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.
arXiv:2510. 12201v2 Announce Type: replace Abstract: As AI becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable.
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.
The paper introduces GenUI‑Harness, a multi‑agent system that pairs a Tool Agent for data retrieval with a GUI Coder Agent that generates structured interfaces to aid human‑agent interaction. It tackles training challenges by using Dynamic UX for lightweight reward collection and Reward Auditor to monitor reward quality. The authors present UI‑TAU Bench, a benchmark of 1,300 database‑backed tasks, and show that GenUI‑Harness improves Pass@3 scores and reduces dialogue rounds compared to existing models.
The paper proposes ‘meta access’—a structured, permissioned view of an interface’s state—to enhance large language model chat UIs. It outlines a ladder of access levels, from feature tutoring to full orchestration, and demonstrates the concept with the Meta Chat prototype, which uses summaries, settings, and message links to enable reversible changes and a browsable tutorial library. Meta access aims to transform chat from a single conversation window into a control layer for the entire workspace while addressing risks such as grounding, privacy, and prompt injection.
arXiv:2607. 20773v1 Announce Type: cross Abstract: Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exchanges.
arXiv:2608. 07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc.
arXiv:2605. 29475v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show remarkable potential in scientific hypothesis discovery.
arXiv:2610.33966v1 Announce Type: cross Abstract: People have long dreamed of an assistant that is always there to guide them and remind what they forget, from new dishes to lost keys. Powered by mul...
The paper introduces Persona Learning from Feedback (PLF), a framework that creates reusable, preference‑grounded assistant personas as LoRA adapters on a frozen backbone. During training, a frozen self‑teacher provides token‑level supervision conditioned on persona specifications and response‑specific feedback, while a feedback gate focuses updates on responses needing improvement. At deployment, a supervised router selects an appropriate adapter or composition based on the request or dialogue, enabling personalized AI assistants without user‑specific fine‑tuning and improving preference fit over existing persona prompting and distillation methods.
arXiv:2609.08977v3 Announce Type: replace-cross Abstract: In this work, we present Gander, a native multimodal duplex interaction model that builds on MiniCPM-o 4.5 and is further adapted for realtim...
arXiv:2608.11460v2 Announce Type: replace Abstract: Human-artificial intelligence (AI) collaboration takes many forms and occur in both professional and educational scenarios. It is important to iden...
arXiv:2610.09163v1 Announce Type: new Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when...