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arXiv · 2603.04737

Interactive Benchmarks

Baoqing Yue·Zihan Zhu·Yutong Han·Brian Fan·Qian Sun·Jichen Feng·Hufei Yang·Yifan Zhang·Mengdi Wang
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Abstract

Existing reasoning evaluation paradigms suffer from different limitations: fixed benchmarks are increasingly saturated and vulnerable to contamination, while preference-based evaluations rely on subjective judgments. We argue that a core aspect of intelligence is the ability to decide what information to acquire and how to use it effectively. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses a model's reasoning ability through budgeted multi-turn interaction. We evaluate models under this framework in two settings: Interactive Proofs, where models interact with a judge to solve Logic, UI2Html, and Mathematics tasks under objective feedback; and Interactive Games, where models reason strategically to maximize long-horizon utilities. Our results show that interactive benchmarks provide a more robust assessment of this dimension of model intelligence, revealing substantial room for improvement in interactive scenarios.

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Baoqing Yue, Zihan Zhu, Yutong Han, Brian Fan, Qian Sun, Jichen Feng, Hufei Yang, Yifan Zhang, Mengdi Wang. 2026-03-05. Interactive Benchmarks. https://arxiv.org/abs/2603.04737

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