Unverified50% confidenceFactExact time
When a model is post-trained on 3D tasks, the high-level semantic pathways in late layers are preferentially reshaped, thereby overwriting the representations that originally supported dynamic temporal reasoning.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedUsing next view prediction as a training objective forces the model to learn spatial consistency, view transformation relationships, and the implicit structure of scenes.77% similarVerifiedTool calling allows models to dynamically invoke external functions during inference and incorporate execution results into the context of subsequent reasoning, forming a closed loop of perception, decision-making, and execution.75% similarUnverifiedOrchestration frameworks extend the practical boundaries of models by building a perception-memory-action enhancement layer outside the model, without retraining the model.75% similarUnverifiedScheduled Sampling is a compromise approach to mitigating exposure bias, gradually introducing the model's own predictions as input during the later stages of training.74% 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.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1037823API
curl https://kongchang.com/api/v1/knowledge/claims/1037823MCP
get_claim(id=1037823)