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Paper Citation Record · LEDGER

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2602.23234.

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

pith.paper-citation-record.v1
2602.23234 v5

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:27:49.815237Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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  • verified fuzzy0
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa2ebf22-f909-4802-990e-d4fe29397e0d · outbound

This paper cites How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models

Reference 1

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source=pdf_text observed=2026-08-02T20:27:45.962398Z digest=sha256:8ecfbd56fd28f038b6ade01b8cef2b09e7cc3fc4b6ec668b283999c4455e251d

Observation fb04062e-0211-4753-83f7-466c3f08c3de · outbound

This paper cites Benchmarking LLM-based Relevance Judgment Methods.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Benchmarking LLM-based Relevance Judgment Methods

Reference 2

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source=pdf_text observed=2026-08-02T20:27:46.060007Z digest=sha256:7509abc7c83a6ff6da2831fbd7a844fa0972e916dc57d5d8f44d8ec2dd622eec

Observation 45cf7666-8990-4a68-9834-1a98fc368b69 · outbound

This paper cites InPars: Data Augmentation for Information Retrieval using Large Language Models.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments InPars: Data Augmentation for Information Retrieval using Large Language Models

Reference 3

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source=pdf_text observed=2026-08-02T20:27:46.184823Z digest=sha256:f7442b1379e2ce6b795780da3185994ab56bc8570efe6027e6e8322799bc9e1f

Observation 1bb66035-7e1c-4d26-be08-e8dfb28ad987 · outbound

This paper cites TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy

Reference 4

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source=pdf_text observed=2026-08-02T20:27:46.259208Z digest=sha256:0ded60f386e7d0af2159a1f82ce2bb9d658eceb33950313b3360a21248b7923f

Observation ff521416-5b21-4874-8821-7ab482878a24 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-02T20:27:46.410809Z digest=sha256:f2ddd18ffffcb90e0ccc096886af3307e1e1cb2bd1b33e4d192aa7e90b44f43f

Observation 84d19515-12fd-4945-affc-4132ad3ed4ce · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-02T20:27:46.474374Z digest=sha256:369af4239e0d964b4b36ab0b0118214868fdf9924e8f0b8a8bf0077ec230e970

Observation 307a16bf-fa15-4034-80d1-267ba353c181 · outbound

This paper cites Perspectives on Large Language Models for Relevance Judgment.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Perspectives on Large Language Models for Relevance Judgment

Reference 7

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source=pdf_text observed=2026-08-02T20:27:46.724633Z digest=sha256:dd8abd81992a155a06f4dd37a6968d77d7fd4cbca027b4a219775792085fc7fb

Observation 4070ce1d-cf7a-40ef-9e84-14636b6817d8 · outbound

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

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments A Survey on LLM-as-a-Judge

Reference 8

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source=pdf_text observed=2026-08-02T20:27:46.847382Z digest=sha256:0fad3a18f914ffee92a84a157aee02fef480bab5db48a7c2bf2b39564fead82f

Observation a923710a-312b-4386-b78b-ee3903c956a8 · outbound

This paper cites Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects

Reference 9

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source=pdf_text observed=2026-08-02T20:27:46.940192Z digest=sha256:c002d502296b70d1aa129ed318b7e329a490a1084e241d21bd9552fc36c050f2

Observation 6c020d2b-a160-4190-8e04-baefe8a756b2 · outbound

This paper cites Retrieve, Annotate, Evaluate, Repeat: Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Retrieve, Annotate, Evaluate, Repeat: Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation

Reference 10

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source=pdf_text observed=2026-08-02T20:27:47.012611Z digest=sha256:3180c2eb3cb39bfc60fe802ea060997348f41beb1654efce5664f8f733fb4f17

Observation 434265ed-e164-4fda-aebc-fc42f8dbd452 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-02T20:27:47.140144Z digest=sha256:e76b54adbf53fee133a8b78c393c04f4315cf80af8655572302f7b4a323047cb

Observation 1f129675-e97f-45ed-8061-10c5c1d5f004 · outbound

This paper cites Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study

Reference 12

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source=pdf_text observed=2026-08-02T20:27:47.271848Z digest=sha256:c5a2ca8d17cb11f12e37312a7f00e47ad4f370a2a190889de5aec1096bba1fd8

Observation 5625f120-1e9d-495e-a306-1b3fce41bc74 · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 13

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source=pdf_text observed=2026-08-02T20:27:47.368816Z digest=sha256:177c34e06f35a8643a2128bee1b7fe4ae814b842315725c18fa677c9a04c8893

Observation bc72ba4f-2515-4b70-821d-7bb1bfa92f0a · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-02T20:27:47.528795Z digest=sha256:68c1cc1dda8c10f33fd3f317567a7e24a07d5b952142d2fb9a9c0d6496cd8624

Observation dff00cbc-e092-4470-8979-193fbf48f884 · outbound

This paper cites One-Shot Labeling for Automatic Relevance Estimation.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments One-Shot Labeling for Automatic Relevance Estimation

Reference 15

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source=pdf_text observed=2026-08-02T20:27:47.788685Z digest=sha256:25a4c655f8adacaa7113e893272ac48b8de7adb9e732b69729a373d4f1060db8

Observation d0631ca9-ec13-4135-a8f5-eaf2408f7df9 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-02T20:27:47.913142Z digest=sha256:5fe2fafa35afdefe6950b41b46e4888b1148348c8663e528348e58a588e4cbab

Observation 0909c3cb-64bd-4ea1-a49a-70ad8020d76e · outbound

This paper cites A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao

Reference 17

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source=pdf_text observed=2026-08-02T20:27:48.078811Z digest=sha256:b953c9869fc85a74e45a44f09472d10fb4081e30cbd8283d3b1a7aef7fd725b0

Observation 53113523-a618-4796-b1d0-726602cd2131 · outbound

This paper cites RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models

Reference 18

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source=pdf_text observed=2026-08-02T20:27:48.209813Z digest=sha256:c1300d50e6ed45e2ffbe0b7a008f97f94cc9429156cd9d9bbe4927a690d8747a

Observation 9ed8e904-6fcb-4f3c-b981-4c73806d2d04 · outbound

This paper cites RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!

Reference 19

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source=pdf_text observed=2026-08-02T20:27:48.327592Z digest=sha256:9c38cbfe54b38fcab93ef15c209eeb7d9fbc8406ff3c89c8644e6a867341bc35

Observation b23e1d06-2a9f-4d50-8854-961e83fb4e53 · outbound

This paper cites Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

Reference 20

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source=pdf_text observed=2026-08-02T20:27:48.439982Z digest=sha256:74a7cb89bf43430ac95b4ca2fc3971463157f19f87598a7c93ff49449a8f39ee

Observation 27ae38dd-6e77-4022-a379-c10ac546c742 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-02T20:27:48.599894Z digest=sha256:b3804f782d6aaeb0c9c4bb11432a680bcd2b75191abc7d43e2127afd2357b6fc

Observation 31519776-21d8-489a-8f07-87807e1285eb · outbound

This paper cites ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems

Reference 22

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Observation 6f065a88-1908-43d9-befa-e3b905c58b2d · outbound

This paper cites Modeling Ranking Properties with In-Context Learning.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Modeling Ranking Properties with In-Context Learning

Reference 23

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Observation 9ad12972-244b-4440-a8a9-c6816881974f · outbound

This paper cites Multi-objective Learning to Rank by Model Distillation.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Multi-objective Learning to Rank by Model Distillation

Reference 24

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source=pdf_text observed=2026-08-02T20:27:48.971057Z digest=sha256:3e7784ee5f37a181b3213e61cada564d708632973d494d856b361aee0b357ba3

Observation d82b6edc-b844-412c-a286-70e6259fb5cc · outbound

This paper cites Large language models can accurately predict searcher preferences.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Large language models can accurately predict searcher preferences

Reference 25

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source=pdf_text observed=2026-08-02T20:27:49.069443Z digest=sha256:66bf7b2c79f71526fcfdcb980f96182adc91fabc5ba35004c9ec9c7d24cbc201

Observation bf82cf5e-2dbb-4797-b3b7-530450052130 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-02T20:27:49.184196Z digest=sha256:406c319158b5357dcacbf710878ed351defe73440ddb9c3081fcf678012f1bf3

Observation 8bcea17a-57a3-4149-b90f-6ac1f93b02f7 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-02T20:27:49.295531Z digest=sha256:5982e684704d85ca547a425e4d9c93d47cde59a4f42097c425e17d50b9822f8b

Observation dd46dd75-3db2-4f73-a8f9-5529f70aca51 · outbound

This paper cites Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment

Reference 28

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source=pdf_text observed=2026-08-02T20:27:49.367798Z digest=sha256:d0237c17b0f5f85bf48d4fb32be76d36a16542ab4b0f7849984032c6efd9fabf

Observation 363754c1-2341-4aa8-b636-4a1958a4a038 · outbound

This paper cites an unresolved cited work.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-02T20:27:49.436726Z digest=sha256:fdf9186502f5ea4bdcbf396d78764bc4fee280f9219c8fee3bf3757ab828f88b

Observation bcb85c11-c90b-4a0b-8317-44df341f11bd · outbound

This paper cites Semantic Search Evaluation.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Semantic Search Evaluation

Reference 30

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source=pdf_text observed=2026-08-02T20:27:49.529106Z digest=sha256:210281dd0ec1ad5427a1f5382eb08b410a7eeeb49345dcc184c3815e83e9b511

Observation 367dc6c0-0192-4d2e-983b-0a93212f9910 · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 31

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source=pdf_text observed=2026-08-02T20:27:49.650865Z digest=sha256:7ca18d6aa4208fa71f7abba17f50a33c8f04f5f217587908978122c5ba6d790e

Observation fd30f938-bcf4-4fc8-999c-59e096805986 · outbound

This paper cites Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labels

Reference 32

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source=pdf_text observed=2026-08-02T20:27:49.725460Z digest=sha256:70ac39933df10d39e0641574f7b31258ab295c3050afc6a392dd67343da7c87b

Observation 70c7dd8e-93cd-42ee-a5ee-a4c39766f458 · outbound

This paper cites A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models

Reference 33

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source=pdf_text observed=2026-08-02T20:27:49.815237Z digest=sha256:7a9f737de4e2f22aa44ee312e74f446893c93962794ecde6b7a4f5e579fa43c0

Observation f9311268-6352-4972-8e94-a0951ddb56cf · outbound

This paper cites InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track

Reference 2024

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Observation 47ada31f-dc65-4f3e-a343-27f531b9e7e5 · outbound

This paper cites LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking.

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking

Reference 2025

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source=pdf_text observed=2026-08-02T20:27:47.663579Z digest=sha256:716a68f2452267684ea3ddee9cc7825b2b14c18a6fb7cf98016e7632e5b94b90

Pith citing papers

No inbound Pith citation observations are available.