mlsanityData to 14 Oct 2026

NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS

arXiv 2502.12267 ↗First posted 17 Feb 2025cs.SE cs.AIcited by 5

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Abstract (arXiv)

Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and control but face trust and safety certification challenges due to inherent uncertainties. The neurosymbolic paradigm replaces stochastic layers with interpretable symbolic AI, enabling determinism. While promising, challenges like multisensor fusion, adaptability, and verification remain. This paper introduces NeuroStrata, a neurosymbolic framework to enhance the testing and verification of autonomous CPS. We outline its key components, present early results, and detail future plans.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 55 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

24 of 55 references.

References · 24

  1. 2017The "something something" video database for learning and evaluating visual common sensecited by 1,103
  2. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  3. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 377
  4. 2017DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Carscited by 251
  5. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 131
  6. 2016Neuro-Symbolic Program Synthesiscited by 84
  7. 2020Verification of Deep Convolutional Neural Networks Using ImageStarscited by 75
  8. 2020An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Modelscited by 68
  9. 2023Scallop: A Language for Neurosymbolic Programmingcited by 63
  10. 2020Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systemscited by 46
  11. 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstractioncited by 46
  12. 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
12 more
  1. 2023Neurosymbolic Reinforcement Learning and Planning: A Surveycited by 24
  2. 2021Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?cited by 19
  3. 2017Deep Predictive Models for Collision Risk Assessment in Autonomous Drivingcited by 15
  4. 2023LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervisioncited by 13
  5. 2017TensorLog: Deep Learning Meets Probabilistic DBscited by 11
  6. 2022Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertaintycited by 11
  7. 2020A Declarative Metamorphic Testing Framework for Autonomous Drivingcited by 10
  8. 2024ScenicNL: Generating Probabilistic Scenario Programs from Natural Languagecited by 10
  9. 2022Scenario-Based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systemscited by 6
  10. 2023Bridging Dimensions: Confident Reachability for High-Dimensional Controllerscited by 5
  11. 2023Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approachcited by 5
  12. 2021Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysiscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Four Principles for Physically Interpretable World Modelscited by 10
  2. 2025NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoningcited by 8
  3. 2025Lobster: A GPU-Accelerated Framework for Neurosymbolic Programmingcited by 4
  4. 2026RT-NeuS: Towards Real-Time Neuro-Symbolic Video Understanding via Adaptive Temporal Verificationcited by 2
  5. 2026From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testingcited by 0