Verified65% confidenceFactExact time
Test-time compute scaling improves performance on complex reasoning tasks by investing more computation during the inference stage through methods such as extending the chain of thought, multi-path sampling with best-of-n selection, and self-verification reflection.
3
Sources
65%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
Unverified推理时扩展在推理阶段增加计算量（如beam search、Best-of-N采样、蒙特卡洛树搜索），模型性能可随计算量增加持续提升并存在幂律关系83% similarUnverifiedMechanisms such as Chain-of-Thought, multi-step verification, and self-reflection are implementations of test-time compute scaling.82% similarVerifiedChain of Thought能显著提升模型在数学推理、逻辑判断等需要多步推导的任务上的表现80% similarVerifiedStreaming inference involves inference optimization techniques such as speculative decoding and continuous batching.79% similarVerifiedTest-time Compute Scaling improves model performance by investing more computational resources during the inference stage, and research indicates it is more efficient than simply increasing model parameter count on certain tasks.79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/572229API
curl https://kongchang.com/api/v1/knowledge/claims/572229MCP
get_claim(id=572229)