AI RESEARCH
ARBITER: Reasoning Trajectory Basins and Majority Vote Failures in Test-Time Sampling
arXiv CS.LG
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ArXi:2605.26172v1 Announce Type: new When language models use test-time sampling, they generate multiple reasoning trajectories and select an answer by majority vote. We show that these trajectories are not independent: for a given question, they concentrate into a small number of clusters, or reasoning basins, each defined by a normalized final answer and the solutions that reach it. A majority vote therefore selects the most stable basin rather than the most accurate one, which creates wrong-majority failures where the correct answer is present but outvoted. We.