Experimental Results from a Self-Improving Retrieval System for Conversational Memory
Earlier, I published a post about porting the immune system's germinal center mechanism to LLM memory. Three arms, two datasets, three mutation strategies, one consistent finding: the biological control loop (adaptive rate, tier lifecycle, decay) is sound engineering, but Gaussian perturbation of pretrained embeddings cannot improve retrieval. The adaptive rate correctly identifies which entries to mutate and how much. It just can't produce a perturbation that helps.
The one thing that worked in that phase was not biological. Cross-encoder reranking on top of bi-encoder retrieval gave a 63% NDCG lift on LongMemEval.
I said two things would need to change: the mutation needed to be semantically informed, and the fitness signal needed to come from outside the embedding space. So I built both. Then I ran another fourteen experiments. The learned MLP adapter produces the right direction. The segmentation mutation finds the wrong granularity. A simple recall diagnostic reframed the...
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