New benchmark tests LLMs on math text continuations

Researchers have developed a new self-supervised benchmark for evaluating language models on mathematical text continuations. This benchmark uses likelihood scoring to assess how well a model's auxiliary forecast string transmits information about a hidden continuation, such as the rest of a displayed equation. Tests on models like GPT-5.5 and Opus 4.7 showed they could distinguish between model families and reasoning efforts, even when scorers were fine-tuned to emulate shortcut vulnerabilities. The findings suggest cross-model likelihood scoring is a viable method for static benchmarking and probing shortcut vulnerabilities before further optimization. AI

IMPACT Introduces a new method for evaluating LLM reasoning and identifying shortcut vulnerabilities in mathematical contexts.

RANK_REASON The cluster describes a new academic paper introducing a novel benchmark for evaluating language models on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]

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• GPT-5.4 nano
• GPT-5.5
• Kimi K2.6
• Opus 4.7
• Qwen3-8B

• paper
• safety

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