New local search engine 'Attemory' uses attention instead of embeddings

Standard retrieval-augmented generation (RAG) works by converting documents into vectors and finding the nearest match by distance. The problem: compressing text into vectors throws away a lot of context, so nearest-vector search often misses evidence a model would recognize if it could actually read the surrounding material.

Once recall starts failing, people pile on fixes — tuning chunk size and overlap, combining keyword and semantic search, adding rerankers, metadata filters, query rewriting, summaries, thresholds — but none of that changes the fact that vector search still isn't the same as reading the evidence. Attemory takes a different approach: instead of embedding chunks and searching by vector distance, it indexes the raw text, documents, or codebase into reusable KV (key-value) state.

At search time, a local Qwen3.5 retrieval model attends directly over that indexed memory and the query, then returns compact evidence — memory IDs, snippets, or file and line ranges — rather than just matching compressed vectors. The developer reports state-of-the-art results on long-memory and code retrieval benchmarks with this approach.