RESEARCH

Research into the systems around intelligence.

We build experiments that make model behaviour, reasoning, interaction, and learning easier to inspect.

OUR THESIS

Westudywhathappensaftermodelsbecomecapable.

As AI systems act across time, capability depends on more than model weights. Tools, state, feedback, reasoning processes, environments, and failure recovery become part of intelligence itself.

We study the machinery around intelligence.
RESEARCH DIRECTIONS

Whatweinvestigate.

01

Agentic Systems

Long-horizon reasoning, tool use, stateful interaction, and autonomous behaviour.

02

Model Intelligence

Internal representations, mechanistic interpretability, model intervention, and behavioural analysis.

03

Reasoning & Verification

Search, symbolic constraints, structured reasoning, and verifiable computation.

04

Simulation & Learning

Interactive environments, synthetic experience, robotics, and adaptive learning systems.

05

AI Red Teaming

Reward-hacking analysis, model-hacking benchmarks, adversarial evaluations, and repeatable tests for unsafe optimisation and deceptive behaviour.

Residual-stream causal map showing dominant routed paths, secondary paths, and the direct-path intervention site across transformer layers
FEATURED RESEARCH

CausalInformationFlowinTransformerResidualStreams

Causal experiments using activation patching, intervention, probing, and ablation to study how specialist attention heads interact through GPT-2's shared residual stream.

Inspectthesystemsbehindtheresearch.

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