Dependency Graphs of Mathematical Arguments: Representation, Structural Measurement, and Proof Review

Authors: Eunji Park

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Abstract: We propose a source-grounded framework for representing and measuring mathematical arguments with assistance from large language models. It includes numbered and unnumbered statements, equations, assumptions, and constructions, distinguishing source occurrences from contextual states and inference records. Proposed dependencies retain source evidence and review status. A measurement contract fixes manuscript versions, boundaries, granularity, proof choices, and macro anchors. On a selected finite directed acyclic graph, depth is the longest prerequisite-path length, and bulk counts distinct ancestor edges. We give a union recurrence that accounts for shared subarguments and identify reachability conditions preserving depth and the macro skeleton; unrestricted refinement need not preserve depth or bulk. A standardized, versioned prompt protocol separates annotation discovery, deterministic recomputation, and review assistance. Cross-paper links support a research agenda on theorem co-use, rare combinations, and connections between dependency communities. These patterns motivate probabilistic premise suggestions and review of possible omissions or unchecked application conditions in human-written and AI-generated proofs. Further applications include source-linked reading support and comparison of individual and collaborative argument shapes. We provide an evaluation design rather than empirical evidence of these benefits. Suggested premises require mathematical validation, and the measures describe a specified representation rather than intrinsic mathematical difficulty, originality, or importance.

Comments:

30 pages

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.36776 [cs.AI]

(or

arXiv:2610.36776v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36776

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Eun-Ji Park [ view email ]

[v1]

Tue, 27 Oct 2026 07:44:14 UTC (37 KB)

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