Unverified50% confidenceFactExact time
The paper derives an error bound between the squared distance of behavior map coordinates and the trajectory KL divergence under explicit assumptions.
1
Sources
50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe paper establishes non-asymptotic, high-probability upper bounds for the convergence of empirical occupation measures to the invariant measure, under the contraction assumption in kernel-induced geometry and the standard spectral gap assumption.71% similarUnverifiedIn static analysis evaluations, common spatial reasoning mistakes by models include: writing relative positions as absolute coordinates, causing offset calculations to be wrong; getting the order of rotation and translation wrong; and ignoring parent-child hierarchy, causing overall misalignment.69% similarUnverifiedThe authors construct a one-dimensional quadratic problem to verify that qualitative differences in the optimization trajectory arise under state-space rounding and preconditioner-space rounding.68% similarUnverifiedThe study theoretically derives that when the encoder merges the distinguishable outcomes of different actions under the same state as well as different appearances, a positive lower bound exists on the squared inverse prediction error.68% similarUnverifiedThe key tools for propagating score estimation error into a KL divergence bound on the path distribution are Girsanov's theorem and the Fokker-Planck equation, with compensated Poisson random measures additionally required for processes with jumps68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1084049API
curl https://kongchang.com/api/v1/knowledge/claims/1084049MCP
get_claim(id=1084049)