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Paper Citation Record · LEDGER

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

As of 8 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2505.24782.

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

pith.paper-citation-record.v1
2505.24782 v2

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:41.561351Z

measured 57 of 57 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T06:31:37.201344Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T06:32:07.564911Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact2
  • verified fuzzy1
  • unresolved52
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e0125e6-fdfa-4e68-b16f-94020effc594 · outbound

This paper cites online" 'onlinestring :=.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings online" 'onlinestring :=

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:35.090979Z digest=sha256:62969b8aa8c776ba99ea54e7a3538f305b86f86270dd09885f444aec7a3b4ccb

Observation 45e6eb54-bdbf-46a3-8095-8e1544bf0f8e · outbound

This paper cites write newline.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings write newline

Reference 2

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no resolver link, observed 2026-08-07T12:35:35.259050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:35.259050Z digest=sha256:659fa13bdca46ad28afaf5ae3b1f82b65482f51342f5f721c78a62e02753dd4b

Observation 0b539695-fa96-4546-a766-f6a4b6143483 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 3

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raw_fallback, observed 2026-08-07T12:35:43.794170Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:35.409282Z digest=sha256:e3bcc0c9932039806063817002f4a9bdd3814b67abd2407bfa86047e7072c0fe

Observation 933443f1-96d4-40e7-a535-bfe8a36284f3 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 4

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raw_fallback, observed 2026-08-07T12:35:43.587233Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:35.569173Z digest=sha256:e82816c35317faf409f0f6ea1014a7f3357411fa5e37d6c2f249f00220b63de6

Observation 48ad719a-41ae-4183-940b-6b06b6bcec88 · outbound

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

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 5

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no resolver link, observed 2026-08-07T12:35:35.831907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:35.831907Z digest=sha256:3e668e8cacf7026b9ad7b844e9ab9f8671ddba26eda5e70903348504fc087d02

Observation 0cbe95c5-4a0d-4823-83be-f70f04488fa1 · outbound

This paper cites EuroBERT: Scaling Multilingual Encoders for European Languages.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings EuroBERT: Scaling Multilingual Encoders for European Languages

Reference 6

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unresolved
no resolver link, observed 2026-08-07T12:35:35.991876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:35.991876Z digest=sha256:f785101f0914a412395318cb61db87bab251156cb96710d24d69fe6e678c9e88

Observation 878a4fa3-cb1e-4189-b9e1-2f6b4b5f1779 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 7

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unresolved
raw_fallback, observed 2026-08-07T12:35:43.432768Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:36.204555Z digest=sha256:8e0dc6168ecefdc13cd63cd8b2dbc06defa0091209c8d13b26f9fd840e7d43ec

Observation 6d7922ce-524f-4a90-a3c6-8ba7177cbdd1 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 8

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raw_fallback, observed 2026-08-07T12:35:43.244482Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:36.351585Z digest=sha256:cd8d8f28e857f58d6137acd0485c3977ba7ae73de678f7d1c17cc2d3dc7ed087

Observation 6dd4677b-02d6-49cf-b549-1bb599c4948f · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 9

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no resolver link, observed 2026-08-07T12:35:36.417209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.417209Z digest=sha256:11ea22c70f16a873ff3f4c3f23c41d43686667693ce05a29a772bdc470d0c303

Observation 3787ed86-7be8-4500-af1c-48f381d8dcd4 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 10

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no resolver link, observed 2026-08-07T12:35:36.557147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.557147Z digest=sha256:793a389c02fadf755d8c94ee400ab2a83362a246058ea44acfa498217643a1ef

Observation bd97c605-210b-4735-9b55-f094265c0ea1 · outbound

This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 11

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no resolver link, observed 2026-08-07T12:35:36.636972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.636972Z digest=sha256:3a40da62b035021e9cf478dc87182550aa4f4b3d4d3f7cfc8d39a15215d00016

Observation db539bec-880a-4ff5-811e-cb9fb0e5de00 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 12

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unresolved
no resolver link, observed 2026-08-07T12:35:36.737456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.737456Z digest=sha256:e9064515f6072ea5c0a5eb9d39ecfbb27428fdb1429b768cfc119ba428d8cbaa

Observation 46cbeff5-fb80-4fa5-a926-92b0e5707d0a · outbound

This paper cites ColPali: Efficient Document Retrieval with Vision Language Models.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings ColPali: Efficient Document Retrieval with Vision Language Models

Reference 13

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unresolved
no resolver link, observed 2026-08-07T12:35:36.862401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.862401Z digest=sha256:7b97138ef4e5b10ef203c93277c004ccd2e5e1c0c0811feb553372aa735ea116

Observation 9162ed4e-03f1-444f-b11e-7b1123c872ba · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 14

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no resolver link, observed 2026-08-07T12:35:36.956843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.956843Z digest=sha256:2cba873359fd3c17a8551dfe1875086ed31332f655198b0c698115c0dfad617d

Observation a9546f77-4c23-418a-aa52-c3c0309dff17 · outbound

This paper cites Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism

Reference 15

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verified exact
local_arxiv, observed 2026-08-07T12:35:42.551776Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:37.055205Z digest=sha256:b9255852d0a3ef0ecee2259fddcee6d28227a6ae98f12ad0b7704dc061156aaf

Observation 5bae5789-f7e9-4879-ad2f-7be9bb0ecaac · outbound

This paper cites Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models

Reference 16

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no resolver link, observed 2026-08-07T12:35:37.173417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.173417Z digest=sha256:575877bf28f6484ee678da9f60b07d6bb9670c13e899df88da0b3e8708b93ed5

Observation d4c5345e-9fa8-48de-8852-286ae79d6512 · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 17

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no resolver link, observed 2026-08-07T12:35:37.236285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.236285Z digest=sha256:aac2e9fa969e277b6070b4ccee6d75e3d75b881d2a7413c03b4b00829cf6e969

Observation e640e5d4-b9fe-4f74-896e-2ea002dd0792 · outbound

This paper cites LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs

Reference 18

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no resolver link, observed 2026-08-07T12:35:37.310372Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.310372Z digest=sha256:51188080c03fcecec1051153752e793b76ed88959c526be587866915be9c3a11

Observation aae5b47c-7a72-46e4-ae37-84362ffaadb7 · outbound

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

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Dense Passage Retrieval for Open-Domain Question Answering

Reference 19

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no resolver link, observed 2026-08-07T12:35:37.411316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.411316Z digest=sha256:658e2e5dfa34e59ff97c5394df9ba2445fffc34fd40fcc653b129c135133f679

Observation b9132ddf-7f39-4c42-94ef-7e412db3e276 · outbound

This paper cites ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Reference 20

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no resolver link, observed 2026-08-07T12:35:37.501961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.501961Z digest=sha256:6343cd646bf27521a42860a922e293d578ff1b9cf12f852fa706e335ab98a859

Observation abb1e945-06d6-40d0-b7b6-8fbaaaab7dda · outbound

This paper cites The NarrativeQA Reading Comprehension Challenge.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings The NarrativeQA Reading Comprehension Challenge

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.597515Z digest=sha256:41bf716880788f1f609d02541e0496c8307d9b4af30548c18b3e300e6c9bb72b

Observation 35fd1d5d-8c7d-402f-a043-d20bd569ade8 · outbound

This paper cites NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 22

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no resolver link, observed 2026-08-07T12:35:37.669424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.669424Z digest=sha256:c57fd2b557ea7d6ea06c4472574b19d881a727d9ebb32c07e01e14841231bc61

Observation 0f04928c-6650-4bdb-a612-03e25b100a1f · outbound

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

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 23

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no resolver link, observed 2026-08-07T12:35:37.755459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.755459Z digest=sha256:eed17204b7791c39175744cf347fda842e36ac1be56e91654a84619359cd1927

Observation 3497bda0-3d56-4b3f-841f-71ba4561057c · outbound

This paper cites Towards General Text Embeddings with Multi-stage Contrastive Learning.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Towards General Text Embeddings with Multi-stage Contrastive Learning

Reference 24

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no resolver link, observed 2026-08-07T12:35:37.872311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.872311Z digest=sha256:983d672af5626f41b4af2935f37290abd9a04b3d272d4f14e70d95903a0c47e5

Observation a327fc0d-c424-43b4-b994-3fddf6dc932b · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 25

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no resolver link, observed 2026-08-07T12:35:37.974636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.974636Z digest=sha256:b2e6120430ddde1c356cb6cf50e9f370518686b224bc915df1819aac1effc08d

Observation c9498298-07a1-4917-9acb-0a3c6fc677da · outbound

This paper cites Unifying Multimodal Retrieval via Document Screenshot Embedding.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unifying Multimodal Retrieval via Document Screenshot Embedding

Reference 26

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no resolver link, observed 2026-08-07T12:35:38.087216Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.087216Z digest=sha256:1004f76459f87475bf93a5d6a3a01748a651bcf7acb619eaf15b3fb67c0b7cfe

Observation 8074480a-de70-4633-bfbc-9ab64e538542 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 27

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no resolver link, observed 2026-08-07T12:35:38.172619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.172619Z digest=sha256:f255234f8d3480e6ccb3d33d9302bedcf46ef49afaad9f2b67e5687458dad61a

Observation 251184c4-8b22-49dc-81d7-3194339f32c3 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 28

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unresolved
raw_fallback, observed 2026-08-07T12:35:43.031925Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:38.271369Z digest=sha256:d10bf74c361d42d4c0ec598fa1ec979ea767183124cb6833f5fd27e2fabd9c12

Observation 461054fd-392b-4fb1-bdd9-7285558d2165 · outbound

This paper cites Contextual Document Embeddings.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Contextual Document Embeddings

Reference 29

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no resolver link, observed 2026-08-07T12:35:38.362337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.362337Z digest=sha256:e6add65f9989a4f0194836da24dabdc9987dc3b70a073e2505ca8a8eb1a39931

Observation c9830f65-2d6f-47ca-a927-afd4ab2bf914 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings MTEB: Massive Text Embedding Benchmark

Reference 30

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unresolved
no resolver link, observed 2026-08-07T12:35:38.435116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.435116Z digest=sha256:47a8fcbbb755d4c2f1533f7f2cb2c447e9991b8a86ae2787b48c6b6f6ca9890a

Observation 41a7b8d1-f28e-4264-8630-29a8c5495534 · outbound

This paper cites Large Dual Encoders Are Generalizable Retrievers.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Large Dual Encoders Are Generalizable Retrievers

Reference 31

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unresolved
no resolver link, observed 2026-08-07T12:35:38.568382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.568382Z digest=sha256:73bff5d9c8fd9610350d3559904c23a674c8f19d0f4e6349a3d067a9fae77e9e

Observation 6d46216c-ebb0-4f07-93c5-735c10480da5 · outbound

This paper cites Nomic Embed: Training a Reproducible Long Context Text Embedder.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Nomic Embed: Training a Reproducible Long Context Text Embedder

Reference 33

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no resolver link, observed 2026-08-07T12:35:38.750679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.750679Z digest=sha256:9b8328f4e6c0312108bf08c5a8435ff720f31a6e2317a777efa0710efc847d07

Observation aa2ddcac-9a64-47b2-ba25-0f590c1ae8a1 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Representation Learning with Contrastive Predictive Coding

Reference 34

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unresolved
no resolver link, observed 2026-08-07T12:35:38.841556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.841556Z digest=sha256:511ef5f4066b646bdf673020f18fbdc9b476a2e8c634bae392666c7c8cbba97a

Observation 9bf29cc9-9e31-4c8d-b8e3-03d7d764118c · outbound

This paper cites Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata

Reference 35

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no resolver link, observed 2026-08-07T12:35:38.942407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.942407Z digest=sha256:97808d3081afd7347b12813f5ef91ba0e6562c50d5613ef5b239405549ea44ec

Observation 39466cfe-09cb-4a20-8f41-284d15d037c5 · outbound

This paper cites Grounding Language Model with Chunking-Free In-Context Retrieval.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Grounding Language Model with Chunking-Free In-Context Retrieval

Reference 36

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no resolver link, observed 2026-08-07T12:35:39.013312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.013312Z digest=sha256:25d43bb2b32bfed1557cc83e127a0b835568b1d393548c1382a279fc8ee42a79

Observation 0cee9502-6127-4fee-9aa4-03847cf047de · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 37

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no resolver link, observed 2026-08-07T12:35:39.087282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.087282Z digest=sha256:9eaccc67e8ef9a37a861546a24682aa06fc4703b7bdb52dad06e185dd682fc23

Observation 2f9eafcf-3e5c-4184-9450-e413823bfbcc · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.164467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.164467Z digest=sha256:8861c8eead910529cc99fd08a96533b08f8007be7c02bc4ce822da7e36e079ec

Observation 13ff1823-3cde-41f9-b8c2-eec5a672f14b · outbound

This paper cites Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:35:42.846289Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:39.241732Z digest=sha256:8d3d29926811efea905feaf91b84845d35c26273014db2f0ee4360585bbf3ed4

Observation 3727f3be-0115-482f-9cd9-63c33c6ee6dd · outbound

This paper cites Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.320076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.320076Z digest=sha256:fb2082fc8901b73498cf000213f738a7f39d5e0619fa9b4c69f3fdfa93bd5541

Observation b97503f3-22e2-4991-8ed5-0b6d835db39c · outbound

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

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.417189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.417189Z digest=sha256:2f3bcac5ad9ee7feeaac5a73f344701a5395c3b78ca61594de3b59430da294ca

Observation 2dda3bf5-eaf2-46a5-a068-21c73f56e54f · outbound

This paper cites FaceNet: A Unified Embedding for Face Recognition and Clustering.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings FaceNet: A Unified Embedding for Face Recognition and Clustering

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.494432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.494432Z digest=sha256:69e83f0b6606f6706108b7303e8077c5b4f19335b877cec741b14843e43f959c

Observation 74b58663-305c-4305-aedd-d3b157c767df · outbound

This paper cites FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:35:42.087570Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T12:35:39.602583Z digest=sha256:2cee8bc7d08a4ab7fdda45757c84905b2bf23ce0fa239cb235b9d90a4a149457

Observation 12980dec-912f-4cbe-acbf-579bf6250d4b · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.686979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.686979Z digest=sha256:2147f90d18bfb623c77ed86eaeae99903b99662c0cc28b1be5cbe287f686480f

Observation 477dcfb1-2139-4930-bc2a-eba929bb92e6 · outbound

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

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.783146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.783146Z digest=sha256:6cf0c3a546158489938ec8f762b34fde5f930b406d31d9673809814d4ec6e81c

Observation a6944cb1-85df-4405-82ff-c4ca880da7eb · outbound

This paper cites LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.879972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.879972Z digest=sha256:8a39639e016eaa0c1374c26e1e055baf25ce670f94886df8603639da5362a10e

Observation 9a341dee-c84b-4134-8fba-e5d267a6da4e · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:39.947606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:39.947606Z digest=sha256:de48f0954f1cc6ebd82fbe568e817f6272f8945c122cf9958bbf2dd8349daf7e

Observation daede230-474e-4411-82d4-9d29a8a01e4a · outbound

This paper cites Improving Text Embeddings with Large Language Models.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Improving Text Embeddings with Large Language Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:40.097134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:40.097134Z digest=sha256:5344532531db3c3b09ee81b6338220c79eda171fa896a4ba1fa331312b7450b6

Observation 0e69deab-fb11-46b0-b876-c18007b5be83 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:40.425906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:40.425906Z digest=sha256:39ae82f20f50f83c555d463c7b053e6e922558c87f4d34e0861240f687a7262a

Observation 8c5235dc-39e0-490d-b718-4d3546f5f625 · outbound

This paper cites Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:40.557193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:40.557193Z digest=sha256:b7e93d4a191a5fae3b1898d9785d7328130e59cd32b1878bedf4e5a5015cc699

Observation 32ecdd65-33a8-4424-9927-2ceaa5cd21da · outbound

This paper cites Retrieval meets Long Context Large Language Models.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Retrieval meets Long Context Large Language Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:40.744965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:40.744965Z digest=sha256:08ce52b2d16a5ddcc09efa1d422562ea18ea9b0790bcfe3d7da0a64cafa467ed

Observation eb2866fd-216e-4769-af7b-4f543dece219 · outbound

This paper cites Qwen2.5-1M Technical Report.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Qwen2.5-1M Technical Report

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:40.930281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:40.930281Z digest=sha256:7e1ea1a3e6aef843c061b1d747bf34624a1b7faf5f0fec34091221ddc5836482

Observation 74b5e41b-3903-4c2d-8ff0-63795698cfc3 · outbound

This paper cites an unresolved cited work.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Unresolved cited work

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:41.083298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:41.083298Z digest=sha256:2ded19a113388d1aa25a2b8e1e1b2ecffc159f8ce0b8f799286c776ac7b27ca3

Observation 3ba27247-7633-4a42-b260-9fe30bf18a82 · outbound

This paper cites Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:41.298206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:41.298206Z digest=sha256:2af3aeefbf223dbc320aa612143299d20feeae732bc158db577edd93e24a4fae

Observation bfdbaa7f-bb57-45e9-a4bb-78d238f20d31 · outbound

This paper cites GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:41.438347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:41.438347Z digest=sha256:828fbd4c7bfccb83f8626d7776bd25ad1831dcbc3bcdedfbdc84abb24d8f191d

Observation 2d576f3c-b7f1-45f7-9736-6a5458b8a13a · outbound

This paper cites LongEmbed: Extending Embedding Models for Long Context Retrieval.

Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings LongEmbed: Extending Embedding Models for Long Context Retrieval

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:41.561351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:41.561351Z digest=sha256:a313265c232ba6ffb8f8b8faa52a50d53e2be50427f0b9a65a39e987e99e9424

Pith citing papers

Observation 6bd3fa9f-96a3-4298-b9ad-2218d14e9d8d · inbound

Should We Still Pretrain Encoders with Masked Language Modeling? cites this paper.

Should We Still Pretrain Encoders with Masked Language Modeling? Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:32:07.568043Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-19T06:31:37.201344Z digest=sha256:c3a603774768dbda56f10c1daee7b6542376aba6b567084979d775e62d101788

Observation 951eee55-96e3-4a73-9b98-e9c3615384da · inbound

Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval cites this paper.

Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:31:04.450710Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T15:26:44.498777Z digest=sha256:7cddafc83aa101d2743f13319b01f4e7ced1900b88c8917450d3ef4c76cd9b02