6 citations · 51 across the 54 of their papers we have counts for
61 papers
Meta Expert: Cross-Layer Expert Sharing for Storage- and Retrieval-Efficient MoE
Ermo Hua, Xiangyu Hong, Baiting Wu +11
As model parameters scale up, sparsely activated Mixture-of-Experts (MoE) approaches have become essential for model scaling. However, mainstream models currently adopt hierarchica…
Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond
Zhiyuan Ma, Jiabao Wei, Tianyu Wang +21
Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining-finetuning"…
NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction
NCP Team, Jiaqi Cao, Chiyu Chen +25
We introduce NCP-ArchPreview, a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction (NTP). Alongside NTP, the model learns thro…
Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
Kai Chen, Jifeng Ding, Ning Ding +44
We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve comp…
PI-Mem: Pushing Long-Context Reasoning to 3.6M Tokens with Parallel-Iterative Memory
Dawei Liu, Haixu Song, Shuang Cheng +9
Long-context reasoning remains a critical bottleneck for large language models, as recent recurrent-memory approaches face two inherent challenges: sequential chunk-wise updates ca…
MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop
Yikun Fu, Bowen Fu, Zhenyu Wu +10
Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, ex…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning
Evelyn Zhang, Fufu Yu, Aoqi Wu +5
Processing long visual token sequences poses a significant computational burden on Multimodal Large Language Models (MLLMs). While token pruning offers a path to acceleration, we f…
SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding
Shuang Cheng, Yuhua Jiang, Zineng Zhou +5
Block-wise discrete diffusion offers an attractive balance between parallel generation and causal dependency modeling, making it a promising backbone for vision-language modeling.…
AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models
Yuhua Jiang, Shuang Cheng, Yan Ding +2
Vision-language-action (VLA) models have recently emerged as a powerful paradigm for building generalist robots. However, traditional VLA models that generate actions through flow…
Diffusion LLM with Native Variable Generation Lengths: Let [EOS] Lead the Way
Yicun Yang, Cong Wang, Shaobo Wang +4
Diffusion-based large language models (dLLMs) have exhibited substantial potential for parallel text generation, which may enable more efficient generation compared to autoregressi…
DePass: Unified Feature Attributing by Simple Decomposed Forward Pass
Xiangyu Hong, Che Jiang, Kai Tian +4
Attributing the behavior of Transformer models to internal computations is a central challenge in mechanistic interpretability. We introduce DePass, a unified framework for feature…
SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation
Shuang Cheng, Yihan Bian, Dawei Liu +8
We propose SDAR, a Synergistic Diffusion-Autoregression paradigm that unifies the training efficiency of autoregressive models with the parallel inference capability of diffusion.…
Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference
Jianuo Huang, Yaojie Zhang, Yicun Yang +4
Diffusion large language models (dLLMs) present a promising alternative to dominant autoregressive models (ARMs) by the ability of parallel decoding at the expense of substantial c…
A Survey of Reinforcement Learning for Large Reasoning Models
Kaiyan Zhang, Yuxin Zuo, Bingxiang He +36
In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontie…
Self Speculative Decoding for Diffusion Large Language Models
Yifeng Gao, Ziang Ji, Yuxuan Wang +3
Diffusion-based Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive models, offering unique advantages through bidirectional attention and par…
Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism
Yuhua Jiang, Shuang Cheng, Yihao Liu +7
Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address this by preserving broad capabilities…
A Survey of Inductive Reasoning for Large Language Models
Kedi Chen, Dezhao Ruan, Yuhao Dan +12
Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamental types, which is characterized by i…
Sequential Diffusion Language Models
Yangzhou Liu, Yue Cao, Hao Li +13
Diffusion language models (DLMs) have strong theoretical efficiency but are limited by fixed-length decoding and incompatibility with key-value (KV) caches. Block diffusion mitigat…
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Zhuo Yang, Jiaqing Xie, Shuaike Shen +13
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce S…
ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data
Zhaoyang Liu, Jingjing Xie, Zichen Ding +27
Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that operate GUIs autonomously, showing great potential, yet progress is limited by the lack of large-scale, o…
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Weiyun Wang, Zhangwei Gao, Lixin Gu +72
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…
Chem3DLLM: 3D Multimodal Large Language Models for Chemistry
Lei Jiang, Shuzhou Sun, Biqing Qi +5
In the real world, a molecule is a 3D geometric structure. Compared to 1D SMILES sequences and 2D molecular graphs, 3D molecules represent the most informative molecular modality.…
ReviewRL: Towards Automated Scientific Review with RL
Sihang Zeng, Kai Tian, Kaiyan Zhang +9
Peer review is essential for scientific progress but faces growing challenges due to increasing submission volumes and reviewer fatigue. Existing automated review approaches strugg…