# Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation

arXiv: https://arxiv.org/abs/2610.14530 · HTML: https://mlsanity.com/paper/2610.14530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 10 Oct 2026.

## Builds on (most cited first, 22 of 22)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [End-to-End Differentiable Proving](https://mlsanity.com/paper/1705.11040/) (2017)
- [Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning](https://mlsanity.com/paper/1905.06088/) (2019)
- [Neural Logic Machines](https://mlsanity.com/paper/1904.11694/) (2019)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Learning Neuro-Symbolic Skills for Bilevel Planning](https://mlsanity.com/paper/2206.10680/) (2022)
- [Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems](https://mlsanity.com/paper/2106.14386/) (2021)
- [VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning](https://mlsanity.com/paper/2410.23156/) (2024)
- [Multi-Robot Collaborative Perception with Graph Neural Networks](https://mlsanity.com/paper/2201.01760/) (2022)
- [A Framework for Neurosymbolic Robot Action Planning using Large Language Models](https://mlsanity.com/paper/2303.00438/) (2023)
- [Learning Neuro-symbolic Programs for Language Guided Robot Manipulation](https://mlsanity.com/paper/2211.06652/) (2022)
- [Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments](https://mlsanity.com/paper/2305.12795/) (2023)
- [Security-Aware Sensor Fusion with MATE: the Multi-Agent Trust Estimator](https://mlsanity.com/paper/2503.04954/) (2025)
- [Exploiting Trust for Resilient Hypothesis Testing with Malicious Robots](https://mlsanity.com/paper/2209.12285/) (2022)
- [Enabling Team of Teams: A Trust Inference and Propagation (TIP) Model in Multi-Human Multi-Robot Teams](https://mlsanity.com/paper/2305.12614/) (2023)
- [Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy](https://mlsanity.com/paper/2403.16956/) (2024)
- [COMRES-VLM: Coordinated Multi-Robot Exploration and Search using Vision Language Models](https://mlsanity.com/paper/2509.26324/) (2025)
- [Dynamic Crowd Vetting: Collaborative Detection of Malicious Robots in Dynamic Communication Networks](https://mlsanity.com/paper/2304.00551/) (2023)
- [Multi-Robot Coordination with Adversarial Perception](https://mlsanity.com/paper/2504.09047/) (2025)

## Abstract (arXiv)

Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks in complex environments. However, reliable cooperation critically depends on inferring trust over both information sources and their reported hypotheses, particularly under partial observability and potential adversarial interference. Existing approaches frequently depend on pre-established trust relationships or side-channel information, which may not be available or reliable in realistic MRS deployments. This work introduces NeST-Bayes, a neural-symbolic framework that jointly infers robot and hypothesis trust using only inter-robot observation consistency, without requiring preassigned robot trust, reputation information, or side-channel identity signals. NeST-Bayes infers instantaneous trust evidence scores for robots and object hypotheses by reasoning over ego-centric relational graphs constructed from cross-robot observation consistency. These evidence scores are accumulated over time through Bayesian updates of Beta-distributed trust beliefs to maintain temporally consistent trust estimates. Extensive evaluations show that NeST-Bayes achieves the highest mean object precision and robot recall across all benchmark settings, and the highest robot precision and object recall in most settings, compared to trust-based methods. Under aggressive optimized attacks, it achieves 98.8% robot precision with 93.8% recall and 98.3% object precision with 95.4% recall. In an occlusion-rich Webots warehouse, it maintains 92.5% robot precision with 92.0% recall and 97.3% object precision with 85.0% recall. These results demonstrate joint improvements in the reliability and coverage of retained information.