Compete Then Collaborate: Multi-Agent Reasoning No Longer Assumes 'More Collaboration Is Better'
Preprint submitted on July 9, 2026, 'Compete Then Collaborate' proposes a reasoning framework where agents first compete independently and then selectively collaborate, reducing premature consensus and error propagation.
Multi-agent systems often use more discussion rounds to improve quality, but agents sharing intermediate conclusions too early can amplify the same errors. This study separates independent exploration from subsequent collaboration, suggesting system design needs to consider viewpoint diversity, selection mechanisms, collaboration timing, and agent count.
The framework first retains multiple independent solution trajectories, then selects candidates for collaboration based on quality and diversity, structurally reducing early information contamination. Technical focus should be on selector reliability, additional inference cost, benefit stability across tasks, and whether failure cases can complement each other.
Competition among agent platforms will shift from orchestrating more roles to managing reasoning diversity and collaboration budgets; enterprises should not equate role count, message count, or workflow complexity with higher reliability.
When designing high-value agent workflows, replace full-group chat with 'independent candidates → quality gating → conditional collaboration', and measure value by total inference cost per correct result.
This result is still preprint evidence. Independent replication of baselines, fair comparison with single-agent cost-equivalent reasoning, and verification of benefits in code, research, and tool-calling tasks are needed.