arXiv AI
From Code Generator to Engineering Companion: A Plant-aware Agent for PLC Programming
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arXiv AI
Aug 26
A Literate Programming Environment for Human and Machine Agents
arXiv:2608.24644v1 Announce Type: cross Abstract: This paper introduces an environment for constructing literate programs in concert with language-aware machine agents. This environment includes a gr...
arXiv AI
Aug 25
Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol
arXiv:2608.21417v1 Announce Type: new Abstract: Flexible manufacturing requires industrial robots to be reprogrammed rapidly as product variants change. This paper presents a language-model-based wor...
arXiv AI
Jul 21
DataFlow-Harness: A Grounded Code-Agent Platform for Constructing Editable LLM Data Pipelines
arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.
arXiv AI
Sep 2
ChatDev 2.0: A No-Code Multi-Agent Platform for Developing Everything
ChatDev 2.0, also called DevAll, is a no-code platform that lets users build, run, and inspect heterogeneous multi‑agent systems (MAS) powered by large language models. It combines a declarative executable graph abstraction with a cycle‑aware execution engine, enabling representation and execution of dynamic, cyclic interactions among diverse agents. The integrated visual interface allows users to author, monitor, and inspect MAS—including human‑in‑the‑loop steps—without writing code, and experiments show it matches state‑of‑the‑art MAS performance across three tasks.
arXiv AI
Jul 3
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models
arXiv:2501. 07892v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicity and effectiveness.
arXiv AI
Oct 9
Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System
The paper surveys how large language models (LLMs) are integrated into software systems under various labels such as chatbot, copilot, retrieval‑augmented generation (RAG), workflow, coding agent, and AI agent. It identifies seven recurring architectural forms—LLM chats, custom agents, RAG, AI‑enhanced workflows, copilots, coding agents, and agentic RAG—and describes each along four structural dimensions: architectural pattern, execution control, user intervention point, and tool usage. The study uses a corpus of 22 systems from research and vendor documentation to illustrate these forms and their limits.