Unverified50% confidenceOpinionExact time
After KV caching, attention optimization, quantization, and other stages have been repeatedly polished, the preprocessing step of tokenization may still be an important contributor to TTFT, especially in long-prompt and Agent multi-turn scenarios.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTTT simultaneously addresses multiple core pain points of current large models, including generalization capability, long-context handling, and test-time self-adaptation.73% similarUnverifiedTime to First Token (TTFT) refers to the interval between the user pressing Enter and the first character appearing on screen, and is a key metric that determines the interactive user experience.71% similarUnverifiedFor batch processing or background tasks, optimizing TTFT is a waste of effort; throughput and cost are the correct metrics in those cases, and TTFT is only dominant in interactive scenarios.71% similarUnverifiedOne strategy for achieving temporal consistency is to share attention weights across adjacent frames during inference, with representative methods including TokenFlow and Video-P2P70% similarUnverified典型的Agent框架如ReAct、AutoGPT、LangGraph采用感知-推理-行动的迭代模式，上下文随轮次累积导致后期每轮Token消耗呈线性甚至指数增长70% similar
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