# XLOG: A CUDA-Native Engine for Neurosymbolic Integration

arXiv: https://arxiv.org/abs/2609.27203 · HTML: https://mlsanity.com/paper/2609.27203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [A Semantic Loss Function for Deep Learning with Symbolic Knowledge](https://mlsanity.com/paper/1711.11157/) (2017)
- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver](https://mlsanity.com/paper/1905.12149/) (2019)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [NeurASP: Embracing Neural Networks into Answer Set Programming](https://mlsanity.com/paper/2307.07700/) (2023)
- [Scallop: A Language for Neurosymbolic Programming](https://mlsanity.com/paper/2304.04812/) (2023)
- [Massively Parallel Continuous Local Search for Hybrid SAT Solving on GPUs](https://mlsanity.com/paper/2308.15020/) (2023)
- [Datalog Disassembly](https://mlsanity.com/paper/1906.03969/) (2019)
- [Composite Event Recognition for Maritime Monitoring](https://mlsanity.com/paper/1903.03078/) (2019)
- [Online Learning Probabilistic Event Calculus Theories in Answer Set Programming](https://mlsanity.com/paper/2104.00158/) (2021)
- [Lobster: A GPU-Accelerated Framework for Neurosymbolic Programming](https://mlsanity.com/paper/2503.21937/) (2025)
- [Optimizing Datalog for the GPU](https://mlsanity.com/paper/2311.02206/) (2023)
- [Column-Oriented Datalog on the GPU](https://mlsanity.com/paper/2501.13051/) (2025)
- [Knowledge compilation languages as proof systems](https://mlsanity.com/paper/1903.04039/) (2019)
- [Free Join: Unifying Worst-Case Optimal and Traditional Joins](https://mlsanity.com/paper/2301.10841/) (2023)
- [Scaling Worst-Case Optimal Datalog to GPUs](https://mlsanity.com/paper/2604.20073/) (2026)

## Abstract (arXiv)

xlog is a CUDA-native logic programming engine integrating neural perception with deterministic Datalog, probabilistic inference, and epistemic world views through a typed frontend and provider-owned CUDA runtime. Its reasoning modes share device data planes, but their execution boundaries differ: ordinary Datalog and exact inference are host-orchestrated, while certified resident recursive and Monte Carlo sampled cores record zero tracked host-device transfers before a bounded terminal receipt. The probabilistic path supports end-to-end gradients through GPU knowledge compilation from provenance to CNF to Decision-DNNF, exact weighted model counting, and backward gradients. A final smoothed circuit is certified against its source formula before caching or evaluation. Circuit caching yields a 2.74x MNIST-addition training speedup; a worst-case-optimal join subsystem yields a 27.96x geometric-mean gain over xlog's binary-join baseline. MNIST-addition accuracy matches Scallop's (0.9561 versus 0.9468), but no per-epoch speed claim is made because baseline epoch time varies with CPU quota. In five hub-skewed triangle-counting cases, the Souffle-to-fused-xlog execution-time ratio rises from 0.88x at 150k edges, where Souffle is faster, to 5.54x at 1.2M; fused peak device allocations are 85-1,033 MB versus 3,287-44,979 MB for the materializing arm. Exact inference is correctness-equivalent to but slower than ProbLog2. On a public video benchmark, a proximity predicate trained only through symbolic credit replaces hand-set geometry at unchanged held-out accuracy; within Event-Calculus rule search it fails ten-fold cross-validation and does not transfer on a leak-free split. On a maritime corpus, weighted clauses beat crisp selection by 0.065 F1, with the result reproduced by one chronological training pass.