# Formal Verification and Control with Conformal Prediction

arXiv: https://arxiv.org/abs/2409.00536 · HTML: https://mlsanity.com/paper/2409.00536/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2024.

## Built on by (most cited first, 26 of 26)

- [Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents](https://mlsanity.com/paper/2502.02561/) (2025)
- [Conformal Prediction: A Data Perspective](https://mlsanity.com/paper/2410.06494/) (2024)
- [Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning](https://mlsanity.com/paper/2505.10547/) (2025)
- [C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets](https://mlsanity.com/paper/2410.09408/) (2024)
- [Conformal Policy Control](https://mlsanity.com/paper/2603.02196/) (2026)
- [Bridging conformal prediction and scenario optimization](https://mlsanity.com/paper/2503.23561/) (2025)
- [Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics](https://mlsanity.com/paper/2505.07594/) (2025)
- [Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees](https://mlsanity.com/paper/2512.04781/) (2025)
- [Statistically Assuring Safety of Control Systems using Ensembles of Safety Filters and Conformal Prediction](https://mlsanity.com/paper/2511.07899/) (2025)
- [Conformal Safety Shielding for Imperfect-Perception Agents](https://mlsanity.com/paper/2506.17275/) (2025)
- [Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network](https://mlsanity.com/paper/2509.11838/) (2025)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [LUCID: Learning-Enabled Uncertainty-Aware Certification of Stochastic Dynamical Systems](https://mlsanity.com/paper/2512.11750/) (2025)
- [Conformal Risk-Averse Decision Making with Action Conditional Guarantee](https://mlsanity.com/paper/2606.05551/) (2026)
- [Conformal Predictive Monitoring for Multi-Modal Scenarios](https://mlsanity.com/paper/2509.01338/) (2025)
- [Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees](https://mlsanity.com/paper/2601.01076/) (2026)
- [Safe learning-based control via function-based uncertainty quantification](https://mlsanity.com/paper/2604.01173/) (2026)
- [Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning](https://mlsanity.com/paper/2412.02951/) (2024)
- [CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planning](https://mlsanity.com/paper/2511.06575/) (2025)
- [Enhancing Conformal Prediction via Class Similarity](https://mlsanity.com/paper/2511.19359/) (2025)
- [Sample-Efficient Expert Query Control in Active Imitation Learning via Conformal Prediction](https://mlsanity.com/paper/2512.00453/) (2025)
- [Conformal Reachability for Safe Control in Unknown Environments](https://mlsanity.com/paper/2602.03799/) (2026)
- [Softmax is not Enough (for Adaptive Conformal Classification)](https://mlsanity.com/paper/2602.19498/) (2026)
- [Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems](https://mlsanity.com/paper/2603.07092/) (2026)
- [A PAC-Bayes Approach for Controlling Unknown Linear Discrete-time Systems](https://mlsanity.com/paper/2605.10493/) (2026)
- [PAC-Bayesian Certificates for Quadratic Closed-Loop Control](https://mlsanity.com/paper/2606.28281/) (2026)

## Abstract (arXiv)

We present recent advances in formal verification and control for autonomous systems with practical safety guarantees enabled by conformal prediction (CP), a statistical tool for uncertainty quantification. This survey is particularly motivated by learning-enabled autonomous systems (LEASs), where the complexity of learning-enabled components (LECs) poses a major bottleneck for applying traditional model-based verification and control techniques. To address this challenge, we advocate for CP as a lightweight alternative and demonstrate its use in formal verification, systems and control, and robotics. CP is appealing due to its simplicity (easy to understand, implement, and adapt), generality (requires no assumptions on learned models and underlying data distributions), and efficiency (real-time capable and accurate). This survey provides an accessible introduction to CP for non-experts interested in applying CP to autonomy problems. We particularly show how CP can be used for formal verification of LECs and the design of safe control as well as offline and online verification algorithms for LEASs. We present these techniques within a unifying framework that addresses the complexity of LEASs. Our exposition spans simple specifications, such as robot navigation tasks, to complex mission requirements expressed in temporal logic. Throughout the survey, we contrast CP with other statistical techniques, including scenario optimization and PAC-Bayes theory, highlighting advantages and limitations for verification and control. Finally, we outline open problems and promising directions for future research.