When Should Self-Consistency Trust Trajectory Signals? Competition-Aware Reselection for LLM Reasoning

Self-consistency (SC) is a strong and widely used aggregation method for LLM reasoning, improving accuracy by majority voting over multiple sampled trajectories. We find that SC selection becomes substantially less reliable under strong competition between the two leading answer clusters, where additional evidence is needed to distinguish the correct candidate. By examining probability-based trajectory signals, we further find that correct answer clusters tend to have higher mean trajectory signals than competing incorrect clusters. Based on these findings, we propose CARS (Competition-Aware Reselection for Self-Consistency), which uses trajectory-level signals to revise SC’s selection between the Top-2 candidates only under strong competition. Across the evaluated models and mathematical and scientific reasoning benchmarks, CARS achieves improvements over Vanilla SC and Trajectory-Signal-Weighted Self-Consistency on the vast majority of tasks. Further cross-model, cross-benchmark, and sensitivity analyses show that the competition-dependent gain pattern remains robust across settings and is not tied to a particular trajectory signal or gating threshold.