Macaron-A2UI: A Model for Generative UI in Personal Agents
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| Authors | Fancy Kong et al. |
| Year | 2026 |
| HF Upvotes | 80 |
| arXiv | 2605.24830 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
The Problem
As personal agents evolve to handle complex, user-centric tasks, static plain-text chat is rapidly becoming a bottleneck.
The Approach
We present Macaron-A2UI, a model for Generative UI in personal agents.
Key Results
We release the models, benchmark, and evaluation protocol to support future work on Generative UI for personal agents.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Macarona2ui
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