Paper Citation Record · LEDGER

Autodata: An agentic data scientist to create high quality synthetic data

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2606.25996.

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

pith.paper-citation-record.v1
2606.25996 v3

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T12:08:06.206832Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T12:17:55.372793Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy0
unresolved38
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 180fa536-5ac7-4830-8f30-d26f7f9aa8f0 · outbound

This paper cites write newline.

Autodata: An agentic data scientist to create high quality synthetic data write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation d4737666-cc86-43a1-ad82-166ace11e432 · outbound

This paper cites Reasoning over mathematical objects: on-policy reward modeling and test time aggregation.

Autodata: An agentic data scientist to create high quality synthetic data Reasoning over mathematical objects: on-policy reward modeling and test time aggregation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 9162f0c7-163c-4102-84ea-162309856634 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

Autodata: An agentic data scientist to create high quality synthetic data GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 2334d965-0a37-4fda-b990-6806f88b942e · outbound

This paper cites Prbench: Large-scale expert rubrics for evaluating high-stakes professional reasoning.

Autodata: An agentic data scientist to create high quality synthetic data Prbench: Large-scale expert rubrics for evaluating high-stakes professional reasoning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 639d0f13-bf92-4404-91af-60ce2dbbcaeb · outbound

This paper cites Ultrafeedback: Boosting language models with high-quality feedback.

Autodata: An agentic data scientist to create high quality synthetic data Ultrafeedback: Boosting language models with high-quality feedback

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 3b627c34-7f65-41b1-b38b-a5cff6f4ddd8 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations.

Autodata: An agentic data scientist to create high quality synthetic data Enhancing chat language models by scaling high-quality instructional conversations

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 38e9ed9e-e8f6-4a06-bf8a-8581047810ad · outbound

This paper cites Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution.

Autodata: An agentic data scientist to create high quality synthetic data Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 7fdde1a0-5123-45c4-82fd-ed38313115cd · outbound

This paper cites DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning.

Autodata: An agentic data scientist to create high quality synthetic data DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation dae35aa8-b607-428e-9d6f-7e7c2c175dfc · outbound

This paper cites Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset.

Autodata: An agentic data scientist to create high quality synthetic data Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation dcb6294a-1d86-4556-9eb8-efd922c63931 · outbound

This paper cites Data interpreter: An llm agent for data science.

Autodata: An agentic data scientist to create high quality synthetic data Data interpreter: An llm agent for data science

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation e03c8727-ade7-4680-b024-445d81e2210c · outbound

This paper cites autoresearch: Ai agents running research on single-gpu nanochat training automatically.

Autodata: An agentic data scientist to create high quality synthetic data autoresearch: Ai agents running research on single-gpu nanochat training automatically

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 8db39b5f-4f30-46b8-933f-6ded800ceea0 · outbound

This paper cites Meta-Harness: End-to-End Optimization of Model Harnesses.

Autodata: An agentic data scientist to create high quality synthetic data Meta-Harness: End-to-End Optimization of Model Harnesses

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation d90ab894-ca72-428a-84fa-1ce45fea481f · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

Autodata: An agentic data scientist to create high quality synthetic data Textbooks Are All You Need II: phi-1.5 technical report

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 5ffbb04e-8b64-49b5-a9aa-ab7225a8e076 · outbound

This paper cites Spice: Self-play in corpus environments improves reasoning.

Autodata: An agentic data scientist to create high quality synthetic data Spice: Self-play in corpus environments improves reasoning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 3a5ad0a2-b680-4595-be2c-d3ec19928364 · outbound

This paper cites S2orc: The semantic scholar open research corpus.

Autodata: An agentic data scientist to create high quality synthetic data S2orc: The semantic scholar open research corpus

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 59a686d9-4f49-48ff-8d0d-42918a4606aa · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

Autodata: An agentic data scientist to create high quality synthetic data The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 7e5efbc6-70d5-4d7c-8ffd-ce346b26b34b · outbound

This paper cites Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources.

Autodata: An agentic data scientist to create high quality synthetic data Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation d988db26-6057-4aa7-863a-e85468f7b1f8 · outbound

This paper cites Autodata: A multi-agent system for open web data collection.

Autodata: An agentic data scientist to create high quality synthetic data Autodata: A multi-agent system for open web data collection

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation f73658fa-590d-4c7d-aa14-c1772eefecc8 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

Autodata: An agentic data scientist to create high quality synthetic data Self-refine: Iterative refinement with self-feedback

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation c4f00990-0eec-4046-a028-9ef6a35ed535 · outbound

This paper cites AgentInstruct: Toward Generative Teaching with Agentic Flows.

Autodata: An agentic data scientist to create high quality synthetic data AgentInstruct: Toward Generative Teaching with Agentic Flows

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation ec946c79-73ee-45f0-a472-93f3f41be6fe · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Autodata: An agentic data scientist to create high quality synthetic data Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 40e65629-249f-4b0f-b6f3-1ab1aaa7a951 · outbound

This paper cites AI-Assisted Generation of Difficult Math Questions.

Autodata: An agentic data scientist to create high quality synthetic data AI-Assisted Generation of Difficult Math Questions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 68804eb7-9908-4981-90fb-9b53ba410c32 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Autodata: An agentic data scientist to create high quality synthetic data DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation ebc735ef-1c8c-4588-aec5-6fbdc9c34bb6 · outbound

This paper cites Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data.

Autodata: An agentic data scientist to create high quality synthetic data Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation db7223da-e3dc-4860-8464-f0fb64a0a717 · outbound

This paper cites Self-instruct: Aligning language models with self-generated instructions.

Autodata: An agentic data scientist to create high quality synthetic data Self-instruct: Aligning language models with self-generated instructions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 4cff64fa-3a6b-40b2-ba00-a48793e42ea1 · outbound

This paper cites Ai & human co-improvement for safer co-superintelligence.

Autodata: An agentic data scientist to create high quality synthetic data Ai & human co-improvement for safer co-superintelligence

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation a5d843e1-d9e4-4342-9292-629c6eee6910 · outbound

This paper cites Wizardlm: Empowering large pre-trained language models to follow complex instructions.

Autodata: An agentic data scientist to create high quality synthetic data Wizardlm: Empowering large pre-trained language models to follow complex instructions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 0aad17bf-3771-4a56-bf62-86ec2a4bdc95 · outbound

This paper cites Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing.

Autodata: An agentic data scientist to create high quality synthetic data Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 4378f57f-1f24-4bc2-b08b-8b9ec0e3306a · outbound

This paper cites Large language models as optimizers.

Autodata: An agentic data scientist to create high quality synthetic data Large language models as optimizers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 2803d00f-723b-4cb3-90ef-1057f931e10f · outbound

This paper cites Metamath: Bootstrap your own mathematical questions for large language models.

Autodata: An agentic data scientist to create high quality synthetic data Metamath: Bootstrap your own mathematical questions for large language models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation d3506294-cd57-45c3-af53-d3e2633180b4 · outbound

This paper cites CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks.

Autodata: An agentic data scientist to create high quality synthetic data CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation e56cea21-9331-4255-b3d0-c802e7338520 · outbound

This paper cites Self-rewarding language models.

Autodata: An agentic data scientist to create high quality synthetic data Self-rewarding language models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 3ddba380-8175-4ae4-8bdd-af5640ffac6c · outbound

This paper cites Naturalreasoning: Reasoning in the wild with 2.8 m challenging questions.

Autodata: An agentic data scientist to create high quality synthetic data Naturalreasoning: Reasoning in the wild with 2.8 m challenging questions

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 650c3652-77a0-4bf4-9a80-0e4e791bf0ef · outbound

This paper cites Mammoth: Building math generalist models through hybrid instruction tuning.

Autodata: An agentic data scientist to create high quality synthetic data Mammoth: Building math generalist models through hybrid instruction tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 87413805-b55b-47f9-a84c-8fc17996ac63 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

Autodata: An agentic data scientist to create high quality synthetic data Star: Bootstrapping reasoning with reasoning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation c38730bd-de32-4d46-9d35-913602c30b36 · outbound

This paper cites Absolute Zero: Reinforced Self-play Reasoning with Zero Data.

Autodata: An agentic data scientist to create high quality synthetic data Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation cd10e78d-3eb5-4460-8ce0-13b9e574f31e · outbound

This paper cites The Majority is not always right: RL training for solution aggregation.

Autodata: An agentic data scientist to create high quality synthetic data The Majority is not always right: RL training for solution aggregation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Observation 876aefd1-24ed-4b36-9741-cf7d9a18fa55 · outbound

This paper cites Self-Challenging Language Model Agents.

Autodata: An agentic data scientist to create high quality synthetic data Self-Challenging Language Model Agents

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

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Pith citing papers

Observation a073bf6e-8d95-41cd-992f-029625ccc0b4 · inbound

A Vocabulary for Multi-Agent Automated Research Systems cites this paper.

A Vocabulary for Multi-Agent Automated Research Systems Autodata: An agentic data scientist to create high quality synthetic data

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T07:06:10.622844Z

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Observation bab2eace-b5a5-4098-9243-af7334eebef1 · inbound

Execution-First Synthetic Tool-Use Trace Generation for LLM Agents cites this paper.

Execution-First Synthetic Tool-Use Trace Generation for LLM Agents Autodata: An agentic data scientist to create high quality synthetic data

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T12:17:55.372793Z

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