Research focus
AI safety, agents, memory and retrieval.
Build useful AI that people can guide, correct and trust.
Knowledge-Intelligence Separation asks what belongs in model parameters, and what belongs in editable memory and tools.
AI safety and human oversight
Make AI useful without losing privacy or human control.
Questions and papers on AI safety and human oversight
- How can an agent complete a task while sharing only the information its recipient needs?
- How can it communicate uncertainty, expose unreliable reasoning and know when to ask a person?
Agents and system evaluation
Improve the whole system, from planning and tool use to coordination between agents.
Questions and papers on Agents and system evaluation
- When an agent fails, was the cause its plan, a tool, retrieved evidence or communication with another agent?
- How can we compare systems and check their reasoning and actions with reproducible evaluations?
Memory and continual adaptation
Help agents learn from new information and user corrections without retraining for every update.
Questions and papers on Memory and continual adaptation
- How should an agent update or delete a memory and revise the facts that depend on it?
- How can it recognise that a previously known answer has become unknown, rather than repeat an outdated fact?
Retrieval and grounded reasoning
Connect models to relevant, current evidence across text and images.
Questions and papers on Retrieval and grounded reasoning
- When should a model rely on its parameters, and when should it search or use external knowledge?
- How can it resolve conflicting evidence and distinguish a correct match from a plausible half-truth?
What would you like to investigate? Tell us in your application.