Github on PKU ZhiClasshttps://zhi-class.ai/en/tags/github/Recent content in Github on PKU ZhiClassHugo -- gohugo.ioenCopyright © Zhi Class 2022-2026Thu, 12 Feb 2026 00:00:00 +0000LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestionhttps://zhi-class.ai/en/research/2602.lda-1b/Thu, 12 Feb 2026 00:00:00 +0000https://zhi-class.ai/en/research/2602.lda-1b/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.Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fieldshttps://zhi-class.ai/en/research/2601.learning-physics-grounded-4d-dynamics-with-neura/Mon, 26 Jan 2026 00:00:00 +0000https://zhi-class.ai/en/research/2601.learning-physics-grounded-4d-dynamics-with-neura/Predicting physical dynamics from visual data remains a fundamental challenge in AI, as it requires both accurate scene understanding and robust physics reasoning.MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Usehttps://zhi-class.ai/en/research/2601.mcpmark/Mon, 26 Jan 2026 00:00:00 +0000https://zhi-class.ai/en/research/2601.mcpmark/The MCP standardizes how LLMs interact with external systems, forming the foundation for general agents.Neural Force Field: Few shot learning of generalized physical reasoninghttps://zhi-class.ai/en/research/2601.neural-force-field/Mon, 26 Jan 2026 00:00:00 +0000https://zhi-class.ai/en/research/2601.neural-force-field/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.Generalized Threshold Optimization with Harmony Multi-Threshold Neurons for Accurate ANN-to-SNN Conversionhttps://zhi-class.ai/en/research/2601.generalized-threshold-optimization-with-harmony/Tue, 20 Jan 2026 00:00:00 +0000https://zhi-class.ai/en/research/2601.generalized-threshold-optimization-with-harmony/Spiking Neural Networks (SNNs) are a promising paradigm designed to emulate the brain’s energy efficient by incorporating the timing of spikes.ProgressGym: Alignment with a Millennium of Moral Progresshttps://zhi-class.ai/en/research/paper8/Tue, 10 Dec 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper8/Benchmarking Open-instruction 6-DoF Object Rearrangement and A VLM-based Approachhttps://zhi-class.ai/en/research/paper2/Mon, 14 Oct 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper2/Exploring Conditional Multi-Modal Prompts for Zero-shot HOI Detectionhttps://zhi-class.ai/en/research/paper4/Sun, 29 Sep 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper4/MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusionhttps://zhi-class.ai/en/research/paper3/Sun, 29 Sep 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper3/Language Models Represent Beliefs of Self and Othershttps://zhi-class.ai/en/research/paper7/Sun, 21 Jul 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper7/Diff-BGM: A Diffusion Model for Video Background Music Generationhttps://zhi-class.ai/en/research/paper9/Mon, 17 Jun 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper9/Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detectionhttps://zhi-class.ai/en/research/paper5/Mon, 17 Jun 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper5/Scaling up dynamic human-scene interaction modelinghttps://zhi-class.ai/en/research/paper1/Mon, 17 Jun 2024 00:00:00 +0000https://zhi-class.ai/en/research/paper1/