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Large Language Model Papers·2026
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TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving

Shuhuai Li, Shengdong Ye +3·Shanghai Jiao Tong University, Stanford University

Jointly decides when waiting agent sessions should run on GPUs and which node should serve them. Scheduling accounts for tool-return readiness, time-decaying node cache value, and token demand.

#paper#LLM#ai-agent#ai-serving#ai-inference+3
AI Video Papers·2026
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Wave Forcing: Towards Speed-of-Light Streaming Video Generation

Yijie Jin, Yuxuan Liu +8·DENG Lab, Shanghai Jiao Tong University, Xi’an Jiaotong University +3

Streams long videos using chunk-wise causal attention and a multi-GPU pipeline. Its five-step model improves motion while maintaining visual quality; on eight H200 GPUs, end-to-end throughput reaches 134.8 FPS.

#paper#ai-video#video#ai-infra#gpu
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