About Skelf Research
Research that ships.
Science that scales.
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
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 20 open-source products encodes a specific research question, and the codebase itself is the proof — runnable, testable, and falsifiable.
Core Principles
Hypotheses as Software
Each project encodes a research question. The codebase is the proof — runnable, testable, and falsifiable.
Open Science by Default
20 public products, each its own repository. 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.
Research Domains
LLM Cognition & Prompt Theory
Formalising the relationship between prompt structure and model behaviour. Declarative prompt specification, automatic optimisation, and routing.
Safe & Verifiable Computing
Memory-safe language design, container sandboxing, and NUMA-aware scheduling for trustworthy autonomous computation.
Formal Optimisation & Decision Science
Bridging human intent and formally provable solutions. Constraint satisfaction, signal compilation, and intelligent ranking.
Edge Intelligence & On-Device AI
On-device LLM execution, mobile agent architectures, and privacy-preserving AI at the edge.
Robotics & Autonomous Systems
Deterministic discrete-event simulation, RL benchmarks, and the systems engineering that turns research code into reproducible robotics experiments.
Get in Touch
We welcome academic collaborators, research partners, and funders who believe the hardest problems in AI deserve open, rigorous, reproducible investigation.