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: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: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.
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: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.
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.
SWE-Journey is a new benchmark designed to evaluate coding assistants in more realistic settings, focusing on long-horizon tasks and multi-turn interactions. It introduces a weak-to-strong synthesis pipeline to create extended coding challenges and a user-simulation agent based on four real user personas to mimic authentic code-assistance dialogues. The benchmark reveals that while models succeed on over 75% of tests with software architects, they manage less than 25% with non-coders, highlighting a significant gap in current assistant capabilities.
arXiv:2607. 14387v1 Announce Type: new Abstract: Validating autonomous driving systems requires diverse, regulation-compliant test scenarios.
The study investigates how users interact with ChatGPT for code generation beyond simple function-level tasks, focusing on project-level benchmarks that involve multi-class dependencies. A user study with 36 participants examined prompting patterns, screen recordings, and chat logs to identify Human‑LLM Interaction (HLI) features that influence productivity. The results highlight three consistently supportive HLI features, five guidelines to boost productivity, and a taxonomy of 29 runtime and logic errors with mitigation strategies.
arXiv:2609.14726v1 Announce Type: cross Abstract: Large language models are increasingly used to scale codebook-based annotation in scientific research, but existing workflows provide limited support...
arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.
PLCWorld is a closed‑loop execution environment and benchmark that evaluates large language model (LLM)–generated programmable logic controller (PLC) programs by coupling Structured Text (ST) execution with simulated plant responses and sensor feedback. It includes 100 synthetic tasks and 473 task‑condition pairs across Motion Control and Material Handling, with difficulty levels based on control‑dependency scope. The benchmark reports Task Success and Safety Violation separately and validates results through practitioner review, reference programs, counterexamples, and comparisons with independent ST runtimes, revealing performance differences among six LLMs and four generation‑and‑verification workflows.