Surprise-gated episodic memory using V-JEPA-2 improves robot QA by ≥12% over prior memory methods and outperforms supervised baselines on event segmentation.
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Fast-Slow Training uses context optimization as fast weights alongside parameter updates as slow weights to achieve up to 3x better sample efficiency, higher performance, and less catastrophic forgetting than standard RL in continual LLM learning.
Auto-Dreamer trains an offline memory consolidator via GRPO on agent performance to abstract cross-session patterns, outperforming baselines by 7 points on ScienceWorld with 12x smaller memory and generalizing to ALFWorld and WebArena.
Explicit memory modeled on the hippocampus is the cornerstone needed to advance LLMs to AGI because their implicit statistical learning cannot produce higher cognitive functions.
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Worth Remembering: Surprise-Gated Robot Episodic Memory
Surprise-gated episodic memory using V-JEPA-2 improves robot QA by ≥12% over prior memory methods and outperforms supervised baselines on event segmentation.