LLMS

I took a look at CC's Memory mechanism, and it's nothing special.The entire memory system's core is just a single MEMORY.md file, no more than 200 lines, which gets stuffed into the context at the start of each conversation. What happens when memories accumulate?A background subprocess called AutoDream runs periodically to scan, merge, and trim, ensuring everything fits.In plain terms: the model can't remember on its own, so it uses the file system + LLM self-management to simulate memory.This solution is solid from an engineering standpoint, but has several fundamental limitations:1. Storage and retrieval depend entirely on the file system + Markdown, cannot scale to cross-project, cross-Agent scenarios; memory becomes isolated silos2. No true semantic indexing, no dynamic recall based on relevance; 200 lines is a hard ceiling3. AutoDream's consolidation is rule-driven (scanning, merging, trimming), not cognition-driven; it can deduplicate and compress, but cannot extract new insights from experience4. No forgetting curve, no memory reinforcement mechanism; memories either exist or are deleted, with no middle groundAfter working on Memory for a while, you realize the ceiling for these solutions isn't actually engineering—it's architecture. As long as the model's attention mechanism itself doesn't support efficient retrieval of large historical contexts, the application layer will always be patching.This is why we chose a different path at EverMind. The MSA (Memory Sparse Attention) we released recently does content-aware sparse routing directly at the Transformer attention layer, letting the model learn itself what to recall and what to ignore, rather than relying on external scripts to make those decisions.A's engineering prowess is undoubtedly top-tier. But this leak happens to prove: the Agent Memory problem is far from solved. [Translated from EN to English]

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