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Paper Citation Record · LEDGER

Info-Coevolution: An Efficient Framework for Data Model Coevolution

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.08070.

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

pith.paper-citation-record.v1
2506.08070 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:28:36.247486Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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

measured 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

44 of 44 outbound references displayed

  • verified exact3
  • verified fuzzy8
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54d225c3-0208-47f6-937c-df08eb6f557e · outbound

This paper cites write newline.

Info-Coevolution: An Efficient Framework for Data Model Coevolution write newline

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:28:32.669509Z digest=sha256:04e7d19832b99c4ad752a63dc90432a979a5a1042bbf52abf67d1b86ce0b4187

Observation 0dea6366-6840-44e9-b4bb-5df754123b1e · outbound

This paper cites Food-101 -- mining discriminative components with random forests.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Food-101 -- mining discriminative components with random forests

Reference 2

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verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:32.729981Z digest=sha256:9f4cff79b65f7fd664c774db78a2880734d30c39074282d88efcaba595b922c9

Observation 97864143-ad64-4c0a-8e3e-e105c60e148d · outbound

This paper cites Language Models are Few-Shot Learners.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Language Models are Few-Shot Learners

Reference 3

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source=arxiv_source observed=2026-08-07T05:28:32.782710Z digest=sha256:683e59b4deff6e5af345877e7c27d34a0ec1a90a21597b1fc82f16c2cb775297

Observation 7c21fb9c-1dee-47e0-aa1a-5bb15bd06aa2 · outbound

This paper cites Semi-supervised Vision Transformers at Scale.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Semi-supervised Vision Transformers at Scale

Reference 4

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source=arxiv_source observed=2026-08-07T05:28:32.845705Z digest=sha256:7f4104b4f901fe6a302e5c65e0f82131fc54693e040169de12188481d0c16276

Observation 896a3b3e-2969-4853-bef4-6ce1b59f5d62 · outbound

This paper cites Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

Reference 5

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source=arxiv_source observed=2026-08-07T05:28:32.928691Z digest=sha256:c5b2f3a40db95f4ab12360e9a3fb584cc6563814666c3296cb2bd64ee868fffa

Observation 2a000902-7aeb-429d-98f2-67fc12d4e7b3 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 6

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no resolver link, observed 2026-08-07T05:28:33.041625Z

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Observation e1c64b40-2772-447f-8978-976f0aef6ac0 · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 7

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source=arxiv_source observed=2026-08-07T05:28:33.095368Z digest=sha256:fbd9bb25c0ab085ccee3cc5901694335ee0abc34a5c02de701491e8918091d20

Observation 94838d54-eb4f-422c-bf16-644af4ecfa83 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Imagenet: A large-scale hierarchical image database

Reference 8

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source=arxiv_source observed=2026-08-07T05:28:33.165596Z digest=sha256:2e6dd25454a65f10a23fe95130f33df1c824c57e805d7d92af54504171ee1be3

Observation 8eb79743-620e-4caf-9897-1a21d46739fb · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

Info-Coevolution: An Efficient Framework for Data Model Coevolution The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 9

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source=arxiv_source observed=2026-08-07T05:28:33.247725Z digest=sha256:d20a01273d798ce48017d5b677f518d96a3d70fb30e01c7e1538a421cb20be0f

Observation 502f85c5-9721-4ae4-b274-76ce7544c6ac · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Info-Coevolution: An Efficient Framework for Data Model Coevolution BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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source=arxiv_source observed=2026-08-07T05:28:33.333337Z digest=sha256:86e396d18764579425bc7860c2f93eea31af6bab25e5c5b8a07d6fd43b3d055d

Observation 83cebced-7d28-4a27-8563-c73d755ded49 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Info-Coevolution: An Efficient Framework for Data Model Coevolution An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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source=arxiv_source observed=2026-08-07T05:28:33.391974Z digest=sha256:3fefe001c96520093e29123857efbad0bf1bcd8b2084828f3811ba1e68bdcc18

Observation 1d693031-6c3a-474c-86f3-3e3678e42daf · outbound

This paper cites Adversarial Active Learning for Deep Networks: a Margin Based Approach.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Adversarial Active Learning for Deep Networks: a Margin Based Approach

Reference 12

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Observation 1524a0ff-8ad7-4d7a-9a07-1ecbad22fdfe · outbound

This paper cites DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

Reference 13

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Observation 3efd6ebc-71bd-43cf-aafd-ded2dbdaf1bf · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Masked Autoencoders Are Scalable Vision Learners

Reference 14

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Observation a918778f-7a78-4e84-b23d-dcc0bd365ab5 · outbound

This paper cites Active Learning: Problem Settings and Recent Developments.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Active Learning: Problem Settings and Recent Developments

Reference 15

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Observation 24b2b3c8-75eb-4f1e-b2dc-b897860f7b19 · outbound

This paper cites Submodular combinatorial information measures with applications in machine learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Submodular combinatorial information measures with applications in machine learning

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:28:33.784616Z digest=sha256:412f8dbfd0655f58f934471a985a65d1c12f614797cfc1b698883c695da4f3e9

Observation 3d42bbf5-7eb5-4242-b89b-d7d79dc5839c · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 17

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source=arxiv_source observed=2026-08-07T05:28:33.852933Z digest=sha256:d0cd35bcb58d27b02dbfca1207a78cb149ad5d5f6fe3e6d26bb818e00b150fd9

Observation e501665d-5376-4c65-bac2-45ee7f77f3b3 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Glister: Generalization based data subset selection for efficient and robust learning

Reference 18

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verified fuzzy
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Source-reported events for the cited work

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

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Observation 1601ca78-a8d5-4a04-af69-22b0369b9b47 · outbound

This paper cites Segment Anything.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Segment Anything

Reference 19

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Source-reported events for the cited work

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Observation d8944ae0-730d-4c40-852d-e8a4b2feb9a1 · outbound

This paper cites Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Reference 20

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Observation 14f3e807-7765-4cc8-b159-631d5608e112 · outbound

This paper cites Cifar-10 (canadian institute for advanced research).

Info-Coevolution: An Efficient Framework for Data Model Coevolution Cifar-10 (canadian institute for advanced research)

Reference 21

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verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:34.217767Z digest=sha256:34806dc0738634a7671c764f92788f74986d418084dc9a0855a00f2cfd5ab62f

Observation d989deae-a48a-41c4-a516-d440b7fb0fb8 · outbound

This paper cites Cifar-100 (canadian institute for advanced research).

Info-Coevolution: An Efficient Framework for Data Model Coevolution Cifar-100 (canadian institute for advanced research)

Reference 22

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verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:34.316048Z digest=sha256:1b333f4cb646207819b0e5cf82da8a264fd5023f308d770beefebf2ccdb405ca

Observation 261898d4-c821-4893-9997-718d28fede54 · outbound

This paper cites A Survey on Deep Active Learning: Recent Advances and New Frontiers.

Info-Coevolution: An Efficient Framework for Data Model Coevolution A Survey on Deep Active Learning: Recent Advances and New Frontiers

Reference 23

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source=arxiv_source observed=2026-08-07T05:28:34.384559Z digest=sha256:a4fa1389b9b0e894f0ca10a2d7b04e3f135297578c4201c5529649ff91b64ca5

Observation 319f357a-1d63-43d6-9d2f-0d6f016e582a · outbound

This paper cites BLIP : Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Info-Coevolution: An Efficient Framework for Data Model Coevolution BLIP : Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 24

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:34.462996Z digest=sha256:9d184aa357af4033bd85008ac708f200f98566db4c75db86f9b717efcb2034f3

Observation 2caa8c80-5ec0-456c-9da7-9043e5133b07 · outbound

This paper cites Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs

Reference 25

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Observation 30f3828d-bcaf-498e-aaf5-c627d85f714a · outbound

This paper cites Active Learning by Acquiring Contrastive Examples.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Active Learning by Acquiring Contrastive Examples

Reference 26

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Observation a54aed7c-1ae8-4237-880d-8fbfd89e6c6b · outbound

This paper cites Coresets for data-efficient training of machine learning models.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Coresets for data-efficient training of machine learning models

Reference 27

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Observation e910dbe4-22f6-4dc2-b315-bab5487cf1da · outbound

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Info-Coevolution: An Efficient Framework for Data Model Coevolution Unresolved cited work

Reference 28

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source=arxiv_source observed=2026-08-07T05:28:34.838354Z digest=sha256:35f345a93762552621842bf5151914965f0904154adf99334e0238cd291efe6e

Observation 0c13258c-4067-46b5-b6bf-0baa1d634bd1 · outbound

This paper cites Im2text: Describing images using 1 million captioned photographs.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Im2text: Describing images using 1 million captioned photographs

Reference 29

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verified fuzzy
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Source-reported events for the cited work

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

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Observation dfbfd959-17cf-4923-a06a-2a6d44bb7132 · outbound

This paper cites an unresolved cited work.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Unresolved cited work

Reference 30

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Source-reported events for the cited work

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

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Observation 83c0fa25-6011-4c25-b7e4-9fa30f08a380 · outbound

This paper cites Dataset growth.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Dataset growth

Reference 31

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Source-reported events for the cited work

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

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Observation 6a654794-8f0a-43c7-8750-5421cb40e8d9 · outbound

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Info-Coevolution: An Efficient Framework for Data Model Coevolution Learning Transferable Visual Models From Natural Language Supervision

Reference 32

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source=arxiv_source observed=2026-08-07T05:28:35.162486Z digest=sha256:dcfe0f6068eb56bf88fed20a473464a573a27c24dd6eb33744b9a3e0e4eaed3e

Observation 96e8c111-6bee-458e-8087-a23424bca0ae · outbound

This paper cites an unresolved cited work.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-08-07T05:28:35.242761Z digest=sha256:e2f2c3cb6f2ee9900db7b55d07b8c998295d4e7aa333c686a68f7f5f62aa73cb

Observation ba820f10-0ea4-440e-94c8-9b67323facf8 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

Info-Coevolution: An Efficient Framework for Data Model Coevolution LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 34

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Observation 5aa3b080-e73a-4f30-82bc-75ff97632f9b · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 36

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Observation c88a639c-6cc9-49c2-9c32-3c76b835d827 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 37

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verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:35.545067Z digest=sha256:d683ba7c53a1ce9d225d2afa9fc55ec53d613cd34ffa1ed5ec8fe6b13bec4713

Observation aa3ccf19-90ad-41f5-b6f5-988b66333bf2 · outbound

This paper cites Small-gan: Speeding up gan training using core-sets.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Small-gan: Speeding up gan training using core-sets

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-07T05:28:37.851727Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:35.628762Z digest=sha256:a3a7e50e25de5d23800ae30c4f5da978dc257cb6e9752d1137468a5dcd0200e6

Observation f7aa379b-84b8-4337-b767-8a5b1dacdb55 · outbound

This paper cites Prediction-Oriented Bayesian Active Learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Prediction-Oriented Bayesian Active Learning

Reference 39

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T05:28:37.133852Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:35.729970Z digest=sha256:52236b6d3a179ec529e427d07add5eba0396b1958808a67159c51e56b05dafa3

Observation dc0d371b-e316-4021-9887-d56a50fe028f · outbound

This paper cites FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence.

Info-Coevolution: An Efficient Framework for Data Model Coevolution FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:35.844364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:28:35.844364Z digest=sha256:ea6c1a6c6a0ad022f9b8ec936e8080a6000cae93649ba015ecd2367b7059d7a2

Observation 86cf6642-fa98-41c6-bf54-bbe543fa0c0f · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:35.921467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:28:35.921467Z digest=sha256:915c522801df271c46b7012dadf849a4fc55d0ae721ce8be92d88d7b5ebf72bc

Observation 7b8044ef-c560-4326-ab3a-a1fd98e0984f · outbound

This paper cites FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning.

Info-Coevolution: An Efficient Framework for Data Model Coevolution FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:36.022024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:28:36.022024Z digest=sha256:7f59996e4d5d9ac2fb230e1f840df5aaac7aa51479d10041f837346d05ac343d

Observation 2b2ab992-2f10-44b0-9264-ace91ff6c75b · outbound

This paper cites FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling.

Info-Coevolution: An Efficient Framework for Data Model Coevolution FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:28:36.840290Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:36.082877Z digest=sha256:da73bebd127a5329c5c867485be57c5683e3a84451ab8edfde3eacefe7de5318

Observation 86422ae2-4d10-4727-b880-b41fae1147b6 · outbound

This paper cites Dataset Quantization.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Dataset Quantization

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:28:36.615922Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T05:28:36.157381Z digest=sha256:822f7c9674cb8e3d700478c03d91747885d86dbeb2fe445ff20979dda0026eb9

Observation c3c2698a-7a61-4c6e-add2-091cbbc9ee59 · outbound

This paper cites Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books.

Info-Coevolution: An Efficient Framework for Data Model Coevolution Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T05:28:36.247486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:28:36.247486Z digest=sha256:eaf530cdd48659ef545740a04ebdacff85a5c84efd2c3606bc16e8c662d409cd

Pith citing papers

No inbound Pith citation observations are available.