# Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system

arXiv: https://arxiv.org/abs/2506.23926 · HTML: https://mlsanity.com/paper/2506.23926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 30 Jun 2025.

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

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models](https://mlsanity.com/paper/2404.05567/) (2024)
- [A free energy principle for a particular physics](https://mlsanity.com/paper/1906.10184/) (2019)
- [The physics of brain network structure, function, and control](https://mlsanity.com/paper/1809.06441/) (2018)
- [How particular is the physics of the free energy principle?](https://mlsanity.com/paper/2105.11203/) (2021)
- [Higher-order percolation processes on multiplex hypergraphs](https://mlsanity.com/paper/2104.05457/) (2021)

## Abstract (arXiv)

Resilience non-equilibrium measurement, the ability to maintain fundamental functionality amidst failures and errors, is crucial for scientific management and engineering applications of industrial chain. The problem is particularly challenging when the number or types of multiple co-evolution of resilience (for example, randomly placed) are extremely chaos. Existing end-to-end deep learning ordinarily do not generalize well to unseen full-feld reconstruction of spatiotemporal co-evolution structure, and predict resilience of network topology, especially in multiple chaos data regimes typically seen in real-world applications. To address this challenge, here we propose industrial brain, a human-like autonomous cognitive decision-making and planning framework integrating higher-order activity-driven neuro network and CT-OODA symbolic reasoning to autonomous plan resilience directly from observational data of global variable. The industrial brain not only understands and model structure of node activity dynamics and network co-evolution topology without simplifying assumptions, and reveal the underlying laws hidden behind complex networks, but also enabling accurate resilience prediction, inference, and planning. Experimental results show that industrial brain significantly outperforms resilience prediction and planning methods, with an accurate improvement of up to 10.8% over GoT and OlaGPT framework and 11.03% over spectral dimension reduction. It also generalizes to unseen topologies and dynamics and maintains robust performance despite observational disturbances. Our findings suggest that industrial brain addresses an important gap in resilience prediction and planning for industrial chain.