Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning
Existing reasoning-time strategies exhibit critical limitations: self-correction tends to reinforce initial biases, multi-agent collaboration (MAC) often suffers from insufficient coordination leading to collective errors, and high-accuracy verifiers require extensive human annotations. This paper proposes AdCo, the first framework to introduce a UCB-based adaptive “coopetition” mechanism—dynamically balancing cooperation and competition—into multi-agent LLM reasoning. AdCo leverages only coarse-grained verification signals to guide uncertainty-aware exploration and enhance trajectory diversity. Its methodology integrates multi-agent collaborative reasoning, iterative refinement, reasoning trajectory analysis, and knowledge diversity modeling. Evaluated on multiple mathematical reasoning benchmarks, AdCo achieves a 20% relative performance gain over state-of-the-art baselines while demonstrating robustness across varying sample sizes and configuration settings.