activity
20242026
collaborators

9 papers

cs.CV2026

Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs

Weixuan Li, Zikun Zhou, Xinyi Zhuang +4

Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention maps or output features to e…

cs.CV2026

MintAct: A Unified Visual Agent for Digital Environments

Mingfei Gao, Rui Tian, Haiming Gang +11

We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and…

cs.CL2026

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

DeepSeek-AI, :, Anyi Xu +585

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…

cs.CL2026

DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

DeepSeek-AI, Anyi Xu, Bangcai Lin +315

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…

cs.CV2025

UniGen-1.5: Enhancing Image Generation and Editing through Reward Unification in Reinforcement Learning

Rui Tian, Mingfei Gao, Haiming Gang +5

We present UniGen-1.5, a unified multimodal large language model (MLLM) for advanced image understanding, generation and editing. Building upon UniGen, we comprehensively enhance t…

cs.CL2025

LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability

Xuan Xu, Zhongliang Yang, Haolun Li +5

Topic modeling aims to produce interpretable topic representations and topic--document correspondences from corpora, but classical neural topic models (NTMs) remain constrained by…

cs.CV2025

UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation

Rui Tian, Mingfei Gao, Mingze Xu +5

We introduce UniGen, a unified multimodal large language model (MLLM) capable of image understanding and generation. We study the full training pipeline of UniGen from a data-centr…

cs.CV2024

REDUCIO! Generating 1K Video within 16 Seconds using Extremely Compressed Motion Latents

Rui Tian, Qi Dai, Jianmin Bao +5

Commercial video generation models have exhibited realistic, high-fidelity results but are still restricted to limited access. One crucial obstacle for large-scale applications is…

cs.CV2024

DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs

Lingchen Meng, Jianwei Yang, Rui Tian +4

Most large multimodal models (LMMs) are implemented by feeding visual tokens as a sequence into the first layer of a large language model (LLM). The resulting architecture is simpl…