Mem0: A Scalable Memory Architecture Enabling Persistent, Structured Recall for Long-Term AI Conversations Across Sessions

Mem0: A Scalable Memory Architecture Enabling Persistent, Structured Recall for Long-Term AI Conversations Across Sessions

Large language models can generate fluent responses, emulate tone, and even follow complex instructions; however, they struggle to retain information across multiple sessions. This limitation becomes more pressing as LLMs are integrated into applications that require long-term engagement, such as personal assistance, health management, and tutoring. In real-life conversations, people recall preferences, infer behaviors, and…

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We tested Intel’s new ‘200S Boost’ feature: 7% higher gaming performance thanks to memory overclocking, now covered by the warranty

We tested Intel’s new ‘200S Boost’ feature: 7% higher gaming performance thanks to memory overclocking, now covered by the warranty

According to documents shared with Tom’s Hardware by a source, Intel will announce a new “Intel 200S Boost” feature for its Arrow Lake processors tomorrow that’s designed to boost gaming performance by providing official warranty coverage for a subset of overclocking features, including memory overclocking. As you can see below, we have put the new…

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RRAM: Non-volatile memory for high-performance embedded applications

RRAM: Non-volatile memory for high-performance embedded applications

Non-volatile memory is an important component in a wide range of high-performance embedded applications. Especially, many consumer, industrial, and medical applications need increased re-writability to support both more frequent code updates as well as increased data logging. These applications require greater memory density to store either a substantially larger code footprint and/or more extensive data…

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Can AI Models Scale Knowledge Storage Efficiently? Meta Researchers Advance Memory Layer Capabilities at Scale

Can AI Models Scale Knowledge Storage Efficiently? Meta Researchers Advance Memory Layer Capabilities at Scale

The field of neural network architectures has witnessed rapid advancements as researchers explore innovative ways to enhance computational efficiency while maintaining or improving model performance. Traditional dense networks rely heavily on computationally expensive matrix operations to encode and store information. This reliance poses challenges when scaling these models for real-world applications that demand extensive knowledge…

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