Jev-Mem paper: a fast scorer makes agent memory building 6.6x faster on LoCoMo
rohanpaul_ai · x · 2026-10-25
A UT Dallas paper introduces Jev-Mem, an agentic memory architecture that offloads small routing/scoring/stopping decisions from the LLM to a fast batched scorer called Jev, calling the LLM only for final answer synthesis. On the LoCoMo long-conversation benchmark it scores 0.777 vs 0.700 for the best baseline, builds memory in 158s vs 1,044s for the fastest rival (6.6x speedup), and answers in 0.93s vs 1.47s. Transferable tip: replace LLM calls in memory pipelines with fast classifiers and save the LLM for answers. Code is public.
Related event: Jev-Mem Speeds Up Agent Memory Construction 6.6x(2 posts)→
More from coding & agent
- Hutash: a two-person team's all-in-one local AI platform now lost in scope — kathi7 · 2026-10-25
- Team ditches Slack for a custom chat app where humans and AI agents work side by side — dee_hw · 2026-10-25
- Musk: Grok Bot can now call businesses for you and summarize the conversation — elonmusk · 2026-10-25
- One prompt let Claude debloat an Android TV over ADB and rebuild a minimal home screen — PrajwalTomar_ · 2026-10-25
- User points Claude at his Samsung TV, spends 11 hours building an ad-free Tizen launcher — PrajwalTomar_ · 2026-10-25
- 10 open-source GitHub repos that put every AI model behind one API — victor_explore · 2026-10-25