Memorilla Compresses LLM Memory into 16 Embeddings, Saving 4,212x Tokens

Memorilla, shared by Stanford AI Lab, compresses long inputs and agent trajectories into 16 latent memory embeddings injected into frozen LLMs, claiming up to 4,212x fewer tokens, 27x faster inference, and even higher accuracy than full-context reasoning—no training required.

2026-11-06 ~ 2026-11-06 · 3 related posts

• Memorilla Compresses LLM Memory Into 16 Embeddings, Beats RAG Pareto Frontier — ShirleyYXWu · 2026-11-06
• Memorilla: Memory System Cuts Tokens 4000x, 27x Faster Than Full Context — ShirleyYXWu · 2026-11-06
• Memorilla adds latent semantic memory to LLMs, positioned beyond RAG — StanfordAILab · 2026-11-06