Instructions to use webbrain-one/webbrain-compass-vld-tiny-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/webbrain-compass-vld-tiny-v5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="webbrain-one/webbrain-compass-vld-tiny-v5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("webbrain-one/webbrain-compass-vld-tiny-v5")
model = AutoModelForMultimodalLM.from_pretrained("webbrain-one/webbrain-compass-vld-tiny-v5", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/webbrain-compass-vld-tiny-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbrain-one/webbrain-compass-vld-tiny-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbrain-one/webbrain-compass-vld-tiny-v5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/webbrain-one/webbrain-compass-vld-tiny-v5

SGLang

How to use webbrain-one/webbrain-compass-vld-tiny-v5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "webbrain-one/webbrain-compass-vld-tiny-v5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbrain-one/webbrain-compass-vld-tiny-v5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "webbrain-one/webbrain-compass-vld-tiny-v5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webbrain-one/webbrain-compass-vld-tiny-v5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use webbrain-one/webbrain-compass-vld-tiny-v5 with Docker Model Runner:
```
docker model run hf.co/webbrain-one/webbrain-compass-vld-tiny-v5
```

webbrain-compass-vld-tiny-v5 / README.md

webbrain-one

Rename to WebBrain Compass VLD Tiny v5; attribution

9d7b87f verified 8 days ago

preview code

Raw History Blame Contribute Delete

8.83 kB

---
license: other
license_name: webbrain-noncommercial-research-restrictions
base_model: openbmb/MiniCPM-V-4.7-1B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- webbrain
- minicpm-v
- browser-agent
- tool-calling
- gui-grounding
- decision-model
- webbrain-compass
---
# WebBrain Compass VLD Tiny v5

`openbmb/MiniCPM-V-4.7-1B` fine-tuned in two rounds as one on-device model for WebBrain: Compass text and tool
calls, VL (the visual fallback) and D (decisions). It covers three jobs:

- **page-state decisions:** 120/126 on d1 with 0 false completions, 0.94 s per request in the browser
- **the local visual fallback:** better grounding than LFM2.5-VL-450M on WebBrain's VL-3 validation
- **offline text and tool calls:** close to Compass Tiny v2.1 on the routing benchmark

This repository holds the **merged float32 weights**. Companion repositories:

- [`webbrain-one/webbrain-compass-vld-tiny-v5-lora`](https://huggingface.co/webbrain-one/webbrain-compass-vld-tiny-v5-lora): the LoRA adapter and the merger weights.
- [`webbrain-one/webbrain-compass-vld-tiny-v5-onnx`](https://huggingface.co/webbrain-one/webbrain-compass-vld-tiny-v5-onnx): the WebGPU ONNX bundle.

Round 1 is kept as `webbrain-one/minicpmv47-webbrain-v1`.

## Training

Two rounds on one RTX 5090 (about 20 hours in total).
**Base:** `openbmb/MiniCPM-V-4.7-1B` (revision `379f120c2ff3588e6e06559ec6b9ec15cc05114e`): a SigLIP vision tower, a
16x vision-to-language merger, and a Qwen3.5-0.8B-style language model (18 Gated DeltaNet + 6 attention layers).

1. **Round 1** (`minicpmv47-webbrain-v1`): one pass over 344,139 samples: WebBrain-VL-4 (335,572 draws of VL-2/curated VL-3 observations,
   MolmoPoint GUI grounding, WebChain trajectories) plus the WebBrain Compass v3 maximal text release (8,583 rows).
   - Trainable: LoRA r = 64, alpha = 64 on all 186 language-model linear layers (43.3M parameters, fp32) and the
     full merger (26.0M, fp32). The vision tower is frozen.
   - AdamW with LoRA LR 1e-4 and merger LR 2e-5; 5,144 steps.
2. **Round 2 (this model):** continues the round 1 adapter and merger with a fresh optimizer (LoRA LR 5e-5, merger LR 1e-5;
   3% warmup, cosine to 10%; effective batch 64; 784 steps; 51,811 samples). The data:
   - **Decision data:** the d1 balanced-V3 **train** split rendered in WebBrain's decision protocol, repeated 6x.
     It has 1,206 samples (918 verify Yes/No continuations and 288 option readouts), token-exact with the extension's
     prompts.
   - **Text data:** the Compass v3 release once more (17% of samples, up from 2.5%).
   - **Replay data:** a 36,000-draw VL-4 replay (WebChain 12k, VL-2 12k, curated VL-3 3k, MolmoPoint 9k).
Loss covers completion tokens only (per-sample mean), with thinking off and a 32K maximum length.
The d1 validation split was used only for the eval loss and probe selection. The d1 final test split never fed
training or selection.

Final validation loss (full sets, round 1 → round 2): VL 0.295 → 0.288, Compass 1.138 → 1.116. On d1 validation decision
samples the loss fell from 4.00 (verify) / 2.32 (readout) at the start of round 2 (first 96 samples) to
0.066 / 0.067 at the end (all 594).

## Results

### Page-state decisions: d1 balanced-V3 final test (126 categorical questions)

Scored through WebBrain's decision protocol in the browser (WebGPU) on an RTX 5090. One request is a screenshot,
its page state and its questions. The d1 final test split was never trained on.

| Model | Correct | Completions found | False completions | Unknown recognized | Time per request |
|---|---:|---:|---:|---:|---:|
| **v5, WebGPU q8 + probe** | **120/126** | **24/24** | **0/48** | **24/24** | 0.94 s |
| **v5, WebGPU q4 (lite) + probe** | **120/126** | 24/24 | 0/48 | 24/24 | 0.94 s |
| Round 1, WebGPU q8 + probe | 103/126 | 22/24 | 11/48 | 13/24 | 0.85 s |
| MiniCPM-V-4.7-1B untuned, WebGPU q8 + probe | 104/126 | 23/24 | 8/48 | 16/24 | n/a |
| Bonsai 2 2B Vision + probe | 110/126 | 21/24 | 4/48 | 20/24 | 2.2 s |
| d1-omni-600M | 92/126 | n/a | 1/48 | n/a | 0.75 s |

- The probe is no longer needed. Without it, v5's raw Yes − No verify margins score 121/126 in PyTorch and 120/126
  in WebGPU q8. In PyTorch the standard and conservative probes (fit on d1 train, chosen on validation) also score
  121/126, and the generated option readout alone scores 120/126.
- A caution on scope: d1 is a synthetic benchmark. v5 was fine-tuned on d1's train families, which are disjoint from
  the test families but follow the same design. Bonsai's backbone was not trained on d1, only its probe was.
  Validate v5 on real extension decisions before relying on these margins.

### Visual fallback: WebBrain VL-3 frozen validation, same 1,022 rows

Greedy generation, scored with the VL-3 pipeline's own scorer. The rows are every MolmoPoint grounding row except
actionable inventory (first 20), the first 80 curated VL-3 rows and every 20th VL-2 row. LFM2.5-VL-450M (VL-3,
step 3750) is WebBrain's current visual fallback.

| Metric | v5 | Round 1 | LFM2.5-VL-450M |
|---|---:|---:|---:|
| Schema-valid outputs | **96.2%** | 95.8% | 92.7% |
| Point within 0.05 (510 rows) | **65.5%** | 58.8% | 45.9% |
| Mean point distance | **0.156** | 0.194 | 0.161 |
| Grounded interaction: point within 0.05 (255) | **57.3%** | 51.0% | 29.8% |
| Intent → point/box: point within 0.05 (255) | **73.7%** | 66.7% | 62.1% |
| Box IoU | **0.175** | 0.113 | 0.050 |
| Point inside the target box | **33.3%** | 23.3% | 10.4% |
| Six-section observation valid, VL-2 / VL-3 | 98.7% / **72.5%** | 99.4% / 65.0% | 97.5% / 38.8% |
| Actionable inventory valid (first 20) | 25% | 30% | 10% |

On intent → point/box the mean point distance is still worse than LFM's (0.190 vs 0.132; round 1 0.248), although more
answers land within 0.05: a minority of far misses. Long inventory lists often do not terminate, for every model.

### Text and tool calls: WebBrain `test/llm` routing benchmark (compact tier)

Served from PyTorch BF16 with seed 3407 and thinking off. The table counts the 89 scenarios scored in the Compass
Tiny v2.1 run: strict = `ideal`, loose = `ideal` + `ideal_name`.

| Model | First-turn structured calls | Strict exact action | Loose tool family | Discouraged actions |
|---|---:|---:|---:|---:|
| **v5** (0.8B language model) | 95/100 | 9/89 | **48/89** | 3 |
| Round 1 | 97/100 | 10/89 | 38/89 | 5 |
| MiniCPM-V-4.7-1B, untuned | 98/100 | 3/89 | 30/89 | 2 |
| Compass Tiny v2.1 (2.6B, WebGPU q4f16) | 98/100 | **15/89** | 43/89 | 3 |

## Usage

```python
import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor
repo = "webbrain-one/webbrain-compass-vld-tiny-v5"
processor = AutoProcessor.from_pretrained(repo)
model = AutoModelForImageTextToText.from_pretrained(repo, dtype=torch.bfloat16, device_map="cuda").eval()
messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe this page."}]}]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, tokenize=False)
inputs = processor(text=[prompt], images=[Image.open("screenshot.png").convert("RGB")], return_tensors="pt").to(model.device)
out = model.generate(**inputs, do_sample=False, max_new_tokens=512)
print(processor.tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

Tool calls use the chat template's native format: `<tool_call>`, then `<function=NAME>`, then one `<parameter=KEY>`
block per argument. Pass OpenAI-style `tools=` to `apply_chat_template`, and parse the reply with a Qwen3.5 /
Qwen3-coder style tool parser that types each argument from the tool's JSON schema.

For decisions, use WebBrain's verify protocol: one "Proposed answer: X ... Is the proposed answer correct? Answer Yes or No." continuation per option, compared by the Yes − No logit. See `minicpmv-decisions.mjs` in the ONNX repository.

## License

The base model is Apache-2.0. This fine-tune was trained on data that includes noncommercial-licensed sources (for
example the WebLINX-derived Compass subset), so the fine-tuned weights are released for **noncommercial research
only** under the WebBrain noncommercial research restrictions. Private hosting does not expand those rights.
Model output is never proof that a browser action succeeded. Keep WebBrain's schema validation, grounding checks and
confirmations in front of any action.

Training data includes WebChain v2 (`webagentlab/webchain`, WebChain Academic Research License v1.0): Fan, Sicheng, Rui Wan, Yifei Leng, Gaoning Liang, Li Ling, Yanyi Shang, and Dehan Kong. "WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces." arXiv:2603.05295, 2026. It also includes WebLINX (McGill-NLP, CC BY-NC-SA 4.0) and MolmoPoint-GUISyn (Ai2, ODC-BY).

	---
	license: other
	license_name: webbrain-noncommercial-research-restrictions
	base_model: openbmb/MiniCPM-V-4.7-1B
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- webbrain
	- minicpm-v
	- browser-agent
	- tool-calling
	- gui-grounding
	- decision-model
	- webbrain-compass
	---

	# WebBrain Compass VLD Tiny v5

	`openbmb/MiniCPM-V-4.7-1B` fine-tuned in two rounds as one on-device model for WebBrain: Compass text and tool
	calls, VL (the visual fallback) and D (decisions). It covers three jobs:

	- page-state decisions: 120/126 on d1 with 0 false completions, 0.94 s per request in the browser
	- the local visual fallback: better grounding than LFM2.5-VL-450M on WebBrain's VL-3 validation
	- offline text and tool calls: close to Compass Tiny v2.1 on the routing benchmark

	This repository holds the merged float32 weights. Companion repositories:

	- [`webbrain-one/webbrain-compass-vld-tiny-v5-lora`](https://huggingface.co/webbrain-one/webbrain-compass-vld-tiny-v5-lora): the LoRA adapter and the merger weights.
	- [`webbrain-one/webbrain-compass-vld-tiny-v5-onnx`](https://huggingface.co/webbrain-one/webbrain-compass-vld-tiny-v5-onnx): the WebGPU ONNX bundle.

	Round 1 is kept as `webbrain-one/minicpmv47-webbrain-v1`.

	## Training

	Two rounds on one RTX 5090 (about 20 hours in total).
	Base: `openbmb/MiniCPM-V-4.7-1B` (revision `379f120c2ff3588e6e06559ec6b9ec15cc05114e`): a SigLIP vision tower, a
	16x vision-to-language merger, and a Qwen3.5-0.8B-style language model (18 Gated DeltaNet + 6 attention layers).

	1. Round 1 (`minicpmv47-webbrain-v1`): one pass over 344,139 samples: WebBrain-VL-4 (335,572 draws of VL-2/curated VL-3 observations,
	MolmoPoint GUI grounding, WebChain trajectories) plus the WebBrain Compass v3 maximal text release (8,583 rows).
	- Trainable: LoRA r = 64, alpha = 64 on all 186 language-model linear layers (43.3M parameters, fp32) and the
	full merger (26.0M, fp32). The vision tower is frozen.
	- AdamW with LoRA LR 1e-4 and merger LR 2e-5; 5,144 steps.
	2. Round 2 (this model): continues the round 1 adapter and merger with a fresh optimizer (LoRA LR 5e-5, merger LR 1e-5;
	3% warmup, cosine to 10%; effective batch 64; 784 steps; 51,811 samples). The data:
	- Decision data: the d1 balanced-V3 train split rendered in WebBrain's decision protocol, repeated 6x.
	It has 1,206 samples (918 verify Yes/No continuations and 288 option readouts), token-exact with the extension's
	prompts.
	- Text data: the Compass v3 release once more (17% of samples, up from 2.5%).
	- Replay data: a 36,000-draw VL-4 replay (WebChain 12k, VL-2 12k, curated VL-3 3k, MolmoPoint 9k).

	Loss covers completion tokens only (per-sample mean), with thinking off and a 32K maximum length.
	The d1 validation split was used only for the eval loss and probe selection. The d1 final test split never fed
	training or selection.

	Final validation loss (full sets, round 1 → round 2): VL 0.295 → 0.288, Compass 1.138 → 1.116. On d1 validation decision
	samples the loss fell from 4.00 (verify) / 2.32 (readout) at the start of round 2 (first 96 samples) to
	0.066 / 0.067 at the end (all 594).

	## Results

	### Page-state decisions: d1 balanced-V3 final test (126 categorical questions)

	Scored through WebBrain's decision protocol in the browser (WebGPU) on an RTX 5090. One request is a screenshot,
	its page state and its questions. The d1 final test split was never trained on.

	\| Model \| Correct \| Completions found \| False completions \| Unknown recognized \| Time per request \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| v5, WebGPU q8 + probe \| 120/126 \| 24/24 \| 0/48 \| 24/24 \| 0.94 s \|
	\| v5, WebGPU q4 (lite) + probe \| 120/126 \| 24/24 \| 0/48 \| 24/24 \| 0.94 s \|
	\| Round 1, WebGPU q8 + probe \| 103/126 \| 22/24 \| 11/48 \| 13/24 \| 0.85 s \|
	\| MiniCPM-V-4.7-1B untuned, WebGPU q8 + probe \| 104/126 \| 23/24 \| 8/48 \| 16/24 \| n/a \|
	\| Bonsai 2 2B Vision + probe \| 110/126 \| 21/24 \| 4/48 \| 20/24 \| 2.2 s \|
	\| d1-omni-600M \| 92/126 \| n/a \| 1/48 \| n/a \| 0.75 s \|

	- The probe is no longer needed. Without it, v5's raw Yes − No verify margins score 121/126 in PyTorch and 120/126
	in WebGPU q8. In PyTorch the standard and conservative probes (fit on d1 train, chosen on validation) also score
	121/126, and the generated option readout alone scores 120/126.
	- A caution on scope: d1 is a synthetic benchmark. v5 was fine-tuned on d1's train families, which are disjoint from
	the test families but follow the same design. Bonsai's backbone was not trained on d1, only its probe was.
	Validate v5 on real extension decisions before relying on these margins.

	### Visual fallback: WebBrain VL-3 frozen validation, same 1,022 rows

	Greedy generation, scored with the VL-3 pipeline's own scorer. The rows are every MolmoPoint grounding row except
	actionable inventory (first 20), the first 80 curated VL-3 rows and every 20th VL-2 row. LFM2.5-VL-450M (VL-3,
	step 3750) is WebBrain's current visual fallback.

	\| Metric \| v5 \| Round 1 \| LFM2.5-VL-450M \|
	\|---\|---:\|---:\|---:\|
	\| Schema-valid outputs \| 96.2% \| 95.8% \| 92.7% \|
	\| Point within 0.05 (510 rows) \| 65.5% \| 58.8% \| 45.9% \|
	\| Mean point distance \| 0.156 \| 0.194 \| 0.161 \|
	\| Grounded interaction: point within 0.05 (255) \| 57.3% \| 51.0% \| 29.8% \|
	\| Intent → point/box: point within 0.05 (255) \| 73.7% \| 66.7% \| 62.1% \|
	\| Box IoU \| 0.175 \| 0.113 \| 0.050 \|
	\| Point inside the target box \| 33.3% \| 23.3% \| 10.4% \|
	\| Six-section observation valid, VL-2 / VL-3 \| 98.7% / 72.5% \| 99.4% / 65.0% \| 97.5% / 38.8% \|
	\| Actionable inventory valid (first 20) \| 25% \| 30% \| 10% \|

	On intent → point/box the mean point distance is still worse than LFM's (0.190 vs 0.132; round 1 0.248), although more
	answers land within 0.05: a minority of far misses. Long inventory lists often do not terminate, for every model.

	### Text and tool calls: WebBrain `test/llm` routing benchmark (compact tier)

	Served from PyTorch BF16 with seed 3407 and thinking off. The table counts the 89 scenarios scored in the Compass
	Tiny v2.1 run: strict = `ideal`, loose = `ideal` + `ideal_name`.

	\| Model \| First-turn structured calls \| Strict exact action \| Loose tool family \| Discouraged actions \|
	\|---\|---:\|---:\|---:\|---:\|
	\| v5 (0.8B language model) \| 95/100 \| 9/89 \| 48/89 \| 3 \|
	\| Round 1 \| 97/100 \| 10/89 \| 38/89 \| 5 \|
	\| MiniCPM-V-4.7-1B, untuned \| 98/100 \| 3/89 \| 30/89 \| 2 \|
	\| Compass Tiny v2.1 (2.6B, WebGPU q4f16) \| 98/100 \| 15/89 \| 43/89 \| 3 \|

	## Usage

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

	repo = "webbrain-one/webbrain-compass-vld-tiny-v5"
	processor = AutoProcessor.from_pretrained(repo)
	model = AutoModelForImageTextToText.from_pretrained(repo, dtype=torch.bfloat16, device_map="cuda").eval()

	messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe this page."}]}]
	prompt = processor.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, tokenize=False)
	inputs = processor(text=[prompt], images=[Image.open("screenshot.png").convert("RGB")], return_tensors="pt").to(model.device)
	out = model.generate(**inputs, do_sample=False, max_new_tokens=512)
	print(processor.tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	Tool calls use the chat template's native format: `<tool_call>`, then `<function=NAME>`, then one `<parameter=KEY>`
	block per argument. Pass OpenAI-style `tools=` to `apply_chat_template`, and parse the reply with a Qwen3.5 /
	Qwen3-coder style tool parser that types each argument from the tool's JSON schema.

	For decisions, use WebBrain's verify protocol: one "Proposed answer: X ... Is the proposed answer correct? Answer Yes or No." continuation per option, compared by the Yes − No logit. See `minicpmv-decisions.mjs` in the ONNX repository.

	## License

	The base model is Apache-2.0. This fine-tune was trained on data that includes noncommercial-licensed sources (for
	example the WebLINX-derived Compass subset), so the fine-tuned weights are released for **noncommercial research
	only** under the WebBrain noncommercial research restrictions. Private hosting does not expand those rights.
	Model output is never proof that a browser action succeeded. Keep WebBrain's schema validation, grounding checks and
	confirmations in front of any action.

	Training data includes WebChain v2 (`webagentlab/webchain`, WebChain Academic Research License v1.0): Fan, Sicheng, Rui Wan, Yifei Leng, Gaoning Liang, Li Ling, Yanyi Shang, and Dehan Kong. "WebChain: A Large-Scale Human-Annotated Dataset of Real-World Web Interaction Traces." arXiv:2603.05295, 2026. It also includes WebLINX (McGill-NLP, CC BY-NC-SA 4.0) and MolmoPoint-GUISyn (Ai2, ODC-BY).