TerminalHorizon-122B-A10B

Scaling Agentic Data for Long-Horizon Terminal Intelligence

Paper · Citation

TerminalHorizon is a fully automated data synthesis engine for long-horizon terminal agents. Grounded in real-world professional work, it constructs executable environments and generates training trajectories that connect agentic behavioral patterns across stages toward a shared goal.

This repository releases TerminalHorizon-122B-A10B, obtained by supervised fine-tuning Qwen3.5-122B-A10B on TerminalHorizon-3K.

Resources

Resource	Description
TerminalHorizon-Environment	Executable terminal-task environments.
TerminalHorizon-3K	Long-horizon training trajectories.
TerminalHorizon-27B	Fine-tuned Qwen3.5-27B.
TerminalHorizon-35B-A3B	Fine-tuned Qwen3.5-35B-A3B.
TerminalHorizon-122B-A10B	Fine-tuned Qwen3.5-122B-A10B.

Model Overview

Property	Value
Base model	Qwen/Qwen3.5-122B-A10B
Training method	Supervised fine-tuning
Training dataset	TerminalHorizon-3K
Training trajectories	2,852
Weight precision	BF16
Checkpoint format	Hugging Face Transformers / Safetensors

The terminal is a shared interaction interface, not a restriction to software tasks. The training environments span 25 professional domains and 463 subdomains, including software engineering, machine learning, education, agriculture, healthcare, finance, and scientific research.

TerminalHorizon focuses on meaningful work and connected agent behavior, rather than task length alone.

Load the Model

Use a version of Transformers that supports Qwen3.5 MoE:

from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration

model_id = "shunzou05/TerminalHorizon-122B-A10B"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5MoeForConditionalGeneration.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

Refer to the base model documentation for inference and serving examples.

Citation

@misc{zou2026scalingagenticdatalonghorizon,
      title={Scaling Agentic Data for Long-Horizon Terminal Intelligence},
      author={Shun Zou and Ziyu Ma and Yi Zou and Yong Wang and Lin Chen and Zehui Chen and Guanhua Chen and Xiangxiang Chu and Feng Zhao},
      year={2026},
      eprint={2610.13166},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2610.13166},
}