HEART: Emotionally-Driven Test-Time Scaling of Language Models

Gabriela Pinto; Palash Goyal; Mihir Parmar; Yiwen Song; Souradip Chakraborty; Zifeng Wang; Jinsung Yoon; Tomas Pfister; Hamid Palangi

arXiv · 2509.22876

HEART: Emotionally-Driven Test-Time Scaling of Language Models

Gabriela Pinto·Palash Goyal·Mihir Parmar·Yiwen Song·Souradip Chakraborty·Zifeng Wang·Jinsung Yoon·Tomas Pfister·Hamid Palangi

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Abstract

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.

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Gabriela Pinto, Palash Goyal, Mihir Parmar, Yiwen Song, Souradip Chakraborty, Zifeng Wang, Jinsung Yoon, Tomas Pfister, Hamid Palangi. 2025-09-26. HEART: Emotionally-Driven Test-Time Scaling of Language Models. https://arxiv.org/abs/2509.22876

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