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.

翻译：近年来，AI推理模型的进展为理解其决策过程提供了前所未有的透明度，使其从传统的黑盒系统转变为能够阐述逐步思维链而非产生不透明输出的模型。这一转变有望提升AI增强开发中的软件质量、可解释性与信任度。然而，软件工程师很少有时间或认知带宽去详细分析、验证和解释每一个AI生成的思维。若缺乏有效的交互界面，这种透明度可能成为负担而非优势。本文提出一种构建AI推理模型与软件工程师间交互的愿景，旨在最大化信任、效率与决策效能。我们认为，仅暴露AI的推理过程并不足够——软件工程师需要能够选择性突出关键洞见、过滤噪声并促进关键假设快速验证的工具与框架。为阐明这一挑战，我们通过动机案例展示AI推理模型在决定使用何种外部库时陈述其假设，并在安全漏洞分析中产生分歧的推理路径与建议，这突显了需要一种能够优先处理可操作洞见、同时管理不确定性与解决冲突的交互界面。随后，我们勾勒了将自动摘要、假设验证与多模型冲突解决集成到软件工程工作流中的研究路线图。实现这一愿景将充分发挥AI推理模型的潜力，使软件工程师能够做出更快速、更明智的决策，而无需被不必要的细节所淹没。