Top Tweets for #lightmem

4 months ago

【コスト1/100】次世代メモリ技術「LightMem」が革命的！✨ LLMの運用コストを劇的に削減する新技術が登場しました。トークン消費を最大1/117に抑えつつ、推論精度も向上させます。自律型エージェント開発の強い味方になりそうです！🚀 #LightMem #生成AI

ai_hakase_'s tweet photo. 【コスト1/100】次世代メモリ技術「LightMem」が革命的！✨
LLMの運用コストを劇的に削減する新技術が登場しました。トークン消費を最大1/117に抑えつつ、推論精度も向上させます。自律型エージェント開発の強い味方になりそうです！🚀
#LightMem #生成AI https://t.co/KecA6hTpYd

579

Ningyu Zhang@ZJU

@zxlzr

8 months ago

Introducing our latest work, LightMem: Lightweight and Efficient Memory-Augmented Generation 🚀. A memory system that cuts cost while preserving (and often improving) long-horizon reasoning for LLM agents. #NLP #LLMs #Memory #LightMem #Agents 📖 Paper: https://t.co/I9EjkePBYI 🔗 Code: https://t.co/MlyaxqfHAw 🧩 Motivation: LLMs struggle in long, multi-turn interactions — context gets noisy, expensive, and models get “lost in the middle.” Existing memory systems are often accurate but heavy on tokens, API calls, and latency. ⚠️ 💡 Solution Overview: LightMem is inspired by human memory and uses a three-stage lightweight pipeline to keep memories compact, topical, and consistent: 1️⃣ Pre-compressing Sensory Memory — remove redundant/low-value tokens before further processing. 2️⃣ Topic-aware Short-Term Memory — group turns by topic and summarize to form precise memory units. 3️⃣ Sleep-time Long-Term Updates + Soft Updates — do only incremental inserts at test time and run high-fidelity consolidation offline to avoid runtime latency. 🔬 Results: On LONGMEMEVAL, LightMem yields notable gains in accuracy (up to ~10.9%) while slashing costs — tokens reduced up to 117×, API calls up to 159×, and runtime reduced >12× in some settings. ⚡ ☑️ Upcoming (README Todo — highlights): - Offline pre-computation of KV cache for update (lossless) - Online pre-computation of KV cache before Q&A (lossy) - MCP (Memory Control Policy) - Integration of more common models & feature enhancements - Coordinated use of context and long-term memory storage We’d love your feedback, issues, and PRs — let’s make memory for agents practical and lightweight! 🎙️

zxlzr's tweet photo. Introducing our latest work, LightMem: Lightweight and Efficient Memory-Augmented Generation 🚀.

A memory system that cuts cost while preserving (and often improving) long-horizon reasoning for LLM agents. #NLP #LLMs #Memory #LightMem #Agents

📖 Paper: https://t.co/I9EjkePBYI
🔗 Code: https://t.co/MlyaxqfHAw

🧩 Motivation: LLMs struggle in long, multi-turn interactions — context gets noisy, expensive, and models get “lost in the middle.”

Existing memory systems are often accurate but heavy on tokens, API calls, and latency. ⚠️

💡 Solution Overview: LightMem is inspired by human memory and uses a three-stage lightweight pipeline to keep memories compact, topical, and consistent:

1️⃣ Pre-compressing Sensory Memory — remove redundant/low-value tokens before further processing.
2️⃣ Topic-aware Short-Term Memory — group turns by topic and summarize to form precise memory units.
3️⃣ Sleep-time Long-Term Updates + Soft Updates — do only incremental inserts at test time and run high-fidelity consolidation offline to avoid runtime latency.

🔬 Results: On LONGMEMEVAL, LightMem yields notable gains in accuracy (up to ~10.9%) while slashing costs — tokens reduced up to 117×, API calls up to 159×, and runtime reduced >12× in some settings. ⚡

☑️ Upcoming (README Todo — highlights):

- Offline pre-computation of KV cache for update (lossless)
- Online pre-computation of KV cache before Q&A (lossy)
- MCP (Memory Control Policy)
- Integration of more common models & feature enhancements
- Coordinated use of context and long-term memory storage

We’d love your feedback, issues, and PRs — let’s make memory for agents practical and lightweight! 🎙️

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