5 citations · 9 across the 16 of their papers we have counts for
18 papers
Two-loop helicity amplitudes for using high-energy and forward expansions
Mateusz Czaja, Ben Rott, Matthias Steinhauser +1
We calculate next-to-leading order QCD corrections for the process with two off-shell bosons, accounting for virtual top quarks in the loop diagrams. Our approa…
OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs
Xianyun Sun, Chaoyou Fu, Zhengye Zhang +6
Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific…
Omni-Prune: Query-Aware Unified Token Pruning for Efficient Omnimodal Large Language Models
Yiming Zhong, Chang Nie, Caifeng Shan
Omnimodal large language models (OmniLLMs) are rapidly extending multimodal reasoning to cover synchronized audio and video. However, the resulting audio-video token sequences are…
Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory
Chang Nie, Jiaju Wei, Junlan Feng +2
Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents o…
Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning
Yinan Zhou, Haokun Lin, Yichen Wu +7
Large multimodal models have achieved strong reasoning on complex visual tasks, but their inference efficiency is often restricted by long chains of thought. A promising solution i…
EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory
Chang Nie, Chaoyou Fu, Junlan Feng +1
Existing embedding models are inherently static: they encode text segments in isolation, ignoring their surrounding context and temporal order. This paper introduces EvoEmbedding,…
OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains
Xinyue Cai, Chaoyou Fu, Yi-Fan Zhang +2
Current automated pipelines for audio-visual Question Answering (QA) generally adopt a ``video-caption-QA'' paradigm. However, these methods typically segment videos into short cli…
VideoDetective: Clue Hunting via both Extrinsic Query and Intrinsic Relevance for Long Video Understanding
Ruoliu Yang, Chu Wu, Caifeng Shan +2
Long video understanding remains challenging for multimodal large language models (MLLMs) due to limited context windows, which necessitate identifying sparse query-relevant video…
SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation
Ruohan Liu, Shukang Yin, Tao Wang +6
Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and…
Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and…
PersonaVLM: Long-Term Personalized Multimodal LLMs
Chang Nie, Chaoyou Fu, Yifan Zhang +2
Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. P…
Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion
Lijiang Li, Zuwei Long, Yunhang Shen +6
While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architecture as their backbone, leaving…
VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting
Xiaoyu Liu, Chaoyou Fu, Chi Yan +15
Current Vision-Language-Action (VLA) models are often constrained by a rigid, static interaction paradigm, which lacks the ability to see, hear, speak, and act concurrently as well…
VITA-VLA: Efficiently Teaching Vision-Language Models to Act via Action Expert Distillation
Shaoqi Dong, Chaoyou Fu, Haihan Gao +12
Vision-Language Action (VLA) models significantly advance robotic manipulation by leveraging the strong perception capabilities of pretrained vision-language models (VLMs). By inte…
VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction
Chaoyou Fu, Haojia Lin, Xiong Wang +13
Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing in…
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin +9
As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Build…
Sparrow: Data-Efficient Video-LLM with Text-to-Image Augmentation
Shukang Yin, Chaoyou Fu, Sirui Zhao +7
Recent years have seen the success of Multimodal Large Language Models (MLLMs) in the domain of vision understanding. The success of these models can largely be attributed to the d…
VITA: Towards Open-Source Interactive Omni Multimodal LLM
Chaoyou Fu, Haojia Lin, Zuwei Long +16
The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas.…