Apple 提出 SCLATE：持续学习智能体训练与评测的执行底座
SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation
Apple 研究团队提出 SCLATE，一个让基准测试与未经修改的智能体各自通过适配器向同一开放事件调度器添加事件的执行底座，用混合模拟时钟在共享时间线上运行事件，可将一个月的场景压缩到数小时。
SCLATE 把基准与智能体事件统一到同一调度器，并给出记忆系统未必优于原生 harness 的对比结果。
AuthorsYoungmok Jung, Sirajul Salekin, Henry Tran, Javier Movellan, Zhao Huang, Manjot Bilkhu
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 loop. We present SCLATE, an execution substrate where benchmarks and unmodified agents each add their events to one open event scheduler through an adapter. A hybrid simulated clock runs these events on a shared timeline, flowing in real time while the agent works and skipping idle gaps, which compresses a month-long scenario into hours. SCLATE also serves as a rollout engine that runs any agent’s harness and memory unmodified, recording the tokens and log probabilities of every model call through an in-container proxy. We port seven benchmarks to SCLATE and compare ten unmodified harness and memory configurations head to head on ten models. The comparison 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. We then post-train Qwen3.5-4B through unmodified harnesses and memory systems. The model learns to use both, reading 6.8× fewer file lines with a 16.7-point higher SWE-bench Verified pass rate, and writing richer memory records, while its held-out MetaClaw accuracy rises by up to 11.8 points.
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来源：Apple Machine Learning Research · machinelearning.apple.com