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Paper Citation Record · LEDGER

Shifting from Ranking to Set Selection for Retrieval Augmented Generation

As of 7 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 2026-08-07T06:34:17.273281+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=sha256:5e855d16b33adbbdb90dd08995b4b46f21030982e7d79b3c2a5bb5597259b5f3

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=sha256:4c5eb3106f09fd9f4f6ac15fc3c8db7902ed6179df81c51ee89c561bc95f647d

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=sha256:6ebfd9e41207df671966f044dca7a10fb9c027a7991ff6314393c1a736369da9

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:fc8cd57496173caa30a053e2d2306493fa63ef8c74c6ed37204c13d1d8c26104

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=sha256:8ce84fc8384e02b5dc1fb4bb84c2514bf4000411e476f8ca98f657a766bf94a9

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=sha256:65828331c5506ada7428374f280f36df36a1f77d0729231d0e89191383728fad

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=sha256:5346ac951b28c0868ca6378f5010273500dcf6a0ca158d06c48d5b758fdb3a4b

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:bff20e61baad4adb2cb336695fbc92c6c42be871cf798bf55a7f04a78be8d065

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=sha256:19efa0515f9eaf83452b790df23e11ae66a91a59e6d76e1c8d410b961f9cdf7d

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=sha256:08984b3e4b1dedce6b98605b9631e1cbb32dc59c694cf8861a785761dcecc66b

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=sha256:5f5589f29e1c1bb70eca868f1d67a7ed1f369eee35ed084463d4fbb79c9108dc

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=sha256:4234f3f4bfc8024a21b2d0295f2ed52356faee7d762374999566698b5fceab6c

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=sha256:154fef2690283982d7d6181fff6eac339d256b6664a1f57b5c8ee029b666acc8

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=sha256:269f46f345ac71a06c65b7dcfc6927ddd46fb82bdecfe84555097157f89d8fbd

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=sha256:25084e52ee96ef0121d486ec55d2a22bfe5e189b82abcc7b49032b5b5026dafe

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=sha256:416863f513537351e19fe333b979f616463f47b9b0a63ff803bcbddd23e2880a

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=sha256:2cb30ce4ba8cf1a0deb502e2ea4b60f417fb880a45d0c0474b66311a259fb404

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=sha256:7dbd5b74ddd2cc4d8aa5ccb9c86c424487dd20863fc68bd47aa03371db3491e8

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=sha256:5a5cf124073f733c4b3fb5e22cb3ad654a14541c14dccdaa430a7b0c007747bb

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=sha256:7a658e737776157edf3971653a4fc62703968ef50de1f909cca0dfba0076a7d0

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:ef9e898cb9f4c9bfda2fda7b72b8f4a55db07fad221140cb443a788acdab72d9

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=sha256:2c441439ef8c648f6988f946fea666274738bf92522ca51a3e422855793424bf

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=sha256:276a230366c9b6b83fea076ea44afa4a983ff09a863b4525253b3221f623c06c

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=sha256:9e88098c951d59a8239a5dd30067f9ac561977dee59fe5f9e5bc71b794165afd

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=sha256:16b9db6ce98261c21318f96e8c38af6ee6284519cd4bb0566ac6ebe3a3a1bb77

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:57:33.663560Z digest=sha256:7ec48782e730de2f9c34aa8ae95cd8b160d788ace473253999f97c924baab9e6

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:d959bea0876dbace2c9540a2fbbaf28803e1112e57cf81cdeb6d91c7223e3c00

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:57:33.669725Z digest=sha256:e120d1edff33dbaace3a8c8275cc7fc8f2cce411b84c3c1bd731aa8319cba7c1

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=sha256:542d6463a0f084b5b4b46bda54aa4b6e77e4e9b3d31856eed991e0dd4968d16d

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=sha256:3cd92012b7a45c2e6b4bdf47a621edb5a34d126551f67ce33f62aa943536eff6

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:abef12adbba5a9df1e2344f3a0061c5c677180f810727fb548677a1f82a7f3e5

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:c8507588f1f65845ad255bb8aeac004f5ad9c460b7e5438725aaffc6a89ac424

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:b4a63730ea7312ef732a95f38b6cbd0239f79baf707bebae8cedc9d69f99f49f

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=sha256:9c403c85e8af066a3beae6b0360a7e5b1987574879e4da338e355993a5e7ce7a

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:ecb8c8d0e00481c54c413e25cc0d7e6963d9b144ac2927df5a1895277650052c

Pith citing papers

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