Research programme
Memory that remains accountable after retrieval.
Long-lived agents need more than similarity search. They need a defensible account of where a memory came from, whether it is still valid, who may use it, what reached the model, and what happened when an interrupted agent resumed work.
My present research studies governed agent memory, execution evidence, crash continuity, structured judgment and causal attribution. Related work examines machine unlearning, generative-model control and evaluation methods for AI systems. This extends an earlier research foundation in nonlinear control, autonomous ground vehicles, soft robotics, system modelling and field experimentation.
Agent memoryAI safety and trustProvenanceMemory integrityAgent evaluationMachine unlearningGenerative AIAutonomous systemsNonlinear control