Ever wonder how AI models pick the best options without getting overwhelmed? That's where CFS — Conditional Field Subtraction — comes in. According to /u/mauro8342 writing in AI, it works by penalizing regions already covered by previous picks, helping models focus on new, relevant data. Now, here’s the clever part: when combined with techniques like BM25 and cosine similarity, CFS boosts retrieval performance significantly — up to a 4% increase in NDCG@10 and 5% in Recall@10, as reported in the Reddit post. This isn’t just tweaking small bits; it’s about sharpening the entire process. As /u/mauro8342 points out, adding CFS to existing fusion methods improves ranking results more consistently than some traditional approaches. So what does this mean for your AI projects? It’s all about smarter, more precise candidate selection — making sure your models don’t waste time on the obvious. The big question now — how quickly will this idea become standard practice in retrieval systems? And get this — if you want to dig deeper, there’s a link in the post for more details.
| CFS selects relevant candidates by penalizing regions already covered by previous picks. Results on retrieval ranking: baseline cosine top-K: NDCG@10 0.5123, Recall@10 0.6924 Against mem0’s additive fusion, rrf(cosine, BM25, CFS) improves retrieval ranking by +4.08 pp NDCG@10 and +5.43 pp Recall@10. Against rrf(cosine, BM25), adding CFS contributes +1.15 pp NDCG@10 and +1.79 pp Recall@10. https://gist.github.com/M-Garcia22/ff4ec80f5a08ca2fd9234bcc35804d1c [link] [comments] |
| CFS selects relevant candidates by penalizing regions already covered by previous picks. Results on retrieval ranking: baseline cosine top-K: NDCG@10 0.5123, Recall@10 0.6924 Against mem0’s additive fusion, rrf(cosine, BM25, CFS) improves retrieval ranking by +4.08 pp NDCG@10 and +5.43 pp Recall@10. Against rrf(cosine, BM25), adding CFS contributes +1.15 pp NDCG@10 and +1.79 pp Recall@10. https://gist.github.com/M-Garcia22/ff4ec80f5a08ca2fd9234bcc35804d1c [link] [comments] |