arXiv cs.AI· Ke Wang, Atakan Topcu, Hossein Momeni, Molin Zhang·· 10 小时前AI 评分34
MRFoundry：面向端到端 MRI 研究的知识引导智能体循环
MRFoundry: Knowledge-guided agent loops for end-to-end MRI research
AI 导读
MRFoundry 是一个开源可扩展工具包，将 MRI 领域知识与 AI 智能体的操作及输出检查连接起来，并配套推出 MRI Agent Bench 评测基准。
正文 · 原文
Abstract:AI agents can write code, use scientific software, and revise computational workflows, opening new possibilities for MRI research. Effective assistance requires connecting acquisition physics, data conventions, specialized tools, and scientific validation. We present MRFoundry, an open-source, extensible package that connects MRI knowledge to agent actions and output checks. Project-local memory preserves decisions and evidence, while reviewed contributions allow researchers to improve shared guidance. Researcher-supervised examples demonstrate skill-guided sequence auditing and acquired-data reconstruction, connecting tool use, output checks, and documented decisions. Alongside MRFoundry, we introduce MRI Agent Bench to compare agents with and without the package across models and MRI task domains. Across 240 paired Core evaluations spanning 24 task types and ten models, all-checks-passed completion increased from 67.5% without MRFoundry to 78.3% with it. Across 134 Hard pairs, completion increased from 36.6% to 44.8%. MRFoundry makes MRI workflow expertise available as reusable guidance that researchers can inspect, adapt, and extend within existing agent and scientific software environments. MRFoundry is openly available at this https URL.
| Comments: | 18 pages, including 6 pages of Supporting Information. Code: this https URL |
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13289 [eess.IV] |
| (or arXiv:2611.13289v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13289 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ke Wang [view email]
[v1]
Mon, 9 Nov 2026 21:54:54 UTC (11,406 KB)
来源：arXiv cs.AI · arxiv.org