# Current Practices for Building LLM-Powered Reasoning Tools Are Ad Hoc -- and We Can Do Better

arXiv: https://arxiv.org/abs/2507.05886 · HTML: https://mlsanity.com/paper/2507.05886/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 1 of its 34 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

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

## Builds on (most cited first, 1 of 1)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)

## Built on by (most cited first, 1 of 1)

- [Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis](https://mlsanity.com/paper/2605.12694/) (2026)

## Abstract (arXiv)

There is growing excitement about building software verifiers, synthesizers, and other Automated Reasoning (AR) tools by combining traditional symbolic algorithms and Large Language Models (LLMs). Unfortunately, the current practice for constructing such neurosymbolic AR systems is an ad hoc programming model that does not have the strong guarantees of traditional symbolic algorithms, nor a deep enough synchronization of neural networks and symbolic reasoning to unlock the full potential of LLM-powered reasoning. I propose Neurosymbolic Transition Systems as a principled computational model that can underlie infrastructure for building neurosymbolic AR tools. In this model, symbolic state is paired with intuition, and state transitions operate over symbols and intuition in parallel. I argue why this new paradigm can scale logical reasoning beyond current capabilities while retaining the strong guarantees of symbolic algorithms, and I sketch out how the computational model I propose can be reified in a logic programming language.