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Paper Citation Record · LEDGER

Mining Math Conjectures from LLMs: A Pruning Approach

As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2412.16177.

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

pith.paper-citation-record.v1
2412.16177 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:24:36.838056Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T04:35:53.975397Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.374528Z

Reference resolution

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy28
  • unresolved3
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22e15fa1-27dc-432c-a23f-b3a927268bac · outbound

This paper cites GPT-4 Technical Report,.

Mining Math Conjectures from LLMs: A Pruning Approach GPT-4 Technical Report,

Reference 1

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unresolved
no resolver link, observed 2026-08-11T19:24:36.689899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:24:36.689899Z digest=sha256:36fdd2e195b4dc5d278c4489a91efe91ec5718239f0b7e73bf777b96527f6ddf

Observation 2e79549c-e0af-4af7-928e-2b9a05fa9bce · outbound

This paper cites Claude AI.

Mining Math Conjectures from LLMs: A Pruning Approach Claude AI

Reference 2

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.694963Z digest=sha256:d81e5d279c2bc4d1d64bd0ae489f2bf508ceafe833a4f1fa4ea89b45e2918bc8

Observation 55c1034a-933d-4a54-8367-fc0969ce984e · outbound

This paper cites Gemini: A family of highly capable multimodal models,.

Mining Math Conjectures from LLMs: A Pruning Approach Gemini: A family of highly capable multimodal models,

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.699657Z digest=sha256:c60766eeeb5b040c32e75a17d6e0d5f97983e1d620acf5512c95c6fdbbe462ef

Observation 909a6b9e-7561-42ed-906b-9d228938108b · outbound

This paper cites Would ChatGPT3 Get a Wharton MBA? A Prediction Based on Its Performance in the Operations Management Course,.

Mining Math Conjectures from LLMs: A Pruning Approach Would ChatGPT3 Get a Wharton MBA? A Prediction Based on Its Performance in the Operations Management Course,

Reference 4

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raw_fallback, observed 2026-08-11T19:24:37.304825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.704105Z digest=sha256:d41642a3008c8e1b24af8a66b09cee75d23a6f3d42911d7a506f13898f5409c0

Observation 2ef73ccf-4e1d-4072-bdad-377bed65bd32 · outbound

This paper cites Mathematical discoveries from program search with large language models,.

Mining Math Conjectures from LLMs: A Pruning Approach Mathematical discoveries from program search with large language models,

Reference 5

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raw_fallback, observed 2026-08-11T19:24:37.290764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.708822Z digest=sha256:fcd48bf90e965567c22695c6105846a5d5e2700cd7b48ed199c5c695938f6cfd

Observation 38efec07-eb00-43b3-9654-4aecc2124562 · outbound

This paper cites Exploring mathematical conjecturing with large language models,.

Mining Math Conjectures from LLMs: A Pruning Approach Exploring mathematical conjecturing with large language models,

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.276526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.713544Z digest=sha256:5dd821ca0ad3a675f07e7df9c22774df9de5569e5164a750075430db63bf88f8

Observation af85bccc-5735-4fbe-b2e6-88862459e8df · outbound

This paper cites Advancing mathematics by guiding human intuition with AI,.

Mining Math Conjectures from LLMs: A Pruning Approach Advancing mathematics by guiding human intuition with AI,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.262344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.718813Z digest=sha256:87dcb3583279f0750cc0dae82a3580d9839eb1c4e8951c3b09940fbee3b37fe4

Observation 9dc1d495-84da-4d71-95f2-06798412711c · outbound

This paper cites Autoformalization with Large Language Models.

Mining Math Conjectures from LLMs: A Pruning Approach Autoformalization with Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T19:24:36.723359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:24:36.723359Z digest=sha256:2ad9a3eebd22035a4b5aaec2fb448a0e047c8d44a0391cdeb96478df91b1ab1f

Observation 150430bd-e6e9-4278-9f71-db1f1891081c · outbound

This paper cites Can LLMs generate novel research ideas? A large-scale human study with 100+ NLP researchers,.

Mining Math Conjectures from LLMs: A Pruning Approach Can LLMs generate novel research ideas? A large-scale human study with 100+ NLP researchers,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.248992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.728117Z digest=sha256:8ae1951f3b29bfe0d656dbad63a9b374efc8c505e6d26e20a157371cb96f2bc8

Observation 40167db2-5896-49c0-977d-b68b0cb6ebd6 · outbound

This paper cites More on the non-solvable graphs and solvabilizers,.

Mining Math Conjectures from LLMs: A Pruning Approach More on the non-solvable graphs and solvabilizers,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.234698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.732471Z digest=sha256:57d9ba8c7f49cc7c76d33a257a73c7c5d1e85a5b89260a3cbbea7f9dda76be63

Observation 36f598e7-48f4-4ec1-97c7-3f3d1dd192c0 · outbound

This paper cites The solubility graph associated with a finite group,.

Mining Math Conjectures from LLMs: A Pruning Approach The solubility graph associated with a finite group,

Reference 11

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.736657Z digest=sha256:94959a20bb731a63ed7cd7843264ea724580aca01e5dac10a5fb7cec5db4035b

Observation b5b8696e-3080-4d33-90d0-d19f38b08c8b · outbound

This paper cites On the solubilizer of an element in a finite group,.

Mining Math Conjectures from LLMs: A Pruning Approach On the solubilizer of an element in a finite group,

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.744778Z digest=sha256:ba8dfe461b0963ceaf17e71cee94e121d0657bca29814f09da9d3b1d7fafb4ea

Observation d57e7a79-7418-498d-8f27-b03e1b6f7837 · outbound

This paper cites Characterization of solubilizers of elements in minimal simple groups,.

Mining Math Conjectures from LLMs: A Pruning Approach Characterization of solubilizers of elements in minimal simple groups,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.190847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.751387Z digest=sha256:b88460e0b50db9b1b186f3d93ce4fbe5763295c50b60736ac1095e52bb28fca8

Observation 6e8d320f-4520-4fc4-bc3b-bea331131013 · outbound

This paper cites Non-solvable graph of a finite group and solvabilizers,.

Mining Math Conjectures from LLMs: A Pruning Approach Non-solvable graph of a finite group and solvabilizers,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.755677Z digest=sha256:f54f986330e8a777500cdfcd3a97ce8559c74c29bc12e4d98bfee05a514e8ea8

Observation 8557d211-0c76-4f10-9e8d-041059fdd38f · outbound

This paper cites Solubilizers in profinite groups,.

Mining Math Conjectures from LLMs: A Pruning Approach Solubilizers in profinite groups,

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 59de24cd-6abb-4bf7-b81f-90adc9f54087 · outbound

This paper cites The impact of the solubilizer of an element on the structure of a finite group,.

Mining Math Conjectures from LLMs: A Pruning Approach The impact of the solubilizer of an element on the structure of a finite group,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.152059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation fc70f236-0140-4f6f-b631-23ffcfa27235 · outbound

This paper cites Is Temperature the Creativity Parameter of Large Language Models?,.

Mining Math Conjectures from LLMs: A Pruning Approach Is Temperature the Creativity Parameter of Large Language Models?,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.137774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.768259Z digest=sha256:88305e60632cc71fcf9291b14c7c70d24a05eb1f376fd032b54c7db3ced648ae

Observation 66ad39f9-bbe5-4fef-9d81-0d346ec2fcb2 · outbound

This paper cites SageMath, the Sage Mathematics Software System (Version 10.0),.

Mining Math Conjectures from LLMs: A Pruning Approach SageMath, the Sage Mathematics Software System (Version 10.0),

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.772402Z digest=sha256:ea8c6c81f8ad296f4484bcf15b40a5322177e526328da9f9ca7d64b789ed6e1b

Observation e6a1ab14-1e62-4653-ab41-67bd57fafba1 · outbound

This paper cites Integrating multiple sources to answer questions in Algebraic Topology.

Mining Math Conjectures from LLMs: A Pruning Approach Integrating multiple sources to answer questions in Algebraic Topology

Reference 19

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verified exact
local_arxiv, observed 2026-08-11T19:24:36.881592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.776416Z digest=sha256:7f02ba2acb26233f622ee25c83408c0b1bab182dbfce8c7374d60c022be5feed

Observation 708fd3f0-5373-4ca3-939d-60701ff91c0b · outbound

This paper cites GSM- Symbolic: Understanding the limitations of mathematical reasoning in large language models,.

Mining Math Conjectures from LLMs: A Pruning Approach GSM- Symbolic: Understanding the limitations of mathematical reasoning in large language models,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.108780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.781091Z digest=sha256:d0480a19c84cf30e66ab7b16410d270954416800574ca3c1849bc3941b3381f2

Observation 111dadb6-d561-4f13-8b1c-4f01a79c7e5b · outbound

This paper cites Solving Olympiad Geometry Without Human Demonstra- tions,.

Mining Math Conjectures from LLMs: A Pruning Approach Solving Olympiad Geometry Without Human Demonstra- tions,

Reference 21

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raw_fallback, observed 2026-08-11T19:24:37.093370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c26c8cc6-1088-4da1-9ba0-078e2a4c5582 · outbound

This paper cites AI solves IMO problems at silver medal level,.

Mining Math Conjectures from LLMs: A Pruning Approach AI solves IMO problems at silver medal level,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.077891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.789604Z digest=sha256:4a8eecbcb3cafa267ef5efd3b38ab201ba4b28493ba06b8b21f921ee1a8c3dd1

Observation 4a562e7b-5d65-48bb-a171-ef851213a5f7 · outbound

This paper cites Nonsolvable finite groups all of whose local subgroups are solvable,.

Mining Math Conjectures from LLMs: A Pruning Approach Nonsolvable finite groups all of whose local subgroups are solvable,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.064435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.794547Z digest=sha256:35a7e858b8e395735151d61746bcfe4de032573bea2bc7b0817f6f60fe8199fa

Observation 41c2c982-8d13-4da1-8e7d-a003096acb15 · outbound

This paper cites Thompson-like characterization of the solvable radical,.

Mining Math Conjectures from LLMs: A Pruning Approach Thompson-like characterization of the solvable radical,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.051194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.799075Z digest=sha256:635e285bc6409e483f9efa6b13c13a87f25d0ffd34944e476beec9659fcd6dd7

Observation 77be555f-49d1-4ccf-ba69-fac677ac8802 · outbound

This paper cites Introducing OpenAI O1-preview.

Mining Math Conjectures from LLMs: A Pruning Approach Introducing OpenAI O1-preview

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:37.037197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.803444Z digest=sha256:2eea90e493204f320dd14fa8f648abb8492adcf611e61ccd053e0950909e0f62

Observation 597b4dbb-5749-4d47-8639-04e161853720 · outbound

This paper cites This is the smallest subgroup of G that contains both x and y.

Mining Math Conjectures from LLMs: A Pruning Approach This is the smallest subgroup of G that contains both x and y

Reference 26

Resolution
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raw_fallback, observed 2026-08-11T19:24:37.022449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.808094Z digest=sha256:194eea9424fabcff23f0eaf4a308597b827d562a27238f4d4c9414ff56419d1c

Observation a0f9793c-a534-4d5d-a655-2a0bce5d39e9 · outbound

This paper cites an unresolved cited work.

Mining Math Conjectures from LLMs: A Pruning Approach Unresolved cited work

Reference 27

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unresolved
raw_fallback, observed 2026-08-11T19:24:37.006646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.812516Z digest=sha256:0947db71390544e2bde57bee2efec1efa52415e09d942b1e57a8811548ee4ff3

Observation 80fe23f8-e5a3-4b66-bba8-065ba0b684fc · outbound

This paper cites This is an interesting definition, and it essentially captures the elements in G that, when paired with x, produce a solvable subgroup.

Mining Math Conjectures from LLMs: A Pruning Approach This is an interesting definition, and it essentially captures the elements in G that, when paired with x, produce a solvable subgroup

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:36.991075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.817087Z digest=sha256:3bba439716a7a586a921718fff12d003414d5b6d8b4d7bf2774c1eb64055dcd2

Observation 7fafd964-15b2-44f1-89d2-8c4675accfd4 · outbound

This paper cites Theorem: For any x ∈ G, x ∈ SolG(x).

Mining Math Conjectures from LLMs: A Pruning Approach Theorem: For any x ∈ G, x ∈ SolG(x)

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-11T19:24:36.974718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.821439Z digest=sha256:dbfdf00ca52321aebfc68434a3f66097b631b22a80a8ec7817e43c960ad56a82

Observation 00546340-dc7b-4969-ae59-a518428b2400 · outbound

This paper cites This is because if ⟨x, y⟩ is solvable, then ⟨x, y−1⟩ is also solvable.

Mining Math Conjectures from LLMs: A Pruning Approach This is because if ⟨x, y⟩ is solvable, then ⟨x, y−1⟩ is also solvable

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:36.959446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.825362Z digest=sha256:6594b57b95a1905b8d7fa6e38f714e5192254ff4073617db9520ad3f04ac950e

Observation b4fbfb70-978a-4ab9-9a9b-775bf3bed0df · outbound

This paper cites Theorem (Conditional): For y1, y2 ∈ SolG(x), if y1y2 ∈ ⟨x, y1⟩ or y1y2 ∈ ⟨x, y2⟩, then y1y2 ∈ SolG(x).

Mining Math Conjectures from LLMs: A Pruning Approach Theorem (Conditional): For y1, y2 ∈ SolG(x), if y1y2 ∈ ⟨x, y1⟩ or y1y2 ∈ ⟨x, y2⟩, then y1y2 ∈ SolG(x)

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:36.944232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.829851Z digest=sha256:e297f39661e95dc9ac22caee83e53d39c7ea9ad318c8230bd277b44534853e45

Observation 7cfa47c8-a20a-4118-b20b-a19709b303e1 · outbound

This paper cites Theorem: If G is solvable, then for all x ∈ G, SolG(x) = G.

Mining Math Conjectures from LLMs: A Pruning Approach Theorem: If G is solvable, then for all x ∈ G, SolG(x) = G

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:36.929140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.834323Z digest=sha256:9a9d2f91faf639755b7267a0fcc09a7da1df7ba7a884f15107d39abbeecf5028

Observation 791adad9-2c9d-470e-82fd-5595793d4ba9 · outbound

This paper cites No Counter - examples !.

Mining Math Conjectures from LLMs: A Pruning Approach No Counter - examples !

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:24:36.913784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T19:24:36.838056Z digest=sha256:9da94b2a4b908e4f4d088617e574fbc05bb3c75605822ac87002864118ab0f4a

Pith citing papers

Observation 209f48cd-2d2c-45ab-99d6-91298c5d36e9 · inbound

Mapping Mathematical Hardness: Machine-Assisted Conjecture Discovery and the Quantification of Non-Triviality cites this paper.

Mapping Mathematical Hardness: Machine-Assisted Conjecture Discovery and the Quantification of Non-Triviality Mining Math Conjectures from LLMs: A Pruning Approach

Reference 3

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arxiv_id, observed 2026-07-03T17:08:43.375958Z

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source=pdf_text observed=2026-06-27T04:35:53.975397Z digest=sha256:2b375292d0389ff715edca203fd7788a316aa4fae61e3c342fd45f804c6593ea