SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation

这条英文动态主要涉及智能体工作流、模型能力与工程、评测与基准。原文要点：Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requires interleaving tasks with agent-side events such as session stop and start, crons, and memory consolidation. Yet existing benchmarks and training frameworks schedule only the benchmark’s own events, leaving each benchmark and agent pair to build a custom scheduling loo...