🤖 AI Summary
This work addresses the limitations of existing large language model–based multi-agent systems, which lack structured deliberation mechanisms and struggle to produce accountable decisions while preserving dissent. The authors propose the Deliberative Collective Intelligence (DCI) framework—the first formal computational model of human-like deliberation—featuring four reasoning roles, 14 typified cognitive behaviors, a shared workspace, and the DCI-CF convergence-flow algorithm. This framework enables phased collaborative reasoning and generates structured decision packages comprising the selected option, residual disagreements, minority reports, and restart conditions. Experiments using Gemini 2.5 Flash demonstrate that DCI significantly outperforms unstructured debate by +0.95 on 40 unconventional tasks, achieves a hidden information integration score of 9.56, produces fully structured decision packages in 100% of cases (with minority reports in 98%), at a computational cost approximately 62 times that of a single agent.
📝 Abstract
Multi-agent LLM systems increasingly tackle complex reasoning, yet their interaction patterns remain limited to voting, unstructured debate, or pipeline orchestration. None model deliberation: a phased process where differentiated participants exchange typed reasoning moves, preserve disagreements, and converge on accountable outcomes. We introduce Deliberative Collective Intelligence (DCI), specifying four reasoning archetypes, 14 typed epistemic acts, a shared workspace, and DCI-CF, a convergent flow algorithm that guarantees termination with a structured decision packet containing the selected option, residual objections, minority report, and reopen conditions. We evaluate on 45 tasks across seven domains using Gemini 2.5 Flash. On non-routine tasks (n=40), DCI significantly improves over unstructured debate (+0.95, 95% CI [+0.41, +1.54]). DCI excels on hidden-profile tasks requiring perspective integration (9.56, highest of any system on any domain) while failing on routine decisions (5.39), confirming task-dependence. DCI produces 100% structured decision packets and 98% minority reports, artifacts absent from all baselines. However, DCI consumes ~62x single-agent tokens, and single-agent generation outperforms DCI on overall quality. DCI's contribution is not that more agents are better, but that consequential decisions benefit from deliberative structure when process accountability justifies the cost.