VISTA-9B

VISTA-9B are GUI-grounding vision-language models trained from Qwen3.5 9B backbones with VISTA: View-Consistent Self-Verified Training for GUI Grounding.

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Model Description

VISTA-9B is a GUI-grounding model that maps a screenshot and a natural-language instruction to a click coordinate in the normalized 0-1000 image frame.

• View-consistent GRPO training. VISTA builds each GRPO comparison group from target-preserving views of the same GUI instance, with exact coordinate remapping across cropped views. This exposes localization behavior under semantically equivalent but geometrically different screenshots.
• Self-verified cross-view anchoring. The training objective adds an oracle-format center-point anchor only when model-generated rollouts have already produced a maximum-reward prediction, stabilizing short coordinate generation without unconditional imitation on all-fail groups.

Evaluation

Accuracy is reported for GUI grounding. The model predicts a normalized coordinate in the 0-1000 frame, and the prediction is counted as correct if the point lies inside the target element. All reported results use deterministic decoding at temperature 0 and single-view inference.

Results on GUI Grounding benchmarks

Quick Start

Use the same image-chat interface as the underlying Qwen3.5 vision-language model. The recommended prompt is:

Output the center point of the position corresponding to the instruction: {instruction}. The output should just be the coordinates of a point, in the format [x,y].

Example:

import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "inclusionAI/VISTA-9B" 

model = AutoModelForImageTextToText.from_pretrained(
 model_id,
 torch_dtype=torch.bfloat16,
 device_map="auto",
 trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

image = Image.open("screenshot.png").convert("RGB")
instruction = "Click the search button"
prompt = (
 "Output the center point of the position corresponding to the instruction: "
 f"{instruction}. The output should just be the coordinates of a point, "
 "in the format [x,y]."
)

messages = [
 {
 "role": "user",
 "content": [
 {"type": "image", "image": image},
 {"type": "text", "text": prompt},
 ],
 }
]

text = processor.apply_chat_template(
 messages,
 tokenize=False,
 add_generation_prompt=True,
)
inputs = processor(
 text=[text],
 images=[image],
 padding=True,
 return_tensors="pt",
).to(model.device)

generated = model.generate(
 **inputs,
 max_new_tokens=32,
 do_sample=False,
)
new_tokens = generated[:, inputs.input_ids.shape[1]:]
response = processor.batch_decode(new_tokens, skip_special_tokens=True)[0].strip()
print(response) # e.g. [512,384]

Citation

Please consider citing if you find our work useful:

@misc{qiu2026vista,
 title={VISTA: View-Consistent Self-Verified Training for GUI Grounding},
 author={Xinyu Qiu, Yunzhu Zhang, Heng Jia, Shuheng Shen, Changhua Meng, Linchao Zhu},
 year={2026},
 eprint={2606.14579},
 archivePrefix={arXiv},
 primaryClass={cs.AI},
 url={https://arxiv.org/abs/2606.14579},
}