Paper Citation Record · LEDGER

Generative Modeling for Mathematical Discovery

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2503.11061.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2503.11061 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:13:28.787627Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T04:06:34.832257Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 81d00992-3f67-46bc-93d7-4ceb070fb283 · inbound

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks cites this paper.

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks Generative Modeling for Mathematical Discovery

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:13:28.787627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34338bc4-9758-4550-8e09-6ff31493dadd · inbound

Using Reasoning Models to Generate Search Heuristics that Solve Open Instances of Combinatorial Design Problems cites this paper.

Using Reasoning Models to Generate Search Heuristics that Solve Open Instances of Combinatorial Design Problems Generative Modeling for Mathematical Discovery

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:43.373622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 706c2c55-9fab-4b84-a01f-6fb7fe6a957a · inbound

AlphaEvolve: A coding agent for scientific and algorithmic discovery cites this paper.

AlphaEvolve: A coding agent for scientific and algorithmic discovery Generative Modeling for Mathematical Discovery

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-10T21:27:24.523396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6a40d76-8a73-4fd0-a16c-0b3a300fa2af · inbound

Mathematical exploration and discovery at scale cites this paper.

Mathematical exploration and discovery at scale Generative Modeling for Mathematical Discovery

Reference 101

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:20:34.549488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 51a444f6-44ff-4aa1-ad27-ad65be0f4f10 · inbound

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search cites this paper.

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search Generative Modeling for Mathematical Discovery

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:06:05.625198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aa49aff0-fa69-47f4-a128-fefe42481011 · inbound

Human agency in initial human-AI proof formalization workflows cites this paper.

Human agency in initial human-AI proof formalization workflows Generative Modeling for Mathematical Discovery

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-02T04:06:34.833587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0b8d72aa-19e7-475d-97ec-be356c3f1a8b · inbound

Mathematical Discovery in the Wild: AI-Guided Proofs in Banach Space Theory cites this paper.

Mathematical Discovery in the Wild: AI-Guided Proofs in Banach Space Theory Generative Modeling for Mathematical Discovery

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T18:16:46.130962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.