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Paper Citation Record · LEDGER

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms

As of 9 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.06499.

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

pith.paper-citation-record.v1
2506.06499 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:04.914665Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 636b0f40-d2ce-4b00-a687-2fa29576a052 · outbound

This paper cites STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving

Reference 3

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no resolver link, observed 2026-08-07T05:59:03.191174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:03.191174Z digest=sha256:25dc1f07c4be8aa8cc4706928446b26171e7a43dfaa3dca05bb2aa97464f719e

Observation 05c392de-edbb-4834-8ecd-65d6c2389b0d · outbound

This paper cites Augmenting Math Word Problems via Iterative Question Composing.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Augmenting Math Word Problems via Iterative Question Composing

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:03.724818Z digest=sha256:2a27a76d4ade1cd89cf19ea3a7424a05667481a33b3b53f3cdf6530989f2ac24

Observation f6f9f615-4df2-4ade-ac70-4f1c89bed220 · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 8

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no resolver link, observed 2026-08-07T05:59:03.833516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:03.833516Z digest=sha256:e1504d15cf8a5d259dff0e0af3757174e468da4ea397efe63b8372baa6536963

Observation 2f318d3f-311a-42fc-b0c6-adc8c5eaff64 · outbound

This paper cites Learning Formal Mathematics From Intrinsic Motivation.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Learning Formal Mathematics From Intrinsic Motivation

Reference 10

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no resolver link, observed 2026-08-07T05:59:04.132004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.132004Z digest=sha256:b501b6b18a6e8d6d4f6773e509950264d1d3ebaacc50a9e9ec2bd4c54ae99653

Observation 96ed1b80-d670-428a-949e-227280c07d61 · outbound

This paper cites Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H

Reference 11

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no resolver link, observed 2026-08-07T05:59:04.269861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.269861Z digest=sha256:ec0c66c5410e75d0b138a4405a8919a72d0a16906c356ccad38604af3a830b9d

Observation e5e4dfcb-47a4-4f45-a5dd-578774dff898 · outbound

This paper cites Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.390203Z digest=sha256:cdd7786c5e8e04c970b7e3fefb0ae10f57f6c5b1ac4017c074a6ae5790a715fa

Observation 53401402-b337-47e6-821e-4f5a5ca25942 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Gemma 2: Improving Open Language Models at a Practical Size

Reference 13

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no resolver link, observed 2026-08-07T05:59:04.477301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.477301Z digest=sha256:b1fd40e085b07eb68cf268e3fd536aefd47e08259eb553df419ef8c9046b6d27

Observation a2efa990-e530-4441-92b9-f9d0df2450d4 · outbound

This paper cites Open-Ended Learning Leads to Generally Capable Agents.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Open-Ended Learning Leads to Generally Capable Agents

Reference 14

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no resolver link, observed 2026-08-07T05:59:04.589837Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.589837Z digest=sha256:0cc5b2dc1487ca442cd7cb743349853c7ac95cb51ba3ab856006b4d5142f0c7f

Observation 79b9410f-4f44-4246-ae22-176812a71ed7 · outbound

This paper cites Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

Reference 15

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no resolver link, observed 2026-08-07T05:59:04.714714Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.714714Z digest=sha256:5c5fe963cd7baf5404823347f5382d7f51bfab595132eb638e85c0cc63b51166

Observation ba525dda-4628-4a8f-affa-26c74f75d3d0 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 16

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no resolver link, observed 2026-08-07T05:59:04.807050Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.807050Z digest=sha256:c9b7d8e564ede6d44beee7c558826c6dc34c695c7e9f502338ba8ac07246487a

Observation 71bb15fe-d816-4bc2-a8f1-9796d2593bc7 · outbound

This paper cites MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

Reference 17

Resolution
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no resolver link, observed 2026-08-07T05:59:04.914665Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:04.914665Z digest=sha256:915c946a1ab22a2fcecb28deb2ee00618010d54d46085e6b9015db5c800b067a

Observation b955b01e-2d9b-4eb3-b853-192167374063 · outbound

This paper cites Illuminating search spaces by mapping elites.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Illuminating search spaces by mapping elites

Reference 2015

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no resolver link, observed 2026-08-07T05:59:03.943693Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:03.943693Z digest=sha256:d42d2ff1040ca08a14fcdbf894280f45406a8d475c751b028a0050f98ff2e010

Observation a70bfe7f-3d3b-4ea5-9228-8126eb576d70 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Measuring Mathematical Problem Solving With the MATH Dataset

Reference 2021

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no resolver link, observed 2026-08-07T05:59:03.328803Z

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source=pdf_text observed=2026-08-07T05:59:03.328803Z digest=sha256:e57c3b1f3a4b76a0bf7d5b793e7170b209e6332133ec32a6d3866af06e70be5e

Observation def2b7f9-068a-4483-a1d2-375dc17bdce1 · outbound

This paper cites Evolution through Large Models.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Evolution through Large Models

Reference 2022

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source=pdf_text observed=2026-08-07T05:59:03.428683Z digest=sha256:033c22801c679ab9773d456362becfeea537cf3215732a697e800c0783877061

Observation 87d472b8-c768-4763-8e56-8f388bf7b4d1 · outbound

This paper cites Quality-Diversity through AI Feedback.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms Quality-Diversity through AI Feedback

Reference 2023

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:02.928475Z digest=sha256:6e1d09bf600ca217e67ae1a4b41ff4ed73e305d0b51e72e92a6d2ac533034876

Observation 66494c96-ae4b-47ff-92a2-624763c5b192 · outbound

This paper cites MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning

Reference 2024

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no resolver link, observed 2026-08-07T05:59:03.565487Z

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source=pdf_text observed=2026-08-07T05:59:03.565487Z digest=sha256:ac904654fc32b2d30554931f0ec05f1cbdb2c58eb4c20b713b00162083d92b06

Observation 93bcd540-c87d-4052-ad87-619049e3ec72 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

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no resolver link, observed 2026-08-07T05:59:03.050740Z

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source=pdf_text observed=2026-08-07T05:59:03.050740Z digest=sha256:1b22ee7e6dd61f8087725af843bf4e1edad3da9ca9114ef59c1b1f4dd1a7b00d

Pith citing papers

No inbound Pith citation observations are available.