SCLATE: a Substrate for Continual-Learning Agent Training and Evaluation
SCLATE is presented, an execution substrate where benchmarks and unmodified agents each add their events to one open event scheduler through an adapter, and shows that an added memory system does not reliably beat the harness's native memory and that models differ widely in how they use the same harness and memory.
Youngmok Jung, Sirajul Salekin, Henry Tran et al.
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