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Comment by langs | Hacker News Reader

I am working on the same thing right now. However, unlike storing conversations in an external retrieval system, I use a local LLM to store the conversation's KV cache and perform retrieval directly on that cache. The method involves running a prefill pass and, after obtaining the attention scores, filtering for the corpus segments that received attention.

This aligns with the "zero tokens" approach described in this paper. :)

I tested it on the LoCoMo used in this paper, and also LongMemEval, both achieved SOTA results.

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I was doing something similar where I saved user input/model output in a multi-depth node style storage system (each depth having more precise details) with the focus on the model having accurate user fed information. I was mostly focused on retrieval of accurate / useful information based on user query (injecting the database node as additional, high confidence information)

Once this(Zero-mem) passes it's peer review, I may have to see if my system can handle something similar instead/in addition.

I'm quite excited to see growth in these different ways of eliminating token's.

Long winded aside, @langs, have you published your work on this?

https://github.com/AttemorySystem/attemory/ stars and issues are welcome :)

Using attention for retrieval was inspired by a comment I saw in here long time ago: Prediction and retrieval are two sides of the same coin; to predict better, you must retrieve more accurately.

I'm still working on the improvement of algorithms, my tests shows the performance and accuracy will be improved a lot in the next release.

"A local Qwen3.5 retrieval model attends over the indexed memory"

Hrm.

What the difference of doing this vs semantic search over indexed fact chunks? This is RAG right?

Yes, it uses modern technology to tackle an old problem: retrieval.

I am not an expert on LLMs, but what's preventing you from treating the context as 'virtual memory', and using the attention matrix to 'blank out' tokens which have very low weights and will not contribute much to the input? I imagine most tokens are like this, and you can skip computation on 95% of an 1M (or practically infinite) context.

Hah I built a similar thing, stored a few million tokens chunked and precomputed winth Qwen a3e (best ratio of kv-size to tokens after chunking).

Some custom kernels and I was able to find all the relevant paragraphs with full force of qwen reasoning within 0.3s, and with a summary round within 0.7s.

Downside - required 200GB ram/vram ;) A few GBs for model and most of it for caching kvs.

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