aiqbench analysis
This research proposes a relational bridge-database to align bibliographic metadata with formal proof artifacts, addressing the fragmentation between mathematical literature and machine-verifiable systems. By introducing a paper-level formalization score derived from cross-document alignment, the framework enables scalable analysis of how much published content is covered in formal libraries like Lean. This interoperability layer represents a foundational step toward integrating disparate mathematical ecosystems into unified, machine-actionable knowledge graphs.
Towards a Bridge Layer Between Bibliographic and Formalized Mathematical Knowledge
arXiv:2606.11430v1 Announce Type: cross Abstract: Mathematical knowledge is split between bibliographic databases (e.g., MathSciNet, zbMATH Open) and formal proof libraries (e.g., Lean mathlib), preventing unified access between published results and their formalizations. We propose a relational bridge-database that aligns publication metadata with formal artifacts, providing an interoperability layer between mathematical literature and machine-verifiable proofs. We introduce a paper-level formalization score that measures how much of a publication is covered in formal systems. As a feasibility study, we show how such scores can be estimated via cross-document alignment between informal texts and Lean formalizations, enabling large-scale analysis of formalization coverage. This framework is a first step toward integrating bibliographic and formal mathematical ecosystems into scalable, machine-actionable knowledge graphs linking publications to formal proof objects.
Abstract:Mathematical knowledge is split between bibliographic databases (e.g., MathSciNet, zbMATH Open) and formal proof libraries (e.g., Lean mathlib), preventing unified access between published results and their formalizations. We propose a relational bridge-database that aligns publication metadata with formal artifacts, providing an interoperability layer between mathematical literature and machine-verifiable proofs. We introduce a paper-level formalization score that measures how much of a publication is covered in formal systems. As a feasibility study, we show how such scores can be estimated via cross-document alignment between informal texts and Lean formalizations, enabling large-scale analysis of formalization coverage. This framework is a first step toward integrating bibliographic and formal mathematical ecosystems into scalable, machine-actionable knowledge graphs linking publications to formal proof objects.
Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
arXiv:2606.11430 [cs.DL]
(or arXiv:2606.11430v1 [cs.DL] for this version)
https://doi.org/10.48550/arXiv.2606.11430 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 220 words
Related coverage
The zbMATH Open Knowledge Graph: Tracing Centuries of Mathematical Research
arXiv · Sep 2, 2026
Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization
arXiv · Jul 21, 2026
Quantifying and detecting quantum-state texture
arXiv · Jun 15, 2026
Prompt Chaining in Practice: A Case Study in Automated Scholarly Report Generation
arXiv · Jul 31, 2026