TL;DR for operators An agent completes a task after retrieving several reusable instructions. Its execution log records the instructions and the successful outcome, but not which instruction actually helped. Keeping every instruction associated with success can preserve redundant or harmful guidance; removing instructions with weak performance records can eliminate capabilities the model still needs. ...
TL;DR for operators A team using an importance-ranking tool must know whether its low-ranked model components are genuinely safe to remove—not merely whether the tool produces the same ranking repeatedly. On LLaMA-3.1-8B at 30% masking, Wanda achieved near-perfect ranking stability of 0.9998, yet removing the components it labelled least important drove perplexity to 360,083. Consensus-2 was less stable at 0.945, but the same removal test produced perplexity of 66.1. The more reproducible ranking was far less causally faithful. ...