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Researcher builds dependency-aware memory system for LLM vulnerability-hunting agents

A security researcher describes building a new memory architecture for LLM agents used in vulnerability research, after noticing that standard retrieval-based memory let models keep reasoning from facts that had since been disproven. Instead of just storing and retrieving past observations, the system tracks logical dependencies between facts, so that when one assumption is invalidated, everything derived from it can be automatically flagged or revised. The approach effectively borrows ideas from program analysis, treating an investigation's knowledge state like a dependency graph rather than a flat log of conversation history.