Eigenform builds empirically grounded recursive self-improvement architectures. Our systems formulate hypotheses, write code to test them, evaluate results, and retrain on what they've learned. Each cycle produces a verifiable reasoning trace.
Reach out to usWe are an AI infrastructure company building empirically grounded recursive models. We don't test our models on synthetic benchmarks or track test loss. We test our models in the real world, allowing them to evolve in response to some of the harshest environments on earth. We believe this is the only path to true superintelligence. If humanity could do it, so can AI.
A living map of our work — from simple rules to recursive minds
Recursive self-improvement (RSI) is the process by which a system improves its own ability to improve itself, so that each cycle of change makes the next cycle faster or more effective. The idea is usually framed as a question about artificial intelligence — could a model rewrite its own code into a better model? — but the mechanism is far older than computing. Cyanobacteria, beaver dams and natural selection are all recursive improvement loops running without any code at all. Today's systems, including AlphaEvolve, the Darwin Gödel Machine and STaR, demonstrate narrow and bounded versions of the loop; open-ended recursive self-improvement remains unproven. Here we map eighty years of the idea across six research traditions, and anticipate future developments.
We don't train AI on synthetic data. Our models must survive the messiest dataset on Earth: Earth itself. We have built self-improving models for maths, cybersecurity and finance, and now we're tackling a global challenge: mineral exploration.
─ Four theses · Four frontiers · One conviction
We develop AI agents capable of autonomous exploration and validation within complex physical environments. By treating the environment as the ultimate reward signal, we move beyond human-labeled constraints. We observed a 15-17% performance uplift per cycle, as the model teaches itself the intermediate skills of geological feature engineering.
Using data probability distributions to build up a conceptual world model of specific sites, our AI systems learn the quirks of your site just like a human researcher would. We apply LLM logics to the "language" of the earth to predict structural shifts and resource distribution.
We turn scanned, translated, 60-year-old reports into structured spatial data in under 60 minutes. Speed is the advantage; the reasoning trace is the proof.
Instead of asking licence owners to pay large upfront fees, we build a financeable exploration vehicle around the asset. The License Owner brings the ground. Eigenform brings the AI engine and fundraising pathway.
Despite rapid advances in AI, much of the world's geological knowledge remains effectively inaccessible to computers: historical reports, complex tables and hand-drawn maps are still extremely difficult to parse reliably into machine-usable data. Eigenform is developing cutting-edge methods to extract, reconstruct and spatially align this information, turning fragmented analogue datasets into a clean digital representation of an exploration area. From there, our system builds a voxelised model of the subsurface and generates and tests competing geological hypotheses, quantifying the relationships between geology, geochemistry, structure and other observed features that best explain prospectivity—producing an interpretable model of what matters, where, and why.
Discover How It WorksMost AI is a black box. Ours produces a verifiable reasoning trace: hypothesis → code → model feature → interpretation. Truth is established by intensive reality-testing and cross-comparison. Every layer becomes training material for the next. We don't just predict; we show our work. You can audit every line. The result is an autonomously evolving local intelligence factory for your business: a model that becomes your IP as it creates it. Want to try? Contact us.
Focus: Autonomous learning without human benchmarks.
Focus: Detecting deterministic patterns in statistical randomness.
Focus: Moving AI beyond the "warm start" problem using empirical validation.
Focus: Low-rank adaptation and the relationship between training categories.
— from theoretical models to physical discovery.