The article discusses how large language models can now generate user interfaces from natural‑language descriptions, and proposes a framework that turns HCI design knowledge into machine‑readable ‘skills’ that guide on‑demand UI construction. By treating the dialogue between user and AI as a shared cognitive workspace, these skills encode established HCI principles (e.g., Nielsen’s heuristics, WCAG criteria) as editable, version‑controlled artifacts. The authors outline a research agenda that envisions HCI moving from heuristic checklists to a machine‑executable, open‑source craft.
By Nathan Conklin, Miranda Capra, Chris North
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.
By Yufan Dang, Shu Yao, Bowen Lai, Chenting Xu, Ruijie Shi, Wai-Shing Leung, Huatao Li, Chen Qian, Zhiyuan Liu
arXiv:2604. 14228v2 Announce Type: replace-cross Abstract: Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user.
By Jiacheng Liu, Xiaohan Zhao, Xinyi Shang, Zhiqiang Shen
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:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.
By Nada Lahjouji, Ashwin Gerard Colaco
The paper introduces the Human‑AI Interaction Description Language (HAIDL), a formal model that treats human‑AI interactions as durable data objects rather than fleeting prompt‑response exchanges. HAIDL captures detailed context—including computational environment, user requests, prompts, AI responses, feedback, provenance, and model metadata—using a Backus‑Naur Form‑inspired syntax. The authors implement HAIDL in HAIDL‑X, a JupyterLab middleware that records interactions, builds contextual prompts, interfaces with AI providers, and stores data for replay, analysis, and learning about user behavior.
By Michela Taufer, Kin Ng, Ian Lumsden, Aashish Pandey, Rajasi Gore, Angelina Ju, Sukanya Moudgalya, Ewa Deelman, Sai Swaminathan
arXiv:2601. 01569v4 Announce Type: replace Abstract: LLM-based agents are increasingly capable of complex task execution, yet current agentic systems remain constrained by text-centric paradigms that struggle with long-horizon tasks due to fragile multi-turn dependencies and context drift.
By Maohao Ran, Zhenglin Wan, Cooper Lin, Yanting Zhang, Hongyu Xin, Hongwei Fan, Yibo Xu, Beier Luo, Yaxin Zhou, Wangbo Zhao, Lijie Yang, Lang Feng, Fuchao Yang, Jingxuan Wu, Yiqiao Huang, Chendong Ma, Yusen Huang, Dailing Jiang, Jianbo Deng, Sirui Han, Yang You, Bo An, Yike Guo, Jun Song
arXiv:2602. 12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.
By Renjun Xu, Yang Yan
arXiv:2606. 30697v1 Announce Type: cross Abstract: Current operating systems expose interfaces optimized for human users but not for AI agents.
By Yogeswar Reddy Thota
arXiv:2607. 01510v1 Announce Type: new Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should they play?
By Natalie Grace Brigham, Eugene Bagdasarian, Tadayoshi Kohno, Franziska Roesner
arXiv:2610.15765v1 Announce Type: cross
Abstract: Large language model agents are emerging as an important new class of users of desktop applications. However, existing desktop interfaces were design...
By Alan Wu, Wei Tao, Yiwen Guo
arXiv:2607. 20709v1 Announce Type: new Abstract: Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs.
By Paul Furgale, Severin Klingler, James Nolan, Matt Staats, Gaia Di Lorenzo, Elisa Martinez Abad, Christian Sch\"uller, Razvan Dinu, Alessio Devoto, Pascal Berard, Gal Kaplun, Elad Sarafian, Riccardo Roveri, Leon Derczynski, Ricardo Silveira Cabral