reuse

AdaPLD is a training-free method that improves model-free speculative decoding by using adaptive retrieval combining lexical and semantic similarity, and constructing branched reuse hypotheses to handle continuation uncertainty, achieving up to 3.10x decoding speedup.

Compares two AI agents handling skill reuse: one rewrites extraction logic from scratch each session while the other packages it into a dedicated, documented file, highlighting the need for agent skill persistence.