KARMA Adapts Advertising Agents Through Validated Knowledge Edits Without Retraining
The KARMA research framework updates six knowledge modules for industrial advertising agents while keeping model weights and executable harness code frozen. It diagnoses errors, compares successful and failed trajectories, and retains atomic knowledge edits only when they pass a fixed validation gate. Across 18 domain–model settings using cases sampled from approximately half a million real-world operation logs, the authors report an average hold-out score increase from 30.19 to 39.15. They also report cross-domain transfer gains without further adaptation.
Why it matters: The work offers a concrete approach to adapting operational agents through inspectable, reversible knowledge changes rather than model retraining.