SCLATE: a Substrate for Continual-Learning Agent Training and Evaluation
Quick summary
arXiv:2609.32391v2 Announce Type: replace Abstract: 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 sche
Key takeaways
- arXiv:2609.32391v2 Announce Type: replace Abstract: 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.
Why it matters
“SCLATE: a Substrate for Continual-Learning Agent Training and Evaluation” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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