Recent research has highlighted the promise of scaling memory embeddings in LLM…
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Recent research has highlighted the promise of scaling memory embeddings in LLM training. While Engram and STEM index memory by token identity or local n-grams, can we design more flexible memory routing that captures how each token’s meaning changes with context? This paper introduces Mixture-of-Memory Embeddings (MoME), which uses a learned router to sparsely select among multiple memory slots for each token. The architecture outperforms strong memory baselines across three model families. Interestingly, both qualitative and quantitative analyses show that the learned routers’ activations
Posted by alphaXiv (55.9k followers) 21 h ago · 104 likes · 5.1k views · view the original post on X. Kept by the AI Radar as AI research. Tools mentioned: alphaXiv.
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