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Nano Banana Pro: the image editing model that turned "edit it by talking to it" into a product

Gemini's native image generation and editing tier, known in the community as Nano Banana. Its point is not peak image quality but the interaction shape: keep editing one image across a multi-turn conversation, read the text inside an image and change it, and fuse several references into one output. That made image generation feel like talking to a designer instead of pulling a slot-machine lever. Most of its ranking comes from preference arenas, which are votes rather than measurements, so graded as needing reproduction.

ProductGoogle DeepMind (Gemini image)Site
Nano Banana Pro: the image editing model that turned "edit it by talking to it" into a product
Show-Harness: Just a VLM Agent Can Play Robots

Show-Harness: Just a VLM Agent Can Play Robots

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete semantic action units that VLMs can naturally reason over, while embodiment-specific interpreters deterministically ground them into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. Through the same interface, Show-Harness demonstrates the feasibility of (1) directly unlocking closed-source frontier VLMs for zero-shot robot control, and (2) adapting small-scale open-source VLMs for low-cost deployment with just a few GPU-hours of fine-tuning. We further develop GUMI (GUI Manipulation Interface), which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to "play" robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms. These results suggest that the right interface can unlock substantial embodied capability from foundation VLMs, without requiring additional model capacity or costly embodiment-specific pretraining.

Yanzhe Chen, Zechen Bai, Zhijun Cao · 1k views · Sep 9, 2026
Vision-language modelsAgentic HarnessSemantic Action InterfaceSep 9, 2026

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