<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title/><link>https://tangent0308.github.io/</link><atom:link href="https://tangent0308.github.io/index.xml" rel="self" type="application/rss+xml"/><description/><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 08 Sep 2026 00:00:00 +0000</lastBuildDate><image><url>https://tangent0308.github.io/media/icon_hu_1c0e9cb08cfb822a.png</url><title/><link>https://tangent0308.github.io/</link></image><item><title>Open-o3-Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence</title><link>https://tangent0308.github.io/publications/open-o3-video-grounded-video-reasoning-with-explicit-spatio-temporal-evidence/</link><pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate><guid>https://tangent0308.github.io/publications/open-o3-video-grounded-video-reasoning-with-explicit-spatio-temporal-evidence/</guid><description>&lt;p&gt;This publication entry was migrated from the previous site.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Citation:&lt;/strong&gt; J Meng, X Li, H Wang, Yue Tan, T Zhang, L Kong, Y Tong, A Wang, Z Teng, et al. (2026). &amp;ldquo;Open-o3-Video: Grounded Video Reasoning with Explicit Spatio-Temporal Evidence.&amp;rdquo; ICML 2026.&lt;/p&gt;</description></item><item><title>Towards One-to-Many Temporal Grounding</title><link>https://tangent0308.github.io/publications/towards-one-to-many-temporal-grounding/</link><pubDate>Mon, 01 Jun 2026 00:00:00 +0000</pubDate><guid>https://tangent0308.github.io/publications/towards-one-to-many-temporal-grounding/</guid><description>&lt;p&gt;This publication entry was migrated from the previous site.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Citation:&lt;/strong&gt; Q Xu, Yue Tan, S Chen, J Meng, A Wang, S Ji, H Fei, J Li (2026). &amp;ldquo;Towards One-to-Many Temporal Grounding.&amp;rdquo; ICML 2026.&lt;/p&gt;</description></item><item><title>Videozerobench: Probing the limits of video MLLMs with spatio-temporal evidence verification</title><link>https://tangent0308.github.io/publications/videozerobench-probing-the-limits-of-video-mllms-with-spatio-temporal-evidence-verification/</link><pubDate>Wed, 01 Apr 2026 00:00:00 +0000</pubDate><guid>https://tangent0308.github.io/publications/videozerobench-probing-the-limits-of-video-mllms-with-spatio-temporal-evidence-verification/</guid><description>&lt;p&gt;This publication entry was migrated from the previous site.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Citation:&lt;/strong&gt; J Meng, Yue Tan, Q Xu, H Wang, Z Ren, W Liu, Y Wang, R Zhang, Y Tong, et al. (2026). &amp;ldquo;Videozerobench: Probing the limits of video MLLMs with spatio-temporal evidence verification.&amp;rdquo; arXiv preprint arXiv:2604.01569.&lt;/p&gt;</description></item><item><title>Cyberv: Cybernetics for test-time scaling in video understanding</title><link>https://tangent0308.github.io/publications/cyberv-cybernetics-for-test-time-scaling-in-video-understanding/</link><pubDate>Sun, 01 Jun 2025 00:00:00 +0000</pubDate><guid>https://tangent0308.github.io/publications/cyberv-cybernetics-for-test-time-scaling-in-video-understanding/</guid><description>&lt;p&gt;This publication entry was migrated from the previous site.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Citation:&lt;/strong&gt; J Meng, S Sun, Yue Tan, L Qi, Y Tong, X Li, L Wen (2025). &amp;ldquo;Cyberv: Cybernetics for test-time scaling in video understanding.&amp;rdquo; arXiv preprint arXiv:2506.07971.&lt;/p&gt;</description></item><item><title>About</title><link>https://tangent0308.github.io/about/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://tangent0308.github.io/about/</guid><description>&lt;p&gt;Yue Tan is a Peking University Class of 2022 undergraduate majoring in Information and Computing Science at the School of Information Science and Technology. Yue Tan is also an incoming Ph.D. student (Class of 2026) at the Wang Xuan Institute of Computer Technology, School of Computer Science, Peking University. Research interests include multimodal large models, video understanding, and AIGC.&lt;/p&gt;
&lt;p&gt;For the latest profile, publications, and contact information, please visit the
.&lt;/p&gt;</description></item></channel></rss>