MARS: Multi-Evidence Adaptive Search with State-Preserving Control for Repository-Level Program Repair

Repository-level program repair aims to resolve real software issues under a bounded compute budget, requiring decisions about how much computation to allocate, which repair state to preserve, and what to commit. Existing staged repair pipelines largely follow fixed search schedules that repeatedly generate, execute, and refine candidate patches while underusing runtime evidence. We refer to this mismatch as Budget–Evidence Decoupling: computation is spent without conditioning on whether current evidence justifies further search, and later refinement may overwrite previously discovered useful repair states. We propose MARS, a training-free controller that formulates candidate search as a budget-aware sequential decision process using execution, verification, review, and resource signals. Specifically, Multi-Evidence Adaptive Search (AS) deterministically selects among verification, refinement, expansion, and termination according to the current repair state. MARS further introduces Incumbent–Challenger Control (IC), separating candidate admission from incumbent replacement, together with Candidate Commitment (CC), which freezes the accumulated candidate state for downstream final selection. These designs jointly condition compute allocation, state preservation, and commitment on runtime evidence while keeping valuable repair states recoverable. Experiments on SWE-bench Lite and SWE-bench Verified show that MARS improves pass@1 over the strongest same-backbone baseline by 2.0 and 2.8 percentage points, respectively, while reducing per-issue cost by 31% and 22%. We further demonstrate its transferability across three backbones and three additional host pipelines.