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Paper Citation Record · LEDGER

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval

As of 13 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2411.15766.

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

pith.paper-citation-record.v1
2411.15766 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:02:28.793051Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved59
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 92c97ab0-fbc0-4626-b909-20e01907f88e · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.613911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.539666Z digest=sha256:3c7cd5c5101614aac7dda649ad180ffccc67b4db23c35e798eb03c02d8f1f53c

Observation 7f0432e0-a510-443f-8071-a958303f037c · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.546032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.546032Z digest=sha256:2b3334a094729a28851952e8de205248f7d3fa999ec6df592074f54bc216ac13

Observation ed7311f9-fb42-424d-8513-7a3fcc0ebb22 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.601587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.552483Z digest=sha256:94d47faf61695631ff269e9ff77cd9e61be989d97732e49088a4c7a62ab06684

Observation 3cbd8b23-e9ac-430c-a1e9-f613290efbcd · outbound

This paper cites Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.557986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.557986Z digest=sha256:c342de39a21b225ecc51177a7db14d6e37e80dab72757854b72c2fd194a26832

Observation 687ffc99-6243-4e87-b325-f2ae39f1dd62 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.589992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.563190Z digest=sha256:85e2e057b61abf66036bb3bf4cf9990de74abab02d7d960d6e1cf830d51a6af1

Observation 34b0177b-35df-4622-8456-7e99f82ae8c3 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.578064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.568635Z digest=sha256:c65311e8317cc328e5b224db188322c19ed1d5fecf2fe6f6daffba0a6c8ba9b7

Observation 8e9861ac-4f27-49c6-b034-8fcb6cbcb46d · outbound

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

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.573430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.573430Z digest=sha256:e7f4a366ef76683564ced940500e878bbbb49548b78d724d313af9200304da4b

Observation efbd5e5a-d9ce-43b3-8a6f-9c818b0491db · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.566076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.579352Z digest=sha256:d5784e29af5beaefd569580e535ef3ed1cab16abf38c001d9f56a48a33b149bc

Observation 25f6f087-f799-4b9f-9ec1-b8b4a6b61016 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Distilling the Knowledge in a Neural Network

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.584073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.584073Z digest=sha256:e789f9521fab6642565ae6ff14876c30a6bb6c6433d7695283b85716babe4d4a

Observation 8c7834c2-a852-4828-a302-247466fd4475 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.553176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.589667Z digest=sha256:4b68c95430b6dc2c3d5fddf9f9e13aaed144487fd6c3f627975d1d0c693991d7

Observation 34333926-d7d5-46de-82b7-845768bcfa96 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.541187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.595027Z digest=sha256:76f49597776efd0583f901d430f04c2f4a1ad37ff1c845c57bfe2a421b8dfc03

Observation 7d490627-e618-4764-8d9f-90b122118322 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.528794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 4e26a82b-76d2-4868-b5a7-e9eb79814eeb · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.516747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.603028Z digest=sha256:87123e8065761b0e1d951d9342e1dd4e3ed5e5e0ee57b25865ea7f44e09da759

Observation 82bf4eed-5da7-41f3-9f38-4eebb389e9d3 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.504769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.606463Z digest=sha256:45341f72a0496ca70063ca6a0f00302fb0a09eabe6ffbc56a4bc9acf226a8ea9

Observation 98e5d9f5-e04a-4d7d-97af-e96b336e07d1 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.610256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.610256Z digest=sha256:fa1dad9f3c29523771eac7e749bac6f08b4bbcfe4d3b43c7c0068c9530d775f5

Observation 45e136b7-addd-4f36-b6d8-7f096f606def · outbound

This paper cites Scaling Laws for Neural Language Models.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Scaling Laws for Neural Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.618666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.618666Z digest=sha256:d544c75578fbafb2f5a931ed948404eba8c56e8bd6579a19e23dd0c31687fd16

Observation 9a12021e-2eef-4b37-b6fd-decb6d74bb20 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.485539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation dabca04a-bcc3-4f22-8c9f-dba2c4c2ca1f · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.473645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.626117Z digest=sha256:5acb635d44ebb4356b2caa9a95239a85eb9a0210706d4d90855708583c7200ba

Observation 8e6c282f-fbd8-4473-9881-7a8b12306372 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.461642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 6b11f066-63c6-4a74-83d7-c24d0a097cc8 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.446064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.633074Z digest=sha256:0a0909bd142f0e24a5e29e28fa1adef6831e1a4fc91a6e5198f8a5efd0f63910

Observation a5f6bea7-db59-48bd-af59-139fdf759338 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.433489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c9fe8151-82a8-4de4-bb9f-4a210fd9b16c · outbound

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

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.640871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.640871Z digest=sha256:e415b3c29105c1798d63a7afd1022b7c97def58375f95da4c3c9acec021c945d

Observation 3e41300a-26f2-4217-a2ed-2b3177e2f26c · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.422084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.644631Z digest=sha256:500f22c366af4602a6ca8f364c292801eb52d88171f9ab4e5e08c5b3b42af744

Observation 9ad82de6-25be-4626-bb91-d4e7555f7939 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.409556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.648295Z digest=sha256:719a100f25c5ece85a7af2c702631ba01bce265ed0743b0898e8844e15aded78

Observation 446c058b-d4f6-4e45-9851-7d8427474b1a · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.398536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.652176Z digest=sha256:3076e8eab8307386a4b91ca0704d221b8165a93d6852e8eabfaec363d8c8fa56

Observation 7ce0b88f-7331-4492-b9d3-fbd944336d0e · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.656608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.656608Z digest=sha256:d647e51b2f6905cb0701900bb44d1071a81d7b56ee10f101c290ffcf14adf870

Observation 4f0cb4cc-f3a2-441f-bbe1-ae54f7f6ed64 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.378874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.660387Z digest=sha256:dd5c1f2d669fe5c363ace7b91a556195ab73bb0a9ecadc1c15b29ee8cb86e7e0

Observation 750b3fe8-0546-4111-89f9-b3698378ac42 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.366999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation bf81440d-ef5a-4589-bcfd-2bb6f2e08c6e · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.356379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 35be1375-7a25-4389-8915-e50638129905 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.345420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.672898Z digest=sha256:c915ebf01d1357e6ef960210835eb542cac797f7b7470fce9a4f5f85630d10fa

Observation 157c20dd-5df1-402f-b382-b01adc3a063c · outbound

This paper cites CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.676669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.676669Z digest=sha256:eade21596b4436ef6c303710ccd1b6ba4ba9ad90d84b03ecfb9991186915126e

Observation 5d832deb-f0d6-4d50-bbcc-b4e13ad74a97 · outbound

This paper cites Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.680734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.680734Z digest=sha256:b0a54b377d7a11780817031a6b5e36991b77b3cc76d9115f21761c36462dfd20

Observation 435682e5-2e2c-459c-92ff-def60a5abcd3 · outbound

This paper cites Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.684763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.684763Z digest=sha256:571a712e8e9835e01efe6ecf0f22f628095370f6d3f6752dd163bbeaf4f1837c

Observation 90d4418f-67b3-4adb-999a-8ff02d67f5cc · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 34

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.333701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.689107Z digest=sha256:9056425c39dfda68294d75b198fe41088d21796e1446572fa489cb9107c78e14

Observation 096ca914-8bb7-4958-bfa7-530cf7879f7d · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.695310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.695310Z digest=sha256:0b2cd7d6e2fe663a6f1e002b862ed8cb6be4d93577609a3a694359f777ebf842

Observation 009d27fd-a130-4f9b-8e6b-5d91ec600a5d · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.317027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.701718Z digest=sha256:aaf09262cc150a1125fa6f498999d9eee7d511fde8035c098394779748b0f878

Observation d9405e35-ab0a-4971-a69b-b4163ba6381d · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 37

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.305953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.705649Z digest=sha256:8071509db12bc85a1c2000915ef85d85215ed2dfe403bfe4ca8b2fc7e8dd524e

Observation 814eeaf2-daea-4690-847d-48fac6ebf28a · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.294182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.709820Z digest=sha256:922578dd3339797fdd883a8441a0c1d752cc70b51f0dec82ed96928e54a708f2

Observation f1e54c6f-fb5d-49fe-afc2-123ebc06d8ba · outbound

This paper cites Let's Think Dot by Dot: Hidden Computation in Transformer Language Models.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Let's Think Dot by Dot: Hidden Computation in Transformer Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.713467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.713467Z digest=sha256:395814702eb968a355ed426ba1a33ade5f8d1fcef2ad2224b98087bb88b7756c

Observation dc8d1e02-381c-4093-a024-112c5f53a1e8 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.282760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.717090Z digest=sha256:887c3fc90885bd718b74133c65210549a5942d1de30d2abfffd2271cd763ad56

Observation 77c4f608-ae16-4cfb-ae71-a66132c0c964 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.271447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.720995Z digest=sha256:ec0cf1fb8599b54f8e4a705aa58d72c946d38f5fcd242eb13103a3162c8992e1

Observation cc51e5cb-474d-4361-b8f3-24eae28d8b48 · outbound

This paper cites 2021.{Zero-offload}: Democratizing{billion-scale} model training.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval 2021.{Zero-offload}: Democratizing{billion-scale} model training

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:02:29.259358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.724750Z digest=sha256:9cf6cb524b67df3940d8fbf667ee994f297681dd12d1df76c51f45665f1d4f38

Observation 0f0c27b3-e90e-443a-9cd7-268334b0641c · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.243701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.728458Z digest=sha256:7ae1b4f0b61ed336d0c3d308f057bc1fa2b037141d5ed3419f56844acc33b742

Observation 98b4982a-cf99-4ebd-9138-2a1434ecd94e · outbound

This paper cites Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.731958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.731958Z digest=sha256:abf6144e9625027a084c5b9cc30ef71ae9114e2b03c65bb47424acb9b3998170

Observation 4f4dd475-ff55-452a-89ec-bf2c8c69d831 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.735315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.735315Z digest=sha256:ca09a51e9a90851062af797c0754ab67eb6481c59c68c3313faca8f1703308dd

Observation 0591a7fc-2ef5-4d8e-b70d-7c8da918474c · outbound

This paper cites Improving Text Embeddings with Large Language Models.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Improving Text Embeddings with Large Language Models

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.738233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.738233Z digest=sha256:0c231371aa66178ef06b8e7e208971d2253f2c4df9d2d7ca379788992afc669d

Observation 0be34ca7-502b-4768-991c-1a5ca835820d · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.223911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.741603Z digest=sha256:199d299e7f0d7c59716f1b3da711dc2aefa2351ade2f117ff3b9ca272a37a4eb

Observation a0d701b6-1535-40aa-a6bb-5421def0640e · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.212818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.744897Z digest=sha256:e7a92a67e48928c86edbabde1702c1c5d980f6c6df47bbb977473720ae2464be

Observation 64632378-6f47-4799-83ac-743100861104 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.202402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.747985Z digest=sha256:3d6ebddf870fe4be6e379993e7874d45d1398ca7338c2939a4b1f858208a3fdd

Observation 1bb61577-7438-4fa4-8b9c-c29c5e67a70a · outbound

This paper cites Large Language Models for Generative Information Extraction: A Survey.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Large Language Models for Generative Information Extraction: A Survey

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.751424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.751424Z digest=sha256:c1fc4e3570e9950ab51065afb004f75f6a5596af6a5d0e7e45fafc8a9ee00217

Observation 9f4c000d-0c9b-42db-9bd5-f2ac36b66a74 · outbound

This paper cites Negative Sampling for Contrastive Representation Learning: A Review.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Negative Sampling for Contrastive Representation Learning: A Review

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.755234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.755234Z digest=sha256:149d8824847b8b44912aa65e18222c646c05624cc7efda04e30d3cac483f367a

Observation c05e6a1d-388c-48a0-b870-b4a164621c34 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.758880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.758880Z digest=sha256:6f6c965cd1c1d57e01c8e40711a1c5a06f3ddf41d20d0351d9e53d26c17575f2

Observation 0de37c3f-4503-4220-8ae2-bccb3bb68e01 · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.171894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.766173Z digest=sha256:f7303f446a75ab82b13083467144bbcd4c8ee020cdeb31d10f9493c308a8685e

Observation e044395d-a13b-49b8-9592-f290fa7900cb · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.160998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.769680Z digest=sha256:7ec77dd376532f47219d115810c0478a31616bb5c8164f6c9ef12902273766b7

Observation a06ba546-136f-4fd7-a5e4-ed8074395b15 · outbound

This paper cites NoteLLM-2: Multimodal Large Representation Models for Recommendation.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval NoteLLM-2: Multimodal Large Representation Models for Recommendation

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.773332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.773332Z digest=sha256:3dcd7a833b532395898058e07fc7382127f268b704f9dd3198faa433718033dd

Observation c230ede2-77b7-43e2-9b3a-0ff17c3cc0fc · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 56

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.149893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.777058Z digest=sha256:0686ebcd1b821d672fae182f9aae437ffd10f50fe064e9a989e5553a06c49772

Observation e081dd71-63a2-44fa-b5b9-dd61f244882c · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-12T14:02:29.139813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.781331Z digest=sha256:ac1781484b5d32537287069b27abbd1280cace31af8858bf89ef41d1800f1f85

Observation 0ea010ee-d85d-41e4-ae35-ab52a21e1962 · outbound

This paper cites A Survey of Large Language Models.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval A Survey of Large Language Models

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.784940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.784940Z digest=sha256:e55a379a601d017a8e07cc6a87e7dabb84460d87583fa5b1082e7b54931ee1a4

Observation 3434de40-ba35-4528-a3ea-9206b98790fc · outbound

This paper cites an unresolved cited work.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Unresolved cited work

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.789063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.789063Z digest=sha256:21817f63ad3ab25f7d234ec1623f244c4bdab854a0a29da6835721d17f9a4a29

Observation 81d03f2b-e7a6-4b7b-a3f5-7dfac89deb82 · outbound

This paper cites Peony" despite the.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Peony" despite the

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:02:29.128625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.793051Z digest=sha256:c0b1f8f38abc5e684cc42aafc2b2a0470c80bb7771165c989fde39c2835bc740

Observation a5577292-e3fa-4dcf-9454-ee8e5a2e61f2 · outbound

This paper cites In SIGIR.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval In SIGIR

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:02:29.183761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:02:28.762502Z digest=sha256:94f3c0f0ffca0262ae6cfa02ab5c7084c35a37b75b098fc75580731b897ac57b

Observation 2aa64f62-e7db-492e-8a6c-465e2155249b · outbound

This paper cites Scaling Sentence Embeddings with Large Language Models.

ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval Scaling Sentence Embeddings with Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-12T14:02:28.614023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:02:28.614023Z digest=sha256:6a8846032a0808292373f7349ba03f02e663c786b5604f6a56c29b115ee6de36

Pith citing papers

No inbound Pith citation observations are available.