Robust Reasoning Benchmark
While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their problem-solving abilities depend on the context and textual formatting. We introduce the Robust Reasoning Benchmark (RRB), a pipeline of 13 deterministic textual perturbations applied to AIME 2024 and AIME 2025. Evaluating 8 state-of-the-art models, we find that frontier models are largely resilient, with the notable exception of Claude, which categorically refuses many transformed prompts. Open-weights reasoning models exhibit a range of failure modes under structural noise (cognitive thrashing, tokenization breakdown, and reasoning collapse), with up to 54% average accuracy drops acros...
| Authors | Pavel Golikov, Evgenii Opryshko, Gennady Pekhimenko et al. |
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| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 21 July 2026 |
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