XLOG is a full typed logic programming language with one compiler and one CUDA runtime spanning four reasoning paradigms: deterministic Datalog, exact and approximate probabilistic inference, epistemic reasoning, and differentiable neural-symbolic training. SAT/MaxSAT verification is available to all four through a shared GPU-resident solver service, not as a paradigm of its own. Neural-symbolic systems today keep symbolic reasoning on the CPU while neural computation runs on the GPU, and every training iteration pays a PCIe round-trip that dominates wall-clock time at scale. XLOG closes that gap: symbolic evaluation is GPU-resident with zero tracked host–device transfers in production data planes. Compiled circuits are cached across training iterations. GPU-resident results are exposed zero-copy through DLPack and the Arrow C Device interface to PyTorch, JAX, and cuDF (Arrow IPC covers host-side interchange). On the MNIST-addition neural-symbolic benchmark, circuit caching yields a measured 2.74× end-to-end training speedup (95% CI [2.29, 3.18]) in a cached-vs-uncached ablation of XLOG’s own pipeline, and steady epochs run 2.80× faster than Scallop at matched accuracy.

Quickstart

Install XLOG, write your first program, and run it on the GPU in five minutes.

Language Reference

The full language surface: types, predicates, rules, modules, aggregation, and pragmas.

Probabilistic Programming

Exact inference via GPU knowledge compilation, plus Monte Carlo sampling with confidence intervals.

Architecture

How the compiler, IR stack, and CUDA runtime fit together.