Interacting with AI Reasoning Models: Harnessing "Thoughts" for AI-Driven Software Engineering
Christoph Treude, Raula Gaikovina Kula
TL;DR
This paper argues that while AI reasoning models expose thought traces, unfiltered exposure can overwhelm software engineers and hinder decision-making. It identifies the need for structured interfaces that filter noise, surface critical assumptions, and enable validation across divergent reasoning paths. The authors propose a concrete vision with components for filtering and summarization, assumption extraction and verification, and multi-model alignment, integrated into software engineering workflows. They outline a research roadmap and open challenges to build automated summarization, assumption checking, and conflict-resolution capabilities, with the aim of turning reasoning traces into actionable, trustworthy guidance for developers.
Abstract
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.