Scaling Discovery Requires Forgetting
Restarting LLM research agents by forgetting search history improves discovery by up to 39 percent versus one agent.
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Restarting LLM research agents by forgetting search history improves discovery by up to 39 percent versus one agent.
A local learning interaction unifies data attribution, forgetting, and plasticity loss in continual updates.
A new compositional benchmark finds modular world models reuse robot skills better, but still forget.
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