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Paper Citation Record · LEDGER

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

As of 3 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 2 inbound Pith citation observations for arXiv:2512.05958.

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

pith.paper-citation-record.v1
2512.05958 v2

Coverage vector

measured 100 of 100 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T17:40:10.135567Z

measured 102 of 102 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:53:25.695298Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:05:56.861299Z

Reference resolution

100 of 100 outbound references displayed

  • verified exact14
  • verified fuzzy77
  • unresolved6
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  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 817caaaf-fa48-49dc-bb0f-f217e478ba8c · outbound

This paper cites https://gist.ai/.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution https://gist.ai/

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation a1ef407e-a8f3-4708-a5d2-23410ce5752c · outbound

This paper cites Geo: Generative engine optimization.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Geo: Generative engine optimization

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation e9f6e963-86e8-42e3-b7f8-d2a03bf354df · outbound

This paper cites Introducing pay per crawl: Enabling content owners to charge AI crawlers for access.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Introducing pay per crawl: Enabling content owners to charge AI crawlers for access

Reference 3

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation ae28303e-50ae-4769-b7b2-151aae50050a · outbound

This paper cites Will Google’s AI Overviews kill news sites as we know them?, 7.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Will Google’s AI Overviews kill news sites as we know them?, 7

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 2b5fcc35-d517-410f-8dcf-f3d9493fc741 · outbound

This paper cites an unresolved cited work.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 7cf2f6da-2112-4479-b5ff-8f70bffce66c · outbound

This paper cites Claude 3.5 Haiku.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Claude 3.5 Haiku

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 15499217-c0cd-4874-ba47-0ae6a29462b4 · outbound

This paper cites Introducing Claude 4.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Introducing Claude 4

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 67612fc8-651a-4d0e-9b47-258744ace3b8 · outbound

This paper cites Pricing.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Pricing

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 46d315ce-27e2-41ae-9f82-898a6885ef0f · outbound

This paper cites deterministic.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution deterministic

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 4aa65d8d-785d-45bb-aa15-cc91ebd9786c · outbound

This paper cites A Turvey-Shapley Value Method for Distribution Network Cost Allocation.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution A Turvey-Shapley Value Method for Distribution Network Cost Allocation

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation aefa2d4b-d9c8-41fe-a25d-89955f94ea0d · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation bab8611d-5c85-4899-a065-9e8edb06493e · outbound

This paper cites Ads in Conversations.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Ads in Conversations

Reference 12

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation b823db9c-6ecb-4032-a28c-e7d469eabe0f · outbound

This paper cites Anthropic PBC, No.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Anthropic PBC, No

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation d0b91ef1-bccb-4033-9a7a-318102081cb1 · outbound

This paper cites Data-driven mechanism design: Jointly eliciting preferences and information.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Data-driven mechanism design: Jointly eliciting preferences and information

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.237819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 0d82181e-0206-462d-858d-a96f42f3642b · outbound

This paper cites Google users are less likely to click on links when an AI summary appears in the results.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Google users are less likely to click on links when an AI summary appears in the results

Reference 15

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 63af827b-c8e3-4d81-9a18-63ce7bee1cc0 · outbound

This paper cites Generative Engine Optimization: How to Dominate AI Search.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Generative Engine Optimization: How to Dominate AI Search

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.260396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation d4829e33-6467-4ef7-b17c-8f0548d2f30d · outbound

This paper cites Glass, Shang-Wen Li, and Wen tau Yih.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Glass, Shang-Wen Li, and Wen tau Yih

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation b2850d75-7d79-4de2-ac52-9d22ececb266 · outbound

This paper cites Learning to Attribute with Attention.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Learning to Attribute with Attention

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation f489ee44-d058-41d2-b54f-241000b5d76c · outbound

This paper cites Contextcite: Attributing model generation to context.NeurIPS, 37:95764–95807.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Contextcite: Attributing model generation to context.NeurIPS, 37:95764–95807

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 6fcf0cd5-64e6-46ad-a392-86d4fa8ff519 · outbound

This paper cites Overview of the trec 2020 deep learning track.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Overview of the trec 2020 deep learning track

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 0a2e9aa7-8a36-4fba-891f-2bc79d586723 · outbound

This paper cites V oorhees.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution V oorhees

Reference 21

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 877e9447-565f-4055-89e6-241e3fce6f7c · outbound

This paper cites Perplexity in talks with top brands on ads model as it challenges google.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Perplexity in talks with top brands on ads model as it challenges google

Reference 22

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 431fde92-580d-4fc0-ae1f-dbb29656c6e4 · outbound

This paper cites an unresolved cited work.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation aa491a1f-5674-46fd-8f76-268d4c07db21 · outbound

This paper cites Google gemini: A multimodal ai model.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Google gemini: A multimodal ai model

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation b72d706e-c16e-4dd6-b08e-69bbf630ae85 · outbound

This paper cites Attention with Dependency Parsing Augmentation for Fine-Grained Attribution.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Attention with Dependency Parsing Augmentation for Fine-Grained Attribution

Reference 25

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arxiv_id, observed 2026-05-21T17:40:26.251226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 48174696-0edc-4f9d-ae48-0db10e9b9047 · outbound

This paper cites Auc- tions with llm summaries.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Auc- tions with llm summaries

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 1a7464aa-50a3-4958-8266-10e65a8bd002 · outbound

This paper cites Mechanism design for large language models.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Mechanism design for large language models

Reference 27

Resolution
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raw_fallback, observed 2026-05-21T17:40:26.713828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation 57e58688-c4a9-40e2-9ac1-a263f07f54e7 · outbound

This paper cites Ai is killing the web.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Ai is killing the web

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Observation a86cfb1e-1bb5-421c-8bdd-a4f6da624638 · outbound

This paper cites Online Advertisements with LLMs: Opportunities and Challenges.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Online Advertisements with LLMs: Opportunities and Challenges

Reference 29

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arxiv_id, observed 2026-05-21T17:40:26.244606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:da3b850750ebcc1c52a37acefb8143cf9e490ef5c82952cf9ccfa7efe5e42fa5

Observation 04ad679c-ced2-40aa-a4e0-d58b9b48b76a · outbound

This paper cites Online advertisements with llms: Opportunities and challenges.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Online advertisements with llms: Opportunities and challenges

Reference 30

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raw_fallback, observed 2026-05-21T17:40:26.643160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:157ab511dd56a012fc185984c5e64d285db29dae7451471bbd48ec0d826a37ad

Observation 4fa44a20-1b5e-4481-b045-622308acd00d · outbound

This paper cites Penske Media sues Google over AI summaries taking traffic.Axios, 9 2025.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Penske Media sues Google over AI summaries taking traffic.Axios, 9 2025

Reference 31

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raw_fallback, observed 2026-05-21T17:40:26.740455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:ee54a32ea67d8aa52b2c4b45beb049f2f0d1f51f0780bd287cf964a64cdc3752

Observation d22ab1f8-a2bc-4393-844f-d04f8b1bb691 · outbound

This paper cites Data shapley: Equitable valuation of data for machine learning.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Data shapley: Equitable valuation of data for machine learning

Reference 32

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raw_fallback, observed 2026-05-21T17:40:26.817366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:39a9ec60df2a87fe7b320b5ae37303257a19b4d15f558de5a36ddf8efc333557

Observation 40b46cf7-d629-493b-9e4f-6e4f602ce77a · outbound

This paper cites A Survey on LLM-as-a-Judge.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution A Survey on LLM-as-a-Judge

Reference 33

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local_arxiv, observed 2026-05-21T17:40:26.257082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:1e777a56d11bc28b4d4306ac27bacc29cb70b628243720ee3a76637f2cde8309

Observation 45bb71ab-37c1-465d-81b7-7499db9fcc71 · outbound

This paper cites RAG" Stands for “Royalties.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution RAG" Stands for “Royalties

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.720692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:64758e9b8a9ab5c26133d0a99aa8e73169fb3d0af1697c545bdf44fddb2e2a1d

Observation 37550eee-b028-4fcc-b88b-0bda8a33569b · outbound

This paper cites Realm: Retrieval-augmented language model pre-training.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Realm: Retrieval-augmented language model pre-training

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:51706a036f7d2cf91cc1e9264d1b1ad9891ae10ee18c6da4474a02533609e5c5

Observation 8e0fbcc0-a396-451c-b0f2-20728a7cbe7e · outbound

This paper cites Ad auctions for llms via retrieval augmented generation.NeurIPS, 37:18445– 18480.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Ad auctions for llms via retrieval augmented generation.NeurIPS, 37:18445– 18480

Reference 36

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raw_fallback, observed 2026-05-21T17:40:26.822679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:52bf628c2478e814d1b202a9e7c27e3202860811d44f445974f0886b5fb00eed

Observation d5b69132-9536-4b0b-83e9-3cf6ecde7d06 · outbound

This paper cites A shapley value-based incentive mechanism in collaborative edge computing.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution A shapley value-based incentive mechanism in collaborative edge computing

Reference 37

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raw_fallback, observed 2026-05-21T17:40:26.648428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:f527ea37d811435e751dffab50f5b63c8578c9087f5b57c82bbacff76e446d31

Observation 69b8c388-dbff-41e9-a072-04ae5184f443 · outbound

This paper cites LAQuer: Localized Attribution Queries in Content-grounded Generation.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution LAQuer: Localized Attribution Queries in Content-grounded Generation

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.215595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:0b578d4e584d83bfd43edd4cf01090546f63081b0b65655e63e430d3d2214ca8

Observation a2593213-6ccd-4648-a612-ea1b7e9e4983 · outbound

This paper cites Datamodels: Understanding predictions with data and data with predictions.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Datamodels: Understanding predictions with data and data with predictions

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.646032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:24bc5b352a545e4e84997a3455b689e56ddbdafe375875776397c6a1d96d527f

Observation 167f43a2-2eed-4c92-b988-a376da0fcbfe · outbound

This paper cites Atlas: Few-shot learning with retrieval augmented language models.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Atlas: Few-shot learning with retrieval augmented language models

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.767961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:6d2c4b9183f8cba7aba0f5705267e29400357123507c58dc9853038abec4d7a5

Observation f9f82a69-0a9d-490b-80a2-ddf1e35172c5 · outbound

This paper cites Virtual machine power accounting with shapley value.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Virtual machine power accounting with shapley value

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.755242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:1629910bb7dbdd02fa5d7916fe73730d67d97a0f35835a741d815a84c0ea43de

Observation b136b4a6-d580-48ad-8fee-8dc101ece480 · outbound

This paper cites an unresolved cited work.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-05-21T17:40:26.700181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:01e33246aecc60641d78459e2f81eb0883880e018bdc1c137d703bb2cd786de1

Observation b2ee82f6-0aad-4c6f-8d43-52d86e2b56f3 · outbound

This paper cites Colbert: Efficient and effective passage search via contextualized late interaction over bert.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Colbert: Efficient and effective passage search via contextualized late interaction over bert

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.814691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:f04ce6dee4866042a147535663296cd6ae68324054629ba828101ddd7cde04d3

Observation 3e773cc2-05a2-484a-9f12-27570a4d1533 · outbound

This paper cites Engaging the many-hands prob- lem of generative-ai outputs: A framework for attributing credit.AI and Ethics.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Engaging the many-hands prob- lem of generative-ai outputs: A framework for attributing credit.AI and Ethics

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.757725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:0db4222fd7190884a0d9db7e2de248f0c994cde626b602f7b36f6cbeca291bb6

Observation 34f85726-2a26-44b5-97da-10aceb923f09 · outbound

This paper cites Understanding black-box predictions via influence functions.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Understanding black-box predictions via influence functions

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.752437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:bef81e700e998753557d1fdb9c28076326c29c303fc9c81bf9c8ee7200739b34

Observation bcdb50a3-4151-48fb-9a2c-c583470d82df · outbound

This paper cites The impact of llms on sponsored search: Evidence from google’s bert.USC Marshall School of Business Research Paper Sponsored by iORB.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution The impact of llms on sponsored search: Evidence from google’s bert.USC Marshall School of Business Research Paper Sponsored by iORB

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.778443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:bb3b5184b0faed1380cef98973659dac0608837a646ad0a33c2b10f6e5c5e438

Observation bf110d09-aeef-4f45-a022-0ee80e5a841e · outbound

This paper cites How to Correctly Report LLM-as-a-Judge Evaluations.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution How to Correctly Report LLM-as-a-Judge Evaluations

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-06-02T03:04:02.584259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:79d06ddde89681287f950a16c41014dda671ef5d8de0649d66c8220fb095d5f7

Observation 4a18f8bb-58d6-47b6-83eb-854b9702087c · outbound

This paper cites Grade: Generating multi-hop qa and fine-grained difficulty matrix for rag evaluation.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Grade: Generating multi-hop qa and fine-grained difficulty matrix for rag evaluation

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.735570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:b6391e1ffc87e0390761028f4ed54bbdc44f32b1d37df6ae4af479ba53674c2b

Observation 1af68438-7fc0-4229-bc0e-9029bc544056 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.737888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:373a1d8fe7aab13fb45450f33ebd95dfbc5b9a92f7058ffbebe042662a047076

Observation 7f203e6b-a43a-440e-98a7-9585e2dfaeef · outbound

This paper cites Llm whisperer: An inconspicuous attack to bias llm responses.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Llm whisperer: An inconspicuous attack to bias llm responses

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.775441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:e6753c2f8eb48c5c5c1d8bda887e058a56e0d78a4bc38be8f4ffb51678582cb4

Observation 45a28dd4-e466-4b6a-9303-e6791aaa9c1c · outbound

This paper cites Attribot: A bag of tricks for efficiently approximating leave-one-out context attribution.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Attribot: A bag of tricks for efficiently approximating leave-one-out context attribution

Reference 51

Resolution
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raw_fallback, observed 2026-05-21T17:40:26.770507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:d31bdef8351d7b37fcea273718d477b76da117b1236d14543eac13adcb05a5a7

Observation e674b769-cb55-4bf7-99ab-a31474648967 · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.786375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:048e818189172a25379c138154ec5f5a2a1dc865ffa7553ab6115454014412a6

Observation 01831593-b05e-4f3e-a5a2-745f7e73c50b · outbound

This paper cites Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.241325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:eaef7e91679333472e3f74def28a7a1d0fca448b47ac02dce3599b87473ffbe3

Observation c2b83c4c-951d-488c-8e4a-dda316142993 · outbound

This paper cites G-eval: Nlg evaluation using gpt-4 with better human alignment.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution G-eval: Nlg evaluation using gpt-4 with better human alignment

Reference 54

Resolution
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raw_fallback, observed 2026-05-21T17:40:26.763244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:cb15cf1e01052adfc92c11f37d96b563567db20a501732e5b954b7de4cf9da85

Observation 72491c91-d709-4791-abe1-6d2c05b6a1c7 · outbound

This paper cites A unified approach to interpreting model predictions.NeurIPS, 30.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution A unified approach to interpreting model predictions.NeurIPS, 30

Reference 55

Resolution
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raw_fallback, observed 2026-05-21T17:40:26.750102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:d00049d6114343eea87332ce99c6ff51017d0a5d5e3b62da8431b0782e3e8c2e

Observation a82a5796-e3bf-4727-8643-63f1927f2d44 · outbound

This paper cites an unresolved cited work.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-05-21T17:40:26.731042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:972253f19d33b1acf7f8565720fc5b88f9df74bfd227fdc607bc40f57ac0045e

Observation 19fd8fa9-6819-47a4-ad67-c43c95040285 · outbound

This paper cites On cooperative settlement between content, transit and eyeball internet service providers.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution On cooperative settlement between content, transit and eyeball internet service providers

Reference 57

Resolution
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raw_fallback, observed 2026-05-21T17:40:26.733309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:8baa14a9f70767f8f9236303fb1b9f1f8c33a76d1127b2b936f28542af6b5dd9

Observation c7a6fbc6-37eb-4b72-aae6-094ada42e1b7 · outbound

This paper cites Efficient computation of the shapley value for game-theoretic network centrality.Journal of Artificial Intelligence Research, 46:607–650.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Efficient computation of the shapley value for game-theoretic network centrality.Journal of Artificial Intelligence Research, 46:607–650

Reference 58

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verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.797020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:7a7596b63b047817868d837f2e186b90723f63ef7d9ed621b4067c73c227ce64

Observation 5b03e723-b3b2-4e5b-9f9b-04b19c3d4696 · outbound

This paper cites In- centivizing peer-assisted services: A fluid shapley value approach.SIGMETRICS.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution In- centivizing peer-assisted services: A fluid shapley value approach.SIGMETRICS

Reference 59

Resolution
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raw_fallback, observed 2026-05-21T17:40:26.831866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:b2127b9406f68e5832ce8c970f7176ff47b5fec0ee90df1cee56f2ba4dedae0d

Observation ce423af9-9c83-4987-8521-ec5e290fdc9c · outbound

This paper cites Sampling permutations for shapley value estimation.Journal of Machine Learning Research, 23(43):1–46.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Sampling permutations for shapley value estimation.Journal of Machine Learning Research, 23(43):1–46

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.728991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:b7d341dab96759b767edf74a76b33da24695469a47837844c4ea34fbc2ffaa52

Observation 7c7ce2cb-cd97-40ef-9555-8e94abc49461 · outbound

This paper cites Sponsored question answering.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Sponsored question answering

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.745172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:f998357c93175f250302d7a24202311f807cf9ef36fed934124bc5f58a39bd78

Observation 9f63c5af-7cb7-41d3-9c42-c7d35471c008 · outbound

This paper cites Source Attribution in Retrieval-Augmented Generation.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Source Attribution in Retrieval-Augmented Generation

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.230222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:20ae26e8f5875f217e4b4def80d381c1154baf030db2b71d4a2cde08815e624a

Observation 9259a03e-167f-4348-ad3f-6a41fc78fca8 · outbound

This paper cites Adversarial Search Engine Optimization for Large Language Models.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Adversarial Search Engine Optimization for Large Language Models

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.248051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:f18368e66c9e46ee5f8d8b074d8f9a2ef4113d4fb28d0c8293f56e8cede121b2

Observation 4456b524-e294-46c1-8581-5417f5371dca · outbound

This paper cites Chegg sues Google for hurting traffic as it considers alternatives.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Chegg sues Google for hurting traffic as it considers alternatives

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.819947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:b3e33d18dbd07edd6ab8ac5c0dcb2eabdecbbcb26b761e16774a64ee1c54c444

Observation 55c8a5b4-bae2-43b6-b516-bb3442c78ac5 · outbound

This paper cites Introducing GPT-4.1 in the API.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Introducing GPT-4.1 in the API

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.659192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:574eaec964f068acdcd943e9958e1926c16d46a828505913be1ae302e4451e96

Observation 67fb7061-7d55-4a93-b787-129b1d465671 · outbound

This paper cites Pricing.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Pricing

Reference 66

Resolution
parse uncertain
raw_fallback, observed 2026-05-21T17:40:26.747481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:1451ba72635d59367d23cbca2567c12e12396584cb3862937a273d0369b4a35e

Observation 56f139f7-8170-4e26-b5c3-ff122432a1c7 · outbound

This paper cites Llm visibility: Ai search statistics.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Llm visibility: Ai search statistics

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.802453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:50a10f62a6d8c54d9a02c3f1fedd338d8b7ddf933286fbe6085998382d9fb2e9

Observation 5875d451-2cd0-4f41-bead-2281b94936c9 · outbound

This paper cites Llm evaluators recognize and favor their own generations.Advances in Neural Information Processing Systems, 37:68772–68802.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Llm evaluators recognize and favor their own generations.Advances in Neural Information Processing Systems, 37:68772–68802

Reference 68

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verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.651216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:853d9c39da17ea65a0d7d2e53c514d76a62ff2a5b53d988baa8a96c7f7671310

Observation 115ebab1-e813-4bf2-a615-1320a9ac656c · outbound

This paper cites TRAK: Attributing model behavior at scale.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution TRAK: Attributing model behavior at scale

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.689692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:ae728554f8054240710a1264d26059b52c79207bb5020b5faf6f56d3618b93d3

Observation 867b6314-c74f-452c-b767-a985ee1bb220 · outbound

This paper cites News publisher files class action antitrust suit against Google, citing AI’s harms to their bottom line, 12 2023.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution News publisher files class action antitrust suit against Google, citing AI’s harms to their bottom line, 12 2023

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.709291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:ccb9f981b3beac50cab4a5f4d2d546e36cf1581af38c13fc0acfb248929467ef

Observation 2e46b0f0-a5c5-4595-8a64-85cbb9463916 · outbound

This paper cites Perplexity AI.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Perplexity AI

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.691644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:5a83e4a32c53b72b159bafdd22e3ff7559a9093ac51e4280afdfec83a927631c

Observation 9bbf5ee3-032f-425d-93ee-0dc3c76c29cb · outbound

This paper cites Anthropic to pay $1.5 billion to settle authors’ copyright lawsuit.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Anthropic to pay $1.5 billion to settle authors’ copyright lawsuit

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.673628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:55b8f75b28cc7d1f46b02648febe1b40c751e95a0eb2e5653cb9567d17e73dfa

Observation 7e187d93-f7c4-4a0c-835c-835fec8bfeff · outbound

This paper cites Esti- mating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Esti- mating training data influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.664141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:c7ba82b0604262eec0ef6462d3359a053c1277e92c216775e3575657f63bd64e

Observation bdcbe430-8fe5-4289-888b-f2dc67548911 · outbound

This paper cites Model internals- based answer attribution for trustworthy retrieval-augmented generation.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Model internals- based answer attribution for trustworthy retrieval-augmented generation

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.702456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:a76af8aa7ec0bf9613a97bdc2262df0dd6f420dfc694210d21540da7a0d28e9a

Observation ce6c4a32-f8fc-476e-9b24-1f0d01af0171 · outbound

This paper cites why should i trust you?.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution why should i trust you?

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.704745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:c248a516021d87ebf0f11d0b3a6a43b5f4132c8c9c57715fc97918d19a485b12

Observation c866f80f-1e16-44e7-8bb7-7309e9d45468 · outbound

This paper cites Ai overviews: How are publishers adapting to the rise of clickless search?.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Ai overviews: How are publishers adapting to the rise of clickless search?

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.693868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:dfca553ef77778893fe0162c27e9f023bc4cb4c537daf9b3be007c99ec093c2c

Observation 70b8dc04-be19-4315-a168-5032f51251e8 · outbound

This paper cites The butterfly effect of altering prompts: How small changes and jailbreaks affect large language model performance.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution The butterfly effect of altering prompts: How small changes and jailbreaks affect large language model performance

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.638040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:a839b564d0bf15438449a724b67552bc81ed9bef7741d58f2653115f6a41956e

Observation 5d3cea7a-d91d-4ed6-bcca-ac985142ac96 · outbound

This paper cites ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.254151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:d411345bd8195826f7e122e8410a19c10dc2914e2830410f6a44dab88d944c68

Observation f657d05d-c1fc-49fb-9f24-e8c89aa45bb2 · outbound

This paper cites A value for n-person games.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution A value for n-person games

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.653195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:08ae61d5f93b6fadec1d5c52052b6941d846b89a8b9510acfb52f2d2bd430fd4

Observation f769ebae-e4da-4c84-b678-f181923fdcc2 · outbound

This paper cites Princeton University Press Princeton.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Princeton University Press Princeton

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.696083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:272388ca895557bc94b01f867e6cb03c6d0e7f87abf9e56546fa7d2765788480

Observation ee40c656-0025-476d-a07e-06ef37c314e9 · outbound

This paper cites A shapley-value mechanism for band- width on demand between datacenters.IEEE Transactions on Cloud Computing, 6(1):19–32.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution A shapley-value mechanism for band- width on demand between datacenters.IEEE Transactions on Cloud Computing, 6(1):19–32

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.799536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:79a75de09f8950d214a159554e8d6022d9418dcecfe381df52d87efebac03d64

Observation 0f877532-05b6-4d2e-a50a-f2dd523971f0 · outbound

This paper cites https://www.similarweb.com/.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution https://www.similarweb.com/

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.726773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:98742430535213ed564626945abd4dfd8052678f82b1186099c88414311956aa

Observation aa94b14b-7c10-45b6-849e-7b2ff32605e3 · outbound

This paper cites Consumer reliance on ai search results signals new era of marketing.Bain & Company.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Consumer reliance on ai search results signals new era of marketing.Bain & Company

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.783548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:65546b4343222ff0e8996703aeebffcb2115be6c2c13a139c35e53413fd11b3a

Observation 8f5047c1-5fc1-4709-9f1b-1f93e9113913 · outbound

This paper cites Goodbye Clicks, Hello AI: Zero-Click Search Redefines Marketing.Bain & Company, 2 2025.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Goodbye Clicks, Hello AI: Zero-Click Search Redefines Marketing.Bain & Company, 2 2025

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.791914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:fe7640718c745579293df6bad48146a37c9cb08cebcae4bbc2f90a9aa6b87d23

Observation d418e56f-2ee3-4b21-8e39-509a3881e21d · outbound

This paper cites On economic heavy hitters: Shapley value analysis of 95th-percentile pricing.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution On economic heavy hitters: Shapley value analysis of 95th-percentile pricing

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.809092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:0578be2cca65ab9d91e7cd9c319bc4da650913f7b788b7f6b0426649df6a0f2e

Observation 7b6c4f21-2928-428c-9a01-5ec1446bbed3 · outbound

This paper cites Microsoft Corporation et al.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Microsoft Corporation et al

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.825591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:9ad93948b2caba86e1f5bd2a154d598fc18887749f206bc27dcbd42d1590ec41

Observation 19cec4dc-8dc1-4269-aa1e-f5ab3ad479ca · outbound

This paper cites Musique: Multihop questions via single-hop question composition.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Musique: Multihop questions via single-hop question composition

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.829095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:82908fabcefa5226901a29e2184dd6f49552fc051fb268d112b11a3be1b5ff09

Observation 8f729105-4e8b-42e9-867c-ef7471d51895 · outbound

This paper cites An Economic Solution to Copyright Challenges of Generative AI.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution An Economic Solution to Copyright Challenges of Generative AI

Reference 88

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:40:26.234171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:8d5efaa32aef6467dc026e218baae54ff2a97fc71398334c0c30d9b2abcf3693

Observation cea2bb4d-008e-4076-8fe9-15928cf90c73 · outbound

This paper cites Wang, Prateek Mittal, Dawn Song, and Ruoxi Jia.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Wang, Prateek Mittal, Dawn Song, and Ruoxi Jia

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.724775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:32c44588f60f7ad538b06cf68e64ba3c2163704bef3925ae6e4d5f68cb417994

Observation 6f8e9ada-6bd8-4c8c-b94d-0d97dd165bde · outbound

This paper cites Tracllm: A generic framework for attributing long context llms.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Tracllm: A generic framework for attributing long context llms

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.698073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:969cb9ea1f4ba664458409fac3ba464d2c6830c1b645e0ae45baabd189ecdf94

Observation 3f3b6685-8364-4bbc-8d91-facf2c007c7e · outbound

This paper cites How to interpret spearman and Kendall correlation coefficients.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution How to interpret spearman and Kendall correlation coefficients

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.657278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:40ac0e16c11024a742bcd66aecb9f59b3a3271f384f969aaa4de9a2cb6f9b64d

Observation 18944334-e4a0-47ad-b70f-156693103a2c · outbound

This paper cites Wang, and Jian Du.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Wang, and Jian Du

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.655262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:108f41c765cae34f2908e7c710650db330bfcddea241b873353a31345fb5fb46

Observation 1d21897f-05e3-41e0-a7c6-dd62f6562569 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 93

Resolution
metadata mismatch
local_arxiv, observed 2026-05-21T17:40:26.223574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:d5d72b58e3b4a17393d8bd257431d6d97c3573be7de9b9d2149330a4fe14fcfb

Observation 1ec6a292-36ae-403b-8c2a-1f77e2203d94 · outbound

This paper cites John Wiley & Sons, Inc.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution John Wiley & Sons, Inc

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.681351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:fd1b04d23c2155965ac236f552fcba737704c80559c17aa5c420a1830eebc1a6

Observation 3c0a72b7-7838-4a86-a118-12be51358e8b · outbound

This paper cites If we assume X, then Y.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution If we assume X, then Y

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.789005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:afda51c0c6d838da1956d604c5b0fd4bcbbf5a6513820eec70712f372112079e

Observation 09faf3df-023b-4240-a2a6-2c40d98fde2c · outbound

This paper cites Do NOT remove anything that is required for correctness.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Do NOT remove anything that is required for correctness

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.683840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:3aa6988591535631fdf1456db5efc87cac50007c8d910a39cd5bc95c8c1d4058

Observation a1eb9d6c-f521-4cf0-acd7-d449096fc5a0 · outbound

This paper cites an unresolved cited work.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Unresolved cited work

Reference 97

Resolution
unresolved
raw_fallback, observed 2026-05-21T17:40:26.804827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:bf9c9e50d46aaf0c30cdff4ca4d957f6ef93eb569b66ada0d1995bafed7a7403

Observation 2a0a6b16-6713-4c37-ae04-ffe32584bd6c · outbound

This paper cites an unresolved cited work.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Unresolved cited work

Reference 98

Resolution
unresolved
raw_fallback, observed 2026-05-21T17:40:26.716088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:a3785e5203a246609e747944ae2d0bdd6d5b10d3025106195882607b8993d4cc

Observation 3ec45253-1802-4f4d-91ba-dad1b4181c1e · outbound

This paper cites Keep each reasoning step separate.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution Keep each reasoning step separate

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.707022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:f727949bf8331b9bff2cb1f4fb4bb1aee410c63b38fbc1e28e0d5773a58daae9

Observation 2d856c50-0ba8-44c3-9666-b6413adbf7f7 · outbound

This paper cites keypoint dis- tillation.

MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution keypoint dis- tillation

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T17:40:26.780933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-21T17:40:10.135567Z digest=sha256:d126203d4243d1f993991bbffbd013368bd3c7fa6f13f20fa8bc246bbe0f5c61

Pith citing papers

Observation 719ad747-8669-4c1f-b302-b89cfef2fc96 · inbound

TRUE: A Trustworthy Unified Explanation Framework for Large Language Model Reasoning cites this paper.

TRUE: A Trustworthy Unified Explanation Framework for Large Language Model Reasoning MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-02T21:53:25.695298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T21:53:25.695298Z digest=sha256:bd376b5c1d55db58d82153e11543b907bec506c97377aaf893647ee2ef60f920

Observation 24b62eae-1970-4698-b87e-0d18c7e52fe0 · inbound

In-Context Credit Assignment via the Core cites this paper.

In-Context Credit Assignment via the Core MaxShapley: Towards Incentive-compatible Generative Search with Fair Context Attribution

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-20T02:04:48.497166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-11T00:52:53.665983Z digest=sha256:94b65451a5fecc2280b9e86b68f904e450f4e660eaf15af073ce179437552299