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Storing knowledge directly in LLM parameters offers a flexible, efficient alternative to in-context learning, bypassing context length limitations and repeated encoding costs.
AI agents fail to invent new concepts or revise existing theories when faced with an epistemically open world, even with abundant evidence.
