7 citations · 11 across the 34 of their papers we have counts for
43 papers
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding
Xuyang Liu, Shihao Wang, Xinyu Wei +5
With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly important. Existing video understa…
MemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
Junxi Wang, Te Sun, Jiayi Zhu +9
Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuously accumulated memory intro…
Editable Visual Design
Junyan Ye, Wei Liu, Dongzhi Jiang +9
While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, their end-to-end generation inherently yields flattened bitmaps with error…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning
Haocong He, Chenfei Liao, Zichen Wen +13
Multimodal Large Language Models (MLLMs) have demonstrated promising spatial reasoning capabilities, while these abilities remain underexplored in the emerging visual modality of p…
ICBCBench: An Industry Consortium Benchmark for Financial Deep Research
Weiya Li, Zhiwei Tang, Yizhou He +10
With the rapid advancement of Deep Research Agents in knowledge-intensive domains such as finance, establishing reliable and domain-aligned evaluation standards remains a critical…
Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning
Junlong Ke, Zichen Wen, Weijia Li +2
On-policy self-distillation trains a reasoning model on its own rollouts while a teacher, often the same model conditioned on privileged context, provides dense token-level supervi…
EvoStreaming: Your Offline Video Model Is a Natively Streaming Assistant
Zichen Wen, Boxue Yang, Junlong Ke +5
Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness against verbosity. Yet most vide…
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
Yulang Chen, Haoxuan Peng, Jinyan Liu +3
Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal communication topologies is lab…
StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding
Junxi Wang, Te Sun, Jiayi Zhu +6
Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memory overhead, leading to high…
AudioKV: KV Cache Eviction in Efficient Large Audio Language Models
Yuxuan Wang, Peize He, Xiyan Gui +5
Large Audio-Language Models (LALMs) have set new benchmarks in speech processing, yet their deployment is hindered by the memory footprint of the Key-Value (KV) cache during long-c…
Flash-Unified: A Training-Free and Task-Aware Acceleration Framework for Native Unified Models
Junlong Ke, Zichen Wen, Boxue Yang +6
Native unified multimodal models, which integrate both generative and understanding capabilities, face substantial computational overhead that hinders their real-world deployment.…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
Innovator-VL: A Multimodal Large Language Model for Scientific Discovery
Zichen Wen, Boxue Yang, Shuang Chen +30
We present Innovator-VL, a scientific multimodal large language model designed to advance understanding and reasoning across diverse scientific domains while maintaining excellent…
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…
IPCV: Information-Preserving Compression for MLLM Visual Encoders
Yuan Chen, Zichen Wen, Yuzhou Wu +6
Multimodal Large Language Models (MLLMs) deliver strong vision-language performance but at high computational cost, driven by numerous visual tokens processed by the Vision Transfo…
VideoCompressa: Data-Efficient Video Understanding via Joint Temporal Compression and Spatial Reconstruction
Shaobo Wang, Tianle Niu, Runkang Yang +6
The scalability of video understanding models is increasingly limited by the prohibitive storage and computational costs of large-scale video datasets. While data synthesis has imp…
OmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learning
Hengrui Kang, Zhuangcheng Gu, Zhiyuan Zhao +4
Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, layout…
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…
AI for Service: Proactive Assistance with AI Glasses
Zichen Wen, Yiyu Wang, Chenfei Liao +10
In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-t…
AudioMarathon: A Comprehensive Benchmark for Long-Context Audio Understanding and Efficiency in Audio LLMs
Peize He, Zichen Wen, Yubo Wang +13
Processing long-form audio is a major challenge for Large Audio Language models (LALMs). These models struggle with the quadratic cost of attention () and with modeling lon…
ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Long Cui, Weiyun Wang, Jie Shao +6
Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Vis…
Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods
Chenfei Liao, Wensong Wang, Zichen Wen +10
Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly…
Efficient Multi-modal Large Language Models via Progressive Consistency Distillation
Zichen Wen, Shaobo Wang, Yufa Zhou +8
Visual tokens consume substantial computational resources in multi-modal large models (MLLMs), significantly compromising their efficiency. Recent works have attempted to improve e…