Paper Citation Record · LEDGER

Data Sharing with a Generative AI Competitor

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 4 inbound Pith citation observations for arXiv:2505.12386.

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

pith.paper-citation-record.v1
2505.12386 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:26.845342Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:50:33.352060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:47:36.030349Z

Reference resolution

56 of 56 outbound references displayed

verified exact4
verified fuzzy31
unresolved20
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81529622-a4d7-4288-955d-962cba767f62 · outbound

This paper cites A game-theoretic approach to recommendation systems with strategic content providers.Advances in Neural Information Processing Systems, 31, 2018.

Data Sharing with a Generative AI Competitor A game-theoretic approach to recommendation systems with strategic content providers.Advances in Neural Information Processing Systems, 31, 2018

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.649132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b133da59-57d1-460c-a2a0-cdb0b49a25c4 · outbound

This paper cites Data, competition, and digital platforms.American Economic Review, 114(8):2553–2595, 2024.

Data Sharing with a Generative AI Competitor Data, competition, and digital platforms.American Economic Review, 114(8):2553–2595, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.631548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c8f08bd-86df-443f-8ad4-6fa5e4538802 · outbound

This paper cites Truthful data acquisition via peer prediction.Advances in Neural Information Processing Systems, 33:18194–18204, 2020.

Data Sharing with a Generative AI Competitor Truthful data acquisition via peer prediction.Advances in Neural Information Processing Systems, 33:18194–18204, 2020

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.614899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14945144-6f50-4ac5-a8f1-3138d7adad13 · outbound

This paper cites Ai partners.https://cloud.google.com/partners/ai.

Data Sharing with a Generative AI Competitor Ai partners.https://cloud.google.com/partners/ai

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.597626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 830fd0ce-2f45-4956-8834-8f00275d02da · outbound

This paper cites Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.

Data Sharing with a Generative AI Competitor Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.586898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f009076b-ecb3-4dce-b81b-9f42f32eae15 · outbound

This paper cites Dataannotation: Flexible remote work to train ai, 2025.

Data Sharing with a Generative AI Competitor Dataannotation: Flexible remote work to train ai, 2025

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.580788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3b3ca028-1ef4-417a-932e-ab0ed5356e06 · outbound

This paper cites Accounting for AI and Users Shaping One Another: The Role of Mathematical Models.

Data Sharing with a Generative AI Competitor Accounting for AI and Users Shaping One Another: The Role of Mathematical Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.598412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdefdbd7-7ba7-4e3c-912d-852305a42852 · outbound

This paper cites Mechanism design for large language models.

Data Sharing with a Generative AI Competitor Mechanism design for large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.566066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d1eb42c-470e-484a-b6db-1c06070e1ed9 · outbound

This paper cites How to Strategize Human Content Creation in the Era of GenAI?.

Data Sharing with a Generative AI Competitor How to Strategize Human Content Creation in the Era of GenAI?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.608835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 227ec070-7986-46d0-897c-e5ec17fac935 · outbound

This paper cites Selling Information in Games with Externalities.

Data Sharing with a Generative AI Competitor Selling Information in Games with Externalities

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:27.071231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db915d9e-2f17-464e-865d-53b5eddff5ea · outbound

This paper cites Game-theoretic mechanisms for eliciting accurate information.

Data Sharing with a Generative AI Competitor Game-theoretic mechanisms for eliciting accurate information

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.553503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77c76bd7-6e59-4245-a166-833471d1258f · outbound

This paper cites Generative Social Choice.

Data Sharing with a Generative AI Competitor Generative Social Choice

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.624831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13fa44ce-67fb-491d-be7f-bd9e67621225 · outbound

This paper cites Prediction-sharing during training and inference.

Data Sharing with a Generative AI Competitor Prediction-sharing during training and inference

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.538184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aade8dd1-79b6-4bbe-80d0-679bf9786dfd · outbound

This paper cites The value of data records.Review of Economic Studies, 91(2):1007–1038, 2024.

Data Sharing with a Generative AI Competitor The value of data records.Review of Economic Studies, 91(2):1007–1038, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.525034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e345872-d53f-4e64-9693-0488bd85789c · outbound

This paper cites Pareto-improving data-sharing.

Data Sharing with a Generative AI Competitor Pareto-improving data-sharing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.511003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa7bc68d-c53d-47d0-9d45-14f27a5f01b8 · outbound

This paper cites Coopetition against an amazon.Journal of Artificial Intelligence Research, 76:1077–1116, 2023.

Data Sharing with a Generative AI Competitor Coopetition against an amazon.Journal of Artificial Intelligence Research, 76:1077–1116, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.497107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe690cf2-1508-4c66-afd0-236e6d91c4fe · outbound

This paper cites Selling Data to a Competitor.

Data Sharing with a Generative AI Competitor Selling Data to a Competitor

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:27.042267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3aaec826-33e6-459f-9f51-883d9ec4564b · outbound

This paper cites Multi-Agent Risks from Advanced AI.

Data Sharing with a Generative AI Competitor Multi-Agent Risks from Advanced AI

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.654732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf6b0967-69fd-4f28-986f-d229e0a10935 · outbound

This paper cites Performative power.Advances in Neural Information Processing Systems, 35:22969–22981, 2022.

Data Sharing with a Generative AI Competitor Performative power.Advances in Neural Information Processing Systems, 35:22969–22981, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.483260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47b0e18c-5dd7-4084-8da0-c4d74b70de80 · outbound

This paper cites Equilibrium of Data Markets with Externality.

Data Sharing with a Generative AI Competitor Equilibrium of Data Markets with Externality

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:27.012449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c317a870-4317-4d59-8a17-871051ac7870 · outbound

This paper cites Modeling Content Creator Incentives on Algorithm-Curated Platforms.

Data Sharing with a Generative AI Competitor Modeling Content Creator Incentives on Algorithm-Curated Platforms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.671195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c52c1e6a-8079-4c7d-b32d-6a37dfa9ab2a · outbound

This paper cites Supply-Side Equilibria in Recommender Systems.

Data Sharing with a Generative AI Competitor Supply-Side Equilibria in Recommender Systems

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.676527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bed34269-bf2c-434b-8c95-2bcae8e2d0e0 · outbound

This paper cites Competition, alignment, and equilibria in digital marketplaces.

Data Sharing with a Generative AI Competitor Competition, alignment, and equilibria in digital marketplaces

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.469558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d33fa7b-8dda-4412-883a-4d8672fb069c · outbound

This paper cites Fine-tuning games: Bargaining and adaptation for general-purpose models.

Data Sharing with a Generative AI Competitor Fine-tuning games: Bargaining and adaptation for general-purpose models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.454199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c0f41ba6-29e2-4c4b-b7a2-12713042c9a9 · outbound

This paper cites Output contract.https://www.law.cornell.edu/wex/output_contract,.

Data Sharing with a Generative AI Competitor Output contract.https://www.law.cornell.edu/wex/output_contract,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.440127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f04671f-45d2-48c4-ba4a-689d18a36ced · outbound

This paper cites The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents.

Data Sharing with a Generative AI Competitor The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:44:26.967842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 829f1673-f206-4c8c-b5d7-399ec656789d · outbound

This paper cites On the convergence of no- regret dynamics in information retrieval games with proportional ranking functions.

Data Sharing with a Generative AI Competitor On the convergence of no- regret dynamics in information retrieval games with proportional ranking functions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.415724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f885186-4bd6-4b2e-ad6a-ff8fd0673b7e · outbound

This paper cites Data partnerships.

Data Sharing with a Generative AI Competitor Data partnerships

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.401000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b407c6e3-1352-4cac-8b35-db64ad495872 · outbound

This paper cites Openai and reddit partnership.

Data Sharing with a Generative AI Competitor Openai and reddit partnership

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.386426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fcc8c36-39c7-43ce-8ee7-2fbf440d2b7d · outbound

This paper cites Instruction-following models.

Data Sharing with a Generative AI Competitor Instruction-following models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.373081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e7f0f40-6caa-4839-8208-a8ed309c7233 · outbound

This paper cites Outlier: Help build the world’s most advanced generative ai, 2025.

Data Sharing with a Generative AI Competitor Outlier: Help build the world’s most advanced generative ai, 2025

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.359849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 35894098-b295-4179-8cc3-67e614489b23 · outbound

This paper cites Competing with big data.The Journal of Industrial Economics, 69(4):967–1008, 2021.

Data Sharing with a Generative AI Competitor Competing with big data.The Journal of Industrial Economics, 69(4):967–1008, 2021

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.731200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83563bed-e602-4c95-bc37-64dff2633124 · outbound

This paper cites Competition and Diversity in Generative AI.

Data Sharing with a Generative AI Competitor Competition and Diversity in Generative AI

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.735597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d136e379-4fd4-475a-a0a2-861b17345a17 · outbound

This paper cites Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap.

Data Sharing with a Generative AI Competitor Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.740587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1394b659-ed4b-4cfc-b5ff-79630583f36d · outbound

This paper cites Privacy and fairness in machine learning: A survey.IEEE Transactions on Artificial Intelligence, 2025.

Data Sharing with a Generative AI Competitor Privacy and fairness in machine learning: A survey.IEEE Transactions on Artificial Intelligence, 2025

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.336296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e3085fa-2426-4429-b415-5c72bd4daa8b · outbound

This paper cites Incentives in private collaborative machine learning.Advances in Neural Information Processing Systems, 36:7555–7593, 2023.

Data Sharing with a Generative AI Competitor Incentives in private collaborative machine learning.Advances in Neural Information Processing Systems, 36:7555–7593, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.321164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c651ff36-1142-49ac-bd9f-9dd8df4700fb · outbound

This paper cites Braess’s paradox of generative ai.

Data Sharing with a Generative AI Competitor Braess’s paradox of generative ai

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.758041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75d63529-0c71-4a51-be75-e3c850f8607f · outbound

This paper cites Selective Response Strategies for GenAI.

Data Sharing with a Generative AI Competitor Selective Response Strategies for GenAI

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.762142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 747c17c2-fa47-4a3f-adc4-e89a04f2c7a9 · outbound

This paper cites Strategic data sharing between competitors.Advances in Neural Information Processing Systems, 36:16483–16514, 2023.

Data Sharing with a Generative AI Competitor Strategic data sharing between competitors.Advances in Neural Information Processing Systems, 36:16483–16514, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.298391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 52c9827d-e175-4eec-afb0-15a937a5bb09 · outbound

This paper cites Data distribution valuation.Advances in Neural Information Processing Systems, 37:2407–2448, 2024.

Data Sharing with a Generative AI Competitor Data distribution valuation.Advances in Neural Information Processing Systems, 37:2407–2448, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.284650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7f33975-41f1-4483-8de1-a21d892a99fe · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:27.271902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e94f0190-c8eb-4bfa-ab81-cec621a48129 · outbound

This paper cites Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?.

Data Sharing with a Generative AI Competitor Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.782382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9924ef6e-9a42-45b8-8db3-6705e109d371 · outbound

This paper cites Rethinking incentives in recommender systems: Are monotone rewards always beneficial?Advances in Neural Information Processing Systems, 36, 2024.

Data Sharing with a Generative AI Competitor Rethinking incentives in recommender systems: Are monotone rewards always beneficial?Advances in Neural Information Processing Systems, 36, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.259304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50b8cc98-cfe0-4e46-afd1-41f6d16ffde4 · outbound

This paper cites User welfare optimization in recommender systems with competing content creators.

Data Sharing with a Generative AI Competitor User welfare optimization in recommender systems with competing content creators

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.247506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2c72517-5183-461a-87aa-e83bbe7a688e · outbound

This paper cites A Survey on Data Markets.

Data Sharing with a Generative AI Competitor A Survey on Data Markets

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:26.795113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35df0935-3e26-49a3-8811-5d876fd5363a · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:27.235539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ba15d99-409b-46dd-b7bc-8c60e1b2b1a3 · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:27.222621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 767662c9-1c8d-487a-bca6-a78ca9ec2391 · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:27.209365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 210d6652-1bd8-4ae2-98d0-21cde9f7b928 · outbound

This paper cites •If(α eq,xeq) = (RF,1−R F )then:.

Data Sharing with a Generative AI Competitor •If(α eq,xeq) = (RF,1−R F )then:

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.195356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 61ec9994-7944-40a1-8f32-a53d3bb9029e · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:27.180981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e58f5a04-6152-4569-9fa1-4209fb85677d · outbound

This paper cites We get the following inequality γ(1−R F )rf +m≥0 By extractingm, we get thatm≥−γr f.

Data Sharing with a Generative AI Competitor We get the following inequality γ(1−R F )rf +m≥0 By extractingm, we get thatm≥−γr f

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.165687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b702953d-e068-4111-9e2b-0fdb604f3407 · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:44:27.151933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d39e09f-e607-4451-8185-94cfa09a1ed6 · outbound

This paper cites Notice that from the condition0≤R F≤ 1, it holds thatm∈ [−γrf,γrf ], and therefore, there is nom under our conditions that induces a Pareto-improving equilibrium.

Data Sharing with a Generative AI Competitor Notice that from the condition0≤R F≤ 1, it holds thatm∈ [−γrf,γrf ], and therefore, there is nom under our conditions that induces a Pareto-improving equilibrium

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.137981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 219880f1-7937-4754-b079-739938e3e7bf · outbound

This paper cites From our conditions onm and our condition forRF∈ [0, 1]we get that mmin is the minimal value in[−rf,m−].

Data Sharing with a Generative AI Competitor From our conditions onm and our condition forRF∈ [0, 1]we get that mmin is the minimal value in[−rf,m−]

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.124060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ecf793b3-7a6a-45fb-8d80-62fed6b6e08d · outbound

This paper cites The maximalm =mmax =m− is optimal only if it holds thatRF (1−λ)+λ≥R G.

Data Sharing with a Generative AI Competitor The maximalm =mmax =m− is optimal only if it holds thatRF (1−λ)+λ≥R G

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:44:27.109382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c45c856c-a7be-47fd-9e57-9e121bfb6229 · outbound

This paper cites an unresolved cited work.

Data Sharing with a Generative AI Competitor Unresolved cited work

Reference 2020

Resolution: parse uncertain; no resolver link, observed 2026-08-15T20:44:26.697512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e030f8fa-de77-4a0b-b73b-0992d814b300 · inbound

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers cites this paper.

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers Data Sharing with a Generative AI Competitor

Reference 198

Resolution: unresolved; no resolver link, observed 2026-08-07T22:50:33.352060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1414e2fb-b175-43ca-b53c-67968c36713b · inbound

Stability in Competitive Search with Results Diversification cites this paper.

Stability in Competitive Search with Results Diversification Data Sharing with a Generative AI Competitor

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-03T03:47:36.031828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5fde0afe-34a5-46ff-8de3-55ac295dd446 · inbound

Freemium Is All You Need cites this paper.

Freemium Is All You Need Data Sharing with a Generative AI Competitor

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T00:20:07.413086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29dc8f61-6444-4aa2-aabc-e5dad4796939 · inbound

Freemium Is All You Need cites this paper.

Freemium Is All You Need Data Sharing with a Generative AI Competitor

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T04:24:46.075160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.