Fx-Work

Fx-Work is a 35B-class multimodal language model for professional work in realistic file-and-tool environments. It is designed to interpret heterogeneous work materials, reason over operational constraints, use tools when required, and produce deliverables that satisfy explicit professional requirements.

Fx-Work is built on Qwen3.6-35B-A3B and post-trained with 20K tool-interaction trajectories. The trajectories are generated from occupational work constructed with real-world artifacts, occupational knowledge, actionable requests, and itemized evaluation criteria. Execution-guided verification is used during data construction to identify and repair inconsistencies between the work request, reference materials, and rubric.

Main Results

Fx-Work achieves the strongest results among the evaluated models at or below the 35B scale on all five reported metrics. It also surpasses DeepSeek-V4-Pro-Preview (1.6T) on four of the five metrics.

Benchmark Results

Results on GDPvalAA-v2, APEX-Agents-AA, and JobBench. GDPval Rubric is the normalized rubric score; GDPval Elo uses the August 4, 2026 snapshot; APEX-Agents reports pass@1; and JobBench reports the Main and Easy splits. AVG is the mean of normalized GDPval Elo, APEX-Agents-AA, and JobBench Main. -- indicates that a result was not publicly available or was not tested. Bold marks the best value among the models at or below the 35B scale.

Quickstart

For streamlined integration, we recommend serving Fx-Work through an OpenAI-compatible API using SGLang or vLLM. The repository requires Hugging Face authentication when access is restricted.

[!Important] Fx-Work uses a native context length of 262,144 tokens. If GPU memory is limited, reduce the context length; for long-horizon professional work, retaining at least 128K tokens is recommended.

SGLang

SGLang provides an OpenAI-compatible server for high-throughput inference.

python -m sglang.launch_server \
  --model-path endless-frontier/Fx-Work \
  --port 8000 \
  --tp-size 8 \
  --mem-fraction-static 0.8 \
  --context-length 262144 \
  --reasoning-parser qwen3

vLLM

vLLM provides a high-throughput OpenAI-compatible server.

vllm serve endless-frontier/Fx-Work \
  --port 8000 \
  --tensor-parallel-size 8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

Both commands expose an endpoint at http://localhost:8000/v1. Tool execution is provided by the surrounding agent harness; the model server exposes generation and, where configured, tool-call responses.

Transformers

import torch
from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration

model_id = "endless-frontier/Fx-Work"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "Summarize the key risks and next actions in this work request.",
    }
]
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(**inputs, max_new_tokens=1024)

new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]
print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])

The processor supports the multimodal image and video message format provided by Qwen3.6.

Citation

If you use Fx-Work, please cite:

@article{zhu2026workgenesis,
  title={WorkGenesis: Building the Worlds That Teach Agents to Work},
  author={Zhu, Xinyu and Liu, Fenyi and Cai, Yuzhu and Tang, Shuo and Ye, Rui and Zhang, Linfeng and Chen, Siheng},
  journal={arXiv preprint arXiv:2609.39325},
  year={2026}
}

License

Fx-Work is released under the Apache 2.0 license, subject to the terms of the base model and third-party components. See the Qwen3.6-35B-A3B license for the upstream terms.