Credit Where the Cut Happens: Candidate Contraction for Repository-Level Code Localization

Repository-level code localization advances mainly by exclusion: an agent rules out code that looks relevant but lies off the fix path. Existing training signals cannot credit such a move: with a free-form answer, a discarded location never appears. We introduce C3L (Candidate Contraction for Code Localization), which makes exclusions first-class learning events. C3L binds the prediction to an explicit survivor set that the agent contracts monotonically from read-only evidence, so every exclusion becomes an irreversible, observable state transition. Two potentials over this state price each cut on the turn it is made: a logarithmic weighted volume rewards effective contraction, and a reachable localization ceiling charges coverage loss the moment a necessary location is discarded, with no learned verifier and no extra rollouts. Because the survivor set summarizes the remaining search, C3L compares turns across rollouts by survivor-set signature even when their histories share nothing, and an execution-verified label cache credits valid repairs outside the reference patch. With a 4B backbone, C3L reaches 59.61/47.60/55.00 mean F1 on SWE-bench Verified, SWE-bench Pro (Python), and LocBench, exceeding the strongest retrained same-size baseline by 6.51/7.22/6.21 points (every 95% CI excludes zero). It outscores a frozen 30B-A3B agent and resolves 40.40% of Verified issues with a fixed repair backend, against 37.33% for the strongest baseline. The gain has three measured sources: the exclusion-aware potentials add 5.9 points over terminal-reward training, grouping turns by survivor signature beats grouping them by action history by 1.35 points, and delivering credit per turn adds 2.11 points at identical total return.