mlsanityData to 14 Oct 2026

IEA-Plugin: An AI Agent Reasoner for Test Data Analytics

arXiv 2504.11496 ↗First posted 14 Apr 2025cs.SEcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

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Abstract (arXiv)

This paper introduces IEA-plugin, a novel AI agent-based reasoning module developed as a new front-end for the Intelligent Engineering Assistant (IEA). The primary objective of IEA-plugin is to utilize the advanced reasoning and coding capabilities of Large Language Models (LLMs) to effectively address two critical practical challenges: capturing diverse engineering requirements and improving system scalability. Built on the LangGraph agentic programming platform, IEAplugin is specifically tailored for industrial deployment and integration with backend test data analytics tools. Compared to the previously developed IEA-Plot (introduced two years ago), IEA-plugin represents a significant advancement, capitalizing on recent breakthroughs in LLMs to deliver capabilities that were previously unattainable.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 12 of its 24 references that are in the map. First posted on arXiv 14 Apr 2025.

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12 of 24 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
  4. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  5. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
  6. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 143
  7. 2024Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluationcited by 38
  8. 2023Skills-in-Context Prompting: Unlocking Compositionality in Large Language Modelscited by 33
  9. 2023Assessing the Reliability of Large Language Model Knowledgecited by 13
  10. 2023Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learningcited by 6
  11. 2023Statistical Knowledge Assessment for Large Language Modelscited by 5
  12. 2023Systematic Assessment of Factual Knowledge in Large Language Modelscited by 5

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