14 citations · 65 across the 30 of their papers we have counts for
30 papers
AnyGS2Mesh: Feed-Forward Mesh Reconstruction from 3D Gaussian Splatting with Arbitrary-Resolution Views
Yuxuan Song, Fan Gao, Yibo Zhao +3
Existing 3D mesh reconstruction methods from Gaussian scene representations predominantly rely on iterative optimization, resulting in slow inference and limited scalability to hig…
Spectral Rewiring for Exploration, Purification, and Model Merging
Zhilong Zhang, Hongli Yu, Huan-ang Gao +5
Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed…
Improved Large Language Diffusion Models
Shen Nie, Qiyang Min, Shaoxuan Xu +7
Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present \emph{iLLaDA}, an 8B masked diffusion language model train…
AMix-2: Establishing Protein as a Native Modality in Large Language Models
Keyue Qiu, Yixin Wu, Lihao Wang +19
We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein understanding and sequence design…
DCFold: Efficient Protein Structure Generation with Single Forward Pass
Zhe Zhang, Yuanning Feng, Yuxuan Song +3
AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This state-of-the-art performance has…
STAC: Plug-and-Play Spatio-Temporal Aware Cache Compression for Streaming 3D Reconstruction
Runze Wang, Yuxuan Song, Youcheng Cai +1
Online 3D reconstruction from streaming inputs requires both long-term temporal consistency and efficient memory usage. Although causal variants of VGGT address this challenge thro…
Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model
Chenghao Fan, Wen Heng, Bo Li +6
Diffusion-based language models (DLLMs) offer non-sequential, block-wise generation and richer data reuse compared to autoregressive (AR) models, but existing code DLLMs still lag…
Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
Xingwei Qu, Shaowen Wang, Zihao Huang +16
Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity…
InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue
Wenwen Tong, Hewei Guo, Dongchuan Ran +23
We introduce InteractiveOmni, a unified and open-source omni-modal large language model for audio-visual multi-turn interaction, ranging from 4B to 8B parameters, designed to lead…
ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation
Yuxuan Song, Zhe Zhang, Yu Pei +7
Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting M…
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
Yuxuan Song, Zheng Zhang, Cheng Luo +19
We present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel gene…
Protenix-Mini: Efficient Structure Predictor via Compact Architecture, Few-Step Diffusion and Switchable pLM
Chengyue Gong, Xinshi Chen, Yuxuan Zhang +3
Lightweight inference is critical for biomolecular structure prediction and other downstream tasks, enabling efficient real-world deployment and inference-time scaling for large-sc…
AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
Changze Lv, Jiang Zhou, Siyu Long +22
We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws,…
Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
Keyue Qiu, Yuxuan Song, Zhehuan Fan +5
Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules. Recent deep generative models are faced with challenges in geometric structure modeling. A major…
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Qiying Yu, Zheng Zhang, Ruofei Zhu +32
Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical details…
Steering Protein Family Design through Profile Bayesian Flow
Jingjing Gong, Yu Pei, Siyu Long +7
Protein family design emerges as a promising alternative by combining the advantages of de novo protein design and mutation-based directed evolution.In this paper, we propose Profi…
A Periodic Bayesian Flow for Material Generation
Hanlin Wu, Yuxuan Song, Jingjing Gong +6
Generative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown e…
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
Jinyi Hu, Shengding Hu, Yuxuan Song +6
Autoregressive and diffusion models have achieved remarkable progress in language models and visual generation, respectively. We present ACDiT, a novel Autoregressive blockwise Con…
Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks
Keyue Qiu, Yuxuan Song, Jie Yu +7
Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exe…
-PO: Generalizing Preference Optimization with -divergence Minimization
Jiaqi Han, Mingjian Jiang, Yuxuan Song +2
Preference optimization has made significant progress recently, with numerous methods developed to align language models with human preferences. This paper introduces -divergenc…
MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
Yanru Qu, Keyue Qiu, Yuxuan Song +5
Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding…
Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Yuxuan Song, Jingjing Gong, Yanru Qu +4
Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to appl…
Equivariant Flow Matching with Hybrid Probability Transport
Yuxuan Song, Jingjing Gong, Minkai Xu +5
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates). Deep generative models, especially…
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Yiming Wang, Yuxuan Song, Yiqun Wang +4
Retrosynthesis poses a key challenge in biopharmaceuticals, aiding chemists in finding appropriate reactant molecules for given product molecules. With reactants and products repre…