762K structured theorems/propositions/proofs, 7 types, 18.5M dependency relations.

Not a simple theorem list, but a structured theorem knowledge graph with clear dependencies.

1)    7 types: axiom, definition, theorem, lemma, corollary, conjecture, law, covering all theorem types.

2)    18.5M dependency relations: clear dependencies between theorems, forming a complete knowledge graph.

3)    772K proof steps: detailed step records for each theorem's proof.

4)    4.19M inference steps: complete logical inference process records, traceable.

5)    Structured storage: conditions, conclusions, proofs all structured for precise queries.

1.38M math problems, 15 subject subcategories, 6 problem types.

Not simple problem text, but structured storage of conditions, targets, paths.

1)    15 subject subcategories: algebra, geometry, number theory, combinatorics, calculus, probability, logic, competition, training.

2)    6 problem types: multiple choice, fill-in, calculation, proof, application, open-ended.

3)    Structured storage: conditions, targets, paths all structured for smart test assembly and recommendation.

4)    Bilingual: all problems in Chinese and English for international needs.

5)    Difficulty labeling: each problem has difficulty labels for filtering and recommendation.

2.11M formulas, 4 rendering formats, formulas not stored as text but rendered from math structures.

Not storing formula text, but math structures, rendered to different formats on demand.

1)    Unicode rendering: render to Unicode characters for console and plain text.

2)    LaTeX rendering: render to LaTeX for KaTeX/MathJax engines.

3)    OpenMath content tree: render to OpenMath content tree with bidirectional precise mapping.

4)    Chinese natural language: render to Chinese natural language for non-expert understanding.

5)    Structured storage: formulas not stored as text, rendered from math structures for correctness.

5 intent types, bilingual, zero-hallucination, deterministic queries.

Not LLM free generation, but precise knowledge base queries with zero hallucination.

1)    5 intent types: prove, find, explain, verify, render.

2)    Bilingual: supports Chinese and English natural language questions.

3)    Zero-hallucination: deterministic queries plus precise entity parsing, no LLM generation.

4)    Three-panel layout: candidate area, proof steps area, verification status area.

5)    DeepSeek SSE compatible: supports DeepSeek's SSE streaming output protocol.

5 fake-proof flags, three-state results, counterexample support.

Not simple accept/reject, but multi-dimensional verification of proof correctness.

1)    5 fake-proof flags: sorry, admit, abort, unreachable, unresolved_metavar.

2)    Three-state results: passed, unverified, failed, precisely expressing verification status.

3)    Counterexample support: provides counterexamples on verification failure.

4)    Step-by-step verification: verify each step of the proof, locating errors.

Knowledge mastery assessment, answer records and step tracking, smart recommendations.

For teaching scenarios, providing complete learning training capabilities.

1)    Knowledge mastery: IRT and Bayesian models assess student knowledge mastery.

2)    Answer records: record student answer history for learning process analysis.

3)    Step tracking: 8 step types, tracking student problem-solving process, identifying difficulties.

4)    Smart recommendations: 4 recommendation types based on student status.

9 LLM providers, 7 math-specific agents, 4 math tools, native Odoo integration.

Math knowledge engine deeply integrated with AI agents for intelligent math services.

1)    9 LLM providers: supports 9 LLM providers for flexible selection.

2)    7 math-specific agents: 7 AI agents optimized for math scenarios.

3)    4 math tools: 4 math tools registered as function calling for AI agents.

4)    Native Odoo integration: native integration with Odoo, no additional deployment.