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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
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},
}
