3 papers
cs.AI2026
Benchmark Everything Everywhere All at Once
Shiyun Xiong, Dongming Wu, Peiwen Sun +5
Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensiv…
cs.CV2026
LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video
Shiqiang Lang, Jing Liu, Haoyang He +6
Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs. Long-horizon tasks such as autonomous driving…
cs.CV2026
AURA: Always-On Understanding and Real-Time Assistance via Video Streams
Xudong Lu, Yang Bo, Jinpeng Chen +9
Video Large Language Models (VideoLLMs) have achieved strong performance on many video understanding tasks, but most existing systems remain offline and are not well-suited for liv…