Latent Semantic Memory Compresses Agent Memory Into 16 Tokens
The result suggests frozen LLMs can accumulate and reuse knowledge over long interactions without retraining, pointing toward cheaper continually learning systems. Developers building RAG pipelines or agent memory should compare this compression approach with Mem0 and MemoRAG before defaulting to full-context retrieval, since it wins most head-to-head benchmarks at a fraction of the token cost.
