SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation
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...
What happened
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...
Why it matters
The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
Affected entities
View evidence
1 reports · 1 original report · 1 independent
- Apple Machine Learning ResearchPrimary source · Supports · EN · 100%SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation ↗
Claims
- SCLATE: A Substrate for Continual-Learning Agent Training and Evaluation Observed
Conflicts
No material conflict detected in the available evidence.
Timeline
- First reported
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