working memory
working memory at Beyond Market Intelligence is a file of 2 stories. The newest of them: “Mapping AI's Memory: Where RNNs, Transformers, and SSMs Store What They Know” and “Explore the Hidden Geometry Inside Your Model's Working Memory”. If you've ever wondered why some AI models seem to remember everything while others compress the past into a tight hidden state, the answer is all about where memory lives. The KV cache isn't a flat list; it's a navigable vector space, and this researcher turned that observation into a working system. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every working memory story on Beyond Market Intelligence, newest first.
Mapping AI's Memory: Where RNNs, Transformers, and SSMs Store What They Know
If you've ever wondered why some AI models seem to remember everything while others compress the past into a tight hidden state, the answer is all about where memory lives. RNNs carry a compact recurrent state forward step by step, creating an elegant bottleneck between compute and memory. Transformers take the opposite path, scattering past representations across a growing KV cache like digital post-it notes.
Explore the Hidden Geometry Inside Your Model's Working Memory
The KV cache isn't a flat list; it's a navigable vector space, and this researcher turned that observation into a working system. On a frozen Qwen3.5-2B at 32k context, geometric routing cuts physical KV reads by 16-31× while still retrieving the planted long-range needle. That's not a theoretical pitch; it's a reproducible demo. The insight is that attention is already similarity search, so indexing old context isn't a hack, it's the natural next step.