@inproceedings{dong-etal-2026-choir,
title = "{CHOIR}: Harmonizing Structured Persona Diversity for Robust Collaborative {LLM} Reasoning",
author = "Dong, Xiangjue and
Wang, Cong and
Teleki, Maria and
Bismay, Millennium and
Huang, Ruihong and
Caverlee, James",
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.2175/",
pages = "46997--47014",
ISBN = "979-8-89176-390-6",
abstract = "Persona-assigned Large Language Models can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas, such as simple pronoun swaps, can alter reasoning trajectories, leading to divergent sets of correct answers on reasoning benchmarks. We explore the potential of these variations as a constructive resource to improve LLM reasoning performance. We propose CHOIR (Collaborative Harmonization fOr Inference Robustness), a test-time framework that harmonizes a set of demographically perturbed, persona-conditioned reasoning signals into a unified prediction. CHOIR orchestrates a collaborative decoding process among counterfactual personas perturbed across dimensions of gender, race, religion, disability, and age, dynamically balancing agreement and divergence in their reasoning paths to improve performance. Experiments demonstrate that CHOIR consistently enhances LLM reasoning across model architectures, scales, and tasks. Improvements reach up to 20.1{\%} for individual groups and 15.1{\%} on average, and we show that CHOIR remains effective even when base personas are suboptimal."
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%0 Conference Proceedings
%T CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning
%A Dong, Xiangjue
%A Wang, Cong
%A Teleki, Maria
%A Bismay, Millennium
%A Huang, Ruihong
%A Caverlee, James
%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 dong-etal-2026-choir
%X Persona-assigned Large Language Models can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas, such as simple pronoun swaps, can alter reasoning trajectories, leading to divergent sets of correct answers on reasoning benchmarks. We explore the potential of these variations as a constructive resource to improve LLM reasoning performance. We propose CHOIR (Collaborative Harmonization fOr Inference Robustness), a test-time framework that harmonizes a set of demographically perturbed, persona-conditioned reasoning signals into a unified prediction. CHOIR orchestrates a collaborative decoding process among counterfactual personas perturbed across dimensions of gender, race, religion, disability, and age, dynamically balancing agreement and divergence in their reasoning paths to improve performance. Experiments demonstrate that CHOIR consistently enhances LLM reasoning across model architectures, scales, and tasks. Improvements reach up to 20.1% for individual groups and 15.1% on average, and we show that CHOIR remains effective even when base personas are suboptimal.
%U https://aclanthology.org/2026.acl-long.2175/
%P 46997-47014
Markdown (Informal)
[CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning](https://aclanthology.org/2026.acl-long.2175/) (Dong et al., ACL 2026)
ACL