# Hybrid Reasoning Systems That Prioritize and Enhance Human Intelligence

arXiv: https://arxiv.org/abs/2504.13477 · HTML: https://mlsanity.com/paper/2504.13477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [Leveraging Rationales to Improve Human Task Performance](https://mlsanity.com/paper/2002.04202/) (2020)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research](https://mlsanity.com/paper/2307.10250/) (2023)
- [A Novel Idea Generation Tool using a Structured Conversational AI (CAI) System](https://mlsanity.com/paper/2409.05747/) (2024)

## Abstract (arXiv)

In a world of accelerating change, there is a need for wise and adaptive human reasoning. Integrating AI capabilities with human guidance offers promise, though human reasoning itself is often hasty, shortsighted, and error-prone, and no clear framework exists for combining human reasoning strategies with AI across diverse tasks. This article proposes a framework for human-centered hybrid reasoning systems that engage and enhance human reasoning abilities ranging from granular data analysis to high-level reflection and wisdom. The framework was developed through a conceptual synthesis combining: (1) established strategies for enhancing human reasoning, (2) AI design approaches that favor pre-conclusive engagement over the generation of conclusions, and (3) the treatment of reasoning as a collection of distinct, individually supportable modes. This synthesis produced a distinctive framework for broad-spectrum reasoning enhancement from which a typology of reasoning modes, a system architecture, and a high-level research agenda were derived.