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

Introduces SCLATE, an execution substrate for continual-learning agent training and evaluation. SCLATE features an open event scheduler, a hybrid simulated clock for time compression, and a rollout engine that records model calls. The post presents empirical comparisons of agent harness and memory configurations, and details the post-training of Qwen3.5-4B using SCLATE, showing improvements in SWE-bench Verified pass rate and MetaClaw accuracy.