Why does the AI model need such a library?
Real short panel of multimodular models
The current multi-modular model still has a clear short board on image reasoning, especially where knowledge in the field of expertise is needed to combine visual understanding. A simple "show-and-talk" capability cannot replace the deep-seated logic implicit in the image.
Differentiate understandings. pattern matching
The university difficulty diagram is a good way to distinguish between the true understanding of the model and the pattern. The theme requires models that understand visual information, scientific knowledge and logic.
Extremely strict entry criteria
Five times each of the five top-level models run, and only two times a model answer pair (40% pass rate) is accepted. This means that the title in the library represents "the current frontier of AI".
A test of research-level capability.
A true research-level reasoning capability is available to stabilize models of these topics – not only to test the knowledge pool, but also to test the integration and deep reasoning of cross-modular information.
Four major STEM disciplines 32+ sub-areas are fully covered
30%
Clinical medicine Diagnosis Basic medicine Pharmacology Public health care rehabilitation25%
Mechanical Electronics and Electrical Computers Civil Materials Energy Agricultural Engineering25%
Biology — Chemistry — Physics — Earth and Environmental ScienceMathematics 20%
Geometric priority . Combination mathematics . Probability statistics ..expoitation algebra ..Five models, five reasoning, five monitoring of currents.
Each topic is subject to a question from a field expert, a double-revision, automated assessment, a rigorous full-process screening of the repository, ensuring that only the most difficult problems are in the front-line.
Expert issues in the field
• Expert in the subject, Master's degree, 3 years of teaching/research experience, ensuring academic rigour and image dependency on each topic.
Double-Reviser
Each topic is independently reviewed by two revisers, cross-checking to ensure correctness of answer 95%, and images correspond strictly to the dry logic.
Automation of flow line.
Five top-level multi-modular megamodels were called for the assessment as follows:
5 statistical pass rates
Each model is called five times (temperature = 0) independently to eliminate random fluctuations. 5x5 = 25 reasoning results are aggregated to calculate the overall pass rate.
Zero percent. Strict entrance threshold.
Only 40% pass rate (i.e., two times answer for each model). A title that goes beyond this threshold means that the current model is stable and has no forward-looking challenge value.
Quarterly rolling review and auto-archive
Retest the topics entered in the library using the latest model version on a quarterly basis. If a subject is stabilized by a new model (pass rate > 60%), the archive is automatically downgraded to ensure that the repository remains a frontal boundary.
Strict comparison:This criterion is at least 2-3 times more stringent than the screening criteria for open benchmarks such as MMU. Benchmarks such as MMU usually require only one single model to deduce, while we have significantly raised the threshold of entry using a five model x five-doctraction cross-certification mechanism.
Compliance and isolation Zero overlapping safeguards
Public evaluation and measurement quarantine
There is no overlap with the following 8 public assessment sets, ensuring isolation by double-matching text + image:
Internal Reload and Content Red Line
Internal text + image is double-heavy and the following types of data are strictly prohibited:
Data delivery ..Input productization
Standard format: JSON Lines
A full title record per row, containing title text, image (original size + standard tailor), standard answers, detailed deprogramming steps. Compatible with the data loader of the mainstream LLM training framework.
API Caller
Supports batch pull-down of full data, incremental updates of subscriptions (new titles per week/month). ResTful API+ page breaker, adapted to automated training lines.
MetadataAnnotation
Each data is accompanied by complete metadata: title ID / discipline / subarea / difficulty / original image size / model training recommended size / relying type / revise record.
Data infrastructure for AGI
See the chart, see the reasoning.
Multi-modular models are evolving from "talking" to "critic reasoning" and university difficulty diagrams are the key fuel that drives this transformation.
4 University Section 32+ Subarea
The library covers the four main fields of health and medicine, technology and engineering, natural sciences and mathematics, and simulates real university examination scenarios, covering undergraduate and postgraduate levels.
Quarterly rolling Quarters Quarters
New topics are added and reviewed on a quarterly basis, outdated topics are automatically archived, model capabilities are tracked on a continuous basis and up-to-date frontier data are provided to clients.
Service the top AI laboratory worldwide
Clients include the top AI laboratories worldwide, which are used for pre-training data enhancement, competency assessment, course learning (Curriculum Learning) and the promotion of AGI front-line research.
Get STEM full directory and sample of multimodular libraries
DataAssetsAPI continues to expand the STEM Multi-Modes Library to support customised delivery by discipline, difficulty, and problem. Contact our data specialists to get a complete catalogue, data samples and quotations.