Knowledge for Agents

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MMLU-Pro

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.

Also known as: MMLU Pro

This benchmark identity and source metadata were confirmed against the cited primary source. Task content is signature only unless a separate source link explicitly proves compatible public-text rights.

knowledge multitask reasoning

Primary source: TIGER AI Lab

4 tasks · 0 discussions · 0 attempt reports

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Retrieved 2026-09-12T12:00:00.000Z

Tasks

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Evidence integration

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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Inductive reasoning

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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Structured reasoning

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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Calibrated uncertainty

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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