Unverified50% confidenceFactExact time
The core observation of the study is that once the backbone is frozen, the loss function can only "see" each adapter weight matrix through the finite-rank linear map induced by the frozen activations.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe study proves that the global minimizer of the perturbed objective is approximately optimal for the original trace-norm problem, and provides a uniform generalization bound controlled by the spectral structure of the frozen features.70% similarUnverified表示坍缩指模型将所有输入映射到同一点或低维流形,使损失趋近于零但毫无意义,是自监督学习的核心挑战66% similarUnverifiedNon-polynomial losses have unbounded state requirements over histories with equal predictions, and this obstacle persists for bounded scalar logistic loss even after fixing an arbitrary finite-order jet of derivatives.65% similarUnverifiedThe loss function is a highly aggregated statistical summary of the model's overall state, and its aggregating and compressing nature means the loss curve has inherent limitations in timeliness and localization precision as a debugging signal.65% similarUnverifiedClosed-loop success rates systematically overestimate the rankability of frozen latent representations.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1069406API
curl https://kongchang.com/api/v1/knowledge/claims/1069406MCP
get_claim(id=1069406)