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cs.DC2026
StateFlow: Sequence Pipeline Parallelism for Long-Context Modeling with Linear Recurrence
Wenxuan Zhao, Yingfa Chen, Xu Han +7
Long-context training is increasingly important for large language models, and linear attention and state space models have become popular for improving long-context efficiency. Ho…
cs.CL2026
The Score Granularity Gap in Black-Box LLM Classification: A Comparative Study of Confidence Constructions
Ao Sun, Tian Sun, Jiaxing Geng
Large language models (LLMs) are increasingly deployed as black-box classifiers in pipelines that automate confident decisions and route uncertain ones to human review. Such select…
cs.CV2026
APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention
Yuxiang Huang, Mingye Li, Xu Han +7
The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods e…