most citedPAS: A Training-Free Stabilizer for Temporal Encoding in Video LLMs

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cs.CV2026

EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation

Bingxuan Li, Yiming Cui, Yicheng He +4

Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-…

cs.CV2026

What Should a Streaming Video Model Remember?

Haonan Ge, Yiwei Wang, Hang Wu +1

Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation bu…

cs.CV2026

Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding

Hang Wu, Sherin Mary Mathews, Yujun Cai +2

Online streaming video understanding requires models to process continuous visual inputs and respond to user queries in real time, where the unbounded stream and unpredictable quer…

cs.CV2026

Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment

Zhixue Song, Boyan Han, Yiwei Wang +1

Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerabili…

cs.CV2026

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools

Zhenlong Yuan, Xiangyan Qu, Chengxuan Qian +8

Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text-centric priors often limits the…

cs.CV2026

VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG

Honghao Fu, Miao Xu, Yiwei Wang +3

Scaling multimodal large language models (MLLMs) to long videos is constrained by limited context windows. While retrieval-augmented generation (RAG) is a promising remedy by organ…