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Tools
Tools
| Item Type: | Preprint |
|---|---|
| Title: | BCER agent: reliable long-horizon MRI workflow execution via compilation, artifact binding, and bounded local recovery |
| Creators Name: | Long, Ziyang, Li, Xinqi, Chen, Junzhou, Gao, Yifan, Li, Debiao and Yang, Hsin-Jung |
| Abstract: | Many recent medical VLM and agent studies are benchmarked on 2D images or comparatively short tool-calling exchanges, whereas real MRI analysis typically demands long, interdependent pipelines that operate on 3D/4D volumetric data. Under these conditions, reactive tool-calling agents are prone to cascading breakdowns triggered by faulty intermediate references, mismatched tool arguments, and limited control over cross-step dependencies. To address this, we introduce BCER (Brain–Cerebellum–Extremity–Reflector), a controller architecture aimed at dependable long-horizon MRI workflow execution. BCER decouples high-level planning from execution and provides bounded local recovery. We assess BCER on a multi-organ MRI benchmark covering brain, prostate, and cardiac tasks with both short- and long-chain workflows, using matched task contracts across controller variants and several backbone models. Relative to reactive baselines, BCER yields consistent improvements in end-to-end execution, with the most pronounced gains observed on long-chain workflows. BCER additionally enables auditability by maintaining explicit links between final outputs and intermediate artifacts and measurements. Code and benchmark are released at https://github.com/Albertlongzi/BCER. |
| Keywords: | Medical Agent, MRI, Tool Orchestration, Auditability |
| Source: | arXiv |
| Publisher: | Cornell University |
| Article Number: | 2605.29163 |
| Date: | 27 May 2026 |
| Official Publication: | https://doi.org/10.48550/arXiv.2605.29163 |
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