Show HN: Beating SOTA embeddings on DeepMind's LIMIT benchmark (94% vs. 18%)

DeepMind's recent paper "On the Theoretical Limitations of Embedding-Based Retrieval" identified a capacity bottleneck in dense embeddings, showing that even SOTA models like E5-Mistral and GritLM struggle on their LIMIT benchmark (scoring ~8-18% Recall@100). I hypothesized that this isn't a retrieval limit, but a compression limit. I built Numen, a retrieval engine based on high-dimensional sparse-dense n-gram hashing (32k dimensions) rather than learned embeddings. The Results (on LIMIT test set): BM25 (Baseline): 93.6% E5-Mistral: 8.3% GritLM 7B: 12.9% Numen (My implementation): 93.9% It beats BM25 while maintaining a vector architecture, completely sidestepping the geometric bottleneck of dense models. The benchmark script ( numen.ipynb ) is in the repo for reproduction.