Abstract:Multi-agent deliberation systems using large language models (LLMs) are increasingly proposed for policy simulation, yet they suffer from artificial consensus: evaluator agents converge on the same option regardless of their assigned value perspectives. We present the AI Council, a three-phase deliberation framework, and conduct 120 deliberations across two policy scenarios to test two interventions. First, architectural heterogeneity (assigning a different 7-9B parameter model to each value perspective) significantly reduces first-choice concentration compared to a homogeneous baseline (child welfare: 70.9% to 46.1%, p < 0.001, r = 0.58; housing: 46.0% to 22.9%, p < 0.001, r = 0.50). This contrasts with accuracy-oriented multi-agent debate, where heterogeneity does not reduce convergence, suggesting model diversity operates differently when no objectively correct answer exists. Second, coherence validation (using a frontier model to assess whether each evaluator's reasoning is grounded in its assigned values) reveals a fidelity-diversity tradeoff: on a scenario with a dominant option, it further reduces concentration (46.1% to 40.8%, p = 0.004), but on a scenario with genuinely competitive options, it increases concentration (22.9% to 26.6%, p = 0.96) by amplifying high-coherence evaluators who cluster on one option. This tradeoff may be a general property of multi-agent systems employing quality weighting. We report negative results from three failed Delphi designs, demonstrate that 8B models exhibit binary rather than graded responses to counter-arguments, and propose the trustworthy tension rate as a diagnostic measure of small-model deliberation capabilities.
| Comments: | 14 pages, 7 tables, 120 deliberations across 2 policy scenarios |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.11 |
| Cite as: | arXiv:2604.26561 [cs.MA] |
| (or arXiv:2604.26561v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26561 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ariel Sela [view email]
[v1]
Wed, 29 Apr 2026 11:47:28 UTC (23 KB)