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.
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?
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.
This is actually quite easy to implement at the harness level and the NER can be way more naive because of the typical nature of LLM dialogue (programming, long running tasks etc).
The useful contribution is not zero token cost; it is removing generative rewriting from memory. Preserving original traces avoids a subtle auditability failure: once an LLM compresses an interaction, retrieval is grounded in the summary's omissions rather than the evidence.
I would still want a harder benchmark around mutation and contradiction. If an entity changes attributes across sessions, can the graph and temporal hierarchy preserve both states, surface the conflict, and show which trace justified the answer? The 57.6% time reduction is compelling, but for production agents I would measure unsupported-answer rate and evidence recall under stale, conflicting, and adversarial traces. Encoder compute and index-maintenance cost should also sit beside token cost; otherwise "zero-token" risks being read as "free."
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.
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?
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.
I would still want a harder benchmark around mutation and contradiction. If an entity changes attributes across sessions, can the graph and temporal hierarchy preserve both states, surface the conflict, and show which trace justified the answer? The 57.6% time reduction is compelling, but for production agents I would measure unsupported-answer rate and evidence recall under stale, conflicting, and adversarial traces. Encoder compute and index-maintenance cost should also sit beside token cost; otherwise "zero-token" risks being read as "free."