Papers by Michael Hinczewski

1 papers
Ranking Reasoning LLMs under Test-Time Scaling (2026.acl-long)

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Challenge: Large language models (LLMs) are increasingly used as general-purpose reasoning systems for tasks such as programming and mathematical problem solving.
Approach: They formalize dense benchmark ranking under test-time scaling and introduce a library that implements statistical ranking methods such as paired-comparison models, item response theory, voting rules, graph- and spectral-based methods.
Outcome: The proposed method is based on paired-comparison models, item response theory (IRT) models, voting rules, graph- and spectral-based methods.

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