inclusionAI/VISTA-9B

image-text-to-texttransformersUpstream checksumstransformerssafetensorsqwen3_5image-text-to-textvistaqwen3.5

License: apache-2.0
Size: not mirrored yet
Downloads in the last 30 days: 0 / 30 days

No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.

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.

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

Model	SSPro	SSV2	OSWorld-G	OSWorld-G-R
Qwen3.5-4B	60.3	90.4	54.4	66.8
GRPO-4B	62.2	94.2	59.9	69.2
VISTA-4B	64.2	93.8	61.2	69.7
Δ	+2.0	-0.4	+1.3	+0.5
Qwen3.5-9B	65.2	91.9	63.1	74.6
GRPO-9B	68.3	95.2	67.5	75.2
VISTA-9B	69.2	95.8	68.1	75.5
Δ	+0.9	+0.6	+0.6	+0.3
Qwen3.5-35B-A3B	68.6	93.8	65.8	72.5
GRPO-35B-A3B	71.7	95.7	70.4	74.3
VISTA-35B-A3B	72.9	95.8	71.5	75.3
Δ	+1.2	+0.1	+1.1	+1.0

Quick Start

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

Example:

python

import torchfrom PIL import Imagefrom 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:

plain

@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},}

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

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:

Example:

python

import torchfrom PIL import Imagefrom 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]

import torchfrom PIL import Imagefrom 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:

plain

@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},}

@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},}