# Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2507.09854 · HTML: https://mlsanity.com/paper/2507.09854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 11 of 11)

- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Automated Curriculum Learning for Neural Networks](https://mlsanity.com/paper/1704.03003/) (2017)
- [ART: Automatic multi-step reasoning and tool-use for large language models](https://mlsanity.com/paper/2303.09014/) (2023)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [Large Language Models Are Neurosymbolic Reasoners](https://mlsanity.com/paper/2401.09334/) (2024)
- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [Mathematical Reasoning in Latent Space](https://mlsanity.com/paper/1909.11851/) (2019)
- [A Framework for Neurosymbolic Robot Action Planning using Large Language Models](https://mlsanity.com/paper/2303.00438/) (2023)
- [Neurosymbolic AI for Enhancing Instructability in Generative AI](https://mlsanity.com/paper/2407.18722/) (2024)

## Abstract (arXiv)

Neurosymbolic artificial intelligence (AI) systems combine neural network and classical symbolic AI mechanisms to exploit the complementary strengths of large scale, generalizable learning and robust, verifiable reasoning. Numerous classifications of neurosymbolic AI illustrate how these two components can be integrated in distinctly different ways. In this work, we propose reinterpreting instruction tuned large language models as model grounded symbolic AI systems where natural language serves as the symbolic layer and grounding is achieved through the models internal representation space. Within this framework, we investigate and develop novel learning and reasoning approaches that preserve structural similarities to traditional learning and reasoning paradigms. Preliminary evaluations across axiomatic deductive reasoning procedures of varying complexity provide insights into the effectiveness of our approach in improving learning efficiency and reasoning reliability.