Test-time scaling has significantly improved how AI models solve problems, yet current methods often get stuck in repetitive, incorrect patterns of thought. We introduce HEART, a framework that uses emotional cues to guide the model's focus, much like how feelings contribute to human decision-making. By alternating between critical tones to sharpen error detection and encouraging tones to spark new ideas, HEART helps the model break out of dead-end reasoning and find the right solution. We evaluate HEART across seven high-difficulty benchmarks--including Humanity's Last Exam, GPQA Diamond, and LiveCodeBench--demonstrating robustness across diverse models. Results show that emotion facilitates deeper reasoning, yielding consistent accuracy gains over affect-sterile baselines. These findings suggest that the next frontier in machine reasoning lies in the strategic integration of affective regulation to guide logical synthesis.

翻译：测试时缩放技术显著提升了人工智能模型解决问题的能力，然而现有方法常陷入重复且错误的思维模式。本文提出HEART框架，该框架利用情感线索引导模型注意力，其机制类似于情感在人类决策过程中的作用。通过交替使用批判性语气以增强错误检测能力，以及鼓励性语气以激发新思路，HEART帮助模型摆脱僵化推理路径并找到正确解决方案。我们在七项高难度基准测试（包括Humanity's Last Exam、GPQA Diamond和LiveCodeBench）上评估HEART，证明了该框架在不同模型间的鲁棒性。实验结果表明，情感机制能促进更深层次的推理，相比无情感干预的基线方法获得持续稳定的准确率提升。这些发现表明，机器推理的下一个前沿在于通过策略性整合情感调节机制来引导逻辑合成过程。