CrossQ: conditional quantization for late interaction
A transparent toy reproduction of the mechanism, not a paper-scale benchmark.
Claim 1 · index pressure
8 × 32
Each synthetic document stores eight token vectors of dimension 32. Full precision uses 32 bits per scalar, illustrating why multi-vector indices grow quickly.
Claim 2 · conditioning
2-bit + context
Document mean context is computed once at indexing time; token residuals are quantized relative to that context and reconstructed before MaxSim scoring.
Claim 3 · result
9.85×
Our toy proxy reaches 9.85× conditional index compression. The released checkout has no paper code, weights, or benchmark data, so 61× and 2.3% are not verified.
Reproducibility status
Local run: deterministic NumPy script, 120 documents, 48 queries. HF GPU Job: blocked before launch by account credit (HTTP 402). See the linked logbook pages for commands and outputs.