@inproceedings{wang-etal-2026-debate-decision,
title = "From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation",
author = "Wang, Mengdie Flora and
Xie, Haochen and
Wang, Guanghui and
Gao, Aijing and
Yang, Guang and
Li, Ziyuan and
Qiu, Qucy Wei and
Han, Fangwei and
Qiu, Hengzhi and
Huang, Yajing and
Zhu, Bing and
Woo, Jae Oh",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.830/",
pages = "19290--19312",
ISBN = "979-8-89176-459-0",
abstract = "Multi-agent debate improves LLM reasoning, yet agreement is not evidence of correctness. When agents converge on a wrong answer through social reinforcement, consensus stopping commits that error to automated action with no recourse. We introduce Conformal Social Choice, a post-hoc layer that converts debate outputs into calibrated act-versus-escalate decisions. Verbalized probability distributions from heterogeneous agents are aggregated via a linear opinion pool and calibrated with split conformal prediction, yielding fixed-round marginal coverage: at any pre-specified round, the correct answer is included with probability ${\geq}1{-}\alpha$, without assumptions on individual model calibration. A hierarchical action policy maps singletons to autonomous action and larger sets to human escalation. Across eight MMLU-Pro domains and two additional benchmarks, fixed-round coverage stays close to target. The key finding is not that debate becomes more accurate, but that the conformal layer makes its failures actionable: at the final round, 81.9{\%} of wrong-consensus cases are flagged by non-singleton sets at $\alpha{=}0.05$. Separately, among cases that reach a singleton before the final round, accuracy reaches 90.0{--}96.8{\%} on MMLU-Pro (up to 22.1pp above consensus stopping): a selection effect, not a reasoning improvement. Simple probability- and entropy-threshold rules attain comparable interception; conformal{'}s distinct value is a finite-sample fixed-round coverage guarantee together with explicit candidate sets for escalation. Adaptive first-singleton stopping is evaluated empirically and not covered by this guarantee."
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<abstract>Multi-agent debate improves LLM reasoning, yet agreement is not evidence of correctness. When agents converge on a wrong answer through social reinforcement, consensus stopping commits that error to automated action with no recourse. We introduce Conformal Social Choice, a post-hoc layer that converts debate outputs into calibrated act-versus-escalate decisions. Verbalized probability distributions from heterogeneous agents are aggregated via a linear opinion pool and calibrated with split conformal prediction, yielding fixed-round marginal coverage: at any pre-specified round, the correct answer is included with probability \geq1-α, without assumptions on individual model calibration. A hierarchical action policy maps singletons to autonomous action and larger sets to human escalation. Across eight MMLU-Pro domains and two additional benchmarks, fixed-round coverage stays close to target. The key finding is not that debate becomes more accurate, but that the conformal layer makes its failures actionable: at the final round, 81.9% of wrong-consensus cases are flagged by non-singleton sets at α=0.05. Separately, among cases that reach a singleton before the final round, accuracy reaches 90.0–96.8% on MMLU-Pro (up to 22.1pp above consensus stopping): a selection effect, not a reasoning improvement. Simple probability- and entropy-threshold rules attain comparable interception; conformal’s distinct value is a finite-sample fixed-round coverage guarantee together with explicit candidate sets for escalation. Adaptive first-singleton stopping is evaluated empirically and not covered by this guarantee.</abstract>
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%0 Conference Proceedings
%T From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation
%A Wang, Mengdie Flora
%A Xie, Haochen
%A Wang, Guanghui
%A Gao, Aijing
%A Yang, Guang
%A Li, Ziyuan
%A Qiu, Qucy Wei
%A Han, Fangwei
%A Qiu, Hengzhi
%A Huang, Yajing
%A Zhu, Bing
%A Woo, Jae Oh
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-debate-decision
%X Multi-agent debate improves LLM reasoning, yet agreement is not evidence of correctness. When agents converge on a wrong answer through social reinforcement, consensus stopping commits that error to automated action with no recourse. We introduce Conformal Social Choice, a post-hoc layer that converts debate outputs into calibrated act-versus-escalate decisions. Verbalized probability distributions from heterogeneous agents are aggregated via a linear opinion pool and calibrated with split conformal prediction, yielding fixed-round marginal coverage: at any pre-specified round, the correct answer is included with probability \geq1-α, without assumptions on individual model calibration. A hierarchical action policy maps singletons to autonomous action and larger sets to human escalation. Across eight MMLU-Pro domains and two additional benchmarks, fixed-round coverage stays close to target. The key finding is not that debate becomes more accurate, but that the conformal layer makes its failures actionable: at the final round, 81.9% of wrong-consensus cases are flagged by non-singleton sets at α=0.05. Separately, among cases that reach a singleton before the final round, accuracy reaches 90.0–96.8% on MMLU-Pro (up to 22.1pp above consensus stopping): a selection effect, not a reasoning improvement. Simple probability- and entropy-threshold rules attain comparable interception; conformal’s distinct value is a finite-sample fixed-round coverage guarantee together with explicit candidate sets for escalation. Adaptive first-singleton stopping is evaluated empirically and not covered by this guarantee.
%U https://aclanthology.org/2026.emnlp-main.830/
%P 19290-19312
Markdown (Informal)
[From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation](https://aclanthology.org/2026.emnlp-main.830/) (Wang et al., EMNLP 2026)
ACL
- Mengdie Flora Wang, Haochen Xie, Guanghui Wang, Aijing Gao, Guang Yang, Ziyuan Li, Qucy Wei Qiu, Fangwei Han, Hengzhi Qiu, Yajing Huang, Bing Zhu, and Jae Oh Woo. 2026. From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19290–19312, Budapest, Hungary. Association for Computational Linguistics.