# Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testing

arXiv: https://arxiv.org/abs/2507.03870 · HTML: https://mlsanity.com/paper/2507.03870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Search-Based Testing of Reinforcement Learning](https://mlsanity.com/paper/2205.04887/) (2022)
- [Differential Assessment of Black-Box AI Agents](https://mlsanity.com/paper/2203.13236/) (2022)
- [WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies](https://mlsanity.com/paper/2502.19308/) (2025)
- [Formal Ethical Obligations in Reinforcement Learning Agents: Verification and Policy Updates](https://mlsanity.com/paper/2408.00147/) (2024)

## Abstract (arXiv)

When an autonomous agent behaves undesirably, including failure to complete a task, it can be difficult to determine whether the behavior is due to a systemic agent error, such as flaws in the model or policy, or an environment error, where a task is inherently infeasible under a given environment configuration, even for an ideal agent. As agents and their environments grow more complex, identifying the error source becomes increasingly difficult but critical for reliable deployment. We introduce AIProbe, a novel black-box testing technique that applies differential testing to attribute undesirable agent behaviors either to agent deficiencies, such as modeling or training flaws, or due to environmental infeasibility. AIProbe first generates diverse environmental configurations and tasks for testing the agent, by modifying configurable parameters using Latin Hypercube sampling. It then solves each generated task using a search-based planner, independent of the agent. By comparing the agent's performance to the planner's solution, AIProbe identifies whether failures are due to errors in the agent's model or policy, or due to unsolvable task conditions. Our evaluation across multiple domains shows that AIProbe significantly outperforms state-of-the-art techniques in detecting both total and unique errors, thereby contributing to a reliable deployment of autonomous agents.