Verified65% confidenceFactExact time
Each additional token generated by an autoregressive model provides one more opportunity for a Transformer forward pass computation, trading sequence length for computational depth.
3
Sources
65%
Confidence
Long-term
Relevance
8/11/2026
First Seen
Sources
潜空间推理：DeepSeek-V4如何让AI在隐藏层中思考
Related Claims
UnverifiedThe Recurrent Transformer introduces the concept of Recurrent Depth, allowing the model to iteratively process information multiple times within the same layer structure.79% similarVerifiedModern large models are based on the Transformer architecture, and generating each token requires one full forward pass over all the model's parameters.78% similarUnverified多模态模型将不同模态数据通过各自编码器转换为统一向量表示，再送入同一Transformer架构进行联合推理76% similarUnverifiedTransformer models accelerate inference through the KV Cache mechanism, but its memory usage is proportional to context length.76% similarUnverifiedIn standard Transformer inference, generating each new token requires accessing the key-value vectors of all historical tokens, and the memory footprint of the KV cache grows linearly with sequence length.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/729942API
curl https://kongchang.com/api/v1/knowledge/claims/729942MCP
get_claim(id=729942)