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Paper Citation Record · LEDGER

Generative Modeling for Mathematical Discovery

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:43.373622Z

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 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.

source=pdf_text observed=2026-08-07T12:45:43.373622Z digest=sha256:553a29ad4fe2134377a070c949161918c93e7ea6880e2abe1b74888203013554

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T21:27:23.987121Z digest=sha256:4629f441ca64d53e11181b3e979012cf2b47690d4c62bce5435d80dfc96834e9

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T01:18:42.333269Z digest=sha256:00756b2a1abbaca219c986e0d9df1791312a2ba522ebf6d05d20f97dc7f86e83

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T02:19:18.121220Z digest=sha256:1d94e7efa1b549499c42028e2463fde03fa2bc29d2ee17f76e7590468356b9cc

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

Characterizing initial human-AI proof formalization workflows cites this paper.

Characterizing 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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T09:29:50.282874Z digest=sha256:2d55d35aac4ec7dc7843a4da87299aa951b848e009b1a73780f840eb4fb5af33

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.

source=pdf_text observed=2026-08-01T18:16:46.130962Z digest=sha256:1dfb38d354e33376b678f8e84432c1e9e0bf8abca3dcf93c5c8402aa9dbc7301