Jev judgment model outperforms cross-encoder reranker on agent memory selection in production

Unblocked reported that its calibrated judgment model Jev outperformed a cross-encoder reranker on agent memory selection, achieving higher precision and recall across 12,927 labeled question-note pairs drawn from 292 production questions. Cost and latency stayed equivalent, and threshold tuning proved more effective than prompt adjustment. Jev replaced the cross-encoder reranker for deciding which saved notes an agent should view.

Jev judgment model outperforms cross-encoder reranker on agent memory selection in production According to Unblocked, Jev, a calibrated judgment model, replaced a cross-encoder reranker for selecting which saved notes an agent should view, and showed higher precision and recall on 12,927 labeled question-note pairs from 292 production questions. Cost and latency remained equivalent, with threshold tuning proving more effective than prompt adjustment. Topics Sources - Press Read article https://getunblocked.com/blog/jev-in-production-vs-cross-encoder/ Go deeper This intelligence is sourced automatically from public sources across the web and synthesised by the Prefactor AI pipeline. Stories are reviewed before publication.