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Paper Citation Record · LEDGER

Shifting from Ranking to Set Selection for Retrieval Augmented Generation

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.06838.

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

pith.paper-citation-record.v1
2507.06838 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:57:33.669725Z

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

  • verified exact0
  • verified fuzzy3
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17bca2c9-7134-4c0f-a7fd-39e5efea9a6b · outbound

This paper cites Provence: efficient and robust context pruning for retrieval-augmented generation.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Provence: efficient and robust context pruning for retrieval-augmented generation

Reference 4

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source=pdf_text observed=2026-08-06T18:57:33.567975Z digest=sha256:1323b872faca381a3d72b4d8dae04dc56310f9be10038b8edd9ef8823ce95ccd

Observation fd0292a0-fb8f-401a-a60b-1bea886ec8c1 · outbound

This paper cites Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

Reference 7

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source=pdf_text observed=2026-08-06T18:57:33.578068Z digest=sha256:d1f8db0d07379a63be5998f19265df8e3cb85b948fa4c683ba6d624d97c68644

Observation d3e4d5d1-b728-48da-b9e1-7fa30e70fc2d · outbound

This paper cites Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise

Reference 8

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source=pdf_text observed=2026-08-06T18:57:33.581971Z digest=sha256:df97799d7ff8147e9bd52566fa2e824da8224ce4ae4ff6a006a2d2a53f8eae35

Observation 2680f6e5-00aa-4cf8-a986-1a31615f08ca · outbound

This paper cites Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

Reference 9

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source=pdf_text observed=2026-08-06T18:57:33.585373Z digest=sha256:15ba4bb3e937acbcf7798111be6b49a5cf7d0230dae2900e8b5ca5b34115deeb

Observation 42691430-e65d-4a17-9b06-f330064e15b2 · outbound

This paper cites Mistral 7B.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Mistral 7B

Reference 10

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source=pdf_text observed=2026-08-06T18:57:33.588478Z digest=sha256:ed4d7cb9c39d8542d5fe6c95f30cd17622ba20cc0d776520d6b1358761f981b0

Observation 90bde8a5-c1d4-46f2-833c-aa61e25f0a2d · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Dense Passage Retrieval for Open-Domain Question Answering

Reference 11

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source=pdf_text observed=2026-08-06T18:57:33.591685Z digest=sha256:71ac3608e56df53f6c49004caf9f1b439e009189ea662e22ed64533fa69761a8

Observation c757cc2f-4550-4393-a0c3-c0cb28e3d359 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 12

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source=pdf_text observed=2026-08-06T18:57:33.595140Z digest=sha256:14a69ce9d0ef687c21642b145e4d2ad1460e58056033dc7fe11660c505a52dfb

Observation 3f13f3b9-f177-4405-b6a1-24c6e3956c63 · outbound

This paper cites JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking

Reference 14

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source=pdf_text observed=2026-08-06T18:57:33.601899Z digest=sha256:2dbd3c9150c3c6a9b5827970323bd60f2fe046e1334835d297a29cc035f1c237

Observation 93f43c6f-1864-435e-8464-8ec7f7cf4f42 · outbound

This paper cites Passage Re-ranking with BERT.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Passage Re-ranking with BERT

Reference 15

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source=pdf_text observed=2026-08-06T18:57:33.605010Z digest=sha256:9f417feee8d3df354b00b769567781767983ca772fe9d56b6700ef7d803a870f

Observation 46f1c541-15aa-454a-9b66-0864fb417106 · outbound

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

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting

Reference 16

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source=pdf_text observed=2026-08-06T18:57:33.609045Z digest=sha256:efbc593dad85c4f3a0926d9bd469eb6238328aea65bab6749d67ebc10372f7ca

Observation e6572969-1979-4a75-91a6-b76a38ae753c · outbound

This paper cites In-Context Retrieval-Augmented Language Models.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation In-Context Retrieval-Augmented Language Models

Reference 17

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source=pdf_text observed=2026-08-06T18:57:33.612325Z digest=sha256:cb7d9af87284465fc95711b6ae3d358c34794495169ff9f0cc7dbf3c3953c976

Observation ad2e8268-a2f0-4682-8046-c5ef44459795 · outbound

This paper cites RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Reference 18

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source=pdf_text observed=2026-08-06T18:57:33.615383Z digest=sha256:7cfe0c6f437539aaddfa1d38de17d286803d141a5c9f52a90f38de8bd32c1625

Observation 44b8da4f-440f-4de6-a303-364d8ba23a69 · outbound

This paper cites Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy

Reference 19

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source=pdf_text observed=2026-08-06T18:57:33.618591Z digest=sha256:6084898134f23ebc34e3e50187b37974e5e1f6b1fe1fcf760bf5eccd83d81a52

Observation 68292781-2f4f-4b83-a404-38d0e03bfaf3 · outbound

This paper cites Large Language Models Can Be Easily Distracted by Irrelevant Context.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Large Language Models Can Be Easily Distracted by Irrelevant Context

Reference 20

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source=pdf_text observed=2026-08-06T18:57:33.622224Z digest=sha256:460baeefea47934c57a342c71dea8b56084da391aa75812481063621d5f24d5d

Observation 56b81bb3-1916-4bf7-8c32-d01441492d52 · outbound

This paper cites Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents

Reference 21

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source=pdf_text observed=2026-08-06T18:57:33.626119Z digest=sha256:11c9d46cb3cac5ba117b17467c9eef75fc769fcd7160d8d81a4a38fe520693e3

Observation 002ae8b1-5ac5-40e9-b7b1-2e67d9fc5c0a · outbound

This paper cites MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries

Reference 22

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source=pdf_text observed=2026-08-06T18:57:33.630029Z digest=sha256:2afabde4e371446def3b0006fc56a8ab516c355c84c1e625991f7e3a339039a2

Observation 6c16b8ff-11bf-4665-b428-2764aa5243ec · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 23

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source=pdf_text observed=2026-08-06T18:57:33.633044Z digest=sha256:b4bf883807e150657b1311988b35f8b7d082d733954078adb2e641c31473d9a0

Observation c8b0663b-b9d1-4789-a4d3-efcf0b4316ad · outbound

This paper cites Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 24

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source=pdf_text observed=2026-08-06T18:57:33.636094Z digest=sha256:6a0613030c96420b8430ceaef5d926795ddbb57d69895ea52394e95bf6708953

Observation 46e7c7f6-098b-4bcd-b2f7-278860f6a0e9 · outbound

This paper cites Zephyr: Direct Distillation of LM Alignment.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Zephyr: Direct Distillation of LM Alignment

Reference 25

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source=pdf_text observed=2026-08-06T18:57:33.639110Z digest=sha256:fc2ad8ce9a47f4af1e3a9b38cc1c6123c4d6f849b53bb81c14c11128f3edcb1f

Observation 576ee7e7-5cfd-4f60-a3c5-1dbef723472b · outbound

This paper cites From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries

Reference 26

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source=pdf_text observed=2026-08-06T18:57:33.641998Z digest=sha256:a716b1e27cce33e9d2853229710a3d7072254ffeddcccdf4b103035789972ecc

Observation ce5ca023-3694-46ac-996c-6e4c05308835 · outbound

This paper cites Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Reference 27

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source=pdf_text observed=2026-08-06T18:57:33.645211Z digest=sha256:7a0b32afbd63cd1c66fe1021222dafed9113514fe7df71237406e86700151616

Observation c392362e-0628-472f-849e-62011e8be68b · outbound

This paper cites Preprint, arXiv:2411.00744.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Preprint, arXiv:2411.00744

Reference 28

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source=pdf_text observed=2026-08-06T18:57:33.648791Z digest=sha256:b6e59355a92b0c0c7728aedaba2845e07a1338b5a2b3d87851eedca71c9b99dc

Observation af4d9c32-ca74-4631-8175-a51ae23cd208 · outbound

This paper cites C-Pack: Packed Resources For General Chinese Embeddings.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation C-Pack: Packed Resources For General Chinese Embeddings

Reference 29

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source=pdf_text observed=2026-08-06T18:57:33.651568Z digest=sha256:92783058b0822ea87e0accb572fbe7c40db66bd81e104f7e8367d05c14a8f8d4

Observation 1f02081f-0221-489b-b7e2-b57ce046af80 · outbound

This paper cites ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval

Reference 31

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source=pdf_text observed=2026-08-06T18:57:33.657516Z digest=sha256:5556fc27603aa31c48f18eea53aab8da95993b8068446f9c1bcbd1df59f4aa18

Observation 12ed2db3-f5b7-4121-a353-a1d994ff2c2e · outbound

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

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 32

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source=pdf_text observed=2026-08-06T18:57:33.660576Z digest=sha256:389d5a8d79368390261710f9c0f2a94ea647e73a59c74a9d280c9e77c24bd6fb

Observation 4c69d6a8-a089-401e-b56f-1363cd139eff · outbound

This paper cites Given a query and a can- didate passage, it outputs a score to reorder retrieved documents by their relevance.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Given a query and a can- didate passage, it outputs a score to reorder retrieved documents by their relevance

Reference 33

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:57:33.663560Z digest=sha256:6c4f8517390de030fae2233e8b2aa2384e8f7ce8230586818d66456ddd2142ad

Observation 9793ff2e-67d1-4d17-9303-6e15dee991ad · outbound

This paper cites It reframes reranking as a single- token decoding task, enabling fast and ef- ficient passage selection.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation It reframes reranking as a single- token decoding task, enabling fast and ef- ficient passage selection

Reference 34

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source=pdf_text observed=2026-08-06T18:57:33.666941Z digest=sha256:82cb245f20f344b47f27911718bc691985ec38e320fda60185c776b1502a156f

Observation 4978cecb-fd90-4476-b3e4-7f319a9e2354 · outbound

This paper cites Let’s think step by step.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Let’s think step by step

Reference 35

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T18:57:33.669725Z digest=sha256:29860075bf7283ff3bf61a4e43ca1f626f9a999553362bacaf88adb2d8469c79

Observation 391a39fc-eb21-4642-8a89-2c543d7284d6 · outbound

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

Shifting from Ranking to Set Selection for Retrieval Augmented Generation HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 2018

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source=pdf_text observed=2026-08-06T18:57:33.654515Z digest=sha256:fff3bfcb9479882d40642571e0f70fe94422f87c03014e298106d7cf27e9df9e

Observation 985e42f8-4358-4fbf-a5c8-b4946596fd49 · outbound

This paper cites Decoupled Weight Decay Regularization.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Decoupled Weight Decay Regularization

Reference 2019

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source=pdf_text observed=2026-08-06T18:57:33.598933Z digest=sha256:7c0f2a561f5d45f735a3e9d6d0a9e296d2c139d140dbad211fc60f7b8343d751

Observation 953be654-07ec-47c2-b19d-22849632f697 · outbound

This paper cites Overview of the TREC 2019 deep learning track.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Overview of the TREC 2019 deep learning track

Reference 2020

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source=pdf_text observed=2026-08-06T18:57:33.574728Z digest=sha256:1227f9bb842688e2bf858d2a3ea62f4f7ff33db36f9a1a8bc8b28b71046bfca3

Observation 142fec3f-3728-4f17-8cac-4b01366916a6 · outbound

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

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Overview of the TREC 2020 deep learning track

Reference 2021

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source=pdf_text observed=2026-08-06T18:57:33.571316Z digest=sha256:2d02e5da0823270e4fcbb2aa8231f3b3c70ebbbfe75f018b9bac0e6e2636df96

Observation 27127e96-871e-462d-94db-4364bd9b75f8 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 2023

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source=pdf_text observed=2026-08-06T18:57:33.560989Z digest=sha256:b63e039f371debeb3e69ced6770c786b24e8befb8153df226afbebccc89d96e4

Observation b2acb541-8b4b-46f9-882f-0fd807bd20c5 · outbound

This paper cites An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking

Reference 2024

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source=pdf_text observed=2026-08-06T18:57:33.564436Z digest=sha256:1db71ed18a5ebe2a81e2eb609040a4646872270c2565d793b8b59790c837c5f9

Observation 7ae4d162-8231-4a87-a6e6-8ee712c9b55c · outbound

This paper cites Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation.

Shifting from Ranking to Set Selection for Retrieval Augmented Generation Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation

Reference 2025

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source=pdf_text observed=2026-08-06T18:57:33.556980Z digest=sha256:e2b49f021c61a37a485bd3d7118752815b022c14142ed3d66c59e2315e5e32a3

Pith citing papers

No inbound Pith citation observations are available.