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

• URL: http://arxiv.org/abs/2602.16812v1
• Date: Wed, 18 Feb 2026 19:22:01 GMT
• Title: NeuDiff Agent: A Governed AI Workflow for Single-Crystal Neutron Crystallography
• Abstract summary: NeuDiff Agent is introduced as a governed, tool-using AI workflow for TOPAZ at the Spallation Neutron Source.<n>NeuDiff Agent takes instrument data products through reduction, integration, refinement, and validation to a validated crystal structure and a publication-ready CIF.
• Score: 10.12215691185414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning [21.927343858709122]
  Restor is a framework that generates executable test assertions from a single observed request-response pair in a black-box setting.<n>We evaluate Restor on an industrial dataset comprising over 2,300 API traces across 246 real-world services.
  arXiv  Detail & Related papers  (2026-07-27T03:20:28Z)
• CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures [4.061481215870679]
  We introduce CausalFlow, an interventional framework that converts failed agent traces into minimal counterfactual repairs and reusable supervision.<n>CaulFlow supports two complementary uses: targeted test-time repair that recovers from failures with minimal behavioral drift, and training-time supervision suitable for offline preference optimization or reward modeling.
  arXiv  Detail & Related papers  (2026-05-25T01:47:01Z)
• LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
  Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
  arXiv  Detail & Related papers  (2026-05-02T11:31:33Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Agentic Observability: Automated Alert Triage for Adobe E-Commerce [0.0]
  This paper presents an agentic observability framework deployed within Adobe's e-commerce infrastructure.<n>The framework autonomously performs alert triage using a ReAct paradigm.<n>Our results show that agentic AI enables an order-of-magnitude reduction in triage latency and a step-change in resolution accuracy.
  arXiv  Detail & Related papers  (2026-01-31T20:20:02Z)
• Veri-Sure: A Contract-Aware Multi-Agent Framework with Temporal Tracing and Formal Verification for Correct RTL Code Generation [4.723302382132762]
  silicon-grade correctness remains bottlenecked by: (i) limited test coverage and reliability of simulation-centric evaluation, (ii) regressions and repair hallucinations, and (iii) semantic drift as intent is reinterpreted across agent handoffs.<n>We propose Veri-Sure, a multi-agent framework that establishes a design contract to align agents' intent and uses a patching mechanism guided by static dependency slicing to perform precise, localized repairs.
  arXiv  Detail & Related papers  (2026-01-27T16:10:23Z)
• Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification [71.98473277917962]
  Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.<n>We propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubrics.<n>We present DeepVerifier, a rubrics-based outcome reward verifier that leverages the asymmetry of verification.
  arXiv  Detail & Related papers  (2026-01-22T09:47:31Z)
• CatMaster: An Agentic Autonomous System for Computational Heterogeneous Catalysis Research [7.778196807731191]
  We present CatMaster, a large-language-model (LLM)-driven agent system that turns natural language requests into complete calculation workspaces.<n>CatMaster maintains a persistent project record of key facts, constraints, and file pointers to inspection and restartability.
  arXiv  Detail & Related papers  (2026-01-20T01:51:12Z)
• The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check [54.08619694620588]
  We present a comprehensive evaluation of dLLMs across two distinct agentic paradigms: Embodied Agents and Tool-Calling Agents.<n>Our results on Agentboard and BFCL reveal a "bitter lesson": current dLLMs fail to serve as reliable agentic backbones.
  arXiv  Detail & Related papers  (2026-01-19T11:45:39Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• Autoformalizer with Tool Feedback [52.334957386319864]
  Autoformalization addresses the scarcity of data for Automated Theorem Proving (ATP) by translating mathematical problems from natural language into formal statements.<n>Existing formalizer still struggles to consistently generate valid statements that meet syntactic validity and semantic consistency.<n>We propose the Autoformalizer with Tool Feedback (ATF), a novel approach that incorporates syntactic and consistency information as tools into the formalization process.
  arXiv  Detail & Related papers  (2025-10-08T10:25:12Z)
• Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval [1.515687944002438]
  The rapid spread of misinformation in the digital era poses significant challenges to public discourse.<n>Traditional human-led fact-checking methods, while credible, struggle with the volume and velocity of online content.<n>This paper proposes a novel multi-agent system for automated fact-checking that enhances accuracy, efficiency, and explainability.
  arXiv  Detail & Related papers  (2025-06-22T02:39:27Z)
• Q-DETR: An Efficient Low-Bit Quantized Detection Transformer [50.00784028552792]
  We find that the bottlenecks of Q-DETR come from the query information distortion through our empirical analyses. We formulate our DRD as a bi-level optimization problem, which can be derived by generalizing the information bottleneck (IB) principle to the learning of Q-DETR. We introduce a new foreground-aware query matching scheme to effectively transfer the teacher information to distillation-desired features to minimize the conditional information entropy.
  arXiv  Detail & Related papers  (2023-04-01T08:05:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.