A visual map of the paper’s core claim: scaling reasoning at inference may require not just more thinking, but better coordination between specialized agents. This page emphasizes architecture, compute tradeoffs, communication patterns, and benchmark behavior.
Instead of using more tokens inside one chain, multi-agent test-time scaling spends budget on communication edges, role specialization, and adaptive routing.
The learned model is trained on dense collaborative traces; the controller decides whether more discussion is useful or wasteful.
Mock data below follows the episode’s qualitative claims: collaborative training helps, CEO orchestration helps further, and the biggest open question is whether the gains justify cost versus stronger single-agent baselines.
The broader debate spans self-consistency, tree search, process verification, and agent frameworks. This map places the paper between learned reasoning and runtime orchestration.
Compact paper list used for the visual framing.