BDA treats council move types as annotator-model observations, achieving best calibration at zero extra calls and improved adversarial robustness
The work introduces Bayesian Dialectical Argumentation (BDA), which treats a multi-LLM council's typed moves—who proposed, challenged, or conceded which answer—as observations of a classical annotator model with per-agent reliabilities, recasting multi-agent deliberation as a reliability estimation problem; across binary and multi-class benchmarks, BDA achieves the best calibration among zero-cost council aggregation methods, requires no additional LLM calls, improves robustness under persistent adversarial coalitions, and remains competitive in clean settings.