| title | Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work |
|---|---|
| authors | Wenhui Chen, Shiwen Cheng, Hao Dong, Chenda Duan, Ruixiang Feng, Zhong Guan, Boqiang Guo, Xueyuan Han, Haojie Hao, Liangmeng Huang, Zhelong Huang, Xinke Kong, Hongyu Li, Jiazheng Li, Junbo Li, Qingchuan Li, Yukun Lian, Chang Liu, Tianyu Liu, Zicheng Liu, Shuyi Ouyang, Yijun Pan, Kunyu Shi, Xiaojun Tang, Bingquan Wang, Kesu Wang, Yuchen Wang, Sibo Wei, Sicong Xie, Xiaoying Xing, Yi Xu, Zhijun Xu, Hongwei Xue, Qingcheng Zeng, Di Zhang, Guannan Zhang, Haochen Zhang, Tianlong Zhang, Tianyu Zhao, Tianyu Zhao, Yanjun Zheng, Jialong Zhu, Zijian Zou |
| date | 2026-09-04 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.11977 |
| affiliation | Accio |
| upvotes | 33 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Co-work agents execute complex workflows that combine information gathering, tool use, coding, and file manipulation across many model invocations. Because cost and latency accumulate over the full episode, their practical value depends not only on peak capability but also on how efficiently that capability is delivered. Yet many steps in everyday work emphasize state tracking, coordination, recovery, and follow-through rather than frontier-scale reasoning. We present Occamy-1.0, a cost-efficient co-work model obtained by further training the post-trained Qwen3.6-35B-A3B checkpoint. We construct execution-grounded data and environments, capture replayable long-horizon trajectories across multiple harnesses, and use staged post-training to develop and consolidate complementary execution capabilities. Across a broad suite of co-work benchmarks, Occamy-1.0 is consistently among the strongest comparably sized models and remains competitive with substantially larger frontier systems on several tasks. Under our stated evaluation and pricing protocol, its aggregate performance across four representative benchmarks places it at the low-cost knee of the observed cost--performance Pareto frontier. Supporting evaluations in tool calling, coding, and instruction following further show that this specialization preserves broad agentic capability. We release the model weights and a subset of the training data to support research on practical co-work agents and agentic post-training. |
cite
@misc{chen2026occamy,
title={Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work},
author={Wenhui Chen and Shiwen Cheng and Hao Dong and Chenda Duan and Ruixiang Feng and Zhong Guan and Boqiang Guo and Xueyuan Han and Haojie Hao and Liangmeng Huang and Zhelong Huang and Xinke Kong and Hongyu Li and Jiazheng Li and Junbo Li and Qingchuan Li and Yukun Lian and Chang Liu and Tianyu Liu and Zicheng Liu and Shuyi Ouyang and Yijun Pan and Kunyu Shi and Xiaojun Tang and Bingquan Wang and Kesu Wang and Yuchen Wang and Sibo Wei and Sicong Xie and Xiaoying Xing and Yi Xu and Zhijun Xu and Hongwei Xue and Qingcheng Zeng and Di Zhang and Guannan Zhang and Haochen Zhang and Tianlong Zhang and Tianyu Zhao and Tianyu Zhao and Yanjun Zheng and Jialong Zhu and Zijian Zou},
year={2026},
eprint={2609.11977},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.11977}
}