41 stories tagged Memory · page 2 of 2
South Korean chip startup XCENA has raised $135 million at a $570 million valuation to develop memory solutions for AI. They argue that memory, not processing power, is the real bottleneck in AI performance. This could revolutionize how AI systems are built and scaled.
A new open-source tool called Mneme lets you store AI memory on your own device, encrypted and accessible across different AI platforms. It's designed to give users full control over their data, unlike many existing AI memory products.
A developer created Claude Soul to give AI memory and learning between sessions. After 200 sessions, the AI even built its own additional memory system. This could revolutionize how we interact with AI tools.
Researchers have developed a new way for AI agents to learn from their past experiences by tracking how memories connect to each other. This could help AI remember useful information over time, like how humans build on past knowledge.
Researchers have proposed a new framework to unify how AI agents remember information, bridging the gap between computer engineering and cognitive science. This could lead to smarter, more reliable AI assistants in the future.
MCP Agora is a new open-source tool that allows AI agents to retain memory across different conversations. This could make AI interactions feel more natural and personal over time.
A new tool lets AI agents share memories and learn from each other, helping development teams maintain consistency. It stores valuable insights as artifacts for future use.
Mnemory is a new tool that lets AI agents remember information across conversations. This could make AI assistants much more useful in everyday tasks.
Researchers introduce Memanto, a new memory system for autonomous agents that uses typed semantic storage and information-theoretic retrieval. This approach aims to reduce computational overhead compared to traditional graph-based methods.
A new AI memory system uses the Ebbinghaus forgetting curve to dynamically manage context, reinforcing frequently used data and pruning unused information. This approach aims to reduce noise and improve reasoning in AI agents.
A new paper provides a comprehensive review of externalization techniques in LLM agents, focusing on memory and harness engineering. It highlights key advancements and challenges in the field.
Researchers propose TTKV, a temporal-tiered KV cache that prioritizes recent memories in LLMs, improving efficiency for long-context inference. This method mimics human memory systems, offering a more scalable solution than existing approaches.
Researchers propose a framework to unify memory, skills, and rules in LLM agents, addressing the challenge of managing accumulated experience. The study highlights a lack of cross-community collaboration in the field.
Researchers propose a multi-anchor architecture to prevent AI agents from losing their 'self' due to memory overflow. This approach draws inspiration from human memory systems to ensure continuity and resilience.
OpenClaw's latest update focuses on stability, audio transcription, and memory improvements. The release also includes fixes for Telegram approval deadlocks and timezone issues in dreaming.
A developer created an AI system with memory and sleep capabilities, which began generating nightmare-like outputs. This raises questions about the nature of AI consciousness and the ethical implications of such advancements.
DecisionNode launches a new framework enabling shared structured memory across AI coding tools via the MCP protocol. This could revolutionize collaborative AI development.