{"type":"ScholarlyArticle","title":"LLM-Orchestrated Diagnose–Plan–Treat for Mixed-Degradation CT Reconstruction","authors":[{"name":"Yongqiang Huang","slug":"yongqiang-huang","given":"Yongqiang","family":"Huang","affiliation":""},{"name":"Yingyu Chen","slug":"yingyu-chen","given":"Yingyu","family":"Chen","affiliation":""},{"name":"Fengzhi Xu","slug":"fengzhi-xu","given":"Fengzhi","family":"Xu","affiliation":""},{"name":"Tao Wang","slug":"tao-wang","given":"Tao","family":"Wang","affiliation":""},{"name":"Wenjun Xia","slug":"wenjun-xia","given":"Wenjun","family":"Xia","affiliation":""},{"name":"Hongming Shan","slug":"hongming-shan","given":"Hongming","family":"Shan","affiliation":""},{"name":"Yi Zhang","slug":"yi-zhang","given":"Yi","family":"Zhang","affiliation":""}],"abstract":"Clinical Computed Tomography (CT) reconstruction often faces mixed degradations, where quantum noise, streak artifacts, and geometric distortions co-occur with various compositions and severities. Recently, all-in-one frameworks have outperformed traditional single-task models through degradation-specialized modulation mechanisms. However, attempting to resolve these mixed degradations simultaneously forces the model into suboptimal trade-offs, failing to balance conflicting reconstruction objectives. Our empirical study shows that decomposing CT reconstruction into a sequence of ordered steps effectively mitigates this conflict, with the execution order being a critical performance factor. Leveraging this insight, we propose AgenticCT, an LLM-orchestrated multi-agent framework designed to autonomously plan the optimal reconstruction trajectory. Specifically, AgenticCT operates through a diagnose-plan-treat workflow: a Supervisor Agent explicitly estimates a structured degradation state; a Planner Agent then selects a topology-optimized execution order conditioned on this state; and a library of Processor Agents sequentially executes the trajectory to achieve high-fidelity reconstruction.  Extensive experiments demonstrate that AgenticCT consistently improves reconstruction fidelity across single and mixed degradations, and generalizes better to out-of-distribution datasets compared with strong specialized and all-in-one baselines. Code is available at: https://github.com/yqhuang2912/AgenticCT.","doi":"10.24963/ijcai.2026/17","url":"https://ijcai.org/proceedings/2026/17","pdf":"https://ijcai.org/proceedings/2026/0017.pdf","markdown":"https://ijcai.org/proceedings/2026/17.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/17","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":17,"pages":{"first":145,"last":153},"keywords":["Computer Vision: Biomedical image analysis","Agent-based and Multi-agent Systems: Coordination and cooperation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}