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122    <img src="images/android-chrome-512x512.png" class="author__avatar" alt="Yuyang Song">
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126    <h3 class="author__name">Yuyang Song</h3>
127    <p class="author__bio">Ph.D. @ UT Austin</p>
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135        <li><i class="fa fa-fw fa-map-marker" aria-hidden="true"></i> Austin, TX, USA</li>
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223<p><span class="anchor" id="about-me"></span></p>
224
225<!-- Yuyang Song is an incoming PhD student in Computer Science at The University of Texas at Austin, advised by Prof. [Yizhong Wang](https://yizhong-wang.com/). He receives his B.S. in Computer Science from the Wu Yuzhang Honors College at Sichuan University in June 2026, under the supervision of  Prof. [Mingjie Tang](https://merlintang.github.io/). Previously, he was a research intern at [Purdue University](https://www.purdue.edu/) advised by Prof. [Jianguo Wang](https://www.cs.purdue.edu/homes/csjgwang/). His research interest lies in **Efficient LLM Reasoning** and **System Optimization**. -->
226
227<!-- Hi! I'm Yuyang Song, an incoming PhD student in Computer Science at [University of Texas at Austin](https://www.cs.utexas.edu/), where I'll be working with Prof. [Yizhong Wang](https://yizhong-wang.com/). I wrap up my B.S. in Computer Science at the Wu Yuzhang Honors College, [Sichuan University](http://en.scu.edu.cn/), advised by Prof. [Mingjie Tang](https://merlintang.github.io/). Before that, I was fortunate to be advised by Prof. [Jianguo Wang](https://www.cs.purdue.edu/homes/csjgwang/) at [Purdue University](https://www.purdue.edu/). -->
228
229<p>Hi! I’m Yuyang Song, a first-year PhD student in Computer Science at <
229a href="https://www.cs.utexas.edu/">University of Texas at Austin</a>, where I feel fortunate to be advised by Prof. <a href="https://yizhongw.github.io/">Yizhong Wang</a>. I earned my B.S. in Computer Science at the Wu Yuzhang Honors College, <a href="http://en.scu.edu.cn/">Sichuan University</a>, under the supervision of Prof. <a href="https://merlintang.github.io/">Mingjie Tang</a>. From October 2025 to July 2026, I was a member of the Foundation Model Group at IQuest AI Lab, Ubiquant.</p>
230
231<p>I’m broadly interested in <strong>how data shapes a model as it flows through training, and how architecture and training should be designed around that flow</strong>, with efficiency as a guiding principle. Rather than treating the model, its data, and its training as separate problems, I see them as forces that continually shape one another, and I want to understand how to harness that interplay to build capable models more efficiently.</p>
232
233<h1 id="-selected-publications">📝 Selected Publications</h1>
234<div class="paper-box"><div class="paper-box-image"><div><div class="badge">Tech Report</div><img src="images/iquest-q1.png" alt="sym" width="100%" /></div></div>
235<div class="paper-box-text">
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237    <p><a href="https://iquestlab.github.io/">IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
238</a> | <a href="https://huggingface.co/IQuestLab/IQuest-Q1"><strong>Code</strong></a></p>
239
240    <p><strong><span class="show_paper_citations" data="DhtAFkwAAAAJ:ALROH1vI_8AC"></span></strong>
241IQuest-Q1 is an open-source agentic foundation model for CLI systems. A sparse Mixture-of-Experts model with 320B total parameters and 15B activated, it combines general and coding agent capabilities with efficient compute for medium- and long-horizon tasks. During development, it also takes part in its own improvement under human supervision.</p>
242  </div>
243</div>
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245<div class="paper-box"><div class="paper-box-image"><div><div class="badge">ICML 2026 (Spotlight)</div><img src="images/terminal.png" alt="sym" width="100%" /></div></div>
246<div class="paper-box-text">
247
248    <p><a href="https://openreview.net/pdf?id=PeFSCRulgy">Large-Scale Terminal Agentic Trajectory Generation
249from Dockerized Environments</a> | <a href="https://github.com/multimodal-art-projection/TerminalTraj"><strong>Code</strong></a></p>
250
251    <p>Siwei Wu*, Yizhi Li*, <strong>Yuyang Song</strong>*, Wei Zhang, Riza Batista-Navarro, Xian yang, Mingjie Tang, Bryan Dai, Jian Yang, Chenghua Lin.</p>
252
253    <p><strong><span class="show_paper_citations" data="DhtAFkwAAAAJ:ALROH1vI_8AC"></span></strong>
254We study how large-scale, execution-grounded terminal trajectories reshape the agentic capabilities of code LLMs. Trained on this data, TerminalTraj-32B achieves state-of-the-art results among models under 100B parameters and matches models more than 10× larger, while showing markedly steeper test-time scaling—evidence that the right training data, not just scale, drives terminal agent capability.</p>
255  </div>
256</div>
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258<div class="paper-box"><div class="paper-box-image"><div><div class="badge">Tech Report</div><img src="images/iquest_result.png" alt="sym" width="100%" /></div></div>
259<div class="paper-box-text">
260
261    <p><a href="https://arxiv.org/pdf/2603.16733">IQuest-Coder-V1 Technical Report</a> | <a href="https://huggingface.co/IQuestLab"><strong>Code</strong></a></p>
262
263    <p>Jian Yang, Wei Zhang, Shawn Guo, Zhengmao Ye, Lin Jing, Shark Liu, Yizhi Li, Jiajun Wu, Cening Liu, X. Ma, <strong>Yuyang Song</strong>, et al.</p>
264
265    <p><strong><span class="show_paper_citations" data="DhtAFkwAAAAJ:ALROH1vI_8AC"></span></strong>
266IQuest-Coder-V1 is a new family of code large language models (LLMs) designed to advance autonomous software engineering and code intelligence. Built on the innovative code-flow multi-stage training paradigm, IQuest-Coder-V1 captures the dynamic evolution of software logic, delivering state-of-the-art performance across critical dimensions.</p>
267  </div>
268</div>
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270<div class="paper-box"><div class="paper-box-image"><div><div class="badge">arXiv'25</div><img src="images/quite.png" alt="sym" width="100%" /></div></div>
271<div class="paper-box-text">
272
273    <p><a href="https://arxiv.org/pdf/2506.07675">QUITE: A Query Rewrite System Beyond Rules with LLM Agents</a> | <a href="https://github.com/Yuyang-Song/QUITE"><strong>Code</strong></a></p>
274
275    <p><strong>Yuyang Song</strong>, Hanxu Yan, Jiale Lao, Yibo Wang, Yufei Li, Yuanchun Zhou, Jianguo Wang, Mingjie Tang</p>
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277    <p><strong><span class="show_paper_citations" data="DhtAFkwAAAAJ:ALROH1vI_8AC"></span></strong>
278QUITE is a training-free and feedback-aware system based on LLM agents that rewrites SQL queries into semantically equivalent forms with significantly better performance, covering a broader range of query patterns and rewrite strategies compared to rule-based methods.</p>
279  </div>
280</div>
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282<!-- - [Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ornare aliquet ipsum, ac tempus justo dapibus sit amet](https://github.com), A, B, C, **CVPR 2020** -->
283
284<!-- # 🔬 Projects
285### Query Rewrite via Large Language Model for DBMS | 2024.07 - 2024.08
286- Leverage the analytical power of Large Language Model to propose the relational rules of SQL optimization.
287- Repeat a LLM-based optimization pipline to reduce the hallucination issues in Large Language Model.
288- Construct a rewrite rule knowledge pool to further gain the learn ability.
289- [Query Rewrite via Large Language Model for DBMS](https://github.com/Orderheart/GenRewrite)
290
291### Construction of targeted anti-caries polypeptides based on machine learning | 2023.11 - 2024.03
292- Characterization of anti-caries antimicrobial peptide protein sequence and construction of data set.
293- Feature extraction and feature scoring index system were constructed by machine learning algorithm.
294- The optimal antimicrobial peptides were found by regression analysis of MIC value.
295- [Construction of targeted anti-caries polypeptides based on machine learning](https://github.com/Orderheart/Machine_Learning_AMP_Analysis) -->
296
297<!-- <font color=red>Nationcal Scholarship</font> -->
298
299<h1 id="-honors-and-awards">🎖 Honors and Awards</h1>
300<ul>
301  <li><em>2026.05</em>, Outstanding Undergraduate Graduate, Sichuan Province.</li>
302  <li><em>2023.11</em>, First Prize, China Undergraduate Mathematical Contest in Modeling.</li>
303  <li><em>2023.10</em>, National Scholarship, Ministry of Education, China (Top 1%).</li>
304  <li><em>2023.10</em>, First-Class Scholarship, Sichuan University (Top 1%).</li>
305  <li><em>2023.08</em>, First Prize, China Undergraduate Internet of Things Design Competition.</li>
306</ul>
307
308<h1 id="-educations">📖 Educations</h1>
309<div class="school-list">
310<div class="school-item">
311  <div class="school-logo"><img src="images/utaustin.png" alt="UT Austin" /></div>
312  <div class="school-text">
313      <p><em>2026.08 - Present</em>, Ph.D. in Computer Science, University of Texas at Austin.</p>
314    </div>
315</div>
316<div class="school-item">
317  <div class="school-logo"><img src="images/scu.png" alt="Sichuan University" /></div>
318  <div class="school-text">
319      <p><em>2022.09 - 2026.06</em>, B.S. in Computer Science, Wuyuzhang College (Honours Programme), Sichuan University.</p>
320    </div>
321</div>
322</div>
323
324<!-- # 💬 Invited Talks
325- *2021.06*, Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ornare aliquet ipsum, ac tempus justo dapibus sit amet. 
326- *2021.03*, Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ornare aliquet ipsum, ac tempus justo dapibus sit amet.  \| [\[video\]](https://github.com/) -->
327
328<h1 id="-internships">💻 Internships</h1>
329<ul>
330  <li><em>2025.10 - 2026.07</em>, Research Internship, IQuest AI Lab, Ubiquant, China.
331<br /></li>
332</ul>
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