SEER: Semantic Evidence for Executable Region Propagation in Open-Vocabulary Semantic Segmentation

Training-free open-vocabulary semantic segmentation (OVSS) has made strong progress by adapting CLIP-like representations to dense prediction, but its remaining errors are often local, structured, and tied to the active label space. A successful correction must be visually plausible, legal under the target vocabulary, and executable in the segmentation score field. We propose SEER, a training-free test-time repair layer for Semantic Evidence and Executable Region propagation. SEER casts post-prediction OVSS repair as evidence-conditioned label propagation. The OVSS score space proposes compact candidate labels, a frozen VLM verifies closed-set regional evidence, and an execution gate certifies whether the evidence is legally and semantically actionable. The accepted label is then propagated by an OVSS-side policy: coherent high-confidence regions allow broad propagation, while object- or boundary-risk cases use conservative score-grounded propagation. Across eight standard OVSS benchmarks, SEER reaches 58.0 average mIoU without updating OVSS weights. Matched-input controls isolate the repair mechanism, while resource measurements report the cost of the complete frozen system.