About Skelf Research
Skelf Research is an independent AI research laboratory based in the United Kingdom. We investigate the foundations of machine reasoning, computational intelligence, and safe AI systems — then publish everything as open-source software.
We operate at the boundary between academic inquiry and real-world systems. We believe the most important questions in AI today — about reasoning, safety, efficiency, and privacy — are best answered by building working prototypes and publishing everything.
Our methodology is simple: identify an open problem, construct a hypothesis as software, stress-test it against real workloads, and release the results. Every repository is a peer-reviewable experiment.
We don't write papers that stay on shelves. We write code that runs in production. Each of our 24 open-source projects encodes a specific research question, and the codebase itself is the proof — runnable, testable, and falsifiable.
Hypotheses as Software
Each project encodes a research question. The codebase is the proof — runnable, testable, and falsifiable.
Open Science by Default
24 public repositories. Every experiment is reproducible, every finding is auditable by the global research community.
Systems-Level Rigour
We choose Rust, Zig, and Go not for fashion but for falsifiability — deterministic performance makes claims measurable.
Privacy as a Research Constraint
On-device inference and zero-trust architectures aren't add-ons — they're design constraints that shape better science.
Formalising the relationship between prompt structure and model behaviour. Declarative prompt specification, automatic optimisation, and routing.
Memory-safe language design, container sandboxing, and NUMA-aware scheduling for trustworthy autonomous computation.
Bridging human intent and formally provable solutions. Constraint satisfaction, signal compilation, and intelligent ranking.
On-device LLM execution, mobile agent architectures, and privacy-preserving AI at the edge.
We welcome academic collaborators, research partners, and funders who believe the hardest problems in AI deserve open, rigorous, reproducible investigation.