Paper detail

TheoremGraph: Bridging Formal and Informal Mathematics

91/100ReadPublished 2026-06-24Fetched 2026-06-30LLM judge, LeanGraph, LeanSearch, TheoremGraph, concept retrieval, formal libraries

Innovation Summary

TheoremGraph: Bridging Formal and Informal Mathematics: We introduce TheoremGraph, a unified statement-level dependency graph spanning both informal and formal mathematics.

Executive Summary

TheoremGraph: Bridging Formal and Informal Mathematics: We introduce TheoremGraph, a unified statement-level dependency graph spanning both informal and formal mathematics. Why it matters: Overall signal 91/100 driven by novelty 100 and practical impact 100. Primary categories: LLM judge, LeanGraph, LeanSearch, TheoremGraph, concept retrieval, formal libraries. Community signal includes 2 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity. Implementation angle: Implementation potential scores 87/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows. No linked repository is present, so expect more translation work before the ideas are production-ready. Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work. Caveat: No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.

Why It Matters

  • Overall signal 91/100 driven by novelty 100 and practical impact 100.
  • Primary categories: LLM judge, LeanGraph, LeanSearch, TheoremGraph, concept retrieval, formal libraries.
  • Community signal includes 2 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity.

Implementation Angle

  • Implementation potential scores 87/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows.
  • No linked repository is present, so expect more translation work before the ideas are production-ready.
  • Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work.

Caveat

No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.

Estimated Reading Priority

High - 91/100 signal; read before acting on adjacent agent, evaluation, inference, or ML systems work.

Observation History

Published 2026-06-24. First fetched 2026-06-30. Observed 2026-06-30.

Paper JSON record

Score Breakdown

Novelty
100
Practical Impact
100
Technical Depth
100
Implementation
87
Relevance
100
Community
33
Confidence
95