Publications

arXiv 2026

OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis

Zhuofeng Li^{*Equal contribution}, Dongfu Jiang^{*Equal contribution}, Haoxiang Zhang, and 7 more authors Dongfu Jiang^{*Equal contribution}, Xueguang Ma, Haoxiang Zhang, Ping Nie, Yuyu Zhang, Kai Zou, Jianwen Xie, Yu Zhang^{✝Corresponding author}, Wenhu Chen^{✝Corresponding author}

Paper | Code | Dataset | Model | Demo | Video | Eval Logs | Abstract | BibTeX

GitHub ⭐

Training deep research agents requires long-horizon trajectories that interleave search, evidence aggregation, and multi-step reasoning. However, existing data collection pipelines typically rely on proprietary web APIs, making large-scale trajectory synthesis costly, unstable, and difficult to reproduce. We present OpenResearcher, a reproducible pipeline that decouples one-time corpus bootstrapping from multi-turn trajectory synthesis and executes the search-and-browse loop entirely offline using three explicit browser primitives: search, open, and find, over a 15M-document corpus. Using GPT-OSS-120B as the teacher model, we synthesize over 97K trajectories, including a substantial long-horizon tail with 100+ tool calls. Supervised fine-tuning a 30B-A3B backbone on these trajectories achieves 54.8% accuracy on BrowseComp-Plus, a +34.0 point improvement over the base model, while remaining competitive on BrowseComp, GAIA, and xbench-DeepSearch. Because the environment is offline and fully instrumented, it also enables controlled analysis, where our study reveals practical insights into deep research pipeline design, including data filtering strategies, agent configuration choices, and how retrieval success relates to final answer accuracy.

@article{li2026openresearcher, title={OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis}, author={Li, Zhuofeng and Jiang, Dongfu and Ma, Xueguang and Zhang, Haoxiang and Nie, Ping and Zhang, Yuyu and Zou, Kai and Xie, Jianwen and Zhang, Yu and Chen, Wenhu}, journal={arXiv preprint arXiv:2603.20278}, year={2026} }

bioRxiv 2026

Eubiota: Modular Agentic AI for Autonomous Discovery in the Gut Microbiome

Pan Lu^{*Equal contribution}, Yifan Gao^{*Equal contribution}, Haoxiang Zhang, and 12 more authors William G. Peng, Haoxiang Zhang, Kunlun Zhu, Elektra K. Robinson, Qixin Xu, Masakazu Kotaka, Harrison G. Zhang, Bingxuan Li, Anthony L. Shiver, Yejin Choi, Kerwyn Casey Huang, Justin L. Sonnenburg^{✝Corresponding author}, James Zou^{✝Corresponding author}

Website | Paper | App | Code | HuggingFace | Discord | Abstract | BibTeX

GitHub ⭐

Eubiota is an open-source modular agentic AI framework for autonomous discovery in the gut microbiome. It features specialized agents for planning, execution, verification, and synthesis that collaborate through shared memory and domain-specific tools. Using reinforcement learning to enhance multi-turn reasoning, Eubiota achieves 87.7% benchmark accuracy, outperforming GPT-4.1 by 10.4%. The system identified the uvr-ruv DNA repair pathway critical for bacterial fitness under inflammatory stress, designed a four-strain bacterial consortium that reduced colitis severity in mice, created an antibiotic combination that preserves beneficial gut bacteria, and discovered diet-associated metabolites that suppress NF-κB signaling.

@article{lu2026eubiota, title={Eubiota: Modular Agentic AI for Autonomous Discovery in the Gut Microbiome}, author={Lu, Pan and Gao, Yifan and Peng, William G. and Zhang, Haoxiang and Zhu, Kunlun and Robinson, Elektra K. and Xu, Qixin and Kotaka, Masakazu and Zhang, Harrison G. and Li, Bingxuan and Shiver, Anthony L. and Choi, Yejin and Huang, Kerwyn Casey and Sonnenburg, Justin L. and Zou, James}, journal={bioRxiv}, year={2026}, doi={10.64898/2026.02.27.708412}, url={https://www.biorxiv.org/content/10.64898/2026.02.27.708412v1} }

ICLR 2026 Oral & NIPS 2025 ER Workshop

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

Zhuofeng Li^{*Equal contribution}, Haoxiang Zhang^{*Equal contribution}, Pan Lu^{✝Corresponding author}, and 5 more authors Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou^{✝Corresponding author}, Pan Lu^{✝Corresponding author}

Paper | Homepage | Demo | Code | Slack | Abstract | BibTeX

Hugging Face Daily Paper #2 • GitHub ⭐

This paper introduces AgentFlow, a trainable agentic framework that coordinates four specialized modules through evolving memory to optimize planning within multi-turn interactions. The authors propose Flow-GRPO, a training method that converts long-horizon optimization into tractable single-turn updates. Testing across ten benchmarks shows the 7B-scale model achieves substantial improvements, including 14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on scientific tasks, even surpassing GPT-4o on some evaluations.

@article{li2025flow, title={In-the-Flow Agentic System Optimization for Effective Planning and Tool Use}, author={Li, Zhuofeng and Zhang, Haoxiang and Han, Seungju and Liu, Sheng and Xie, Jianwen and Zhang, Yu and Choi, Yejin and Zou, James and Lu, Pan}, journal={arXiv preprint arXiv:2510.05592}, year={2025} }

TMLR

Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation

Haoxiang Zhang, Zhuofeng Li, Shichao Pei^{✝Corresponding author}, and 3 more authors Qiannan Zhang, Ziyi Kou, Juncheng Li, Shichao Pei^{✝Corresponding author}

Paper | Code | Abstract | BibTeX

Pre-trained graph neural networks (GNNs) have demonstrated significant success in leveraging large-scale graph data to learn transferable representations. However, their performance often degrades under distribution shifts, particularly in real-world scenarios where test labels are unavailable. To address this challenge, we propose Graph Optimization via Augmented Transformations (GOAT), a novel self-supervised test-time tuning paradigm that adapts pre-trained GNNs to distribution-shifted test data by focusing exclusively on node feature transformations. By avoiding complex and often suboptimal graph structure transformations, GOAT overcomes the limitations of existing data-centric methods. To further address the issue of transformation collapse, where feature transformations converge to trivial solutions—such as when the test-time learned data-centric transformation degenerates into a constant or identity mapping across different inputs, we introduce a parameter-efficient low-rank adapter that generates diverse transformations tailored to individual input graphs. This design not only enhances adaptation performance but also improves interpretability by avoiding modifications to the graph structure. Through extensive experiments on six real-world datasets with diverse distribution shifts, we demonstrate that GOAT achieves consistent performance improvements across different pre-trained GNN backbones, outperforming state-of-the-art test-time adaptation methods.

@article{zhang2025avoiding, title={Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation}, author={Haoxiang Zhang and Zhuofeng Li and Qiannan Zhang and Ziyi Kou and Juncheng Li and Shichao Pei}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2025}, url={https://openreview.net/forum?id=yiS6q42LLt}, note={} }

CIKM 2024

Learning from Novel Knowledge: Continual Few-shot Knowledge Graph Completion

Zhuofeng Li^{*Equal contribution}, Haoxiang Zhang^{*Equal contribution}, Shichao Pei^{✝Corresponding author}, and 2 more authors Qiannan Zhang, Ziyi Kou, Shichao Pei^{✝Corresponding author}

Paper | Code | Abstract | BibTeX

This work addresses the challenge of continual few-shot learning in knowledge graph completion, enabling models to learn from novel knowledge while preserving previously learned information.

@inproceedings{li2024learning, title={Learning from novel knowledge: Continual few-shot knowledge graph completion}, author={Li, Zhuofeng and Zhang, Haoxiang and Zhang, Qiannan and Kou, Ziyi and Pei, Shichao}, booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}, pages={1326--1335}, year={2024} }

Dataset

Sci-Bench-AIME25: A Multi-Modal Chain-of-Thought Dataset for Advanced Tool-Intergrated Mathematical Reasoning

Haoxiang Zhang^{❧Corresponding author}, Siyuan Wang^{❧Corresponding author}, Xueji Fang, and 5 more authors Xueji Fang, Xinkai Zou, Tiange Lyu, Siyuan Cao, Jingyuan Huang, Jie Xing

Dataset | Abstract | BibTeX

A comprehensive multi-modal dataset designed for advancing mathematical reasoning capabilities through chain-of-thought methodologies and tool integration.

@dataset{zhang2025scibench_aime25, title = {{Sci-Bench-AIME25}: A Multi-Modal Chain-of-Thought Dataset for Advanced Tool-Intergrated Mathematical Reasoning}, author = {Zhang, Haoxiang and Wang, Siyuan and Fang, Xueji and Zou, Xinkai and Lyu, Tiange and Cao, Siyuan and Huang, Jingyuan and Xing, Jie}, year = {2025}, publisher = {Hugging Face}, doi = {10.5281/zenodo.17112481}, url = {https://huggingface.co/datasets/IPF/AIME25-CoT-CN}, note = {Also available on Zenodo} }

CIKM 2025

GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction

Zhijie Lin^{*Equal contribution}, Zhuofeng Li^{*Equal contribution}, Haoxiang Zhang, and 4 more authors Chenglei Dai, Wentian Bao, Enyun Yu, Haoxiang Zhang, Liang Zhao^{❧Corresponding author}

Paper | Abstract | BibTeX

A novel generative framework that enhances reranking efficiency through ordered multi-token prediction, providing improved performance in information retrieval tasks.

@article{lin2025gref, title={GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction}, author={Lin, Zhijie and Li, Zhuofeng and Dai, Chenglei and Bao, Wentian and Lin, Shuai and Yu, Enyun and Zhang, Haoxiang and Zhao, Liang}, journal={arXiv preprint arXiv:2510.25220}, year={2025} }

Mathematics

SGNet: Efficient Snow Removal Deep Network with a Global Windowing Transformer

Shan Lie, Haoxiang Zhang, Bodong Cheng^{❧Corresponding author}

Paper | Abstract | BibTeX

An efficient deep learning approach for snow removal in images using a global windowing transformer architecture, achieving state-of-the-art performance in image restoration tasks.

@article{shan2024sgnet, title={SGNet: Efficient Snow Removal Deep Network with a Global Windowing Transformer}, author={Shan, Lie and Zhang, Haoxiang and Cheng, Bodong}, journal={Mathematics}, volume={12}, number={10}, pages={1424}, year={2024}, publisher={MDPI} }

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