---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- world-model
- digital-agents
- qwen3.5
- wia
---

# WIA-4B

**WIA-4B** is a 4B-parameter text world model from the World Intelligence Agent (WIA) project. It predicts the next observation produced by an action in heterogeneous digital environments, including Android, MCP, desktop OS, Search, SWE, Terminal, and Web. It is intended for world-model evaluation, synthetic agent training, and test-time action lookahead; it is not a general-purpose chat assistant.

The model is initialized from a Qwen3.5 4B backbone and trained with the WIA pipeline: continual pre-training (WIA-CPT), observation-only supervised fine-tuning (WIA-SFT), and GSPO-based world-model RL (WIA-WM).

## Evaluation

Results below are from the WIA technical report. Scores are on a 0--100 scale, evaluated by `DeepSeek-V4-Pro`; `Avg` is sample-weighted and `MAvg` is the macro average over seven domains.

### WIA-Bench OOD

The complete OOD comparison from the technical report is reproduced below. Scores use the `DeepSeek-V4-Pro` judge; `Avg` is sample-weighted and `MAvg` is the macro average over seven domains.

| Group | Model | Android | MCP | OS | Search | SWE | Terminal | Web | Avg | MAvg |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Proprietary | GPT-5.6-luna | 48.37 | 44.51 | 54.96 | 40.01 | 55.88 | 62.42 | 53.87 | 51.91 | 51.43 |
| Proprietary | GPT-5.6-terra | 48.34 | 43.55 | 56.14 | 40.46 | 57.68 | 61.79 | 54.57 | 52.38 | 51.79 |
| Proprietary | GPT-5.6-sol | 49.39 | 45.75 | 59.70 | 39.99 | 58.61 | 63.63 | 55.00 | 53.58 | 53.15 |
| Proprietary | Claude-Sonnet-5 | 47.76 | 49.85 | 63.22 | 35.26 | 44.58 | 46.65 | 54.24 | 48.03 | 48.79 |
| Proprietary | Claude-Opus-4.8 | 48.98 | 53.97 | 63.02 | 39.10 | 58.15 | 57.93 | 55.26 | 53.40 | 53.77 |
| Open-source | GLM-5.3 | 47.20 | 50.23 | 55.58 | 31.10 | 32.68 | 53.88 | 51.03 | 44.98 | 45.96 |
| Open-source | MiMo-v2.5-pro | 45.80 | 46.18 | 59.78 | 38.20 | 49.73 | 53.43 | 51.50 | 49.09 | 49.23 |
| Open-source | Qwen3.8-27B | 41.08 | 39.40 | 53.34 | 34.88 | 48.47 | 51.84 | 47.51 | 45.46 | 45.22 |
| Open-source | Qwen3.8-max | 47.60 | 45.32 | 57.97 | 36.82 | 57.62 | 63.42 | 55.48 | 52.40 | 52.03 |
| Open-source | DeepSeek-V4-Flash | 46.74 | 44.46 | 58.93 | 38.40 | 50.51 | 55.53 | 50.77 | 49.40 | 49.33 |
| Open-source | DeepSeek-V4-Pro | 47.87 | 43.92 | 61.93 | 38.77 | 51.14 | 57.77 | 52.63 | 50.73 | 50.57 |
| Qwen3.5 | Qwen3.5-4B | 39.92 | 38.38 | 55.29 | 30.44 | 37.26 | 39.50 | 45.24 | 40.46 | 40.86 |
| Qwen3.5 | Qwen3.5-9B | 43.00 | 41.37 | 58.19 | 30.99 | 44.05 | 43.81 | 47.42 | 43.76 | 44.12 |
| Qwen3.5 | Qwen3.5-27B | 47.84 | 42.56 | 64.29 | 33.75 | 48.10 | 55.09 | 52.78 | 49.15 | 49.20 |
| Qwen3.5 | Qwen3.5-35B-A3B | 47.09 | 42.69 | 56.59 | 32.03 | 45.70 | 49.80 | 49.55 | 46.01 | 46.21 |
| Qwen3.5 | Qwen3.5-122B-A10B* | 43.92 | 32.43 | 41.86 | 34.93 | 48.65 | 55.70 | 44.15 | 44.07 | 43.09 |
| Qwen3.5 | Qwen-AgentWorld | 47.43 | 42.63 | 64.21 | 45.02 | 56.31 | 57.22 | 53.68 | 52.87 | 52.36 |
| WIA-WM | WIA-WM-4B | 27.19 | 44.10 | 48.40 | 36.84 | 60.06 | 55.87 | 49.27 | 46.34 | 45.96 |
| WIA-WM | WIA-WM-9B | 46.82 | 52.12 | 62.70 | 46.06 | 60.77 | 59.38 | 53.43 | 54.49 | 54.47 |
| WIA-WM | WIA-WM-27B | 50.51 | 52.53 | 63.42 | 46.36 | 59.92 | 60.73 | 54.52 | 55.45 | 55.43 |
| WIA-WM | WIA-WM-35B-A3B | 48.76 | 43.72 | 63.86 | 51.33 | 59.19 | 62.52 | 54.95 | 55.72 | 54.90 |
| WIA-WM | WIA-WM-122B-A10B* | 50.59 | 40.00 | 64.71 | 58.18 | 60.46 | 64.62 | 53.59 | 57.35 | 56.02 |

### WIA-Bench ID

The complete ID comparison from the technical report is reproduced below.

| Group | Model | Android | MCP | OS | Search | SWE | Terminal | Web | Avg | MAvg |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Proprietary | GPT-5.6-sol | 43.62 | 61.02 | 33.08 | 50.66 | 53.11 | 59.96 | 62.38 | 52.04 | 51.98 |
| Proprietary | GPT-5.6-terra | 41.57 | 61.98 | 33.08 | 47.32 | 52.81 | 59.25 | 60.94 | 51.05 | 50.99 |
| Proprietary | GPT-5.6-luna | 41.29 | 60.03 | 32.43 | 45.40 | 51.29 | 55.28 | 58.33 | 49.11 | 49.15 |
| Proprietary | Claude-Opus-4.8 | 41.59 | 59.08 | 33.00 | 63.50 | 56.46 | 65.91 | 66.21 | 55.76 | 55.11 |
| Proprietary | Claude-Sonnet-5 | 40.08 | 60.28 | 32.27 | 58.46 | 38.72 | 60.40 | 63.96 | 50.75 | 50.59 |
| Open-source | GLM-5.3 | 41.20 | 58.63 | 33.20 | 54.74 | 32.09 | 63.22 | 64.52 | 49.64 | 49.66 |
| Open-source | MiMo-v2.5-pro | 40.93 | 59.61 | 32.39 | 51.34 | 47.36 | 54.35 | 64.16 | 50.12 | 50.02 |
| Open-source | Qwen3.8-27B | 36.69 | 55.67 | 30.75 | 50.87 | 43.50 | 52.67 | 61.15 | 47.59 | 47.33 |
| Open-source | Qwen3.8-max | 41.18 | 58.32 | 32.39 | 61.00 | 51.06 | 65.09 | 65.82 | 54.06 | 53.55 |
| Open-source | DeepSeek-V4-Flash | 40.64 | 59.23 | 32.39 | 56.41 | 44.87 | 56.97 | 64.40 | 50.90 | 50.70 |
| Open-source | DeepSeek-V4-Pro | 41.54 | 59.06 | 32.31 | 55.72 | 45.65 | 55.51 | 64.51 | 50.76 | 50.61 |
| Qwen3.5 | Qwen3.5-4B | 35.61 | 54.57 | 30.96 | 45.63 | 33.14 | 40.22 | 56.33 | 42.20 | 42.35 |
| Qwen3.5 | Qwen3.5-9B | 36.80 | 55.09 | 31.81 | 38.29 | 37.16 | 38.97 | 60.23 | 42.39 | 42.62 |
| Qwen3.5 | Qwen3.5-27B | 41.24 | 57.26 | 32.09 | 45.92 | 43.77 | 57.37 | 64.72 | 48.91 | 48.91 |
| Qwen3.5 | Qwen3.5-35B-A3B | 37.50 | 56.75 | 32.21 | 39.15 | 41.76 | 50.03 | 61.36 | 45.47 | 45.54 |
| Qwen3.5 | Qwen-AgentWorld | 39.03 | 56.99 | 32.49 | 63.68 | 45.60 | 61.33 | 64.54 | 52.51 | 51.95 |
| WIA-WM | WIA-WM-4B | 27.12 | 65.26 | 30.76 | 82.59 | 57.52 | 75.76 | 63.24 | 59.17 | 57.46 |
| WIA-WM | WIA-WM-9B | 41.61 | 63.89 | 32.79 | 84.31 | 60.87 | 73.96 | 68.69 | 61.99 | 60.87 |
| WIA-WM | WIA-WM-27B | 43.21 | 67.74 | 35.28 | 88.54 | 66.24 | 80.85 | 73.84 | 66.41 | 65.10 |
| WIA-WM | WIA-WM-35B-A3B | 43.49 | 67.45 | 34.39 | 89.30 | 64.18 | 76.36 | 71.86 | 65.04 | 63.86 |
| WIA-WM | WIA-WM-122B-A10B* | 41.44 | 64.33 | 33.01 | 86.66 | 57.74 | 71.43 | 66.21 | 61.16 | 60.12 |

*The 122B-A10B configuration differs from the other WIA-WM scales; it is RL-only and uses a different RL data and reward setup.

### Stage ablation

OOD-only ablation under the `DeepSeek-V4-Pro` judge:

| Stage | Android | MCP | OS | Search | SWE | Terminal | Web | Avg |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| Qwen3.5-4B-Base | 35.88 | 33.29 | 47.07 | 26.43 | 33.14 | 36.00 | 40.21 | 35.74 |
| WIA-CPT-4B | 39.07 | 35.27 | 59.75 | 28.57 | 34.84 | 37.52 | 42.69 | 39.24 |
| WIA-SFT-4B | 37.82 | 39.14 | 44.81 | 34.40 | 49.57 | 51.58 | 46.88 | 43.84 |
| **WIA-WM-4B** | **27.19** | **44.10** | **48.40** | **36.84** | **60.06** | **55.87** | **49.27** | **46.34** |

The main gains are concentrated in trajectory-oriented domains: relative to the base model, the final model improves OOD SWE by 26.92 points and OOD Terminal by 19.87 points. The report also notes a small 4B RL regression on some UI-structured domains, especially Android.

## Download

Install the Hugging Face tools and download the complete sharded checkpoint:

```bash
pip install -U "transformers>=5.8.0" huggingface_hub torch
hf download darwinagent/WIA-4B --local-dir ./WIA-4B
```

## Usage

WIA-4B predicts an observation after an action. Keep the WIA prompt format and append `###[Observation]` before generation.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "darwinagent/WIA-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "system", "content": "Operate as a language world model and report only the resulting observation."},
    {"role": "user", "content": "Task: inspect the repository.\nCurrent observation: the shell is open.\nAction: shell_exec(command='pwd', cwd='/workspace', timeout_sec=30)\n###[Observation]"},
]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
with torch.inference_mode():
    output = model.generate(inputs, max_new_tokens=16384, do_sample=False)
print(tokenizer.decode(output[0, inputs.shape[-1]:], skip_special_tokens=True))
```

Use a GPU configuration appropriate for the model and context length. The report evaluates with long contexts; practical memory use depends on dtype, context length, batching, and attention implementation.

## Citation

Please cite the WIA technical report when using this model. The report covers WIA-Corpus, WIA-Bench, WIA-WM, and WIA-WAM.