Researchers build fleeting memory transformers with human-like memory decay, proving memory limits help AI learn grammar ...

Researchers at the Tokyo-based startup Sakana AI have developed a new technique that enables language models to use memory more efficiently, helping enterprises cut the costs of building applications ...

Stanford research finds AI models agree with users 49% more than humans, while memory mismanagement causes up to 39% performance drops across 15 major LLMs.

What if your AI could remember every meaningful detail of a conversation—just like a trusted friend or a skilled professional? In 2025, this isn’t a futuristic dream; it’s the reality of ...

Transformer networks, driven by self-attention, are central to large language models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at ...

As humans and machine learning systems often face similar computational challenges 1, there has been synergy between machine learning and cognitive science research, leveraging machine learning ...

In the fast-paced world of artificial intelligence, memory is crucial to how AI models interact with users. Imagine talking to a friend who forgets the middle of your conversation—it would be ...

Microsoft’s new Surface RTX Spark Dev Box packs Nvidia Blackwell AI power and 128GB of unified memory to run large AI models locally, helping developers cut cloud costs and rethink enterprise AI ...

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during ...

A new study reveals that the memory for a specific experience is stored in multiple parallel 'copies'. These are preserved for varying durations, modified to certain degrees, and sometimes deleted ...

Microsoft Research’s Mirage stores 3D scene data directly in diffusion latent space, cutting GPU memory 55x and generation ...