Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning

Abhijith Babu; Ramneet Kaur; Vishal Pramanik; Olivera Kotevska; Nathaniel D. Bastian; Susmit Jha; Sunny Raj; Yanzhao Wu

cs.AIAug 20, 2026

Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning

Authors: Abhijith Babu, Ramneet Kaur, Vishal Pramanik, Olivera Kotevska, Nathaniel D. Bastian, Susmit Jha, Sunny Raj, Yanzhao Wu,

Organizations: Knight Foundation School of Computing and Information Sciences, Florida International University · SRI International · Department of Computer & Information Science & Engineering, University of Florida · Oak Ridge National Laboratory ·

Abstract

Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particularly in hidden-profile settings where each agent holds only part of the evidence required for a correct decision. Existing protocols, including fixed schedules, round-robin exchange, and unstructured debate, provide no guarantee that a conversational action is appropriate. We propose Consilience, an inference-time orchestration framework that both steers and certifies multi-agent communication under distributed private information. At each turn, Consilience summarizes the discussion using a compact state capturing uncertainty, disagreement, evidence gain, redundancy, and premature consensus, then selects both a communication intervention (challenge, clarify, seek evidence, or route) and an appropriate speaker. Its central contribution is a round-wise conformal calibration procedure that provides a distribution-free, finite-sample guarantee: at each discussion round, conditional on reaching that round, the one-step regret of a controller's proposed action is bounded by a calibrated threshold with marginal probability at least 1 - alpha; an acceptance mechanism enforces the same guarantee for the executed action by replacing inadmissible proposals. On HiddenBench-style hidden-profile tasks spanning 12 open and closed weight language models, Consilience improves decision accuracy and communication efficiency over fixed and unstructured discussion protocols, sometimes surpassing a full-information baseline where every agent observes all evidence. These results demonstrate that certified adaptive communication control can be more valuable than increasing information availability, providing a practical mechanism for reliable multi-agent LLM coordination.

Figures & tables

Figure 1: Proposed framework Consilience : (1) Each agent receives shared task information and a private clue. (2) Agents report beliefs conditioned on the public transcript, which Consilience aggregates with transcript-level signals into the collective state $s_{t}$ . (3) A controller proposes a communication action, and round-wise conformal acceptance retains or replaces it using calibrated one-step regret. (4) An action-conditional router selects the speaker, whose message updates the transcript and agent beliefs. The loop repeats until termination, followed by plurality voting with confidence-based tie-breaking.

Method	Task Accuracy	Vote Accuracy
Hidden Pre	0.2537	0.2475
Hidden Post	0.4982	0.4925
Full Info	0.3657	0.3358
Rules (Ours)	0.6544	0.6542
MLP (Ours)	0.5071	0.5025
LLM (Ours)	0.6150	0.6125

Table 2: Task accuracy and vote accuracy for the benchmark generated from GroupTravelBench Cheng et al. (2026) averaged across 12 models.

Figure 2: Three agents choose between Apartments A and B. Shared evidence initially favors Apartment A, but complementary private clues collectively identify Apartment B as the correct choice. Round-robin discussion repeats common evidence further suppresses support for B (as Agent 1 has no clue about any utility surcharges for Apartment B in Round 1), and the final decision converges on A. Consilience instead adaptively selects interventions and speakers to surface unshared evidence, reverse premature consensus, and recover B.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Features retained	Success $\uparrow$	Communication cost $\downarrow$	Final objective $\downarrow$
All features	82.22%	0.5228	0.0239
Information only	82.22%	0.5254	0.2277
Belief + information	77.78%	0.5285	0.3509
Belief + cost	77.78%	0.5253	0.1582
Belief only	75.56%	0.4625	0.2212

Appendix

Table 6: State-feature ablation for Qwen3-8B. Each row retrains the MLP using only the retained feature groups. Higher success is better; lower communication cost and final objective are better.

Configuration	Rules	MLP	LLM	LLM+Vote	Average
Full	92.3	87.7	89.2	83.1	88.1
– Challenge	90.8 ( $-1.5$ )	86.2 ( $-1.5$ )	87.7 ( $-1.5$ )	83.1 (0.0)	86.9 ( $-1.2$ )
– Clarify	93.8 (+1.5)	87.7 (0.0)	89.2 (0.0)	81.5 ( $-1.5$ )	88.1 (0.0)
– New Evidence	92.3 (0.0)	92.3 (+4.6)	81.5 ( $-7.7$ )	70.8 ( $-12.3$ )	84.2 ( $-3.8$ )
– Routing	87.7 ( $-4.6$ )	89.2 (+1.5)	87.7 ( $-1.5$ )	69.2 ( $-13.8$ )	83.5 ( $-4.6$ )
– Voting	92.3 (0.0)	NA	NA	83.1 (0.0)	87.7 (0.0)

Appendix

Table 7: Action ablation study on HiddenBench. Each row removes one communication action from the full controller. Entries report task accuracy (%), with the change relative to the corresponding full action set shown in parentheses. Negative values indicate performance degradation.

Routing policy	Mean $\Delta J$ $\downarrow$	Normalized regret $\downarrow$
Consilience (action-conditional)	-0.0079	0.4868
Random	0.0145	0.4930
Round-robin	-0.0017	0.4839
Most-disagreeing	0.1205	0.5735
Most-spoken	0.2815	0.7184

Appendix

Table 8: Counterfactual speaker-routing ablation with Qwen3-8B. The controller-selected action is held fixed and only the speaker-selection policy is changed. Lower objective change is better, while lower normalized regret indicates routing decisions closer to the counterfactual optimum.

Method	Mean Success (%)	Mean Rounds/Trial
LLM	82.75	9.54
MLP	82.75	9.87
Rules	64.50	3.24
LLM+Voting	63.00	5.43

Appendix

Table 9: Comparison of controller methods over 400 trials. Using Qwen3-8B model.

Multi-Agent Communication

Multi-agent communication studies how autonomous agents exchange information to coordinate decisions, share knowledge, and act together under constraints.

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May 24, 2026

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Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and high communication overhead. When agents exchange raw responses or reasoning traces, incorrect intermediate reasoning may be adopted and amplified, leading to confident but wrong consensus; multi-round communication also increases token consumption, latency, and inference cost. In this paper, we propose a controlled-communication coordination framework named DarkForest. DarkForest first keeps agents independent, so each agent produces an answer without seeing the others' outputs. It then parses the raw responses into structured candidate records, groups semantically equivalent candidates into clusters, and estimates a calibrated belief distribution over these clusters using agent reliability, confidence, parse quality, support-pattern reliability, and independence corrections. A coordinator receives only policy-permitted evidence from this belief state with controlled communication. Experiments on six reasoning benchmarks show that DarkForest achieves leading overall quality, improves the strongest baseline by up to 30.7% on benchmark metrics, and reduces token consumption by up to

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compared with communication-heavy baselines.

Yi Li, Songtao Wei, Dongming Jiang +3

University of Texas at Dallas · Independent Researcher · University of California, Davis

Multi-Agent LLM Systems Multi-Agent System Optimization Multi-Agent Coordination LLM Agent Workflow Optimization Agentic Reasoning Multi-Agent Systems+4

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Apr 9, 2026

From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberation

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

Mengdie Flora Wang, Haochen Xie, Guanghui Wang +9

AWS Generative AI Innovation Center · HSBC Holdings Plc., HSBC Technology Center, China

LLM Agent Safety Selective Prediction Multi-Agent Deliberation Conformal Prediction Multi-Agent Debate Multi-Agent Consensus

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Oct 13, 2026

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Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference. A fixed protocol cannot allocate computation differently to resolved and unresolved instances, and it may mistake repeated support for independent confirmation. We study this failure as semantic false consensus: a confident group answer supported by narrow, duplicated, or correlated rationales. We introduce ECMR, an evidence-aware controller over frozen role-conditioned LLMs. At each step, ECMR summarizes answer concentration, support diversity and independence, contradictions, role behavior, verification history, and remaining budget, then selects whether to propose, challenge, verify, arbitrate, diversify, correct a role, or stop. We formulate this decision as finite-horizon budgeted control and derive sufficient conditions under which state conditioning, risk-triggered verification, and role correction improve cost-adjusted utility. Across a declared held-out evaluation of 9,000 records from MedQA, CaseHOLD, and FinQA, ECMR-Full increases pooled accuracy from 62.1% to 84.9%, reduces false-consensus rate from 40.7% to 17.0%, and raises evidence-support rate from 58.0% to 88.7%. State shuffling loses 11.7 accuracy points with only 0.7 fewer rounds, while harder traces shift from early stopping toward arbitration. ECMR-Full uses the largest token budget, yielding the strongest absolute reliability rather than uniformly superior token efficiency.

Yuan Liu, Haoyue Peng, Fanliang Hu +2

Medical Artificial Intelligence Laboratory, Chongqing University Cancer Hospital, Chongqing University, Chongqing 400030, China

Multi-Agent Reasoning LLM Agent Reliability Multi-Agent Consensus

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Abstract

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