Unverified50% confidenceFactExact time
This approximation error is independent of model capacity: even with a perfect marginal distribution predictor trained on infinite data, the product approximation will inevitably deviate from the joint distribution when writing to multiple positions with dependencies in parallel.
1
Sources
50%
Confidence
Long-term
Relevance
10/2/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedClone copies from the same model share nearly identical weights, training data, and inductive biases, making their errors highly correlated and preventing systematic biases from being eliminated through averaging.79% similarUnverifiedNaïve post-training quantization causes individual weight errors to accumulate and amplify across multiple layers—a phenomenon known as compounding error—and at 2-bit or 1-bit precision the model structure collapses75% similarUnverifiedWriting only one position or conditionally independent positions per step can theoretically match the distribution exactly, while writing more dependent positions causes errors to accumulate.73% similarUnverified研究团队证明学习分布与真实分布之间的总变差距离可分解为初始化误差、Score误差和求解器离散化误差三项73% similarUnverified实际部署中因并行计算的浮点非确定性可能产生细微差异并随链式生成放大73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/968475API
curl https://kongchang.com/api/v1/knowledge/claims/968475MCP
get_claim(id=968475)