mlsanityData to 14 Oct 2026

Directed Neuro-Symbolic Stochastic Execution for Verification of Distributed Parallel AI Programs

arXiv 2608.07947 ↗First posted 8 Aug 2026cs.AI cs.DCcited by 0

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Abstract (arXiv)

Distributed parallel Artificial Intelligence (AI) programs expose reliability gaps that conventional testing cannot close: parallel executions are non-deterministic, and AI workloads bring high-dimensional inputs and non-linear operations that defeat fuzzing and symbolic execution in isolation. We present Directed Neuro-Symbolic Stochastic Execution (DNSSE), a hybrid testing framework that couples schedule prediction guided by a Large Language Model (LLM) with symbolic constraint solving and coverage-guided stochastic mutation. We model distributed AI executions as non-deterministic transition systems, specify correctness in linear temporal logic, and prove soundness, bounded completeness, and probabilistic completeness of the hybrid solver, together with an expected-cost analysis of LLM-guided schedule exploration. A scalable implementation on PyTorch and Ray detects 2.9% more concurrency bugs than the strongest baseline and raises average branch coverage from 68.6 % to 91.6 % across five realistic distributed AI benchmarks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 8 Aug 2026.

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References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  5. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
  6. 2017DeepXplore: Automated Whitebox Testing of Deep Learning Systemscited by 311
  7. 2022Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Modelscited by 103
  8. 2023Fuzz4All: Universal Fuzzing with Large Language Modelscited by 88
  9. 2022Fuzzing Deep-Learning Libraries via Automated Relational API Inferencecited by 16
  10. 2025Large Language Model Powered Symbolic Executioncited by 2
  11. 2021Facilitating Parallel Fuzzing with mutually-exclusive Task Distributioncited by 1
  12. 2026Hybrid Concolic Testing with Large Language Models for Guided Path Explorationcited by 1

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