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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
V2V-Bench: A Comprehensive Benchmark for Video-to-Video Generation Evaluation
Tao Liu, Leela Krishna, Gouti Pavan Kumar +2
Video-to-video (V2V) generation is difficult to evaluate because outputs must both follow editing instructions and preserve frame-level correspondence with the source video, which…
cs.LG2026
MODE: Modality-Decomposed Expert-Level Mixed-Precision Quantization for MoE Multimodal LLMs
Yuanteng Chen, Nanxin Zeng, Peisong Wang +9
Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential. Among PTQ methods…