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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 2026-08-07T06:34:17.273281+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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unresolved
no resolver link, observed 2026-08-07T12:35:35.090979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:35.090979Z digest=sha256:e5dc693a85ce038856b58fed1cffa622ce4bb535c41b5ace251137edbf05bff0

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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unresolved
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:d1bcd80067ddf7ddd46966477054ae7c29a05e7866b2978e9af76b028b832c2e

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

source=arxiv_source observed=2026-08-07T12:35:35.409282Z digest=sha256:0edfe0e9eb94dd098ac6b56630a7b9d152a47437966ef0b274d3e91efdb1ab61

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

source=arxiv_source observed=2026-08-07T12:35:35.569173Z digest=sha256:32a25aa901942c3e45578c9f3627e15e7372a03ace6f5da8cb339643a7cc7d38

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=sha256:2b631664480577f619f6b50fdd207c8b38ba8ae9ab4496b75e183a8e925c0f14

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:dcd2bd00d91670633eee7cd72707987f0f8b1f9abc2564bb635e17f5470f41f3

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

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

source=arxiv_source observed=2026-08-07T12:35:36.204555Z digest=sha256:0fe1119812b8e8dc3e19de5a0c881c50303b8e394a7abb66cbe3c135ea3c50c2

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

source=arxiv_source observed=2026-08-07T12:35:36.351585Z digest=sha256:a85c7fac5fdd4a36aa341f451865eef20d6c6de3a79ec8c8a8c5b800d8c88c84

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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unresolved
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:dbb9d9a87a5312ea6cb2fb6c10dfacdb2ce8245aba17b7a4a0eccd496cb7d43b

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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unresolved
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:b9b532f93cde56f4c251324c609a82b0b686759b482a2684d0da1da66ada7e25

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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unresolved
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=sha256:000fb20a100594193e839957aebee66444ad760226f0317922c42875d8916ba0

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

Resolution
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=sha256:3d75163c8e61e67b8af37c1599bf09810fa393fea8c2ccc43e38d01117b1fbaa

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:f8d0c6cc103caa33cf55955bbabeedc9ca38a0cef4019821b35ea090d97de489

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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unresolved
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=sha256:7580c9a46675332107c960bb311c27104da71f8e4bc15e672d006110bb6cf142

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

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

source=arxiv_source observed=2026-08-07T12:35:37.055205Z digest=sha256:c390c7e79ee974caa97b1045849570c8745e0c11ced05819c05833432a35af89

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:f93ba232f3aeac3e6ac8992793d8aa8b166e48527930e3bfcc4b4eb46cab0e79

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

Resolution
unresolved
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:d2eec9b5251db8b087f6c4b6b19130f1f85ec14c2c3f753790d3f9765a8f3b6b

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.310372Z digest=sha256:b14280e8c9b9c681024135bd948db8772c45a7db85be942e230122dd7ef43de4

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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unresolved
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=sha256:023536401308428a6b32495c7a57cebdcc950798aee8b6d0ea0f144109cdb02b

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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unresolved
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:fe66c7cebd33a7428fffbfdbcf4bcbc839914908c8257503b048ba577fab24e9

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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unresolved
no resolver link, observed 2026-08-07T12:35:37.597515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:37.597515Z digest=sha256:761c88b20fa4eae55a56e7d5d8677bb44a473787e11cf57bc6b8e8beed7fc6f8

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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unresolved
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=sha256:988aecabf3d3ac5a785d1a5b84f2f7fd9e3cb534ef76654fd80b326f22f57ccc

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

Resolution
unresolved
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=sha256:25a4bef2f7abecf65324431f42d6bf914f9de8f05c4f34096e6f78898cb32526

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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unresolved
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:fa56e3b6cb13045a4c84c06c78a841fe2145a1946ff9c2ad8ae24f7b6064fb71

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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unresolved
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=sha256:9501cfa75f3c262bc694ad3a7a22e38105fdffecc2e9966dee2970b158319972

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:38.087216Z digest=sha256:cc218d697caf9cdb8d851c78b72492166be90e3db8c43c78d8a9aa8e9ed81b2e

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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unresolved
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=sha256:0500fd1caa647db6c748ba8fb86b08215d561ee2920a85d1b75a34ecf993dac3

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

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

source=arxiv_source observed=2026-08-07T12:35:38.271369Z digest=sha256:c4c2a0b47ce72b07190b0867153379bc47be3763f8d3e7ff4c1914d0f4d13c31

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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unresolved
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=sha256:3b72ebdf99187e7219181689176dda9bc324c045075747bfc353423f64c6bb49

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:e27f8cccdb8e18edfd0c2969894affdc0932f868fc1540043ae0838075d5a486

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=sha256:4d4de27c99f057da0cd5aa486e292f567e5df2eabdea2d48f02dcce3905e9d7f

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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unresolved
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=sha256:6cde3ca6e6137414b15f8ef2697c556891463ad9e57b95d6b385200d0ebdf22f

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=sha256:16e2464d4a6f4674ad940da4a479762e93d9ecfacfa163ac700d6ac600c1e9b3

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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unresolved
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:f4150f115f0abe709423098e5e2ba2d693a0acb1d6cb6abb7c0979e1d428891d

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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unresolved
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=sha256:93b29c65a17f9484b985fef30a4c66348d807e3a7cf81a7b1917d316d3ede556

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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unresolved
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=sha256:58dd52e4d8d979c70697b279a08f44b87a8a1d09f7a2fcafb2ac9f98daf7eb7c

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

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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:d39eb9fe997cc0ed7111f10dc18b359c289889a613b1fd3633160b2852e5f99a

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

source=arxiv_source observed=2026-08-07T12:35:39.241732Z digest=sha256:a7504b8c53e627ff8927a2bc643705185c2e9637beb79c35e3b7f8edca2cccbb

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=sha256:7d310e290081d4c61a4707a4b4c706dd7a4829f95cf633b9c656792b689a3aae

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=sha256:3f821030ba1926900fd479400668240a7ee191c5e7072694ffef5ba68ca64253

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:b2b8789b8368e332f8fdb95f0280b243d53900c608f8a0c4929ac98516e297a8

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

source=arxiv_source observed=2026-08-07T12:35:39.602583Z digest=sha256:69b849df6b84e3f29ec2fe585c391eb2fa1162b65d958a0262253f1bb01a7836

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=sha256:459a5bc36dc86d445bb8d21409d9f2dc3991d1f539efca2aef3aab12970c50ad

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:e1e40856b4ea3ed58296a44255b1760314fd4cf823659b0fa6dda07d85082f48

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=sha256:83f3bb4a45bda02d63402ec1fbd7e7ec279b394db83e993252e974fcc0735c80

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=sha256:9647d43de7a3cb336a05ff8208b195d132e7d2dd141998ad794a556ffa716403

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=sha256:2377ea2bfab64214c78b9653df5b876b1ae025f709e2ff2400bfbaae25e23023

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:c2c31a9190174a0aa4c10c2a35ac086af4b23837d86a495c53f62fbd3900c206

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:e87d7718b63aaafeabda9bee2da7e71e0acd130f6aa55e28abc1e325b0dd762c

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=sha256:26da709fb924cb1d6ba25ae5e425c69c84f2e491f070b9a05a3732ed1c84fef8

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=sha256:2536f067e0894173816551091b82ceb9664232cb9bc017c4c9baf5778fbce3dc

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:b490c92fe4a7b13ada9b0ae282a517af35fa9236cbe8ffdc266a74a992bc1626

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:dee864bfd520a7eed1e3143e9422036540d73315cbe52ef9ae21f53d13357f6e

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:a5ae2d2ae5d7c6cf1323abbff7d955cded2d1a17e972c4a2b39b6118e1a7d1ab

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:b7c986e9815f6299ba25aa4907913c69293bbb85b601296ad1a13cdbf8944401

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

source=arxiv_source observed=2026-05-19T06:31:37.201344Z digest=sha256:3976113c3c6d7162c3dda1940c9ebb1dbf02ba7d10bd163e1e2ba02351d0a563

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

source=pdf_text observed=2026-05-10T15:26:44.498777Z digest=sha256:c6d6137e5495bebbad2a5974e24625d9c97a8f29986fa40d3b2b2f27e975aa87