Paper Citation Record · LEDGER

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences

As of 16 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2502.03472.

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

pith.paper-citation-record.v1
2502.03472 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:51:52.937586Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

34 of 34 outbound references displayed

verified exact0
verified fuzzy22
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a047c74-b592-4fe6-9b41-a4f7620a1ccc · outbound

This paper cites On the Limitations of Compute Thresholds as a Governance Strategy.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences On the Limitations of Compute Thresholds as a Governance Strategy

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.738542Z

Source-reported events for the cited work

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Observation aacb74c3-b0dc-459e-a27f-72c334693790 · outbound

This paper cites Rubicon: Rubric-based evaluation of domain-speciﬁc human ai conversations,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Rubicon: Rubric-based evaluation of domain-speciﬁc human ai conversations,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.657477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c8d3e87-a505-4894-8225-8bfc32a0d4bb · outbound

This paper cites Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.750466Z

Source-reported events for the cited work

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Observation f7a973d2-2abd-4058-80ca-a5d1454a69ff · outbound

This paper cites Artiﬁcial in- telligence risk management framework: Generative artiﬁci al intelligence proﬁle,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Artiﬁcial in- telligence risk management framework: Generative artiﬁci al intelligence proﬁle,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.637101Z

Source-reported events for the cited work

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Observation b49b6025-88ce-4112-9c83-035e00d05e78 · outbound

This paper cites Grounding ai policy towards researcher ac cess to ai usage data,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Grounding ai policy towards researcher ac cess to ai usage data,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.618950Z

Source-reported events for the cited work

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Observation ca8f4d23-537c-417e-ac91-3dc069023df2 · outbound

This paper cites Executive order on the safe, secure, an d trustworthy development and use of artiﬁcial intelligence,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Executive order on the safe, secure, an d trustworthy development and use of artiﬁcial intelligence,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.602611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 015ecf58-a960-4787-b6dc-75b1e9d8d68d · outbound

This paper cites Sb 1047: Safe and secure innovation for frontier artiﬁcial intelligence mod els act,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Sb 1047: Safe and secure innovation for frontier artiﬁcial intelligence mod els act,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.586083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 25bc7157-c89b-497a-a72a-f1e6c3225090 · outbound

This paper cites Exclusive: Anthropic weighs in on california a i bill,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Exclusive: Anthropic weighs in on california a i bill,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.568677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 499422df-89f0-46c2-9551-7e43134e56f3 · outbound

This paper cites We are writing to express signiﬁcant concer ns about sb 1047, the “safe and secure innovation for frontier artiﬁcial intelligence mod els act.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences We are writing to express signiﬁcant concer ns about sb 1047, the “safe and secure innovation for frontier artiﬁcial intelligence mod els act

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.549930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a407b33c-bfb5-4a71-a244-4f2d7392f6a3 · outbound

This paper cites Re:senatebill1047(wiener)-safeandsecure innovationforfrontierartiﬁcialintelligence modelsact-oppose,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Re:senatebill1047(wiener)-safeandsecure innovationforfrontierartiﬁcialintelligence modelsact-oppose,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.532681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 213d803d-9120-4093-89c9-9395fbc81988 · outbound

This paper cites an unresolved cited work.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:51:53.516314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 950f98ad-37af-4a8d-961c-97de6311ad78 · outbound

This paper cites In pursuit of regulatable llm s,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences In pursuit of regulatable llm s,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.499464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12e710a6-a278-4290-895f-7d4f30dc3f3f · outbound

This paper cites Scaling monosemanticity : Extracting interpretable fea- tures from claude 3 sonnet,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Scaling monosemanticity : Extracting interpretable fea- tures from claude 3 sonnet,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.484179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1acd8c87-77db-46c9-b17e-8af626c1b7b7 · outbound

This paper cites Artiﬁcial intelligence/ machine learning (ai/ml)-based software as a medical device (samd) action plan,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Artiﬁcial intelligence/ machine learning (ai/ml)-based software as a medical device (samd) action plan,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.468098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 53af5e1c-06be-4b96-8761-fa742de46781 · outbound

This paper cites Policy for device softw are functions and mobile medical ap- plications. guidance for industry and food and drug adminis tration staff,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Policy for device softw are functions and mobile medical ap- plications. guidance for industry and food and drug adminis tration staff,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.451044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2c8ebf2-cbcb-4533-b2cd-f32f92a705d9 · outbound

This paper cites The imperative for regulator y oversight of large language models (or generative ai) in healthcare,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences The imperative for regulator y oversight of large language models (or generative ai) in healthcare,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.434337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cba5dea3-50cb-454d-8789-1a4b2ecbf412 · outbound

This paper cites Generativ e ai and large language models in health care: pathways to implementation,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Generativ e ai and large language models in health care: pathways to implementation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.415671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 189a3cf6-4cb5-4f02-99c0-d15a5832c6ca · outbound

This paper cites Sb 1047, ai regulation, and unlikely allie s for open models,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Sb 1047, ai regulation, and unlikely allie s for open models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.398268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7699843f-1069-4b5c-ad06-606019c6c49a · outbound

This paper cites Ofﬁce of the governor,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Ofﬁce of the governor,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.381117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bcb84dec-e9cf-480c-ad1a-569ca577824b · outbound

This paper cites Sb24-205 consumer protec tions for artiﬁcial intelligence,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Sb24-205 consumer protec tions for artiﬁcial intelligence,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.362186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbfdf799-17c3-4117-b9d0-7eabdebdfc58 · outbound

This paper cites Open llm leaderboard,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Open llm leaderboard,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.341083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 706c8388-aaf6-4f52-ad7d-565da31aaef2 · outbound

This paper cites Holistic Evaluation of Language Models.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Holistic Evaluation of Language Models

Reference 22

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Observation abd78e69-183d-4418-869f-54ffb513b8cc · outbound

This paper cites Generative AI for Synthetic Data Generation: Methods, Challenges and the Future.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.866371Z

Source-reported events for the cited work

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Observation 0489f116-08d3-4d06-ad97-61ea6ebd9f67 · outbound

This paper cites GPT-4 Technical Report.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences GPT-4 Technical Report

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.871843Z

Source-reported events for the cited work

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Observation 5771b311-fad2-47f3-985d-f8f6baa975a5 · outbound

This paper cites Towards Expert-Level Medical Question Answering with Large Language Models.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Towards Expert-Level Medical Question Answering with Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.877187Z

Source-reported events for the cited work

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Observation 8bb86f1f-9355-480f-9d8c-d773ecfd7d62 · outbound

This paper cites The evolving landscape of llm evaluation,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences The evolving landscape of llm evaluation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.324596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bd960c56-569e-452e-bd57-2f0773afa84a · outbound

This paper cites Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.890433Z

Source-reported events for the cited work

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Observation bbf95a3c-83af-496a-9ed3-03c22207d1ab · outbound

This paper cites Walking a Tightrope -- Evaluating Large Language Models in High-Risk Domains.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Walking a Tightrope -- Evaluating Large Language Models in High-Risk Domains

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.899304Z

Source-reported events for the cited work

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Observation f25c9310-ceb0-45a6-b316-6773f4780eae · outbound

This paper cites LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

Reference 29

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Observation 11c52d50-a5f6-43ac-aa8f-4380c76a6fb4 · outbound

This paper cites M-24-10 advancing governance, innovation and risk management for agency use o f artiﬁcial intelligence,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences M-24-10 advancing governance, innovation and risk management for agency use o f artiﬁcial intelligence,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.306688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1dab4539-ce5b-4c0d-9a4b-5e3bce0d8025 · outbound

This paper cites Ai models collapse when trained on recursively generated data,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Ai models collapse when trained on recursively generated data,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.287720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ce50e0e8-9ada-4352-b873-8ca3cc4fca91 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatb ot arena,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Judging llm-as-a-judge with mt-bench and chatb ot arena,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:51:53.269985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ed6c35d4-f3d1-4970-aeb2-e85c2ff5f5a9 · outbound

This paper cites Evaluating the evaluator: Measuring llms’ adhe rence to task evaluation instruc- tions,.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Evaluating the evaluator: Measuring llms’ adhe rence to task evaluation instruc- tions,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.932496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17976ebf-5732-4c9d-b237-710493f46537 · outbound

This paper cites Best Practices and Lessons Learned on Synthetic Data.

Powering LLM Regulation through Data: Bridging the Gap from Compute Thresholds to Customer Experiences Best Practices and Lessons Learned on Synthetic Data

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T20:51:52.937586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.