Buckets:
| <html lang="en"> | |
| <head><meta charset="utf-8"><title>CrossQ toy reproduction poster</title> | |
| <style> | |
| @page{size:12.5in 8.333in;margin:0} | |
| body{margin:0;background:#0b1020;color:#f7f8ff;font:24px Arial,sans-serif;text-wrap:pretty} | |
| .poster{box-sizing:border-box;width:1200px;min-height:800px;padding:56px;background:linear-gradient(135deg,#111a38,#172b48)} | |
| h1{font-size:56px;margin:0 0 16px;color:#8ee6d2;text-wrap:balance} h2{font-size:30px;color:#ffd166;text-wrap:balance} | |
| .grid{display:grid;grid-template-columns:1fr 1fr;gap:24px;align-items:start}.card{padding:26px;border:2px solid #355070;border-radius:18px;background:#14233d} | |
| .big{font-size:62px;font-weight:700;color:#ffd166}.note{font-size:18px;color:#b9c5d8;line-height:1.45} | |
| a{color:#8ee6d2;text-decoration:none} | |
| </style></head> | |
| <body><main class="poster" data-measure-role="poster"> | |
| <h1>CrossQ: conditional quantization for late interaction</h1> | |
| <p class="note">A transparent toy reproduction of the mechanism, not a paper-scale benchmark.</p> | |
| <div class="grid" data-measure-role="column"> | |
| <section class="card" data-measure-role="card" data-logbook-target="claim-1-late-interaction-retrievers-like-colbert-achieve-high-quality-but-suffer-from-large-multi-vector-indices"><h2>Claim 1 · index pressure</h2><div class="big">8 × 32</div><p>Each synthetic document stores eight token vectors of dimension 32. Full precision uses 32 bits per scalar, illustrating why multi-vector indices grow quickly.</p></section> | |
| <section class="card" data-measure-role="card" data-logbook-target="claim-2-crossq-adaptively-allocates-precision-by-conditioning-token-codes-on-lightweight-document-context-computed-at-indexing-time"><h2>Claim 2 · conditioning</h2><div class="big">2-bit + context</div><p>Document mean context is computed once at indexing time; token residuals are quantized relative to that context and reconstructed before MaxSim scoring.</p></section> | |
| <section class="card" data-measure-role="card" data-logbook-target="claim-3-achieves-61x-compression-while-narrowing-gap-with-full-precision-colbert-to-just-2-3-mrr-10-loss"><h2>Claim 3 · result</h2><div class="big">9.85×</div><p>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.</p></section> | |
| <section class="card" data-measure-role="card"><h2>Reproducibility status</h2><p>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.</p><p class="note"><a href="https://openreview.net/forum?id=jxHF6z0S1L">OpenReview paper</a> · <a href="https://github.com/Chenruishuo/posterly">Posterly</a></p></section> | |
| </div></main></body></html> | |
Xet Storage Details
- Size:
- 2.83 kB
- Xet hash:
- 1ea2060f2d122c6747e109f6dfdd5193d8541284b6b3693f262f75ad51a14eee
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.