AutoRecLab is a Python-based autonomous laboratory for recommender‑system research that transforms natural‑language prompts into executable experiments. It automatically derives experiment requirements, builds and validates prototypes, and iteratively expands them into full studies using retrieval‑augmented generation, static type verification, and execution‑steered tree search. In a demonstration, AutoRecLab autonomously conducted an explicit‑to‑implicit feedback conversion study, achieving 8 of 9 successful runs at an average cost of about $1 per run with GPT‑5.4‑mini.
By Moritz Baumgart, Philipp Meister, Justus Krell, Michael Schmidt, Bela Gipp, Joeran Beel
arXiv:2608.29889v1 Announce Type: new
Abstract: Programming with AI is increasingly agentic, users prompt LLMs to directly edit their code and review the changes, with adoption growing especially for...
By Nishant Balepur, Connor Baumler, Valerie Chen, Eunsol Choi, Rachel Rudinger, Jordan Boyd-Graber
arXiv:2604.20779v2 Announce Type: replace
Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful...
By Joachim Baumann, Vishakh Padmakumar, Xiang Li, John Yang, Diyi Yang, Sanmi Koyejo
UPPAAI extends the UPPAAL tool by integrating large language models (LLMs) to assist users in constructing, explaining, debugging, and specifying models through dialogue. The system follows a workflow where the user requests, the LLM proposes and explains, and UPPAAL performs verification, exposing its editor, queries, and verifier via a Tools API. Seven case studies demonstrate UPPAAI’s effectiveness across timed automata, stochastic hybrid automata, and strategy learning, showing that LLMs not only generate solutions but also proactively suggest relevant UPPAAL features.
By Peter Dolog, Kim G. Larsen, Marius Miku\v{c}ionis, Frederik F. K. Rasmussen
The paper investigates whether tailoring coding agent skills to individual developers improves collaboration. It introduces a framework that bootstraps rule‑based skills and refines them with LLMs to capture developer‑specific preferences, and evaluates this approach using an interactive replay system with a simulated developer. Experiments on real interaction logs show that while task‑specific skills generally perform best, personalized skills become more effective when developers frequently express consistent preferences, especially on related tasks.
By Shuyan Huang, Kai Du, Andrew Lan
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
By Shuo Hao, You Lu, Bihuan Chen, Xin Peng
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
By Yutian Tang, Yuming Zhou, Huaming Chen
arXiv:2507.21831v2 Announce Type: replace-cross
Abstract: LLMs are seeing widespread use for task automation, including automated coding in the social sciences. However, even though researchers have...
By Andreas Reich, Claudia Thoms, Tobias Schrimpf
arXiv:2608. 10319v1 Announce Type: cross Abstract: Large language model (LLM)-powered agents have rapidly evolved from code-completion tools into solvers of complex software engineering tasks.
By Shuyan Huang, Kai Du, Andrew Lan
The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization. Reports from large technology companies showed that around 20% to 30% of their code are generated by LLMs.
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.
By Irene Weber (University of Applied Sciences Kempten, Germany)
arXiv:2601. 19072v3 Announce Type: replace-cross Abstract: Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated review comments are ungrounded in the actual code -- poses a significant challenge to the adoption of LLMs in code review workflows.
By Kla Tantithamthavorn, Hong Yi Lin, Patanamon Thongtanunam, Wachiraphan Charoenwet, Minwoo Jeong, Ming Wu