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

arXiv: https://arxiv.org/abs/2502.12267 · HTML: https://mlsanity.com/paper/2502.12267/

## 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 (most cited first, 24 of 24)

- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Neuro-Symbolic Program Synthesis](https://mlsanity.com/paper/1611.01855/) (2016)
- [Verification of Deep Convolutional Neural Networks Using ImageStars](https://mlsanity.com/paper/2004.05511/) (2020)
- [An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models](https://mlsanity.com/paper/2002.02175/) (2020)
- [Scallop: A Language for Neurosymbolic Programming](https://mlsanity.com/paper/2304.04812/) (2023)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction](https://mlsanity.com/paper/2306.01439/) (2023)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?](https://mlsanity.com/paper/2106.12233/) (2021)
- [Deep Predictive Models for Collision Risk Assessment in Autonomous Driving](https://mlsanity.com/paper/1711.10453/) (2017)
- [LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision](https://mlsanity.com/paper/2304.07647/) (2023)
- [TensorLog: Deep Learning Meets Probabilistic DBs](https://mlsanity.com/paper/1707.05390/) (2017)
- [Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty](https://mlsanity.com/paper/2210.05989/) (2022)
- [A Declarative Metamorphic Testing Framework for Autonomous Driving](https://mlsanity.com/paper/2012.10672/) (2020)
- [ScenicNL: Generating Probabilistic Scenario Programs from Natural Language](https://mlsanity.com/paper/2405.03709/) (2024)
- [Scenario-Based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systems](https://mlsanity.com/paper/2209.01546/) (2022)
- [Bridging Dimensions: Confident Reachability for High-Dimensional Controllers](https://mlsanity.com/paper/2311.04843/) (2023)
- [Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach](https://mlsanity.com/paper/2311.07377/) (2023)
- [Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis](https://mlsanity.com/paper/2111.03781/) (2021)

## Built on by (most cited first, 5 of 5)

- [Four Principles for Physically Interpretable World Models](https://mlsanity.com/paper/2503.02143/) (2025)
- [NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2509.18041/) (2025)
- [Lobster: A GPU-Accelerated Framework for Neurosymbolic Programming](https://mlsanity.com/paper/2503.21937/) (2025)
- [RT-NeuS: Towards Real-Time Neuro-Symbolic Video Understanding via Adaptive Temporal Verification](https://mlsanity.com/paper/2602.23553/) (2026)
- [From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing](https://mlsanity.com/paper/2604.18918/) (2026)

## 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.