I accidentally turned LLM memory into program analysis
A developer is exploring a new approach to LLM memory that focuses on maintaining a 'state of knowledge' rather than just retrieving past conversation logs. This method aims to reduce hallucinations in complex tasks like vulnerability research by allowing the model to update or invalidate previous assumptions.
Why it matters
Improving LLM reasoning and state management is critical for the reliability of AI agents in technical fields like cybersecurity.
Over the past few months I have been playing around quite a bit with LLM agents, particularly for vulnerability research.
They are becoming surprisingly good at navigating large codebases, explaining unfamiliar subsystems and helping explore potential attack surfaces. However, once an investigation starts taking a few hours, I kept running into the same problem: the model would slowly lose track of what we had actually established.
It might suggest an approach that we had already ruled out, forget that an assumption turned out to be false, or confidently continue reasoning from an observation that was no longer valid. Obviously, telling an LLM that something is wrong does not necessarily mean that it will stop believing all of the things that depended on it :)
I initially started looking into memory systems because I wanted to make LLMs more useful for complex vulnerability research and reduce this type of hallucination.
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