{"ID":5935864,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03062","arxiv_id":"2607.03062","title":"Lightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller","abstract":"While lightweight polyp segmentation is highly desirable for low-cost deployment, reported performance gains often stem from upgraded backbone encoders, complex decoders, or heavy refinement branches. Consequently, it remains difficult to isolate whether a lightweight correction mechanism is inherently effective on its own. We address this limitation by formulating refinement as a prediction-space recursive correction task, introducing a recursive controller that operates directly on backbone logits. Under a fixed recursion budget, this controller aggregates discrepancy and uncertainty evidence, updates a compact state tracking recent correction utility, and applies additive residual logit corrections. By design, this correction path remains small, host-portable, and deployment-explicit. Utilizing a unified Kvasir-trained protocol, we evaluate our approach across seven lightweight backbones on Kvasir-SEG and three transfer datasets, measuring segmentation accuracy (Dice/IoU) alongside deployment efficiency (parameters, GMACs, and peak memory). The controller yields consistent improvements in the source domain, achieves competitive performance against both training-side baselines and heavier structural refiners on representative hosts, and delivers selective transfer gains with minimal static overhead. Code is available at https://github.com/tyui99/Gain-Aware-Prediction-Space-Recursive-Controller.","short_abstract":"While lightweight polyp segmentation is highly desirable for low-cost deployment, reported performance gains often stem from upgraded backbone encoders, complex decoders, or heavy refinement branches. Consequently, it remains difficult to isolate whether a lightweight correction mechanism is inherently effective on its...","url_abs":"https://arxiv.org/abs/2607.03062","url_pdf":"https://arxiv.org/pdf/2607.03062v1","authors":"[\"Jiachi Zhang\",\"Zhuoyu Wu\",\"Quanjun Wang\",\"Wenhui Ou\",\"Wenqi Fang\"]","published":"2026-07-03T07:53:08Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613936,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T01:22:02.77346169Z","DeletedAt":null,"paper_id":5935864,"paper_url":"https://arxiv.org/abs/2607.03062","paper_title":"Lightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller","repo_url":"https://github.com/tyui99/Gain-Aware-Prediction-Space-Recursive-Controller","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}