AI Agents for Ground-Based Gamma Astronomy

• URL: http://arxiv.org/abs/2503.00821v1
• Date: Sun, 02 Mar 2025 09:55:54 GMT
• Title: AI Agents for Ground-Based Gamma Astronomy
• Authors: D. Kostunin, V. Sotnikov, S. Golovachev, A. Strube,
• Abstract summary: We present two prototypes that integrate with the Cherenkov Telescope Array Observatory pipelines for operations and offline data analysis.<n>These AI agents offer a transformative approach to system management and data analysis by automating complex tasks and providing intelligent assistance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next-generation instruments for ground-based gamma-ray astronomy are marked by a substantial increase in complexity, featuring dozens of telescopes. This leap in scale introduces significant challenges in managing system operations and offline data analysis. Methods, which depend on advanced personnel training and sophisticated software, become increasingly strained as system complexity grows, making it more challenging to effectively support users in such a multifaceted environment. To address these challenges, we propose the development of AI agents based on instruction-finetuned large language models (LLMs). These agents align with specific documentation and codebases, understand the environmental context, operate with external APIs, and communicate with humans in natural language. Leveraging the advanced capabilities of modern LLMs, which can process and retain vast amounts of information, these AI agents offer a transformative approach to system management and data analysis by automating complex tasks and providing intelligent assistance. We present two prototypes that integrate with the Cherenkov Telescope Array Observatory pipelines for operations and offline data analysis. The first prototype automates data model implementation and maintenance for the Configuration Database of the Array Control and Data Acquisition (ACADA). The second prototype is an open-access code generation application tailored for data analysis based on the Gammapy framework.

Related papers

• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• 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)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• CoDA: Agentic Systems for Collaborative Data Visualization [57.270599188947294]
  Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations.<n>Existing approaches, including simple single- or multi-agent systems, often oversimplify the task.<n>We introduce CoDA, a multi-agent system that employs specialized LLM agents for metadata analysis, task planning, code generation, and self-reflection.
  arXiv  Detail & Related papers  (2025-10-03T17:30:16Z)
• Enhancing the development of Cherenkov Telescope Array control software with Large Language Models [35.18016233072556]
  We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO)<n>These agents align with project-specific documentation and interact with external APIs, and communicate with users in natural language.
  arXiv  Detail & Related papers  (2025-10-01T14:14:41Z)
• OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use [101.57043903478257]
  The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations.<n>With the evolution of (multi-modal) large language models ((M)LLMs), this dream is closer to reality.<n>This survey aims to consolidate the state of OS Agents research, providing insights to guide both academic inquiry and industrial development.
  arXiv  Detail & Related papers  (2025-08-06T14:33:45Z)
• A Survey on Code Generation with LLM-based Agents [61.474191493322415]
  Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm.<n>LLMs are characterized by three core features.<n>This paper presents a systematic survey of the field of LLM-based code generation agents.
  arXiv  Detail & Related papers  (2025-07-31T18:17:36Z)
• Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations [0.0]
  Large language models (LLMs) and their associated agent-based frameworks have significantly advanced automated information extraction.<n>This paper presents Agent0, an agent-based system designed to automate information extraction and feature construction from raw, unstructured text.
  arXiv  Detail & Related papers  (2025-07-25T06:45:10Z)
• Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems [8.816332263275305]
  Traditional Data+AI systems rely heavily on human experts to orchestrate system pipelines.<n>Existing Data+AI systems have limited capabilities in semantic understanding, reasoning, and planning.<n>We propose the concept of a 'Data Agent' - a comprehensive architecture designed to orchestrate Data+AI ecosystems.
  arXiv  Detail & Related papers  (2025-07-02T11:04:49Z)
• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities [117.49715661395294]
  Data structurization can play a promising role by transforming intricate and disorganized data into well-structured forms.<n>This survey presents a first systematic review of how graphs can empower AI agents.
  arXiv  Detail & Related papers  (2025-06-22T12:59:12Z)
• Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation [58.799397354312596]
  Large language models (LLMs) have demonstrated remarkable capabilities in various domains, particularly in system 1 tasks.<n>Recent research on System2-to-System1 methods surge, exploring the System 2 reasoning knowledge via inference-time computation.<n>In this paper, we focus on code generation, which is a representative System 2 task, and identify two primary challenges.
  arXiv  Detail & Related papers  (2025-02-18T03:20:50Z)
• Towards Human-Guided, Data-Centric LLM Co-Pilots [53.35493881390917]
  CliMB-DC is a human-guided, data-centric framework for machine learning co-pilots.<n>It combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing.<n>We show how CliMB-DC can transform uncurated datasets into ML-ready formats.
  arXiv  Detail & Related papers  (2025-01-17T17:51:22Z)
• 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>A trajectory reward model is then employed to ensure the quality of the generated trajectories.
  arXiv  Detail & Related papers  (2024-12-27T16:21:58Z)
• Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs [0.0]
  Large Language Models (LLMs) have revolutionized various aspects of engineering and science. This thesis serves as a comprehensive guide that elucidates a multi-faceted approach for empowering LLMs with the capability to leverage Application Programming Interfaces (APIs) We propose an on-device architecture that aims to exploit the functionality of carry-on devices by using small models from the Hugging Face community.
  arXiv  Detail & Related papers  (2024-12-17T14:14:04Z)
• An LLM Agent for Automatic Geospatial Data Analysis [5.842462214442362]
  Large language models (LLMs) are being used in data science code generation tasks. Their application to geospatial data processing is challenging due to difficulties in incorporating complex data structures and spatial constraints. We introduce GeoAgent, a new interactive framework designed to help LLMs handle geospatial data processing more effectively.
  arXiv  Detail & Related papers  (2024-10-24T14:47:25Z)
• Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report [3.4632900249241874]
  This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. The practical implications of this research lie in enhancing the reliability of generative AI systems in various sectors.
  arXiv  Detail & Related papers  (2024-10-21T12:21:49Z)
• QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis [1.9521598508325781]
  We introduce QUIS, a fully automated EDA system that operates in two stages: insight generation (ISGen) driven by question generation (QUGen) The ISGen module analyzes data to produce multiple relevant insights in response to each question, requiring no prior training and enabling QUIS to adapt to new datasets.
  arXiv  Detail & Related papers  (2024-10-14T08:21:25Z)
• LAMBDA: A Large Model Based Data Agent [7.240586338370509]
  We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system. LAMBDA is designed to address data analysis challenges in complex data-driven applications. It has the potential to enhance data analysis paradigms by seamlessly integrating human and artificial intelligence.
  arXiv  Detail & Related papers  (2024-07-24T06:26:36Z)
• AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning [98.26836657967162]
  textbfAgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. textbfxLAM-v0.1, a large action model tailored for AI agents, demonstrates exceptional performance across various benchmarks.
  arXiv  Detail & Related papers  (2024-02-23T18:56:26Z)
• Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future [130.87142103774752]
  This review systematically assesses over seventy open-source autonomous driving datasets. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets. It also delves into the scientific and technical challenges that warrant resolution.
  arXiv  Detail & Related papers  (2023-12-06T10:46:53Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

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.