New AI Memory System Makes Language Models More Transparent and Controllable
Researchers have developed SPIRAL Cortex, a memory architecture that lets AI systems retrieve and explain their reasoning in auditable ways. The approach could help companies deploy language models in regulated industries by making it clearer how—and why—these systems reach their decisions.
Originaltitel: SPIRAL Cortex: A Policy-First Noetic Memory Architecture for Controlled Retrieval and Auditable Recall
This working paper introduces SPIRAL Cortex, a policy-first memory architecture for controlled retrieval and auditable recall. Rather than proposing a new model backbone, it studies when structured external memory can causally improve an existing policy under ambiguity. Using selected SWE-bench protocol-gap cases, the paper separates semantic same-subsystem memory from generic retrieval, lesions, and wrong-memory controls, and frames the resulting traces as a future bridge to NDT-style trajectory diagnostics.