Post
35
Something quietly caught my attention while going through this week's trending papers — Three independent teams, all converging on the same problem — and all trending on HuggingFace right now.
That kind of convergence usually means the field is quietly agreeing something needs fixing.
The problem, as I understand it:
Most of us are still stuffing conversation history into a context window and calling it memory. That's not memory — that's a very expensive clipboard.
📄 Three papers worth reading:
🔹 SkillOpt — Microsoft Research ( @Yifan Yang, @Ziyang Gong et all )
Agent skills stored as natural language that improves with real usage, no retraining needed
→ SkillOpt: Executive Strategy for Self-Evolving Agent Skills (2605.23904)
🔹 Mem0 — Mem0 (YC S24, 41K ⭐) ( @Prateek Chhikara & all )
Graph-structured memory that retrieves what's relevant not just what's recent
→ https://arxiv.org/abs/2504.19413
🔹 EverMemOS — EverMind (@Chuanrui Hu, @Xingze Gao et all )
Separates memory into episodic, semantic and procedural types — closer to how human memory actually works
→ EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2601.02163)
Great work from all three teams 🙌
💬 What are you actually using for agent memory in production today? Still ConversationBufferMemory, a custom schema, something else entirely? And do you think bigger context windows eventually make this problem disappear?
#AgentMemory #MemoryAugmentedAgents #LongContextLLM #AgenticAI #LangChain #SkillOpt #Mem0 #EverMemOS
That kind of convergence usually means the field is quietly agreeing something needs fixing.
The problem, as I understand it:
Most of us are still stuffing conversation history into a context window and calling it memory. That's not memory — that's a very expensive clipboard.
📄 Three papers worth reading:
🔹 SkillOpt — Microsoft Research ( @Yifan Yang, @Ziyang Gong et all )
Agent skills stored as natural language that improves with real usage, no retraining needed
→ SkillOpt: Executive Strategy for Self-Evolving Agent Skills (2605.23904)
🔹 Mem0 — Mem0 (YC S24, 41K ⭐) ( @Prateek Chhikara & all )
Graph-structured memory that retrieves what's relevant not just what's recent
→ https://arxiv.org/abs/2504.19413
🔹 EverMemOS — EverMind (@Chuanrui Hu, @Xingze Gao et all )
Separates memory into episodic, semantic and procedural types — closer to how human memory actually works
→ EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2601.02163)
Great work from all three teams 🙌
💬 What are you actually using for agent memory in production today? Still ConversationBufferMemory, a custom schema, something else entirely? And do you think bigger context windows eventually make this problem disappear?
#AgentMemory #MemoryAugmentedAgents #LongContextLLM #AgenticAI #LangChain #SkillOpt #Mem0 #EverMemOS
