From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915926316679168 |
|---|---|
| author | Wang, Mengdie Flora Xie, Haochen Wang, Guanghui Gao, Aijing Yang, Guang Li, Ziyuan Qiu, Qucy Wei Han, Fangwei Qiu, Hengzhi Huang, Yajing Zhu, Bing Woo, Jae Oh |
| author_facet | Wang, Mengdie Flora Xie, Haochen Wang, Guanghui Gao, Aijing Yang, Guang Li, Ziyuan Qiu, Qucy Wei Han, Fangwei Qiu, Hengzhi Huang, Yajing Zhu, Bing Woo, Jae Oh |
| contents | Multi-agent debate improves LLM reasoning, yet agreement among agents is not evidence of correctness. When agents converge on a wrong answer through social reinforcement, consensus-based stopping commits that error to an automated action with no recourse. We introduce Conformal Social Choice, a post-hoc decision 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 prediction sets with a marginal coverage guarantee: the correct answer is included with probability ${\geq}\,1{-}α$, without assumptions on individual model calibration. A hierarchical action policy maps singleton sets to autonomous action and larger sets to human escalation. On eight MMLU-Pro domains with three agents (Claude Haiku, DeepSeek-R1, Qwen-3 32B), coverage stays within 1--2 points of the target. The key finding is not that debate becomes more accurate, but that the conformal layer makes its failures actionable: 81.9% of wrong-consensus cases are intercepted at $α{=}0.05$. Because the layer refuses to act on cases where debate is confidently wrong, the remaining conformal singletons reach 90.0--96.8% accuracy (up to 22.1pp above consensus stopping) -- a selection effect, not a reasoning improvement. This safety comes at the cost of automation, but the operating point is user-adjustable via $α$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07667 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation Wang, Mengdie Flora Xie, Haochen Wang, Guanghui Gao, Aijing Yang, Guang Li, Ziyuan Qiu, Qucy Wei Han, Fangwei Qiu, Hengzhi Huang, Yajing Zhu, Bing Woo, Jae Oh Artificial Intelligence Multiagent Systems Social and Information Networks Multi-agent debate improves LLM reasoning, yet agreement among agents is not evidence of correctness. When agents converge on a wrong answer through social reinforcement, consensus-based stopping commits that error to an automated action with no recourse. We introduce Conformal Social Choice, a post-hoc decision 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 prediction sets with a marginal coverage guarantee: the correct answer is included with probability ${\geq}\,1{-}α$, without assumptions on individual model calibration. A hierarchical action policy maps singleton sets to autonomous action and larger sets to human escalation. On eight MMLU-Pro domains with three agents (Claude Haiku, DeepSeek-R1, Qwen-3 32B), coverage stays within 1--2 points of the target. The key finding is not that debate becomes more accurate, but that the conformal layer makes its failures actionable: 81.9% of wrong-consensus cases are intercepted at $α{=}0.05$. Because the layer refuses to act on cases where debate is confidently wrong, the remaining conformal singletons reach 90.0--96.8% accuracy (up to 22.1pp above consensus stopping) -- a selection effect, not a reasoning improvement. This safety comes at the cost of automation, but the operating point is user-adjustable via $α$. |
| title | From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation |
| topic | Artificial Intelligence Multiagent Systems Social and Information Networks |
| url | https://arxiv.org/abs/2604.07667 |