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This paper proposes that AI memory consolidation should recombine knowledge across domains (like dreaming) rather than merely replaying experiences, and demonstrates that cross-domain consolidation improves performance in both neural (LoRA fine-tuning) and symbolic systems.
This paper proposes using intrinsic device noise on analog neuromorphic hardware as a resource for continual learning by conditioning each weight's stochastic dynamics to avoid crossing memory-critical barriers, demonstrating non-monotonic retention improvement and validation on BrainScaleS-2 silicon.
Proposes a cognitively grounded multi-factor value function for agentic memory in LLM agents, learning interpretable weights to decide what to encode, forget, and retrieve under memory constraints. Improves gold-evidence retention significantly over similarity-only or recency-based baselines.
FadeMem introduces a distance-aware key-value memory consolidation mechanism that organizes historical video data into a temporal hierarchy, improving long-video generation under fixed cache constraints.
This paper introduces a 'Sleep' paradigm for large language models that enables continual learning through memory consolidation and dreaming phases, allowing models to distill short-term knowledge into long-term parameters and self-improve without human supervision.
This paper introduces a sleep-like consolidation mechanism for Transformer-based LLMs that periodically converts recent context into persistent fast weights in SSM blocks, clearing the KV cache to improve long-horizon reasoning without increasing inference latency.
Auto-Dreamer introduces a learned offline memory consolidation method for language agents, decoupling fast memory acquisition from slow cross-session consolidation, and achieving higher performance with smaller memory banks, generalizing to unseen environments.