Academic

Interactive Benchmarks

arXiv:2603.04737v1 Announce Type: new Abstract: Standard benchmarks have become increasingly unreliable due to saturation, subjectivity, and poor generalization. We argue that evaluating model's ability to acquire information actively is important to assess model's intelligence. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses model's reasoning ability in an interactive process under budget constraints. We instantiate this framework across two settings: Interactive Proofs, where models interact with a judge to deduce objective truths or answers in logic and mathematics; and Interactive Games, where models reason strategically to maximize long-horizon utilities. Our results show that interactive benchmarks provide a robust and faithful assessment of model intelligence, revealing that there is still substantial room to improve in interactive scenarios. Project page: https://github.com/interactivebench/interactivebench

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Baoqing Yue, Zihan Zhu, Yifan Zhang, Jichen Feng, Hufei Yang, Mengdi Wang · · 1 min read · 2 views
#cs.AI #cs.CL #cs.LG

arXiv:2603.04737v1 Announce Type: new Abstract: Standard benchmarks have become increasingly unreliable due to saturation, subjectivity, and poor generalization. We argue that evaluating model's ability to acquire information actively is important to assess model's intelligence. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses model's reasoning ability in an interactive process under budget constraints. We instantiate this framework across two settings: Interactive Proofs, where models interact with a judge to deduce objective truths or answers in logic and mathematics; and Interactive Games, where models reason strategically to maximize long-horizon utilities. Our results show that interactive benchmarks provide a robust and faithful assessment of model intelligence, revealing that there is still substantial room to improve in interactive scenarios. Project page: https://github.com/interactivebench/interactivebench

Executive Summary

The article proposes Interactive Benchmarks, a novel evaluation paradigm that assesses a model's intelligence through interactive processes under budget constraints. This approach aims to address the limitations of standard benchmarks, which often suffer from saturation, subjectivity, and poor generalization. The framework is instantiated in two settings: Interactive Proofs and Interactive Games, demonstrating a robust and faithful assessment of model intelligence.

Key Points

  • ▸ Introduction of Interactive Benchmarks as a unified evaluation paradigm
  • ▸ Assessment of model's reasoning ability in interactive processes under budget constraints
  • ▸ Instantiation of the framework in Interactive Proofs and Interactive Games settings

Merits

Comprehensive Evaluation

Interactive Benchmarks provide a more comprehensive assessment of model intelligence by evaluating its ability to acquire information actively and reason strategically.

Demerits

Complexity

The interactive nature of the benchmarks may introduce additional complexity, potentially making it more challenging to design and implement.

Expert Commentary

The introduction of Interactive Benchmarks represents a significant step forward in the evaluation of AI models. By assessing a model's ability to acquire information actively and reason strategically, this approach has the potential to provide a more accurate and comprehensive assessment of model intelligence. However, the complexity of the interactive benchmarks may require careful consideration and further research to ensure their effective implementation.

Recommendations

  • ✓ Further research on the design and implementation of Interactive Benchmarks
  • ✓ Exploration of the potential applications of Interactive Benchmarks in various fields, including artificial intelligence and machine learning.

Sources

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