@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://preview.aclanthology.org/ingest-acl/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."
}