[{"content":"","date":"10 July 2026","externalUrl":null,"permalink":"/en/activities/","section":"","summary":"","title":"","type":"activities"},{"content":"","date":"10 July 2026","externalUrl":"https://mp.weixin.qq.com/s/ibyTrQgYKh_rrzLoVR1QZw","permalink":"/en/activities/2026-07-10_0/","section":"","summary":"2022级智班毕业专访——徐佳怡学姐篇","title":"2026毕业特辑-徐佳怡 | 风物长宜放眼量","type":"activities"},{"content":"","date":"10 July 2026","externalUrl":null,"permalink":"/en/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","date":"10 July 2026","externalUrl":null,"permalink":"/en/authors/pku%E6%99%BA%E7%8F%AD/","section":"Authors","summary":"","title":"PKU智班","type":"authors"},{"content":"","date":"10 July 2026","externalUrl":null,"permalink":"/en/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"","date":"10 July 2026","externalUrl":null,"permalink":"/en/tags/%E6%B4%BB%E5%8A%A8/","section":"Tags","summary":"","title":"活动","type":"tags"},{"content":"","date":"7 July 2026","externalUrl":"https://mp.weixin.qq.com/s/XzD0E3gzBrkq32X1nJFywg","permalink":"/en/activities/2026-07-07_0/","section":"","summary":"2022级智班毕业专访——戴冠哲学长篇","title":"2026毕业特辑-戴冠哲 | 破而后立，在平淡与抗挫中寻找生活的解","type":"activities"},{"content":"","date":"6 July 2026","externalUrl":"https://mp.weixin.qq.com/s/sesy3KjlzcvYSBkP-YKS0w","permalink":"/en/activities/2026-07-06_0/","section":"","summary":"2022级智班毕业专访——陈灵钧学长篇","title":"2026毕业特辑-陈灵钧 | You will never walk alone.","type":"activities"},{"content":"","date":"4 July 2026","externalUrl":"https://mp.weixin.qq.com/s/LUVA2rSOrkvCHRKKX2ZUiA","permalink":"/en/activities/2026-07-04_0/","section":"","summary":"祝22级智班同学们毕业快乐，前程似锦！","title":"22级智班毕业啦|青春不散场，智启新征程","type":"activities"},{"content":"","date":"4 July 2026","externalUrl":"https://mp.weixin.qq.com/s/2g_-6JCWf0dKeaot1CTSKQ","permalink":"/en/activities/2026-07-04_1/","section":"","summary":"祝22级智班同学们毕业快乐，前程似锦！","title":"青春不散场，智启新征程 ——22级智班毕业啦","type":"activities"},{"content":"","date":"12 June 2026","externalUrl":"https://mp.weixin.qq.com/s/_uDG4jkyCbB2MnIsA9heZA","permalink":"/en/activities/2026-06-12_0/","section":"","summary":"2022级智班毕业专访——王梓帆学长篇","title":"2026毕业特辑-王梓帆 | 保持热爱，奔赴山海","type":"activities"},{"content":"","date":"8 June 2026","externalUrl":"https://mp.weixin.qq.com/s/_njaMRRYnlr9B1Foj1irlw","permalink":"/en/activities/2026-06-08_0/","section":"","summary":"2026智班黄奕聪奖学金申报通道开启","title":"2026年度智班“黄奕聪奖学金”申报通知","type":"activities"},{"content":"","date":"6 June 2026","externalUrl":"https://mp.weixin.qq.com/s/2ye0Ma8uCtPehvC33Qw66Q","permalink":"/en/activities/2026-06-06_0/","section":"","summary":"2022级智班毕业专访——张子沐学长篇","title":"2026毕业特辑-张子沐 | 潜龙勿用","type":"activities"},{"content":"","date":"2 June 2026","externalUrl":"https://mp.weixin.qq.com/s/NbiwQASS7WDHYgPqX6DEEA","permalink":"/en/activities/2026-06-02_0/","section":"","summary":"2022级智班毕业专访——林宇辰学长篇","title":"2026毕业特辑-林宇辰 | Do important things","type":"activities"},{"content":"","date":"2 June 2026","externalUrl":"https://mp.weixin.qq.com/s/VF91SLmV80gK0J4fJiAn8Q","permalink":"/en/activities/2026-06-02_1/","section":"","summary":"2022级智班毕业专访——林宇辰学长篇","title":"2026毕业特辑-林宇辰 | Do important things","type":"activities"},{"content":"本网站的初版搭建始于2024年春，感谢以下同学的付出（按拼音排序）：\n丁昱菲、牛艺达、沈睿弘、王敏齐、殷绍峰、于旻扬、赵瑞捷\n","date":"1 June 2026","externalUrl":null,"permalink":"/en/thanks/","section":"致谢","summary":"本网站的初版搭建始于2024年春，感谢以下同学的付出（按拼音排序）：","title":"致谢","type":"thanks"},{"content":"","date":"29 May 2026","externalUrl":"https://mp.weixin.qq.com/s/J5Kab-bwYTYal1uc0Dqq0w","permalink":"/en/activities/2026-05-29_0/","section":"","summary":"2022级智班毕业专访——张洋学长篇","title":"2026毕业特辑-张洋 | Explore \u0026 Exploit（择路与深耕）","type":"activities"},{"content":"","date":"28 May 2026","externalUrl":"https://mp.weixin.qq.com/s/YySEik0ZeZxSVyg78BDiHg","permalink":"/en/activities/2026-05-28_0/","section":"","summary":"2022级智班毕业专访——周骏学长篇","title":"2026毕业特辑-周骏 | AI 筑桥，逐梦前行","type":"activities"},{"content":"","date":"27 May 2026","externalUrl":"https://mp.weixin.qq.com/s/JqNvDhwcGZoPLX_TLfuHWA","permalink":"/en/activities/2026-05-27_0/","section":"","summary":"2022级智班毕业专访——王敏齐学长篇","title":"2026毕业特辑-王敏齐 | 不要担心被命运的大雨淋湿","type":"activities"},{"content":"","date":"26 May 2026","externalUrl":"https://mp.weixin.qq.com/s/re7Ot99vLV2nbTK_KaLdBA","permalink":"/en/activities/2026-05-26_0/","section":"","summary":"2022级智班毕业专访——殷绍峰学长篇","title":"2026毕业特辑-殷绍峰 | 培养感受幸福的能力","type":"activities"},{"content":"","date":"25 May 2026","externalUrl":"https://mp.weixin.qq.com/s/q3UoW8sSPHPQ7PfGTl4qVw","permalink":"/en/activities/2026-05-25_0/","section":"","summary":"2022级智班毕业专访——王浩翔学长篇","title":"2026毕业特辑-王浩翔 | 纵有疾风起，人生不言弃","type":"activities"},{"content":"","date":"21 May 2026","externalUrl":"https://mp.weixin.qq.com/s/-yvPxkCP-UZ7xhm2aa2iOg","permalink":"/en/activities/2026-05-21_0/","section":"","summary":"2022级智班毕业专访——于旻扬学长篇","title":"2026毕业特辑-于旻扬 | 思考不止，步履不息","type":"activities"},{"content":"","date":"20 May 2026","externalUrl":"https://mp.weixin.qq.com/s/4Lmcy1-_LP68VP8g1KqP2Q","permalink":"/en/activities/2026-05-20_0/","section":"","summary":"2022级智班毕业专访——汤家淇学长篇","title":"2026毕业特辑-汤家淇 | 自信人生二百年，会当水击三千里","type":"activities"},{"content":"","date":"16 April 2026","externalUrl":null,"permalink":"/en/tags/zhi-talk/","section":"Tags","summary":"","title":"Zhi Talk","type":"tags"},{"content":"","date":"16 April 2026","externalUrl":"https://mp.weixin.qq.com/s/sZNNdXyMwx74DG0gqycc0g","permalink":"/en/activities/2026-04-16_0/","section":"","summary":"2026/04/11                                          ZhiTong Talk                                          总结回顾                                                                            嘉宾介绍          \u0026hellip;","title":"Zhitong-Talk 总结回顾","type":"activities"},{"content":"","date":"3 April 2026","externalUrl":"https://mp.weixin.qq.com/s/OQG2qUtJ4UumnClj1rDeNA","permalink":"/en/activities/2026-04-03_0/","section":"","summary":"4月11日 北京时间上午10:00","title":"Zhi Talk |  VIGA: An Agent That \"Thinks\" with the Blender Engine","type":"activities"},{"content":"","date":"2 April 2026","externalUrl":"https://mp.weixin.qq.com/s/J8BxMU96ec8y89mFU92EiQ","permalink":"/en/activities/2026-04-02_0/","section":"","summary":"春日之约，智班同行","title":"智汇春光 定向未来 | 记2026智班团建","type":"activities"},{"content":"","date":"3 March 2026","externalUrl":"https://mp.weixin.qq.com/s/Unkd8PAWj8dwe3jlfDO5HQ","permalink":"/en/activities/2026-03-03_0/","section":"","summary":"3月7日 北京时间下午3:00 | Guest: Boyuan Chen | Title:  AI Alignment and the World Model Paradigm","title":"Zhi Talk |  AI Alignment and the World Model Paradigm","type":"activities"},{"content":"","date":"17 February 2026","externalUrl":"https://mp.weixin.qq.com/s/VTROaUnIrkHqM33Wsq9Esg","permalink":"/en/activities/2026-02-17_0/","section":"","summary":"咚咚咚，新春快乐！2026 丙午马年的正式到来，智学会为大家准备了智班吉祥物的专属红包封面，两种款式各100个，先到先得哦~                                                   最后，一场赛博烟花秀送给屏幕前的你：金马送吉，祝新岁如愿，春风得意！","title":"马年到 鸿运照|智班红包封面来啦","type":"activities"},{"content":"","date":"16 February 2026","externalUrl":"https://mp.weixin.qq.com/s/SZHTcAqYfnUVbjeStTytiw","permalink":"/en/activities/2026-02-16_0/","section":"","summary":"2026，马到成功 共赴新程！","title":"马年大吉|智班邀您领取新年祝福！","type":"activities"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/tags/arxiv/","section":"Tags","summary":"","title":"Arxiv","type":"tags"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/dongbin-zhao/","section":"Authors","summary":"","title":"Dongbin Zhao","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/tags/github/","section":"Tags","summary":"","title":"Github","type":"tags"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/haoran-li/","section":"Authors","summary":"","title":"Haoran Li","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/haoran-liao/","section":"Authors","summary":"","title":"Haoran Liao","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/he-wang/","section":"Authors","summary":"","title":"He Wang","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/jiangran-lyu/","section":"Authors","summary":"","title":"Jiangran Lyu*","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/jiayi-chen/","section":"Authors","summary":"","title":"Jiayi Chen","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/jiazhao-zhang/","section":"Authors","summary":"","title":"Jiazhao Zhang","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/kai-liu/","section":"Authors","summary":"","title":"Kai Liu*","type":"authors"},{"content":"Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous embodied data. While the Unified World Model (UWM) formulation has the potential to leverage such diverse data, existing instantiations struggle to scale to foundation-level due to coarse data usage and fragmented datasets. We introduce LDA-1B, a robot foundation model that scales through universal embodied data ingestion by jointly learning dynamics, policy, and visual forecasting, assigning distinct roles to data of varying quality. To support this regime at scale, we assemble and standardize EI-30k, an embodied interaction dataset comprising over 30k hours of human and robot trajectories in a unified format. Scalable dynamics learning over such heterogeneous data is enabled by prediction in a structured DINO latent space, which avoids redundant pixel-space appearance modeling. Complementing this representation, LDA-1B employs a multi-modal diffusion transformer to handle asynchronous vision and action streams, enabling stable training at the 1B-parameter scale. Experiments in simulation and the real world show LDA-1B outperforms prior methods (e.g., ) by up to 21%, 48%, and 23% on contact-rich, dexterous, and long-horizon tasks, respectively. Notably, LDA-1B enables data-efficient fine-tuning, gaining 10% by leveraging 30% low-quality trajectories typically harmful and discarded.\n","date":"12 February 2026","externalUrl":"https://pku-epic.github.io/LDA/","permalink":"/en/research/2602.lda-1b/","section":"Research","summary":"Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous embodied data.","title":"LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion","type":"research"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/li-yi/","section":"Authors","summary":"","title":"Li Yi","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/mi-yan/","section":"Authors","summary":"","title":"Mi Yan","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/ming-yu-liu/","section":"Authors","summary":"","title":"Ming-Yu Liu","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/research/","section":"Research","summary":"","title":"Research","type":"research"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/tags/rss-%E5%9C%A8%E6%8A%95/","section":"Tags","summary":"","title":"RSS 在投","type":"tags"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/senmao-qi/","section":"Authors","summary":"","title":"Senmao Qi","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/shuo-wang/","section":"Authors","summary":"","title":"Shuo Wang","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/tingrui-shen/","section":"Authors","summary":"","title":"Tingrui Shen","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/wenbo-cui/","section":"Authors","summary":"","title":"Wenbo Cui","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/wenxuan-zhu/","section":"Authors","summary":"","title":"Wenxuan Zhu","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/xuesong-shi/","section":"Authors","summary":"","title":"Xuesong Shi","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/xuheng-zhang/","section":"Authors","summary":"","title":"Xuheng Zhang*","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/yifei-dong/","section":"Authors","summary":"","title":"Yifei Dong","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/yixin-zheng/","section":"Authors","summary":"","title":"Yixin Zheng","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/yizhou-wang/","section":"Authors","summary":"","title":"Yizhou Wang","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/yusen-feng/","section":"Authors","summary":"","title":"Yusen Feng","type":"authors"},{"content":"","date":"12 February 2026","externalUrl":null,"permalink":"/en/authors/zhizheng-zhang/","section":"Authors","summary":"","title":"Zhizheng Zhang","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/hao-dong12/","section":"Authors","summary":"","title":"Hao Dong1,2,‡","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/haolin-yang/","section":"Authors","summary":"","title":"Haolin Yang*","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/tags/icra/","section":"Tags","summary":"","title":"ICRA","type":"tags"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/jiapeng-xu/","section":"Authors","summary":"","title":"Jiapeng Xu","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/lei-kang/","section":"Authors","summary":"","title":"Lei Kang","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/minghan-wang/","section":"Authors","summary":"","title":"Minghan Wang","type":"authors"},{"content":"Instruction-following navigation is a key step toward embodied intelligence. Prior benchmarks mainly focus on semantic understanding but overlook systematically evaluating navigation agents\u0026rsquo; spatial perception and reasoning capabilities. In this work, we introduce the NavSpace benchmark, which contains six task categories and 1,228 trajectory-instruction pairs designed to probe the spatial intelligence of navigation agents. On this benchmark, we comprehensively evaluate 22 navigation agents, including state-of-the-art navigation models and multimodal large language models. The evaluation results lift the veil on spatial intelligence in embodied navigation. Furthermore, we propose SNav, a new spatially intelligent navigation model. SNav outperforms existing navigation agents on NavSpace and real robot tests, establishing a strong baseline for future work.\n","date":"31 January 2026","externalUrl":"https://navspace.github.io/","permalink":"/en/research/2601.navspace/","section":"Research","summary":"Instruction-following navigation is a key step toward embodied intelligence.","title":"NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions","type":"research"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/wenzhe-cai/","section":"Authors","summary":"","title":"Wenzhe Cai","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/yihan-wang/","section":"Authors","summary":"","title":"Yihan Wang","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/yuxing-long/","section":"Authors","summary":"","title":"Yuxing Long*","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/zhuoyuan-yu/","section":"Authors","summary":"","title":"Zhuoyuan Yu","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/zihan-yang/","section":"Authors","summary":"","title":"Zihan Yang","type":"authors"},{"content":"","date":"31 January 2026","externalUrl":null,"permalink":"/en/authors/ziyan-yu/","section":"Authors","summary":"","title":"Ziyan Yu","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/arvin-xu/","section":"Authors","summary":"","title":"Arvin Xu","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/chang-pan/","section":"Authors","summary":"","title":"Chang Pan","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/chi-zhang/","section":"Authors","summary":"","title":"Chi Zhang","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/fanqing-meng/","section":"Authors","summary":"","title":"Fanqing Meng","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/fanshi-zhang/","section":"Authors","summary":"","title":"Fanshi Zhang","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/tags/iclr/","section":"Tags","summary":"","title":"ICLR","type":"tags"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/tags/iclr-2026/","section":"Tags","summary":"","title":"ICLR 2026","type":"tags"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/jiawei-wang/","section":"Authors","summary":"","title":"Jiawei Wang","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/jinjie-ni/","section":"Authors","summary":"","title":"Jinjie Ni","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/junfeng-ni/","section":"Authors","summary":"","title":"Junfeng Ni","type":"authors"},{"content":"Predicting physical dynamics from visual data remains a fundamental challenge in AI, as it requires both accurate scene understanding and robust physics reasoning. While recent video generation models achieve impressive visual quality, they lack explicit physics modeling and frequently violate fundamental laws like gravity and object permanence. Existing approaches combining 3D Gaussian splatting with traditional physics engines achieve physical consistency but suffer from prohibitive computational costs and struggle with complex real-world multi-object interactions. The key challenge lies in developing a unified framework that learns physics-grounded representations directly from visual observations while maintaining computational efficiency and generalization capability. Here we introduce NGFF, an end-to-end neural framework that learns explicit force fields from 3D Gaussian representations to generate interactive, physically realistic 4D videos from multi-view RGB inputs, achieving two orders of magnitude speedup over prior Gaussian simulators. Through explicit force field modeling, NGFF demonstrates superior spatial, temporal, and compositional generalization compared to SOTA methods, including Veo3 and NVIDIA Cosmos, while enabling robust sim-to-real transfer. Comprehensive evaluation on our GSCollision dataset\u0026mdash;640k rendered physical videos (~4TB) spanning diverse materials and complex multi-object interactions\u0026mdash;validates NGFF\u0026rsquo;s effectiveness across challenging scenarios. Our results demonstrate that NGFF provides an effective bridge between visual perception and physical understanding, advancing video prediction toward physics-grounded world models with interactive capabilities.\n","date":"26 January 2026","externalUrl":"https://neuralgaussianforcefield.github.io/","permalink":"/en/research/2601.learning-physics-grounded-4d-dynamics-with-neura/","section":"Research","summary":"Predicting physical dynamics from visual data remains a fundamental challenge in AI, as it requires both accurate scene understanding and robust physics reasoning.","title":"Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields","type":"research"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/lingjun-chen/","section":"Authors","summary":"","title":"Lingjun Chen","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/lingxiao-du/","section":"Authors","summary":"","title":"Lingxiao Du","type":"authors"},{"content":"The MCP standardizes how LLMs interact with external systems, forming the foundation for general agents. However, existing MCP benchmarks remain narrow in scope: they focus on read-heavy tasks or tasks with limited interaction depth, and fail to capture the complexity and realism of real-world workflows. To address this gap, we propose MCPMark, a benchmark designed to evaluate MCP use in a more realistic and comprehensive manner. It consists of 127 high-quality tasks collaboratively created by domain experts and AI agents, each with a curated initial state and programmatic verification script. These tasks demand diverse CRUD operations and richer environmental interactions. We evaluate cutting-edge LLMs using a minimal agent framework. The best-performing model, gpt-5-medium, reaches only 52.56% pass@1 and 33.86% passˆ4, while other strong models including claude-sonnet-4 and o3 fall below 30% pass@1 and 15% passˆ4. On average, LLMs require 16.2 turns and 17.4 tool calls per task, highlighting the stress-testing nature of MCPMark.\n","date":"26 January 2026","externalUrl":"https://arxiv.org/abs/2509.24002","permalink":"/en/research/2601.mcpmark/","section":"Research","summary":"The MCP standardizes how LLMs interact with external systems, forming the foundation for general agents.","title":"MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use","type":"research"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/michael-qizhe-shieh/","section":"Authors","summary":"","title":"Michael Qizhe Shieh","type":"authors"},{"content":"We present NFF, a modeling framework built on NODE that learns interpretable force field representations which can be efficiently integrated through an ODE solver to predict object trajectories. Unlike existing approaches that rely on high-dimensional latent spaces, NFF captures fundamental physical concepts such as gravity, support, and collision in an interpretable manner. Experiments on two challenging physical reasoning tasks demonstrate that NFF, trained with only a few examples, achieves strong generalization to unseen scenarios. This physics-grounded representation enables efficient forward-backward planning and rapid adaptation through interactive refinement.\n","date":"26 January 2026","externalUrl":"https://neuralforcefield.github.io/","permalink":"/en/research/2601.neural-force-field/","section":"Research","summary":"We present NFF, a modeling framework built on NODE that learns interpretable force field representations which can be efficiently integrated through an ODE solver to predict object trajectories.","title":"Neural Force Field: Few shot learning of generalized physical reasoning","type":"research"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/ruihong-shen/","section":"Authors","summary":"","title":"Ruihong Shen","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/shiqian-li/","section":"Authors","summary":"","title":"Shiqian Li","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/xiangyan-liu/","section":"Authors","summary":"","title":"Xiangyan Liu","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/xinyuan-zhang/","section":"Authors","summary":"","title":"Xinyuan Zhang","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/yaoqi-ye/","section":"Authors","summary":"","title":"Yaoqi Ye","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/yaoyu-tao/","section":"Authors","summary":"","title":"Yaoyu Tao","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/yiran-zhao/","section":"Authors","summary":"","title":"Yiran Zhao","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/yixin-zhu/","section":"Authors","summary":"","title":"Yixin Zhu","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/yufan-yang/","section":"Authors","summary":"","title":"Yufan Yang","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/zijian-wu/","section":"Authors","summary":"","title":"Zijian Wu","type":"authors"},{"content":"","date":"26 January 2026","externalUrl":null,"permalink":"/en/authors/zirui-wang/","section":"Authors","summary":"","title":"Zirui Wang","type":"authors"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/tags/aaai-26/","section":"Tags","summary":"","title":"AAAI-26","type":"tags"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/tags/ccf-a/","section":"Tags","summary":"","title":"CCF A","type":"tags"},{"content":"Spiking Neural Networks (SNNs) are a promising paradigm designed to emulate the brain’s energy efficient by incorporating the timing of spikes. Conversion is an efficient way to obtain high-performance SNNs from Artificial Neural Networks (ANNs). Existing conversion methods often face a trade-off between accuracy and time steps, which is largely caused by the incomplete release of residual membrane potentials. To minimize the conversion error, this paper proposed a harmonious mathematical property-based neuron, called Harmony Multi-Threshold Neurons (H-MT Neuron), which utilizes multiple spikes to minimize residual membrane potentials. The proposed neuron is further enhanced with an optional effective communication mechanism to achieve more accurate conversion. In addition, we propose a threshold optimization method applicable to a broader range cases of spiking neurons to to find the optimal neuron thresholds. Experiment results demonstrate that our method achieve superior accuracy on ImageNet benchmark datasets while significantly reducing the required time steps and energy consumption.\n","date":"20 January 2026","externalUrl":"https://doi.org/10.48448/64g5-3g71","permalink":"/en/research/2601.generalized-threshold-optimization-with-harmony/","section":"Research","summary":"Spiking Neural Networks (SNNs) are a promising paradigm designed to emulate the brain’s energy efficient by incorporating the timing of spikes.","title":"Generalized Threshold Optimization with Harmony Multi-Threshold Neurons for Accurate ANN-to-SNN Conversion","type":"research"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/authors/tiejun-huang/","section":"Authors","summary":"","title":"Tiejun Huang","type":"authors"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/authors/tong-bu/","section":"Authors","summary":"","title":"Tong Bu","type":"authors"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/authors/wenhan-zhang/","section":"Authors","summary":"","title":"Wenhan Zhang","type":"authors"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/authors/zhaofei-yu/","section":"Authors","summary":"","title":"Zhaofei Yu","type":"authors"},{"content":"","date":"20 January 2026","externalUrl":null,"permalink":"/en/authors/zihan-huang/","section":"Authors","summary":"","title":"Zihan Huang","type":"authors"},{"content":"","date":"9 January 2026","externalUrl":"https://mp.weixin.qq.com/s/0QoNPGEEA3c6TpF0x-zNDw","permalink":"/en/activities/2026-01-09_0/","section":"","summary":"全文共2941字，阅读约需9分钟    [图片][图片]    关键词：未名学子说[图片]    编者按“未名湖是个海洋”，燕园蕴藏着无数种生活。或耀眼，或沉静，每个学子都能在北大找到独属自己的成长之路。是严师益友的陪伴，是九州四海的际遇，是兼容并包的无限可能。在“未名学子说”中，一同深入探索最真\u0026hellip;","title":"未名学子说 | 徐灵昀：寻找属于自己的动机","type":"activities"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/di-he/","section":"Authors","summary":"","title":"Di He","type":"authors"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/jiayi-xu/","section":"Authors","summary":"","title":"Jiayi Xu","type":"authors"},{"content":"In this paper, we introduce \\emph{Luminark｝, a training-free and probabilistically-certified watermarking method for general vision generative models. Our approach is built upon a novel watermark definition that leverages patch-level luminance statistics. Specifically, the service provider predefines a binary pattern together with corresponding patch-level thresholds. To detect a watermark in a given image, we evaluate whether the luminance of each patch surpasses its threshold and then verify whether the resulting binary pattern aligns with the target one. A simple statistical analysis demonstrates that the false positive rate of the proposed method can be effectively controlled, thereby ensuring certified detection. To enable seamless watermark injection across different paradigms, we leverage the widely adopted guidance technique as a plug-and-play mechanism and develop the \\emph{watermark guidance｝. This design enables Luminark to achieve generality across state-of-the-art generative models without compromising image quality. Empirically, we evaluate our approach on nine models spanning diffusion, autoregressive, and hybrid frameworks. Across all evaluations, Luminark consistently demonstrates high detection accuracy, strong robustness against common image transformations, and good performance on visual quality.\n","date":"3 January 2026","externalUrl":"https://arxiv.org/abs/2601.01085","permalink":"/en/research/2601.luminark/","section":"Research","summary":"In this paper, we introduce \\emph{Luminark｝, a training-free and probabilistically-certified watermarking method for general vision generative models.","title":"Luminark: Training-free, Probabilistically-Certified Watermarking for General Vision Generative Models","type":"research"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/ruitao-chen/","section":"Authors","summary":"","title":"Ruitao Chen","type":"authors"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/tianyu-he/","section":"Authors","summary":"","title":"Tianyu He","type":"authors"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/yixian-xu/","section":"Authors","summary":"","title":"Yixian Xu","type":"authors"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/yuanrui-zhang/","section":"Authors","summary":"","title":"Yuanrui Zhang","type":"authors"},{"content":"","date":"3 January 2026","externalUrl":null,"permalink":"/en/authors/zhang-zhang/","section":"Authors","summary":"","title":"Zhang Zhang","type":"authors"},{"content":"","date":"2 January 2026","externalUrl":"https://mp.weixin.qq.com/s/Jyr4SEksGMgvVGDBH_ONLQ","permalink":"/en/activities/2026-01-02_0/","section":"","summary":"","title":"致青春正闪耀-智班师生2026新年晚会节目","type":"activities"},{"content":"","date":"28 December 2025","externalUrl":"https://mp.weixin.qq.com/s/0Tai8XSisXwMQ6Sq7bsFTA","permalink":"/en/activities/2025-12-28_0/","section":"","summary":"寒意渐浓，交流会如期而至。","title":"智chat | 初雪纷飞日，温暖相聚时","type":"activities"},{"content":"","date":"21 December 2025","externalUrl":"https://mp.weixin.qq.com/s/M7f55mWOyFoQTbYccf1CCQ","permalink":"/en/activities/2025-12-21_0/","section":"","summary":"本文第一作者是来自北京大学的本科生殷绍峰，合作者包含来自北京大学的博士生雷廷，通讯作者为北京大学王选计算机研究","title":"智知 | 智班论文精选","type":"activities"},{"content":"","date":"20 December 2025","externalUrl":"https://mp.weixin.qq.com/s/bC_1pf-pse0jEAGvRxbfZQ","permalink":"/en/activities/2025-12-20_0/","section":"","summary":"初雪燕园，                                禄岛暖饺                                                                                           北风卷着碎雪掠过燕园的檐角时，青砖黛瓦已被初雪晕染成一片温柔的雪白。恰逢这京城第一场雪落的日子，禄岛成了我们冬日相聚的坐标，滚沸的饺子馅\u0026hellip;","title":"初雪燕园，禄岛暖饺","type":"activities"},{"content":"","date":"6 December 2025","externalUrl":"https://mp.weixin.qq.com/s/7G53krEgytPG_35XWxlhPA","permalink":"/en/activities/2025-12-06_0/","section":"","summary":"【智Chat：学长学姐有“话”说】温暖对谈，听见彼此的声音 冬至虽未至，温暖已启程。我们将在12月初于禄岛举办一次轻松而真诚的跨年级交流活动——不是单向的“分享”，而是双向的“对话”。在这里，你可以听到学长学姐真实的成长故事，也可以提出自己最关心的问题，甚至随时加入讨论、随时提问。我们希望这是一次真正“听得见每个人”的相聚。我们理解大家时间安排不同，欢迎随时加入、也可随时离开，只需安静进出即可。 📅 活动时间：12月12日，16：00-17：30 📍 活动地点：禄岛活动室 👥 参与对象：22-25级智班同学 🎯 活动内容： ·话题引导，自由交流：我们将提前征集同学们关心的问题，归类为学习、生活、科研、未来规划等方向。学长学姐将以自身经历为引，不局限于具体问题，更注重真实的感受与建议。 ·随时提问，轻松互动：你可以随时举手发言、追问，甚至接受学长学姐的“反问”。Host会协助暖场、鼓励发言，并把握交流节奏。 ·零食茶点，抽奖活动：现场将准备茶点零食，并且有神秘抽奖活动等着大家 🤔 我们期待这样的交流： ·不是“你应该怎么做”，而是“我曾经这样走过”； ·不是单向的聆听，而是彼此的倾听； ·不追求覆盖所有问题，但希望回应你此刻最真实的困惑。 📝 报名方式：通过问卷星链接报名https://v.wjx.cn/vm/QCVUVA8.aspx# 期待与你，在冬日的禄岛，温暖对话。 智学会学术部","title":"智Chat:学长学姐有“话”说","type":"activities"},{"content":"","date":"5 December 2025","externalUrl":"https://mp.weixin.qq.com/s/POVVnHt0lUG4UmQcYWZRlw","permalink":"/en/activities/2025-12-05_0/","section":"","summary":"冬至将至，昼短情长。智班同学发起的一年一度的温馨团聚即将开启！我们将在禄岛共度一个充满欢笑、美食与游戏的暖冬夜晚。 📅 活动时间：12月12日（周五）晚 17:30 📍 活动地点：禄岛活动室 👥 参与对象：智班，通班，图班，信班及智能学院同学 🎯 活动内容： · 🥟 饺子DIY：我们准备了面皮与馅料，大家可以在现场亲自参与饺子包制，并品尝自己和同学们亲手制作的各色水饺！ · 🎲 桌游自由局：活动部准备多种桌游，轻松开局，畅聊互动～ · ❄️ 团建小游戏：知识问答、猜谜互动……PPT已就位，等你来挑战！ · 🎵 氛围与记录：背景音乐、零食饮料俱全，现场更有照片记录温馨时刻～ 📝 报名方式：扫描海报中的二维码。 让我们暂缓脚步，借冬至之夜相聚言欢，在游戏与热饺之间，联结你我，共蓄暖意。期待与你，不见不散！","title":"共聚禄岛，暖意“饺”汇","type":"activities"},{"content":"","date":"29 November 2025","externalUrl":"https://mp.weixin.qq.com/s/Yd9czfQq1cNkovbdyks5jw","permalink":"/en/activities/2025-11-29_0/","section":"","summary":"本文转自大信科公众号                                       REVIEW                            智班学子海外交流小记                                  1                                  背景概要                                  \u0026hellip;","title":"智班学子海外交流小记丨在海德堡，与科学和浪漫撞个满怀","type":"activities"},{"content":"","date":"20 November 2025","externalUrl":"https://mp.weixin.qq.com/s/Wr4OyJ3pboTuAHiQL5PL4g","permalink":"/en/activities/2025-11-20_0/","section":"","summary":"11月22日 北京时间上午10:30 | Guest: Kaile Wang | Title:  Language Models Resist Alignment","title":"Zhi Talk |  Language Models Resist Alignment","type":"activities"},{"content":"","date":"14 November 2025","externalUrl":"https://mp.weixin.qq.com/s/NvhYJLppErIT4sqB7NG8iw","permalink":"/en/activities/2025-11-14_0/","section":"","summary":"告别盛夏，暑气消散                          天高云阔，秋凉宜人                          浪漫的秋日                          总是酝酿着诗意                                \u0026ndash; the beauty of autumn                                     \u0026hellip;","title":"北平的秋 | 观香山红叶","type":"activities"},{"content":"","date":"8 November 2025","externalUrl":null,"permalink":"/en/tags/aaai/","section":"Tags","summary":"","title":"AAAI","type":"tags"},{"content":"","date":"8 November 2025","externalUrl":null,"permalink":"/en/authors/chengyan-zeng/","section":"Authors","summary":"","title":"Chengyan Zeng","type":"authors"},{"content":"Existing vision-and-language navigation models often deviate from the correct trajectory when executing instructions. However, these models lack effective error correction capability, hindering their recovery from errors. To address this challenge, we propose Self-correction Flywheel, a novel post-training paradigm. Instead of considering the model\u0026rsquo;s error trajectories on the training set as a drawback, our paradigm emphasizes their significance as a valuable data source. We have developed a method to identify deviations in these error trajectories and devised innovative techniques to automatically generate self-correction data for perception and action. These self-correction data serve as fuel to power the model\u0026rsquo;s continued training. The brilliance of our paradigm is revealed when we re-evaluate the model on the training set, uncovering new error trajectories. At this time, the self-correction flywheel begins to spin. Through multiple flywheel iterations, we progressively enhance our monocular RGB-based VLA navigation model CorrectNav. Experiments on R2R-CE and RxR-CE benchmarks show CorrectNav achieves new state-of-the-art success rates of 65.1% and 69.3%, surpassing prior best VLA navigation models by 8.2% and 16.4%. Real robot tests in various indoor and outdoor environments demonstrate \\method\u0026rsquo;s superior capability of error correction, dynamic obstacle avoidance, and long instruction following.\n","date":"8 November 2025","externalUrl":"https://correctnav.github.io/","permalink":"/en/research/2511.correctnav/","section":"Research","summary":"Existing vision-and-language navigation models often deviate from the correct trajectory when executing instructions.","title":"CorrectNav: Self-Correction Flywheel Empowers Vision-Language-Action Navigation Model","type":"research"},{"content":"","date":"8 November 2025","externalUrl":null,"permalink":"/en/authors/hao-dong/","section":"Authors","summary":"","title":"Hao Dong†","type":"authors"},{"content":"","date":"8 November 2025","externalUrl":null,"permalink":"/en/authors/hongwei-fan/","section":"Authors","summary":"","title":"Hongwei Fan","type":"authors"},{"content":"","date":"8 November 2025","externalUrl":null,"permalink":"/en/authors/jiyao-zhang/","section":"Authors","summary":"","title":"Jiyao Zhang","type":"authors"},{"content":"","date":"8 November 2025","externalUrl":null,"permalink":"/en/authors/zhuoyuan-yu/","section":"Authors","summary":"","title":"Zhuoyuan Yu*","type":"authors"},{"content":"","date":"7 November 2025","externalUrl":"https://mp.weixin.qq.com/s/YdO3Op180eFuaL0U7NEN2Q","permalink":"/en/activities/2025-11-07_0/","section":"","summary":"2025 AI Tech Day                                   INTRO                            概况                                      2025年10月25日下午，由北京大学人工智能研究院主办的第三届科技节（AI Tech Day）在百周年纪念讲堂拉开帷幕。科技节不仅面向全校师生开放，而\u0026hellip;","title":"跨界交融，同赴智能之约 | 第三届科技节圆满落幕","type":"activities"},{"content":"","date":"6 November 2025","externalUrl":"https://mp.weixin.qq.com/s/zxIG20Gnuu33HlsxIxnvEQ","permalink":"/en/activities/2025-11-06_0/","section":"","summary":"前言                                                                           2025年10月25日，秋意浸润燕园，由人工智能研究院主办的2025年度学生大会于北京大学百周年纪念讲堂李莹厅如约启幕。这场盛会也是智班大会系列活动的第三篇章，它打破群体界限——北大智班学子与清北通班、通计划成员、智能学科硕博群体欣然相聚，在“交\u0026hellip;","title":"智班学生大会系列活动 | 2025年度学生大会顺利召开","type":"activities"},{"content":"","date":"5 November 2025","externalUrl":"https://mp.weixin.qq.com/s/72gNEVsMBETUYpw3MG-8zQ","permalink":"/en/activities/2025-11-05_0/","section":"","summary":"ZHI🚶🏻‍➡️2025                                                                                                                                              金秋十月，燕园层林尽染。智班启动智班学生大会系列活动，师生齐聚一堂，在回望专业建设步履铿锵的\u0026hellip;","title":"矢志一流，同心致远 | 2025智班学生大会系列活动圆满举行","type":"activities"},{"content":"","date":"11 September 2025","externalUrl":"https://mp.weixin.qq.com/s/nk4KmY3C2cJaeHnHbt-bsA","permalink":"/en/activities/2025-09-11_0/","section":"","summary":"2025级智班迎新活动顺利举行","title":"薪火相传，智启新章 | 智班举办2025级新生见面会","type":"activities"},{"content":" Recent # 2026毕业特辑-徐佳怡 | 风物长宜放眼量 \u0026#8599; \u0026#8598; 10 July 2026 PKU智班 活动 2022级智班毕业专访——徐佳怡学姐篇 2026毕业特辑-戴冠哲 | 破而后立，在平淡与抗挫中寻找生活的解 \u0026#8599; \u0026#8598; 7 July 2026 PKU智班 活动 2022级智班毕业专访——戴冠哲学长篇 2026毕业特辑-陈灵钧 | You will never walk alone. \u0026#8599; \u0026#8598; 6 July 2026 PKU智班 活动 2022级智班毕业专访——陈灵钧学长篇 LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion \u0026#8599; \u0026#8598; 12 February 2026 Jiangran Lyu* ,\u0026nbsp; Kai Liu* ,\u0026nbsp; Xuheng Zhang* ,\u0026nbsp; Haoran Liao ,\u0026nbsp; Yusen Feng ,\u0026nbsp; Wenxuan Zhu ,\u0026nbsp; Tingrui Shen ,\u0026nbsp; Jiayi Chen ,\u0026nbsp; Jiazhao Zhang ,\u0026nbsp; Yifei Dong ,\u0026nbsp; Wenbo Cui ,\u0026nbsp; Senmao Qi ,\u0026nbsp; Shuo Wang ,\u0026nbsp; Yixin Zheng ,\u0026nbsp; Mi Yan ,\u0026nbsp; Xuesong Shi ,\u0026nbsp; Haoran Li ,\u0026nbsp; Dongbin Zhao ,\u0026nbsp; Ming-Yu Liu ,\u0026nbsp; Zhizheng Zhang ,\u0026nbsp; Li Yi ,\u0026nbsp; Yizhou Wang ,\u0026nbsp; He Wang Arxiv Github RSS 在投 Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert … NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions \u0026#8599; \u0026#8598; 31 January 2026 Haolin Yang* ,\u0026nbsp; Yuxing Long* ,\u0026nbsp; Zhuoyuan Yu ,\u0026nbsp; Zihan Yang ,\u0026nbsp; Minghan Wang ,\u0026nbsp; Jiapeng Xu ,\u0026nbsp; Yihan Wang ,\u0026nbsp; Ziyan Yu ,\u0026nbsp; Wenzhe Cai ,\u0026nbsp; Lei Kang ,\u0026nbsp; Hao Dong1,2,‡ Arxiv ICRA Instruction-following navigation is a key step toward embodied intelligence. Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields \u0026#8599; \u0026#8598; 26 January 2026 Shiqian Li ,\u0026nbsp; Ruihong Shen ,\u0026nbsp; Junfeng Ni ,\u0026nbsp; Chang Pan ,\u0026nbsp; Chi Zhang ,\u0026nbsp; Yixin Zhu Arxiv Github ICLR Predicting physical dynamics from visual data remains a fundamental challenge in AI, as it requires … ","date":"30 August 2025","externalUrl":null,"permalink":"/en/","section":"","summary":"Recent # 2026毕业特辑-徐佳怡 | 风物长宜放眼量 \u0026#8599; \u0026#8598; 10 July 2026 PKU智班 活动 2022级智班毕业专访——徐佳怡学姐篇 2026毕业特辑-戴冠哲 | 破而后立，在平淡与抗挫中寻找生活的解 \u0026#8599; \u0026#8598; 7 July 2026 PKU智班 活动 2022级智班毕业专访——戴冠哲学长篇 2026毕业特辑-陈灵钧 | You will never walk alone.","title":"","type":"page"},{"content":"","date":"26 August 2025","externalUrl":"https://mp.weixin.qq.com/s/_nTF0C9K6FpObSR_m7Hy6A","permalink":"/en/activities/2025-08-26_0/","section":"","summary":"智学会换届招新- Waiting for You -    新学期伊始，智学会即将迎来2025-2026学年换","title":"2025智学会换届通知","type":"activities"},{"content":"","date":"14 July 2025","externalUrl":null,"permalink":"/en/tags/iccv-2025/","section":"Tags","summary":"","title":"ICCV 2025","type":"tags"},{"content":"","date":"14 July 2025","externalUrl":null,"permalink":"/en/authors/shaofeng-yin/","section":"Authors","summary":"","title":"Shaofeng Yin","type":"authors"},{"content":"","date":"14 July 2025","externalUrl":null,"permalink":"/en/authors/ting-lei/","section":"Authors","summary":"","title":"Ting Lei","type":"authors"},{"content":"","date":"14 July 2025","externalUrl":"https://fugtemypt123.github.io/ToolVQA-website/","permalink":"/en/research/2507.toolvqa/","section":"Research","summary":"","title":"ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools","type":"research"},{"content":"","date":"14 July 2025","externalUrl":null,"permalink":"/en/authors/yang-liu/","section":"Authors","summary":"","title":"Yang Liu†","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/hao-dong/","section":"Authors","summary":"","title":"Hao Dong","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/haoran-geng/","section":"Authors","summary":"","title":"Haoran Geng","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/tags/iros-2025/","section":"Tags","summary":"","title":"IROS 2025","type":"tags"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/jiahe-ying/","section":"Authors","summary":"","title":"Jiahe Ying","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/jinzhou-li/","section":"Authors","summary":"","title":"Jinzhou Li","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/lehong-wu/","section":"Authors","summary":"","title":"Lehong Wu","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/long-yang/","section":"Authors","summary":"","title":"Long Yang","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/mingdong-wu/","section":"Authors","summary":"","title":"Mingdong Wu","type":"authors"},{"content":"Autonomous learning of dexterous, long-horizon robotic skills has been a longstanding pursuit of embodied AI. Recent advances in robotic reinforcement learning (RL) have demonstrated remarkable performance and robustness in real-world visuomotor control tasks. However, applying RL in the real world faces challenges such as low sample efficiency, slow exploration, and significant reliance on human intervention. In contrast, simulators offer a safe and efficient environment for extensive exploration and data collection, while the visual sim-to-real gap, often a limiting factor, can be mitigated using real-to-sim techniques. Building on these, we propose SimLauncher, a novel framework that combines the strengths of real-world RL and real-to-sim-to-real approaches to overcome these challenges. Specifically, we first pre-train a visuomotor policy in the digital twin simulation environment, which then benefits real-world RL in two ways: (1) bootstrapping target values using extensive simulated demonstrations and real-world demonstrations derived from pre-trained policy rollouts, and (2) Incorporating action proposals from the pre-trained policy for better exploration. We conduct comprehensive experiments across multi-stage, contact-rich, and dexterous hand manipulation tasks. Compared to prior real-world RL approaches, SimLauncher significantly improves sample efficiency and achieves near-perfect success rates. We hope this work serves as a proof of concept and inspires further research on leveraging large-scale simulation pre-training to benefit real-world robotic RL.\n","date":"6 July 2025","externalUrl":"https://simlauncher.github.io/","permalink":"/en/research/2507.simlauncher/","section":"Research","summary":"Autonomous learning of dexterous, long-horizon robotic skills has been a longstanding pursuit of embodied AI.","title":"SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training","type":"research"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/weiyao-huang/","section":"Authors","summary":"","title":"Weiyao Huang","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/yizhuo-wu/","section":"Authors","summary":"","title":"Yizhuo Wu","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/yuanpei-chen/","section":"Authors","summary":"","title":"Yuanpei Chen","type":"authors"},{"content":"","date":"6 July 2025","externalUrl":null,"permalink":"/en/authors/zheyuan-hu/","section":"Authors","summary":"","title":"Zheyuan Hu","type":"authors"},{"content":"","date":"26 June 2025","externalUrl":null,"permalink":"/en/authors/haiwen-feng/","section":"Authors","summary":"","title":"Haiwen Feng","type":"authors"},{"content":"","date":"26 June 2025","externalUrl":"https://mp.weixin.qq.com/s/OFLuEumqC-whPgARiS860g","permalink":"/en/activities/2025-06-26_0/","section":"","summary":"","title":"Zhi Talk | Counterfactual Imagination via World Simulator","type":"activities"},{"content":"","date":"26 June 2025","externalUrl":null,"permalink":"/en/authors/%E7%88%B1%E5%90%ACtalk%E7%9A%84%E6%99%BA%E5%AD%A6%E4%BC%9A/","section":"Authors","summary":"","title":"爱听talk的智学会","type":"authors"},{"content":"","date":"18 June 2025","externalUrl":null,"permalink":"/en/authors/%E6%99%BA%E7%8F%AD/","section":"Authors","summary":"","title":"智班","type":"authors"},{"content":"","date":"18 June 2025","externalUrl":null,"permalink":"/en/tags/%E6%AF%95%E4%B8%9A/","section":"Tags","summary":"","title":"毕业","type":"tags"},{"content":"","date":"18 June 2025","externalUrl":"https://mp.weixin.qq.com/s/s4lkXCh96NAvSlcSC_8ccg","permalink":"/en/activities/2025-06-18_0/","section":"","summary":"","title":"青春未散场，未来正启航——记智班首届毕业生欢送会","type":"activities"},{"content":"","date":"2 June 2025","externalUrl":"https://mp.weixin.qq.com/s/oxwnJE6kVodQUHaoUDpRxg","permalink":"/en/activities/2025-06-02_0/","section":"","summary":"","title":"以香为礼，端午安康——记智班手作香囊活动","type":"activities"},{"content":"","date":"1 June 2025","externalUrl":null,"permalink":"/en/authors/jiang-bian/","section":"Authors","summary":"","title":"Jiang Bian","type":"authors"},{"content":"","date":"1 June 2025","externalUrl":null,"permalink":"/en/authors/junliang-guo/","section":"Authors","summary":"","title":"Junliang Guo","type":"authors"},{"content":"","date":"1 June 2025","externalUrl":null,"permalink":"/en/tags/neurips-2025/","section":"Tags","summary":"","title":"NeurIPS 2025","type":"tags"},{"content":"In this work, we present Next-Frame Diffusion (NFD), an autoregressive diffusion transformer that incorporates block-wise causal attention, enabling iterative sampling and efficient inference via parallel token generation within each frame. Nonetheless, achieving real-time video generation remains a significant challenge for such models, primarily due to the high computational cost associated with diffusion sampling and the hardware inefficiencies inherent to autoregressive generation. To address this, we introduce two innovations: (1) We extend consistency distillation to the video domain and adapt it specifically for video models, enabling efficient inference with few sampling steps; (2) To fully leverage parallel computation, motivated by the observation that adjacent frames often share the identical action input, we propose speculative sampling. In this approach, the model generates next few frames using current action input, and discard speculatively generated frames if the input action differs. Experiments on a large-scale action-conditioned video generation benchmark demonstrate that NFD beats autoregressive baselines in terms of both visual quality and sampling efficiency. We, for the first time, achieves autoregressive video generation at over 30 Frames Per Second (FPS) on an A100 GPU using a 310M model.\n","date":"1 June 2025","externalUrl":"https://nextframed.github.io/","permalink":"/en/research/2506.nextframed/","section":"Research","summary":"In this work, we present Next-Frame Diffusion (NFD), an autoregressive diffusion transformer that incorporates block-wise causal attention, enabling iterative sampling and efficient inference via parallel token generation within each frame.","title":"Playing with Transformer at 30+ FPS via Next-Frame Diffusion","type":"research"},{"content":"","date":"1 June 2025","externalUrl":null,"permalink":"/en/authors/tianyu-he/","section":"Authors","summary":"","title":"Tianyu He†","type":"authors"},{"content":"","date":"1 June 2025","externalUrl":null,"permalink":"/en/authors/xinle-cheng/","section":"Authors","summary":"","title":"Xinle Cheng","type":"authors"},{"content":"","date":"7 May 2025","externalUrl":"https://hierarchical-chain-of-generation.github.io/","permalink":"/en/research/2505.gascol/","section":"Research","summary":"","title":"Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation","type":"research"},{"content":"","date":"7 May 2025","externalUrl":null,"permalink":"/en/tags/cvpr-2025/","section":"Tags","summary":"","title":"CVPR 2025","type":"tags"},{"content":"","date":"7 May 2025","externalUrl":null,"permalink":"/en/authors/yiming-qin/","section":"Authors","summary":"","title":"Yiming Qin","type":"authors"},{"content":"","date":"7 May 2025","externalUrl":null,"permalink":"/en/authors/zhu-xu/","section":"Authors","summary":"","title":"Zhu Xu","type":"authors"},{"content":"","date":"24 April 2025","externalUrl":null,"permalink":"/en/authors/chenguo-lin/","section":"Authors","summary":"","title":"Chenguo Lin†","type":"authors"},{"content":"","date":"24 April 2025","externalUrl":null,"permalink":"/en/tags/iclr-2025/","section":"Tags","summary":"","title":"ICLR 2025","type":"tags"},{"content":"","date":"24 April 2025","externalUrl":null,"permalink":"/en/authors/jianjin-xu/","section":"Authors","summary":"","title":"Jianjin Xu","type":"authors"},{"content":"","date":"24 April 2025","externalUrl":"https://wgsxm.github.io/projects/omniphysgs/","permalink":"/en/research/2504.omniphysgs/","section":"Research","summary":"","title":"OmniPhysGS: 3D Constitutive Gaussians for General Physics-based Dynamics Generation","type":"research"},{"content":"","date":"24 April 2025","externalUrl":null,"permalink":"/en/authors/yadong-mu/","section":"Authors","summary":"","title":"Yadong Mu‡","type":"authors"},{"content":"","date":"24 April 2025","externalUrl":null,"permalink":"/en/authors/yuchen-lin/","section":"Authors","summary":"","title":"Yuchen Lin","type":"authors"},{"content":"","date":"30 March 2025","externalUrl":null,"permalink":"/en/tags/activities/","section":"Tags","summary":"","title":"Activities","type":"tags"},{"content":"","date":"30 March 2025","externalUrl":"https://mp.weixin.qq.com/s/lGYN6_4h70L_5oT6j__KIw","permalink":"/en/activities/2025-03-30_0/","section":"","summary":"","title":"通智天下 友谊长青——智班通班团建活动回顾","type":"activities"},{"content":"","date":"14 March 2025","externalUrl":null,"permalink":"/en/authors/hao-zhu/","section":"Authors","summary":"","title":"Hao Zhu","type":"authors"},{"content":"","date":"14 March 2025","externalUrl":"https://mp.weixin.qq.com/s/lGYN6_4h70L_5oT6j__KIw","permalink":"/en/activities/2025-03-14_0/","section":"","summary":"","title":"Zhi Talk | Training Language Agents in the Open Social World","type":"activities"},{"content":"","date":"8 March 2025","externalUrl":null,"permalink":"/en/authors/jiaxin-ge/","section":"Authors","summary":"","title":"Jiaxin Ge","type":"authors"},{"content":"","date":"8 March 2025","externalUrl":"https://mp.weixin.qq.com/s/JngFcMDX6yRN5opLIM7jJQ","permalink":"/en/activities/2025-03-08_0/","section":"","summary":"","title":"Zhi Talk | Visual Understanding and Generation through Code","type":"activities"},{"content":"","date":"17 February 2025","externalUrl":null,"permalink":"/en/tags/%E4%BA%BA%E7%89%A9/","section":"Tags","summary":"","title":"人物","type":"tags"},{"content":"","date":"17 February 2025","externalUrl":null,"permalink":"/en/tags/%E5%85%B6%E4%BB%96/","section":"Tags","summary":"","title":"其他","type":"tags"},{"content":"","date":"17 February 2025","externalUrl":"https://mp.weixin.qq.com/s/q827ndvWHDnp4THAxd04tw","permalink":"/en/activities/2025-02-17_0/","section":"","summary":"","title":"未名学子说 | 张洋：追逐“风浪”的勇气","type":"activities"},{"content":"","date":"17 February 2025","externalUrl":null,"permalink":"/en/authors/%E6%A0%A1%E6%8B%9B%E7%94%9F%E5%8A%9E/","section":"Authors","summary":"","title":"校招生办","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/arman-cohan/","section":"Authors","summary":"","title":"Arman Cohan","type":"authors"},{"content":"We present ChemAgent, a novel framework designed to improve the performance of LLMs through a dynamic, self-updating library. This library is developed by decomposing chemical tasks into sub-tasks and compiling these sub-tasks into a structured collection that can be referenced for future queries. Then, when presented with a new problem, ChemAgent retrieves and refines pertinent information from the library, which we call memory, facilitating effective task decomposition and the generation of solutions. Our method designs three types of memory and a library-enhanced reasoning component, enabling LLMs to improve over time through experience. Experimental results on four chemical reasoning datasets from SciBench demonstrate that ChemAgent achieves performance gains of up to 46% (GPT-4), significantly outperforming existing methods. Our findings suggest substantial potential for future applications, including tasks such as drug discovery and materials science.\n","date":"3 January 2025","externalUrl":"https://arxiv.org/abs/2501.06590","permalink":"/en/research/2501.chemagent/","section":"Research","summary":"We present ChemAgent, a novel framework designed to improve the performance of LLMs through a dynamic, self-updating library.","title":"ChemAgent: Self-updating Memories in Large Language Models Improves Chemical Reasoning","type":"research"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/mark-gerstein/","section":"Authors","summary":"","title":"Mark Gerstein","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/muyang-ye/","section":"Authors","summary":"","title":"Muyang Ye*","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/pan-lu/","section":"Authors","summary":"","title":"Pan Lu","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/siru-ouyang/","section":"Authors","summary":"","title":"Siru Ouyang","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/tianyu-hu/","section":"Authors","summary":"","title":"Tianyu Hu*","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/wangchunshu-zhou/","section":"Authors","summary":"","title":"Wangchunshu Zhou","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/xiangru-tang/","section":"Authors","summary":"","title":"Xiangru Tang*","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/xunjian-yin/","section":"Authors","summary":"","title":"Xunjian Yin","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/yanjun-shao/","section":"Authors","summary":"","title":"Yanjun Shao*","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/yilun-zhao/","section":"Authors","summary":"","title":"Yilun Zhao","type":"authors"},{"content":"","date":"3 January 2025","externalUrl":null,"permalink":"/en/authors/zhuosheng-zhang/","section":"Authors","summary":"","title":"Zhuosheng Zhang","type":"authors"},{"content":"","date":"30 December 2024","externalUrl":"https://mp.weixin.qq.com/s/n395roLVPbnaN0AjwK2ZzQ","permalink":"/en/activities/2024-12-30_0/","section":"","summary":"","title":"智能学院2025新年晚会 | 岁末嘉年庆新春，智班学子放华彩","type":"activities"},{"content":"","date":"10 December 2024","externalUrl":null,"permalink":"/en/authors/jasmine-xinze-li/","section":"Authors","summary":"","title":"Jasmine Xinze Li","type":"authors"},{"content":"","date":"10 December 2024","externalUrl":null,"permalink":"/en/authors/jiaming-ji/","section":"Authors","summary":"","title":"Jiaming Ji","type":"authors"},{"content":"","date":"10 December 2024","externalUrl":null,"permalink":"/en/tags/neurips2024/","section":"Tags","summary":"","title":"NeurIPS2024","type":"tags"},{"content":"","date":"10 December 2024","externalUrl":"","permalink":"/en/research/paper8/","section":"Research","summary":"","title":"ProgressGym: Alignment with a Millennium of Moral Progress","type":"research"},{"content":"","date":"10 December 2024","externalUrl":null,"permalink":"/en/authors/tianyi-qiu/","section":"Authors","summary":"","title":"Tianyi Qiu*†","type":"authors"},{"content":"","date":"10 December 2024","externalUrl":null,"permalink":"/en/authors/xuchuan-huang/","section":"Authors","summary":"","title":"Xuchuan Huang","type":"authors"},{"content":"","date":"10 December 2024","externalUrl":null,"permalink":"/en/authors/yang-zhang/","section":"Authors","summary":"","title":"Yang 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