistics.