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Paper Citation Record · LEDGER

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis

As of 19 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2505.18710.

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

pith.paper-citation-record.v1
2505.18710 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:30:56.248436Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:30:50.951365Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T00:30:51.481502Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa7a07b4-38b9-4a05-9f0e-7ba932e97873 · outbound

This paper cites online" 'onlinestring :=.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis online" 'onlinestring :=

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:30:52.656266Z digest=sha256:9b5eedb41de0b207e2b63b5ea82219504261f4ebb9709a6f722cce958ed639aa

Observation 827cc3ec-7073-4edf-848c-916604b681f8 · outbound

This paper cites write newline.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis write newline

Reference 2

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source=arxiv_source observed=2026-08-07T14:30:52.744638Z digest=sha256:b1ebaa273453855863ae5ddf1faee4907f9d892ff41fa026558ab2f7e84d1f17

Observation 87afa78c-e2da-4f02-8807-e3cc02f209ef · outbound

This paper cites GPT-4 Technical Report.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis GPT-4 Technical Report

Reference 3

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source=arxiv_source observed=2026-08-07T14:30:52.824716Z digest=sha256:b163f44a4c8d26a79410b8b1a3b0d29f9d8eec0f9e5cb23a9569aa0dd14e6678

Observation 601b515b-1d0b-4725-ad7a-786a0b5938af · outbound

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

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 4

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source=arxiv_source observed=2026-08-07T14:30:52.906135Z digest=sha256:dad3483dd38d3669caa7ac4ecbba00395a1c5490ef6ed95638704791e221d926

Observation 88cf084c-d5db-4e09-8c81-fdfbb46acf90 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-07T14:30:52.991748Z digest=sha256:d344a59e263cd0ea3b244573f353e24a599501430a61ec97a3a330fb3a91aeaa

Observation c2e10192-1437-4c0c-bc53-7e0f3a3f83d8 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-08-07T14:30:53.066347Z digest=sha256:72e99a049b49e9e10c98a1225464d2381cc75a397fb57848ce447e9d8c6f77d4

Observation ad9cd22b-fbbe-4ebb-9c00-2c8b7f58c2fc · outbound

This paper cites Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation

Reference 7

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source=arxiv_source observed=2026-08-07T14:30:53.191782Z digest=sha256:ec82ca52468e900a2bad812c90988618af9ea30f0401f04fd84e1d1a4ec9e133

Observation f49228e1-a807-43db-9428-ac8d35c7c656 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-07T14:30:53.268669Z digest=sha256:403ff8462ff8f38b4d5c89203c72f642bb7c9c3552591915fb4d219504ba1c83

Observation 18ce72a4-bd86-4e42-8d56-c5081cd4c15c · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 9

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source=arxiv_source observed=2026-08-07T14:30:53.364959Z digest=sha256:7582f32aba2133aef77d7846dd67edc8012169c0e08aef1616ae3875aca1e32a

Observation 76782d3d-30a3-4beb-8af6-18bac0c54bf3 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-07T14:30:53.493153Z digest=sha256:909f2fee6906b56574519ad55c6660ed349bf9d49ffea6a395a0b2e9c6965d97

Observation a6f8e021-66f6-4ce1-ad52-d60167946a12 · outbound

This paper cites Rethinking with Retrieval: Faithful Large Language Model Inference.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Rethinking with Retrieval: Faithful Large Language Model Inference

Reference 11

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source=arxiv_source observed=2026-08-07T14:30:53.569078Z digest=sha256:cbffd145680621c5bfb21b300d44be3170493439565fb815eb3825fda0d66338

Observation 8fd37d16-3671-4b6d-ac6b-9315399543ae · outbound

This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 13

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source=arxiv_source observed=2026-08-07T14:30:53.778386Z digest=sha256:a8dc1e4c31705f221273f371844872e51a77f0ff8fcaf8492acd1793bef622f2

Observation d4d33b04-423b-4b82-9ef7-5e07ef3b272f · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 14

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source=arxiv_source observed=2026-08-07T14:30:53.863306Z digest=sha256:56486e1a136c71fa906e8e29ca24cc785619f254158619e9b1b3ac6bf2b73775

Observation c9d7a0dc-c480-4619-a58c-a5a6acce4039 · outbound

This paper cites Atlas: Few-shot Learning with Retrieval Augmented Language Models.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Atlas: Few-shot Learning with Retrieval Augmented Language Models

Reference 15

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source=arxiv_source observed=2026-08-07T14:30:53.935877Z digest=sha256:62da7fcbdce64a6aa536237ddef9bd3a176e519ff3e34b1ed292196273cca507

Observation 6558374a-5019-4a13-8cc3-5da717212d2e · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 16

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source=arxiv_source observed=2026-08-07T14:30:54.049064Z digest=sha256:5d752f23616b13fc6ceb00fc62db5c979d9ac403f8eb0e9224b8809e05f5c4d1

Observation 165378e4-0ccf-4964-a194-3fffa7d9d5d2 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-07T14:30:54.180427Z digest=sha256:b0166923ec221f504660bece11020da7dad15be1502518b223d2d8a379fc57cf

Observation ff323985-0754-448b-8c2d-e81093071341 · outbound

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

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Dense Passage Retrieval for Open-Domain Question Answering

Reference 18

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source=arxiv_source observed=2026-08-07T14:30:54.311608Z digest=sha256:8b95f9cd5ff7ac0bbfac9d5d2ab9c39e7061f37976cf0371be62d8d450386c9b

Observation 1affa6fc-d9f7-4c14-92e9-8bc5a265ddf3 · outbound

This paper cites Bridging the Preference Gap between Retrievers and LLMs.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Bridging the Preference Gap between Retrievers and LLMs

Reference 19

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source=arxiv_source observed=2026-08-07T14:30:54.419840Z digest=sha256:a3cf92355c8eb9b0ea6955f32b253ce10e86166faf913e77123f7cdcc3541ce0

Observation 4bcc27a7-6561-4d05-9ee4-67b563263318 · outbound

This paper cites SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs

Reference 20

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source=arxiv_source observed=2026-08-07T14:30:54.525693Z digest=sha256:2545baa56d6194df2c8ca7c1824ecd0a83e1a9bd83b90982856bc1e0e25acb27

Observation e03c58cc-7794-4f8d-9ded-fdb2dad4336f · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 21

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source=arxiv_source observed=2026-08-07T14:30:54.617743Z digest=sha256:30d1bba931750120b7ecc72330b1f26ad01747c2f5e810d61242596b44b8f459

Observation ce72a03e-1f67-46df-858f-b73c9e180a73 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 22

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source=arxiv_source observed=2026-08-07T14:30:54.732812Z digest=sha256:d4bf9aa8e9a29d6334b969b4748592b889a55fea5198908408eb9b383e5bfc02

Observation c2327ce6-34a5-4800-bd70-56a1f7ae1353 · outbound

This paper cites Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning

Reference 23

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source=arxiv_source observed=2026-08-07T14:30:54.840814Z digest=sha256:2d0404c59bb3919399701c76b74e0ea24e5aba14279c440fcf38b7ddf9189c9f

Observation 12b92e94-51eb-4fd0-80d3-53314c049101 · outbound

This paper cites From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 24

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source=arxiv_source observed=2026-08-07T14:30:54.951220Z digest=sha256:4dca21641816be847800094f90cebd1b439eea3306f44637e5841892d934bb30

Observation 6b8c7b36-ca21-4f30-817a-bb3f80a2c3d6 · outbound

This paper cites Contrastive Decoding: Open-ended Text Generation as Optimization.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Contrastive Decoding: Open-ended Text Generation as Optimization

Reference 25

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source=arxiv_source observed=2026-08-07T14:30:55.026727Z digest=sha256:9e04232b7f19e5a769a7137104b2aadabe4a8432d79dd225d690a546d875ff4d

Observation c82370a8-376d-4f87-9573-92c31dbfc723 · outbound

This paper cites RA-DIT: Retrieval-Augmented Dual Instruction Tuning.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis RA-DIT: Retrieval-Augmented Dual Instruction Tuning

Reference 26

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source=arxiv_source observed=2026-08-07T14:30:55.094747Z digest=sha256:caa718a42b2d124f4aca23fbb7f5fd059c590743ea1c941db764a4c89dc88754

Observation 1b6e3abe-38bd-4c22-a3a9-ce02adda65d7 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 27

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source=arxiv_source observed=2026-08-07T14:30:55.234743Z digest=sha256:7e36add499d39f731a35b40e50771f84a44f520f615f3bd8c932fb79dffbdf4b

Observation 6644aa7a-a242-43bd-aaa4-08d46ab6867e · outbound

This paper cites When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Reference 28

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source=arxiv_source observed=2026-08-07T14:30:55.288600Z digest=sha256:5c5684624f14625c0fb6b0f00c755a58854434754a1ead2180b5d0d4bd400f87

Observation adb12687-863a-41ea-bbd9-217a2d1eb359 · outbound

This paper cites W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering

Reference 29

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verified exact
local_arxiv, observed 2026-08-07T14:30:56.488179Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T14:30:55.381245Z digest=sha256:7b9098c2b60465380893da6cc74eeb5ca7272de5bbbfbc4aa41273ea0e7c9db1

Observation f1c7f738-b0b5-468d-8338-edba84b03221 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 30

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source=arxiv_source observed=2026-08-07T14:30:55.450490Z digest=sha256:d7e07a2fbfa853f2b8b9da595dd45964270e2323f80f843dc5cdb8412ab665fa

Observation acc833d7-0d8b-4748-ab72-e0eb79b56a70 · outbound

This paper cites Trusting Your Evidence: Hallucinate Less with Context-aware Decoding.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

Reference 31

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source=arxiv_source observed=2026-08-07T14:30:55.518056Z digest=sha256:73fc88f7ace393f4e2e9b16fe119d056c6e60e1e1316999653c68e1570a0f55a

Observation 20ff52fd-7335-4fac-b45c-d3d2a277dfc6 · outbound

This paper cites REPLUG: Retrieval-Augmented Black-Box Language Models.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis REPLUG: Retrieval-Augmented Black-Box Language Models

Reference 32

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source=arxiv_source observed=2026-08-07T14:30:55.611939Z digest=sha256:a34700e2f3545bbb36db0242fa65ff05f33759ea50c27093c158527fd86c96ff

Observation f4c7dd07-665b-4c20-96cb-95d596040a73 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis LLaMA: Open and Efficient Foundation Language Models

Reference 33

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source=arxiv_source observed=2026-08-07T14:30:55.659579Z digest=sha256:931d0f41751460d0f51bd2725d3b92b822bce87de36bbbe38b527040fc658a0c

Observation f38dd14a-f8b5-4e7f-8037-cc43e4becb77 · outbound

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

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 34

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source=arxiv_source observed=2026-08-07T14:30:55.744806Z digest=sha256:9539e2800f50d500655ed4db9376c226f1798a1ffa19b4237f77d48e3bc6c651

Observation 087cf98d-516d-47ff-aa06-ab6025e330e6 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 35

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raw_fallback, observed 2026-08-07T14:30:56.776049Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T14:30:55.826272Z digest=sha256:47822cb48ece19a364e4323195178107c92066d97c076b0ff0cfe6ed5a07ca92

Observation ffe53512-5524-44d4-b874-201c9dc22e7a · outbound

This paper cites REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering

Reference 36

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source=arxiv_source observed=2026-08-07T14:30:55.881903Z digest=sha256:707de36856bc5287579079e442670f11fc07ca7a5980cd2efca590713f172615

Observation 8605a773-ab50-49ec-9faa-d3a67b960120 · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 37

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source=arxiv_source observed=2026-08-07T14:30:56.006069Z digest=sha256:f9aee4f466cd7836e9840a6af7826507373b1bf055f9b726347af97a5418ce54

Observation 0b2bf267-3578-454e-a3cf-55b80cbbc04b · outbound

This paper cites an unresolved cited work.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Unresolved cited work

Reference 38

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unresolved
no resolver link, observed 2026-08-07T14:30:56.082065Z

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source=arxiv_source observed=2026-08-07T14:30:56.082065Z digest=sha256:287b5d616f73ae8cb7fda196d98cb24a70a21f1d0d03cef5cb38e5e8dbd6d67f

Observation 58b4c4e0-f525-4b39-a15a-1f80447ad69c · outbound

This paper cites Generate rather than Retrieve: Large Language Models are Strong Context Generators.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 39

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no resolver link, observed 2026-08-07T14:30:56.152073Z

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source=arxiv_source observed=2026-08-07T14:30:56.152073Z digest=sha256:f7d8dc1a15de2258bab0b7a7a7a6fdcb16b26a2069768e82f50b1008f94f166a

Observation da3a6cc5-e9a5-451f-8805-ee1d4d2b3719 · outbound

This paper cites RAFT: Adapting Language Model to Domain Specific RAG.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis RAFT: Adapting Language Model to Domain Specific RAG

Reference 40

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no resolver link, observed 2026-08-07T14:30:56.248436Z

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source=arxiv_source observed=2026-08-07T14:30:56.248436Z digest=sha256:7b74619cfe95a9717a0c26a6bede9a4571df72f948641f2cebad0c7e43a526a4

Pith citing papers

Observation 9ea1f465-654f-4a95-beeb-795bea38866d · inbound

LODESTAR: Trustworthy Entropy Is Navigated, Not Merely Measured -- Reinforced Polarizer Keeps a Frozen LLM from Being Confidently Misled by the Wrong Evidence cites this paper.

LODESTAR: Trustworthy Entropy Is Navigated, Not Merely Measured -- Reinforced Polarizer Keeps a Frozen LLM from Being Confidently Misled by the Wrong Evidence GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis

Reference 10

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local_arxiv, observed 2026-08-16T00:30:51.485559Z

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

source=pdf_text observed=2026-08-16T00:30:50.951365Z digest=sha256:4476c180ca2790a1dbf63440050839a99b737a3259f7936f7c70cd89f09b5db0