arXiv:2607. 10531v1 Announce Type: cross Abstract: Recent advances in LLMs and existing work on programming by demonstration have made it possible for end users to create automations by explicitly demonstrating their behavior to LLMs.
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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.
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...
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...
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.
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.
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.