SCLATE
A unified execution substrate framework for continual learning agents, providing an open event scheduler and a hybrid simulation clock. It supports benchmarks and agent systems via adapters, addressing the fragmentation problem in continual learning evaluation.
Timeline (last 90 days)
SCLATE is a unified execution substrate built for continual-learning agent training and evaluation (Substrate for Continual-Learning Agent Training and Evaluation).
SCLATE provides an open event scheduler into which both benchmarks and unmodified agents can register their respective events via adapters.
SCLATE employs a hybrid simulated clock to coordinate the timing of events such as session start and stop, scheduled tasks, and memory consolidation.
SCLATE's no-agent-modification property means that existing agent systems can be directly plugged into the evaluation pipeline without altering their internal logic.
All Facts (4)
SCLATE is a unified execution substrate built for continual-learning agent training and evaluation (Substrate for Continual-Learning Agent Training and Evaluation).
50%UnverifiedSCLATE provides an open event scheduler into which both benchmarks and unmodified agents can register their respective events via adapters.
50%UnverifiedSCLATE employs a hybrid simulated clock to coordinate the timing of events such as session start and stop, scheduled tasks, and memory consolidation.
50%UnverifiedSCLATE's no-agent-modification property means that existing agent systems can be directly plugged into the evaluation pipeline without altering their internal logic.
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