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This work forms this missing dimension of harness optimization as an automated curriculum learning problem and introduces ActiveSaddler, which establishes automated curriculum learning as a new crucial optimization dimension for harness optimization.
A systematic study of observability in agent-generated systems reveals a consistent gap between diagnostic semantics at the source level and fault signals at runtime, suggesting that current evaluations focusing primarily on functional correctness may overlook observability as an important dimension of practical softwa...
The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent...
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