paper-with-me

홈 › Papers

NeuDiff Agent: A Governed AI Workflow for Single-Crystal Neutron Crystallography

2026-02-18 · Zhongcan Xiao, Leyi Zhang, Guannan Zhang, Xiaoping Wang arxiv

Large-scale facilities increasingly face analysis and reporting latency as the limiting step in scientific throughput, particularly for structurally and magnetically complex samples that require iterative reduction, integration, refinement, and validation. To improve time-to-result and analysis efficiency, NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source that takes instrument data products through reduction, integration, refinement, and validation to a validated crystal structure and a publication-ready CIF. NeuDiff Agent executes this established pipeline under explicit governance by restricting actions to allowlisted tools, enforcing fail-closed verification gates at key workflow boundaries, and capturing complete provenance for inspection, auditing, and controlled replay. Performance is assessed using a fixed prompt protocol and repeated end-to-end runs with two large language model backends, with user and machine time partitioned and intervention burden and recovery behaviors quantified under gating. In a reference-case benchmark, NeuDiff Agent reduces wall time from 435 minutes (manual) to 86.5(4.7) to 94.4(3.5) minutes (4.6-5.0x faster) while producing a validated CIF with no checkCIF level A or B alerts. These results establish a practical route to deploy agentic AI in facility crystallography while preserving traceability and publication-facing validation requirements.

📄 PDF Abstract BibTeX arXiv:2602.16812

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Autonomous Diffractometry Enabled by Visual Reinforcement Learning

2026-04-13 · J. Oppliger, M. Stifter, A. Rüegg, I. Biało 외 arxiv

Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual information remain challenging. For example, crystal alignment strongly relies…

Reinforcement Learning

Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production

2026-07-08 · Arun Malik arxiv

AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lif…

Correct Is Not Governed: Provenance Integrity in Agentic Workflows

2026-08-13 · Jesus Salas arxiv

Agentic workflows are commonly evaluated by whether they reach the correct outcome. That is insufficient in institutional settings, where a correct action may rely on the wrong authority, an unsupported completion claim,…

Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration

2026-06-04 · Dutao Zhang, Liaotian arxiv

Enterprise agent systems increasingly need to connect large language models to private tools, internal knowledge, and Model Context Protocol (MCP) interfaces. In this setting, raw task capability is insufficient: organiz…

Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments

2026-04-02 · Rajat Khanda, Mohammad Baqar, Sambuddha Chakrabarti, Satyasaran Changdar arxiv

Autonomous AI agents operating in dynamic environments face a persistent challenge: acquiring new capabilities without erasing prior knowledge. We present Adaptive Memory Crystallization (AMC), a memory architecture for …

Reinforcement Learning