Strategist: Evolving Reasoning with Agents
Strategist uses a reusable library of executable reasoning blueprints that agents retrieve, test, and revise across tasks. The paper reports similar average accuracy to its strongest baseline at lower inference cost.
**Strategist** retrieves related executable blueprints, writes candidate inference strategies, tests them on a small development set, and revises them. New candidates join a shared library for later tasks. Across **30 benchmarks**, it reports 74.7% average accuracy versus 74.0% for the strongest baseline, at 39% lower inference cost.
For multi-task agent systems, consider storing reusable strategy code and testing it against task samples before deployment. The authors also report transfer to unseen tasks and other models without additional search.
**Strategist** retrieves related executable blueprints, writes candidate inference strategies, tests them on a small development set, and revises them. New candidates join a shared library for later tasks. Across **30 benchmarks**, it reports 74.7% average accuracy versus 74.0% for the strongest baseline, at 39% lower inference cost. For multi-task agent systems, consider storing reusable strategy code and testing it against task samples before deployment. The authors also report transfer to unseen tasks and other models without additional search. These are paper-reported benchmark results; the supplied material does not describe the benchmark mix or implementation details needed to judge fit for a particular coding-agent workflow.
This describes a reusable strategy library built from executable blueprints that are tested on development samples and revised, with reported benchmark accuracy and inference-cost results. SkillProx adds a maintenance implication: measured retries, rollback, and pruning can help govern accumulated reusable procedures. The supplied material does not establish that either approach fits coding-agent workflows.