A GitOps-Driven Annotation Catalog for Fully Automatic Railway Operations
- URL: http://arxiv.org/abs/2608.04724v1
- Date: Wed, 05 Aug 2026 11:44:44 GMT
- Title: A GitOps-Driven Annotation Catalog for Fully Automatic Railway Operations
- Abstract summary: This paper introduces an innovative, lightweight GitOps-based architecture for metadata management.<n>By leveraging Data-as-Code principles, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and Static Site Generation (SSG), the proposed approach establishes a seamless, developer-centric workflow.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Automatic train operation (ATO) at grade of automation 3 and above (GoA3-GoA4) requires robust AI-based perception systems capable of reliably detecting obstacles and railway-specific objects under real-world conditions. The effectiveness of these modern artificial intelligence approaches depends heavily on large-scale, high-quality, and highly dynamic annotated datasets. However, managing metadata, maintaining provenance, and tracking the iterative evolution of these annotations impose significant infrastructural and regulatory requirements. Existing monolithic data catalogs often suffer from massive operational overhead, poor integration into developer workflows, and severe documentation drift. This paper introduces an innovative, lightweight GitOps-based architecture for metadata management. By leveraging Data-as-Code principles, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and Static Site Generation (SSG), the proposed approach establishes a seamless, developer-centric workflow. This ensures an traceability, enforces strict regulatory compliance, and automatically generates a highly performant dataset overview.
Related papers
- DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations [106.74348504930124]
We introduce DocOps, a deterministically verifiable evaluation framework underpinned by a hierarchical taxonomy.<n>Based on DocOps, we systematically evaluate representative closed- and open-source models across various agentic harnesses.<n>Our work exposes the capability boundaries of agents in maintaining global document consistency, shedding light on the future design of robust, non-destructive agents.
arXiv Detail & Related papers (2026-07-22T07:52:59Z) - DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams [48.434767119224375]
We propose a paradigm shift towards Agentic Data Tailoring.<n>To overcome the data scarcity bottleneck in training such high-order capabilities, we design a two-stage pipeline.<n>$textDataClaw_0$-9B model synergizes Supervised Fine-Tuning with Group Relative Policy Optimization.
arXiv Detail & Related papers (2026-06-19T11:31:43Z) - DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch [89.87798465720181]
We introduce textbfDeNovoSWE, a large-scale dataset for whole-repository generation.<n>DeNovoSWE comprises 4,818 high-quality instances, where each instance requires generating a complete repository from documentation.<n>Fine-tuning Qwen3-30B-A3B on DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challenging BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.
arXiv Detail & Related papers (2026-06-09T11:37:15Z) - GTA: Generating Long-Horizon Tasks for Web Agents at Scale [82.43869456830664]
We introduce a scalable framework, GTA, that integrates crawling, retrieval-based seeding, in-context generation, and automated quality control.<n>We instantiate the pipeline on over 50 websites covering e-commerce, government, forums, and news, with multilingual and multi-hop coverage.<n>Our contributions are three-fold: (i) formalizing multi-hop web-agent task generation, (ii) proposing an efficient and validated pipeline for automatic data creation, and (iii) releasing a dynamic benchmark with reproducible evaluation.
arXiv Detail & Related papers (2026-05-28T01:05:50Z) - Adaptive Multimodal Agents-Based Framework for Automatic Workflow Execution [1.2132786136735432]
We propose a novel multi-agent framework that achieves automatic workflow execution through a distinct two-phase pipeline.<n>During inference, agents leverage Adaptive Retrieval-Augmented Generation (RAG) over this fixed, pre-established graph.<n>We validate our framework in a real-world context, demonstrating its ability to maintain high reliability and semantic awareness even with limited training data.
arXiv Detail & Related papers (2026-05-27T15:23:22Z) - GraphMind: From Operational Traces to Self-Evolving Workflow Automation [8.929269898719822]
We present GraphMind, a system that constructs, executes, and evolves action-centric workflow graphs with minimal human effort.<n>The system has been deployed across four production cloud database services for incident investigation.<n>A 12-week field study confirms practical value: 97% of scored conversations yield actionable results within interactive latency.
arXiv Detail & Related papers (2026-05-17T19:22:22Z) - HeteroHub: An Applicable Data Management Framework for Heterogeneous Multi-Embodied Agent System [10.1098903025112]
Heterogeneous Multi-Embodied Agent Systems involve coordinating multiple embodied agents with diverse capabilities to accomplish tasks in dynamic environments.<n>Existing frameworks lack a unified data management infrastructure to support the real-world deployment of such systems.<n>We present textbfHeteroHub, a data-centric framework that integrates static metadata, task-aligned training corpora, and real-time data streams.
arXiv Detail & Related papers (2026-03-30T04:01:05Z) - LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks [4.6880826836662814]
We introduce textbfLOGIGEN, a logic-driven framework that synthesizes verifiable training data.<n>On $2$-Bench, LOGIGEN-32B(RL) achieves a textbf79.5% success rate, substantially outperforming the base model.
arXiv Detail & Related papers (2026-02-28T08:35:30Z) - A Survey of Data Agents: Emerging Paradigm or Overstated Hype? [66.1526688475023]
"Data agent" currently suffers from terminological ambiguity and inconsistent adoption.<n>This survey introduces the first systematic hierarchical taxonomy for data agents.<n>We conclude with a forward-looking roadmap, envisioning the advent of proactive, generative data agents.
arXiv Detail & Related papers (2025-10-27T17:54:07Z) - TD-Suite: All Batteries Included Framework for Technical Debt Classification [5.669063174637433]
TD-Suite provides a seamless end-to-end pipeline, managing everything from initial data ingestion to model training.<n>To ensure the generated models are robust and perform reliably on real-world, often imbalanced, datasets, TD-Suite incorporates critical training methodologies.<n>The framework integrates tracking and reporting of carbon emissions associated with the computationally intensive model training process.
arXiv Detail & Related papers (2025-04-15T11:31:17Z) - OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis [55.390060529534644]
We propose OS-Genesis, a novel data synthesis pipeline for Graphical User Interface (GUI) agents.<n>Instead of relying on pre-defined tasks, OS-Genesis enables agents first to perceive environments and perform step-wise interactions.<n>We demonstrate that training GUI agents with OS-Genesis significantly improves their performance on highly challenging online benchmarks.
arXiv Detail & Related papers (2024-12-27T16:21:58Z)
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.