Ant Group Releases LingBot-VLA | Generative UI, AG-UI, and the Stack Behind Agent-Driven Interfaces

Here is your today’s AI Dev Brief from Marktechpost, covering core research, models, infrastructure tools, and applied updates for AI developers and researchers.

LingBot VLA is a vision language action foundation model trained on about 20,000 hours of real world dual arm teleoperation data from 9 robot embodiments and designed for strong cross morphology and cross task generalization. The model combines a Qwen2.5 VL backbone, a Flow Matching based action expert, and depth aware spatial perception via LingBot Depth distillation, so robots can reason more accurately about 3D structure. On the GM 100 benchmark across 3 platforms LingBot VLA with depth reaches about 17.30 percent average Success Rate and 35.41 percent Progress Score, outperforming π0.5, GR00T N1.6, and WALL OSS under a shared protocol, while simulation tests show similar gains under domain randomization. The open source toolkit provides an efficient post training stack that reaches about 261 samples per second per GPU on 8 GPUs, delivering 1.5 to 2.8 times higher throughput than existing open VLA frameworks.......… Read the full analysis/article here.

Most AI applications still showcase the model as a chat box. That interface is simple, but it hides what agents are actually doing, such as planning steps, calling tools, and updating state. Generative UI is about letting the agent drive real interface elements, for example tables, charts, forms, and progress indicators, so the experience feels like a product, not a log of tokens.

What is Generative UI?

The CopilotKit team explains Generative UI as to any user interface that is partially or fully produced by an AI agent. Instead of only returning text, the agent can drive:

✅ stateful components such as forms and filters

✅ visualizations such as charts and tables

✅ multistep flows such as wizards

✅ status surfaces such as progress and intermediate results...… Read the full analysis/article here.

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