ADNC

In my opinion current research should focus on revisiting older concepts to figure out if they can be applied to transformers. Transformers are superior "database" encodings as the hype about LLMs points out, but there have been promising ML models that were focusing on memory parts for their niche use cases, which could be promising concepts if we could make them work with attention matrixes and/or use the frequency projection idea on their neuron weights. The way RNNs evolved to LSTMs, GRUs, and eventually DNCs was pretty interesting to me. In my own implementations and use cases I wasn't able to reproduce Deepmind's claims in the DNC memory related parts. Back at the time the "seeking heads" idea of attention matrixes wasn't there yet, maybe there's a way to build better read/write/access/etc gates now. [1] a fairly good implementation

Well, agents can't discover bypass attacks because they don't have memory. That was what DNCs [1] (Differentiable Neural Computers) tried to accomplish. Correlating scan metrics with analytics is btw a great task for DNCs and what they are good at due to how their (not so precise) memory works. Not so much though at understanding branch logic and their consequences. However, I currently believe that forensic investigations will change post LLMs, because they're very good at translating arbitrary bytecode, assembly, netasm, intel asm etc syntax to example code (in any language). It doesn't have to be 100% correct in those translations, that's why LLMs can be really helpful for the discovery phase after an incident. Check out the ghidra MCP server which is insane to see real-time [2] [1] https://github.com/JoergFranke/ADNC [2] https://githu