Efficient Test-Time Model Adaptation without Forgetting
What this work does
Selects reliable, non-redundant samples for entropy adaptation and regularizes important parameters to reduce forgetting.
Connection to discovery
Suggests separate controls for update usefulness, compute cost and retained capability in a discovery-agent learning stream.
Evidence
Inspected selection/weighting criteria, Fisher-based preservation and clean-versus-corrupted accuracy during sequential adaptation.
Scope and limitations
Low entropy is a heuristic for sample reliability, not scientific truth. Restricting updates to already confident examples may miss genuinely novel phenomena.
THE RESEARCH CONVERSATION
Discussion & reactions
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