I rebuilt search using physics instead of statistics. +18.5% NDCG@10. No ML. Yes its Open Source
BM25 is from 1994. Vector search is just nearest neighbor lookup. Every AI search product is one of those two things with a weighted sum on top.
I wanted to try something different so I built Resonance Search.
The idea: everything you can embed has gravity. So products, documents, code, images, database rows, whatever. Instead of a one-shot nearest neighbor lookup, your query actually moves through the space from gradient descent and settles into the deepest cluster of relevant results.
Scoring uses interference terms instead of linear addition so a result has to be strong across multiple signals at once to rank, it can't fake it by crushing just one.
The numbers on 50 real legal contracts, 980 chunks, 200 queries:
| Method | NDCG@10 |
|---|---|
| Resonance | 0.2751 |
| Linear Fusion | 0.2321 |
| BM25 | 0.1685 |
| Vector Only | 0.1103 |
+18.5% over linear fusion, you can run it yourself.
Pure Rust, zero ML at query time, sub-millisecond latency. AGPL open source.
(No this isn't AI Slop) and Yes I use Opus for assisting the Code/Comments:)
github dot com /Razshy/resonance-search