# Artificial Expert Intelligence through PAC-reasoning

arXiv: https://arxiv.org/abs/2412.02441 · HTML: https://mlsanity.com/paper/2412.02441/

## Key facts

- Cited by 3 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 16 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Placed in

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

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

- [On the Measure of Intelligence](https://mlsanity.com/paper/1911.01547/) (2019)

## Built on by (most cited first, 3 of 3)

- [Execution Guided Line-by-Line Code Generation](https://mlsanity.com/paper/2506.10948/) (2025)
- [From Reasoning to Super-Intelligence: A Search-Theoretic Perspective](https://mlsanity.com/paper/2507.15865/) (2025)
- [L0-Reasoning Bench: Evaluating Procedural Correctness in Language Models via Simple Program Execution](https://mlsanity.com/paper/2503.22832/) (2025)

## Abstract (arXiv)

Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with critical, precise reasoning capabilities akin to those of top human experts. Existing AI systems often excel at predefined tasks but struggle with adaptability and precision in novel problem-solving. To overcome this, AEI introduces a framework for ``Probably Approximately Correct (PAC) Reasoning". This paradigm provides robust theoretical guarantees for reliably decomposing complex problems, with a practical mechanism for controlling reasoning precision. In reference to the division of human thought into System 1 for intuitive thinking and System 2 for reflective reasoning~citeptversky1974judgment, we refer to this new type of reasoning as System 3 for precise reasoning, inspired by the rigor of the scientific method. AEI thus establishes a foundation for error-bounded, inference-time learning.