8 stories tagged AI Memory
Researchers introduced Akashic, a low-overhead memory system for LLM inference that uses MemAttention to organize context into bounded chunks and model semantic relationships. This could make chatbots and AI assistants much more efficient and accurate by reducing prefill costs and avoiding context limits.
Researchers introduced TrustMem, a framework designed to improve long-term memory in AI agents. It addresses the problem of AI systems forgetting important information or generating hallucinated content that becomes permanently stored. This could make AI assistants more reliable over extended interactions.
ChatGPT now has a 'Dreaming' feature that helps it remember your preferences better by reviewing past interactions when idle. This makes interactions more personal and relevant over time. The feature is gradually rolling out to all users, starting with Plus and Enterprise.
Scientists argue that AI memory systems need to evolve beyond simple databases. Current approaches lead to issues like unchecked growth and forgotten information. Long-term AI agents need more sophisticated memory structures to function effectively.
Researchers have developed a new AI memory system called Lossless Context Management (LCM) that handles long texts better than Claude Code. This could make AI assistants more reliable for tasks requiring large amounts of information.
A new paper proposes a two-counter system to evaluate AI memory quality by tracking co-occurrence with success or failure. This could revolutionize how agents manage and prioritize memories over time.
A new research paper proposes a shift from retrieval-augmented generation to personal wiki-style memory architectures for LLMs. This approach could revolutionize how AI systems retain and utilize knowledge over time.
A new paper introduces a mathematical framework for how environments can act as memory for agents in reinforcement learning. Experiments show that spatial paths can reduce the information needed to represent history.