Large language models (LLMs) demonstrate strong reasoning capabilities, yet the inference-time performance of existing solutions remains limited by self-biases, coordination inefficiencies, lack of robust error detection, and dependency on high-quality verifiers. To address these challenges, we propose Adaptive Coopetition (AdCo), a lightweight, multi-agent multi-round inference-time framework that enhances collective reasoning through adaptive decision-making guided by coarse verifier signals. Without relying on high-performance verifiers, AdCo achieves a 20% relative accuracy improvement on math reasoning benchmarks, with consistent performance on different sample sizes and agent configurations. This adaptive, signal-guided ‘coopetition’ framework enhances reasoning robustness by leveraging diverse model knowledge and reasoning traces, while also promoting uncertainty-driven exploration, especially when participants have comparable capabilities.