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Abstract:As personal agents evolve to handle complex, user-centric tasks, static plain-text chat is rapidly becoming a bottleneck. Generative UI emerges as the necessary new interface layer, dynamically synthesizing the right controls, options, and state from the interaction context in real time. We present Macaron-A2UI, a model for Generative UI in personal agents. Our goal is to move beyond text-only interaction by enabling agents to generate natural language together with lightweight, executable UI actions for information collection, preference refinement, confirmation, and multi-goal organization. We build a large-scale Generative UI corpus from heterogeneous dialogue sources, introduce A2UI-Bench for controlled evaluation, and train 30B, 235B and 754B models with parameter-efficient LoRA-based supervised fine-tuning followed by reward-driven reinforcement learning. The best Macaron-A2UI model reaches 75.6 overall on A2UI-Bench without explicit schema hints, surpassing the strongest full-schema frontier baseline. We release the models, benchmark, and evaluation protocol to support future work on Generative UI for personal agents.
| Subjects: | Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2605.24830 [cs.HC] |
| (or arXiv:2605.24830v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24830 arXiv-issued DOI via DataCite (pending registration) |
From: Xiaoteng Ma [view email]
[v1]
Sun, 24 May 2026 02:51:07 UTC (3,034 KB)
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