Unverified50% confidenceFactExact time
During inference, the model processes input triplets group by group in an autoregressive manner along the depth axis.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedBy externalizing intermediate steps as text, chain-of-thought equivalently extends the model's effective computational depth from the number of layers in a single forward pass to the number of layers multiplied by the number of reasoning steps.78% similarUnverifiedIn deep thinking mode, the model first generates intermediate reasoning steps internally, then derives the final conclusion based on those steps, performing significantly better than direct generation when handling multi-step logical reasoning tasks.76% similarUnverifiedThis study systematically examines three ways of integrating deep sequence models into conformal prediction for time series: conditional quantile regression, conditional quantile function estimation, and localized conformal prediction.76% similarUnverified模型每个token向量同时扮演词的表示、点吸引子和几何读出三重角色,所有功能压缩进共享的井表中75% similarUnverifiedLLM graph agents typically employ multi-hop traversal when reasoning over graphs, starting from an initial entity and progressively collecting relational triples as evidence by following edges.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/986943API
curl https://kongchang.com/api/v1/knowledge/claims/986943MCP
get_claim(id=986943)