Fast with defaults, but struggles with structured output & UI grounding

Описание: I’ve been spending serious time hacking on UI grounding and multimodal models, and in this session I walk through my hands-on experience with some of the most popular ones right now: OS Atlas, Show, GUI Actors, and others.

We’ll cover what each model does well, where it struggles, and what to watch for if you’re considering them for single-target detection, OCR, UI navigation, or structured output. I highlight issues like prompt sensitivity, robustness across environments (Linux, Windows, web, mobile), grounding speed, and the need for more consistent output standards across the ecosystem.

If you’re experimenting with agent models, multimodal perception, or grounding tasks, this breakdown will save you time and frustration.

⏱️ Chapters

00:00 Intro: hacking on grounding models
00:08 OS Atlas — robust, structured output, great at single-target detection
00:35 Limitations when pushing prompts beyond training scope
01:09 A popular model with 120k+ downloads — but way too prompt-sensitive
01:38 Fast with defaults, but struggles with structured output & UI grounding
02:24 Show model — fast, gives coordinates/action dictionaries
02:54 Weak at identifying environment (Linux/Windows/web/mobile), OCR struggles
03:34 Built with Qwen2 backbone, future releases may improve
03:50 GUI Actors (Qwen 2.5 backbone) — fast, consistent, but requires exact prompts
04:45 Newer release defaults to bounding boxes, supports points too
05:01 OCR trade-offs: strong grounding OCR vs traditional OCR speed
05:34 Navigation strengths, outputs “thinking tokens” trend
06:15 Frustrations with subtle prompt changes & inconsistent outputs
06:42 Call for a standard in output formats and labels

What you’ll learn
How OS Atlas handles localization and robust structured outputs
Why some popular models are held back by extreme prompt sensitivity
What the Show model can (and can’t) do for environment awareness & OCR
How GUI Actors (Qwen 2.5 backbone) balance speed, consistency, and prompting constraints
OCR performance differences across models (grounding OCR vs traditional)
Why standardized output formats are urgently needed in grounding models
Where the ecosystem might be heading with “thinking tokens” and improved backbones

This is an unfiltered practitioner’s take: the wins, the frustrations, and the reality check on where UI grounding models stand today.

👉 Which grounding model have you tried, and what’s your biggest pain point—speed, accuracy, or output consistency? Comment below and let’s compare notes.