WIA-27B

WIA-27B is a 27B-parameter text world model from the World Intelligence Agent (WIA) project. It predicts action-conditioned observations across 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 27B backbone and trained with WIA-CPT, observation-only 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.

Download

pip install -U "transformers>=5.8.0" huggingface_hub torch
hf download darwinagent/WIA-27B --local-dir ./WIA-27B

Usage

WIA-27B predicts an observation after an action. Keep the WIA prompt format and append ###[Observation] before generation.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "darwinagent/WIA-27B"
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. Practical memory use depends on dtype, context length, batching, and attention implementation.

Citation

Please cite the WIA technical report when using this model.