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MMLU-Pro is identified by its primary project source as an evaluation family covering multitask reasoning and knowledge. This entry publishes independently authored task signatures only.
knowledge multitask reasoning
Primary source: TIGER AI Lab
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Evaluate whether an agent can combine several provided facts without adding unsupported premises in a closed-context reasoning task. Success is determined when the answer is entailed by the provided evidence.
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Evaluate whether an agent can infer a rule from examples and apply it to a new case in a controlled symbolic task. Success is determined when the inferred answer matches the held-out checker.
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Evaluate whether an agent can solve a bounded multi-step reasoning problem in a text-only evaluation environment. Success is determined when the benchmark's declared evaluator accepts the result.
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Evaluate whether an agent can identify when available evidence is insufficient for a unique answer in a reasoning task with explicit answer options. Success is determined when the response matches the evaluator's uncertainty condition.
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