About Ufinq
Ufinq is a focused research effort on closed-form Symbolic AI — algorithms that discover small, readable programs describing how data behaves, instead of large opaque models. The work is closed-source by default and open in its ideas: papers, designs, and benchmarks are public; the productized system is part of Diafunc.
The name follows from the work: Ufinq denotes the universal function space the algorithm searches — the (effectively infinite) space of programs composed from a value system and a fixed set of elementary operators, navigated by an evolutionary process. Ufinq, as a system, makes that search practical at cluster scale.
Ufinq is a long bet on Symbolic AI: that the right output of a learning system, in most regulated and high-stakes domains, is code — not weights.
What Ufinq is built for
Most contemporary AI research compresses data into ever-larger weight matrices. The resulting models are difficult to audit, expensive to deploy, and hard to explain — three properties that matter most precisely where the stakes are highest. Ufinq starts from the opposite premise: search over programs directly, return a program as the model, and trade learning capacity for transparency.
The research effort is organized around four commitments:
Relationship to Diafunc
Diafunc is the company and the platform; Ufinq is its research arm and the technology powering it. The split is deliberate: research output (papers, designs, benchmarks) lives under the Ufinq brand on this site, while the productized platform — projects, datasets, notebooks, automated analyses — lives at diafunc.com. There is no separate Ufinq download, package, or installer; the only way to use Ufinq commercially is through Diafunc.
The asymmetry between reading a paper and reproducing a full distributed system is deliberate. Papers describe ideas. Productizing AHISTER, DEBOHAC, the simplification pipeline, the cluster runtime, the refinement chain, and the universal value substrate is a multi-year engineering effort — and that is what Diafunc sells.
Principal investigator
Markus Pollak
Markus designs and writes the Ufinq system end-to-end: the algorithms (AHISTER, DEBOHAC, CRESCENT), the symbolic substrate, the refinement pipeline, the distributed runtime, and the benchmarks. He is the author of the current Ufinq papers and the founder of Diafunc, the company that distributes Ufinq commercially. He works from Austria.
The orientation behind Ufinq is long-form: a multi-year research bet that the right form of a learning system, in most regulated and high-stakes domains, is readable code — and that the algorithmic and engineering work to make that practical at scale is worth doing in depth, in one consistent system, instead of spread across loosely coupled prototypes. Ufinq is that system; Diafunc is how it reaches the people who need it.
Continue reading
For the short version of the system, see Technology. The active research themes are listed under Research; the corresponding papers under Papers. Empirical results live on Benchmarks. To get in touch, see Contact.