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This work formalizes task-agnostic environment preprocessing, in which a studying system explores an environment under a budget and produces artifacts for a frozen solver, and compares unaided and archive-equipped meta-agents with fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks.
Harness optimization reveals that LLMs can significantly enhance their performance, but surprisingly, native harnesses aren't always the best choice for optimization.
