https://hackerpaper.kaipfister.de/p/2306.04039/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=45099503urn:hackerpapers:mention:2306.04039:45099503Tue, 02 Sep 2025 05:43:59 GMT<p>Mixture of Logits was actually already deployed on 100M scale+ datasets at Meta and at LinkedIn (https://arxiv.org/abs/2306.04039 https://arxiv.org/abs/2407.13218 etc.). The crucial departure from traditional embedding/multi-embedding approaches is in learning a query-/item- dependent gating function, which enables MoL to become a universal high-rank approximator (assuming we care about recall@1) even when the input embeddings are low rank.</p><p><a href="https://hackerpaper.kaipfister.de/p/2306.04039/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LG