AdaMAST: Adaptive Failure Taxonomies Improve Agent Systems Without Weight Changes
AdaMAST is a method that converts agent execution traces into compact, evidence-grounded failure taxonomies with named codes organized along three axes. These taxonomies serve as a shared feedback interface, enabling improvements in agent search, runtime performance (e.g., SWE-agent from 60% to 70%, Claude Code from 64.0% to 70.7%), and trajectory selection (8-15 point gains on Terminal-Bench 2.0). The approach is fully automated, compact, human-faithful, and adaptive across domains.
Why it matters: AdaMAST demonstrates a principled, automated way to improve agent systems by learning from failures without modifying model weights, yielding notable performance gains across multiple benchmarks.
Full story at: arXiv Software Engineering ↗