Voting, Self-Consistency, and Debate Topology
The cheapest aggregation: sample N independent agents, majority-vote. Wang et al. 2022 self-consistency did this with one model sampled N times. Multi-agent extends it with heterogeneous agents to escape monoculture — different models, different prompts, different temperatures, different contexts. Beyond majority vote, debate topology matters: MultiAgentBench (arXiv:2503.01935, ACL 2025) evaluated star / chain / tree / graph coordination and found graph best for research, with a "coordination tax" past 4 agents. AgentVerse (ICLR 2024) documents two emergent patterns — volunteer behaviors and conformity behaviors — and conformity is both a feature (finding consensus) and a risk (groupthink, Lesson 24). This lesson maps the topology space, builds each variant, and measures the coordination tax. Debate can improve accuracy (Du et al., arXiv:2305.14325). It can also degrade it. Whether debate helps depends on four structural choices: Who talks to whom (topology). How many rounds (Du 2023: both rounds and agents matter independently). Whether agents are heterogeneous (different base models break monoculture). Whether an adversarial voice is present (steel-manning vs. straw-manning). Teams that bolt "run 5 agents and vote" onto a task often regress vs. a single agent. The failures are not random. They track topology and heterogeneity. This lesson is the topology map. Wang et al. 2022 ("Self-Consistency Improves Chain of Thought Reasoning") sampled the same model N times at temperature >…
Voting, Self-Consistency, and Debate Topology: The cheapest aggregation: sample N independent agents, majority-vote. Wang et al. 2022 self-consistency did…
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