BENCHMARK-TARGETED TRAINING

Expert-level academic questions for frontier-model reasoning.

Overview Production
INTERDISCIPLINARY KNOWLEDGECLOSED-FORM REASONINGRETRIEVAL RESISTANCE

01 / DATASET OVERVIEW

EXPERT-LEVEL TASK DATA

SCOPE & PURPOSE

The training task.

  • INTERDISCIPLINARY KNOWLEDGE
  • CLOSED-FORM REASONING
  • RETRIEVAL RESISTANCE

Designed to evaluate frontier models on expert-level interdisciplinary knowledge and closed-form reasoning, HLE provides original, verifiable, and retrieval-resistant academic question-answering data. It covers mathematics, natural sciences, humanities, and social sciences, and is used to evaluate deep reasoning, knowledge mastery, and confidence calibration.

02 / DATASET PROFILE & TECHNICAL SIGNALS

SCOPE · SCALE · CONSTRUCTION
01KNOWLEDGE SCOPE
Interdisciplinary
Expert-level academic question answering.
02QUESTION DESIGN
Original
High-difficulty domain-specific problems.
03ANSWER VALIDATION
Unique answers
Answer uniqueness verification.
04RETRIEVAL
Resistance checks
Retrieval-resistant academic data.

03 / DATA PRODUCTION & CONSTRUCTION

METHOD & WORKFLOW

Data production strengths

We have expert-level, interdisciplinary capabilities for producing challenging academic problems, supported by domain experts and professional task design and review resources. Our capabilities cover task design, answer uniqueness verification, retrieval-resistance validation, and quality review, supporting scalable production and automated evaluation of high-difficulty domain-specific data.

  1. 01DESIGN

    Expert academic problems

    Use domain experts and professional resources to design challenging interdisciplinary problems.

  2. 02VERIFY

    Answer uniqueness

    Verify the uniqueness of answers.

  3. 03VALIDATE

    Retrieval resistance

    Validate resistance to retrieval-based answering.

  4. 04REVIEW

    Domain-specific quality

    Review quality to support scalable production and automated evaluation.

04 / COVERAGE & CAPABILITY MATRIX

TASKS · METHODS · APPLICATIONS
DIMENSIONCOVERAGE
01Knowledge domains
MathematicsNatural sciencesHumanitiesSocial sciences
02Question format
Academic question answeringClosed-form reasoning
03Data characteristics
OriginalVerifiableRetrieval-resistant
04Evaluation scope
Deep reasoningKnowledge masteryConfidence calibration
Explore the production method

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