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Blog Release Xueguang

Zhuofeng Li*$^{1}$, Dongfu Jiang*$^{2}$, Xueguang Ma*$^{2}$, Haoxiang Zhang$^{3}$, Yuyu Zhang$^{4}$, Kai Zou$^{5}$, Ping Nie$^{2}$, Jianwen Xie$^{6}$, Yu Zhang†$^{1}$, Wenhu Chen†$^{2}$

$^{1}$Texas A&M University $^{2}$Waterloo University $^{3}$UC San Diego $^{4}$Verdent AI $^{5}$NetMind AI $^{6}$Lambda

*****: Equal Contribution; **†**Corresponding Authors

February 2026


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TL;DR

👨‍💻 Github, 🤗 HF Model, 🤗 HF Dataset, 📈 Wandb Logs, 🔎 Eval Logs

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1. Open Source Gaps in Deep Research Agents

Deep research agents—systems that perform iterative search, evidence gathering, and multi-step reasoning—have become a key frontier for LLM capabilities. Commercial systems like Perplexity Deep Research, OpenAI Deep Research, and Gemini Deep Research demonstrate impressive performance, but open-source alternatives lack key components.

Work Weights Trajectories Code Environment
Search-R1 [1] ✅ (Wikipedia)
WebShaper [2] ❌ (API-based)
MiroMind [3] ❌ (API-based)
Ours

Key gaps in existing work:

This post provides a fully open pipeline for synthesizing long-horizon deep research trajectories with 100+ turns, releasing all components for the community.