Unverified50% confidenceFactExact time
Recurrent depth technology allows the model to iteratively compute on the same layer or module repeatedly, completing multiple rounds of thinking within hidden states (latent space) without needing to convert each step into visible textual output.
1
Sources
50%
Confidence
Long-term
Relevance
9/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedRecurrent depth is more likely to complement chain-of-thought rather than fully replace it, and a hybrid architecture can be adopted: first performing multiple rounds of silent computation in latent space, then outputting a refined reasoning chain or final answer.79% similarUnverifiedThe core technology of multimodal large models is a unified semantic embedding space, where inputs from different modalities are encoded as representations in the same high-dimensional vector space for cross-modal reasoning.75% similarUnverifiedLatent space reasoning allows the model to complete thinking in a continuous vector space composed of hidden states, only decoding the result into text at the final stage.75% similarUnverifiedBy 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.73% similarUnverifiedLatent space reasoning refers to the model thinking internally in more abstract, high-dimensional vector representations rather than converting each reasoning step into text tokens.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/852179API
curl https://kongchang.com/api/v1/knowledge/claims/852179MCP
get_claim(id=852179)