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I find AI roleplay therapeutic
用AI进行深度角色扮演，通过记忆与Lorebook系统打造沉浸式互动体验
Article URL: https://chatbrat.ai/bratlog/ultimate-ai-roleplay-setup-guide-memory-lorebooks Comments URL: https://news.ycombinator.com/item?id=48877439…
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Show HN: Trace – open-source, self-organizing memory for LLM agents (PyPI)
开源自组织记忆工具TRACE，专为LLM代理设计，实现多路径检索与背景树管理，提升代理记忆效率。
Article URL: https://github.com/husain34/TRACE Comments URL: https://news.ycombinator.com/item?id=48829843 Points: 2 # Comments: 0
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AutoMem: Automated Learning of Memory as a Cognitive Skill
让大模型像人类一样学习记忆管理，将文件系统操作变为可训练的认知技能
arXiv:2607.01224v1 Announce Type: new Abstract: Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize kno…
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Show HN: A Claude skill that prunes your AI's memory file, one diff at a time
免费工具解决AI记忆文件膨胀问题，堪称Claude用户的救命稻草
I made a memory cleaner for Claude Code. I guess it'll probably work in Codex, OpenCode, Composer etc, but I've only tested it in Claude Code. It's fo…
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Show HN: Callimachus – Local search across your AI coding-agent history
本地搜索AI编码代理历史，轻松管理每个仓库的决策、陷阱和待办事项，提升开发效率。
Article URL: https://github.com/BetaBots-LLC/callimachus Comments URL: https://news.ycombinator.com/item?id=48613813 Points: 4 # Comments: 2
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AI Agents Don't Need More Memory. They Need Governed Recall.
这篇文章颠覆了AI代理记忆的主流认知，指出关键不在于容量，而在于权限和语义检索的受控召回。
Most AI Agent Memory discussions start from the same assumption: If the agent forgets, give it more memory. More chat history. More retrieved document…
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Show HN: Push vs. Pull Agent Memory?
实测对比Agent记忆的推拉模式，开源项目帮你搞清楚哪个更适合你的AI系统。
Article URL: https://github.com/H-XX-D/recall-memory-substrate Comments URL: https://news.ycombinator.com/item?id=48580203 Points: 1 # Comments: 0
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G-Long: Graph-Enhanced Memory Management for Efficient Long-Term Dialogue Agents
用图结构优化大模型长期对话记忆，效率与连贯性双提升的学术新突破
arXiv:2606.13115v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have advanced open-domain dialogue systems, maintaining long-term…
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MemRefine: LLM-Guided Compression for Long-Term Agent Memory
大模型做智能体记忆压缩，用LLM引导算法优化长期存储效率，读这篇就对了。
arXiv:2606.13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, …
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Show HN: Lore – LLM proxy for coding agent context and memory management
比压缩算法好2倍：Lore用智能记忆管理让AI编码代理告别68分钟/天的重复解释，总召回率提升至2.6倍。
Article URL: https://withlore.ai/ Comments URL: https://news.ycombinator.com/item?id=48464573 Points: 4 # Comments: 0
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REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs
提出REAL推理增强图框架，让LLM长期记忆管理更高效、更可靠。
arXiv:2606.10694v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons.…
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Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents
反事实推理优化LLM Agent记忆管理，创新性地解决工具使用中的上下文选择与压缩难题。
arXiv:2606.08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is…
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Show HN: LLM memory without context bleed; 100% precision vs. <10% vector search
终结LLM上下文泄露：100%精度记忆，告别向量搜索的10%低效。
Article URL: https://tenureai.dev/ Comments URL: https://news.ycombinator.com/item?id=48409678 Points: 4 # Comments: 1
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DMF: A Deterministic Memory Framework for Conversational AI Agents
确定性记忆框架让对话AI不再"失忆"，21页论文详解DMF如何提升对话一致性与可控性。
arXiv:2606.03463v1 Announce Type: new Abstract: Conversational AI agents require memory systems that are both scalable and semantically coherent acros…
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I spent a week fixing my chatbot's memory — here's what worked
管理AI对话记忆的API服务，支持自动总结和持久化线程，减少延迟与成本损失。
Two months ago, I shipped a customer support chatbot for my SaaS product. It worked great for the first three messages. Then it started forgetting wha…
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从零手搓 AI Agent，我学到了什么？
从零手搓AI Agent的经历，暴露了token消耗和记忆管理的痛，值得开发者借鉴。
AI 知识「干中学」——用两天时间梳理完 AI 发展脉络，理解 Agent 能力边界。 查看全文