Research that ships.
Science that scales.

Abstract visualization of interconnected 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

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.

Core Principles

01

Hypotheses as Software

Each project encodes a research question. The codebase is the proof — runnable, testable, and falsifiable.

02

Open Science by Default

24 public repositories. Every experiment is reproducible, every finding is auditable by the global research community.

03

Systems-Level Rigour

We choose Rust, Zig, and Go not for fashion but for falsifiability — deterministic performance makes claims measurable.

04

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.

Get in Touch

We welcome academic collaborators, research partners, and funders who believe the hardest problems in AI deserve open, rigorous, reproducible investigation.