# Interacting with AI Reasoning Models: Harnessing "Thoughts" for AI-Driven Software Engineering

arXiv: https://arxiv.org/abs/2503.00483 · HTML: https://mlsanity.com/paper/2503.00483/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 8 of 8)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks](https://mlsanity.com/paper/2304.01665/) (2023)
- [Generative AI and Empirical Software Engineering: A Paradigm Shift](https://mlsanity.com/paper/2502.08108/) (2025)
- [Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs](https://mlsanity.com/paper/2308.11914/) (2023)
- [Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems](https://mlsanity.com/paper/2408.15550/) (2024)
- [A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research](https://mlsanity.com/paper/2401.14617/) (2024)

## Built on by (most cited first, 2 of 2)

- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Interleaved Reasoning for Large Language Models via Reinforcement Learning](https://mlsanity.com/paper/2505.19640/) (2025)

## Abstract (arXiv)

Recent advances in AI reasoning models provide unprecedented transparency into their decision-making processes, transforming them from traditional black-box systems into models that articulate step-by-step chains of thought rather than producing opaque outputs. This shift has the potential to improve software quality, explainability, and trust in AI-augmented development. However, software engineers rarely have the time or cognitive bandwidth to analyze, verify, and interpret every AI-generated thought in detail. Without an effective interface, this transparency could become a burden rather than a benefit. In this paper, we propose a vision for structuring the interaction between AI reasoning models and software engineers to maximize trust, efficiency, and decision-making power. We argue that simply exposing AI's reasoning is not enough -- software engineers need tools and frameworks that selectively highlight critical insights, filter out noise, and facilitate rapid validation of key assumptions. To illustrate this challenge, we present motivating examples in which AI reasoning models state their assumptions when deciding which external library to use and produce divergent reasoning paths and recommendations about security vulnerabilities, highlighting the need for an interface that prioritizes actionable insights while managing uncertainty and resolving conflicts. We then outline a research roadmap for integrating automated summarization, assumption validation, and multi-model conflict resolution into software engineering workflows. Achieving this vision will unlock the full potential of AI reasoning models to enable software engineers to make faster, more informed decisions without being overwhelmed by unnecessary detail.