Re²Math is a new benchmark that evaluates AI models on retrieving and verifying the applicability of theorems from math literature to advance steps in partial proofs, accepting any sufficient theorem while controlling for leakage.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
unclear 1representative citing papers
The thesis proposes specialized algebraic, logical, and geometric methods to enable scalable reasoning over imprecise attributes, probabilistic triples, and incomplete schemas in knowledge graphs.
citing papers explorer
-
Re$^2$Math: Benchmarking Theorem Retrieval in Research-Level Mathematics
Re²Math is a new benchmark that evaluates AI models on retrieving and verifying the applicability of theorems from math literature to advance steps in partial proofs, accepting any sufficient theorem while controlling for leakage.
-
Scalable Uncertainty Reasoning in Knowledge Graphs
The thesis proposes specialized algebraic, logical, and geometric methods to enable scalable reasoning over imprecise attributes, probabilistic triples, and incomplete schemas in knowledge graphs.