GPQA

AI2 Reasoning Challenge (ARC) tests reasoning through grade-school science questions.

Discrete Reasoning Over Paragraphs (DROP) requires models to resolve references in a passage and perform discrete operations.

MRCR (Multi-Round Coreference Resolution) is part of the Michelangelo benchmark suite that evaluates long-context reasoning in LLMs. It tests models' ability to track identities and references in adversarial conversation histories up to 1M tokens. Unlike retrieval-based benchmarks, MRCR requires synthesis, reasoning, and contextual understanding over extended contexts. The benchmark is synthetically generated to avoid pretraining contamination and provides unambiguous scoring metrics. MRCR is recommended as a superior alternative to traditional retrieval benchmarks for evaluating long-context reasoning capabilities.