Unverified50% confidenceFactExact time
The paper derives the joint long-memory limit across multiple expectile levels and characterizes its limiting covariance structure.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe bottleneck in agent memory lies in the write path, yet most memory benchmarks (such as LongBench and Needle-in-a-Haystack) only test long-context retrieval and reasoning, implicitly assuming that memory has already been written correctly.67% similarUnverifiedThe 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.67% similarUnverifiedThe paper derives generalization error upper bounds separately for the preliminary estimator and the constrained estimator; these bounds are expressed in terms of complexity measures such as the covering number or VC dimension of the family of measurable sets, and do not depend on the specific structure of the response space.67% similarUnverifiedThe autocovariance of long-memory linear processes (such as FARIMA) decays slowly as a power law |k|^{2β−1}, with memory parameter β∈(1/2,1); in this case the sample mean converges at rate n^{β−1/2}, and the limiting process is the integral of fractional Brownian motion with Hurst index H=β+1/2>1/2.66% similarUnverifiedThe paper proves fully matching approximate upper and lower bounds for exponential-mode and finite-state-mode cases of algebraically decaying forecast memory.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1023457API
curl https://kongchang.com/api/v1/knowledge/claims/1023457MCP
get_claim(id=1023457)