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Paper Citation Record · LEDGER

NoLoCo: No-all-reduce Low Communication Training Method for Large Models

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 5 inbound Pith citation observations for arXiv:2506.10911.

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

pith.paper-citation-record.v1
2506.10911 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:20:04.267992Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T12:16:10.904456Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:07:37.154141Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17186e22-de31-4b7b-80cc-87472de0c03a · outbound

This paper cites an unresolved cited work.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Unresolved cited work

Reference 2

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T04:20:04.267992Z digest=sha256:face8480cff45a9f26b613edb6ce0cb7b9d74005759509cb21b7d570b2adc301

Observation b44aafe4-29b1-4f1c-b8fb-76420366909f · outbound

This paper cites Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo

Reference 3

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source=pdf_text observed=2026-08-07T04:20:01.105792Z digest=sha256:6f4885359be8e2ad61dcc39c170b14e3ff2be0bc1c4062b5af73194e05ebfeb0

Observation dc4ae879-c1d3-40eb-9951-968107914fb6 · outbound

This paper cites Efficient Training of Large Language Models on Distributed Infrastructures: A Survey.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Efficient Training of Large Language Models on Distributed Infrastructures: A Survey

Reference 7

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source=pdf_text observed=2026-08-07T04:20:01.632451Z digest=sha256:8ca4d21b9a6094e3efafde9de518057bd2d0c662857fb3f6c44b08dabf1965d8

Observation 6d204c69-99c5-4a21-bd5b-525ec5c9bf7f · outbound

This paper cites Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator

Reference 8

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source=pdf_text observed=2026-08-07T04:20:01.748761Z digest=sha256:8fe2a440d65ead0932cc12c85a5468bc90ba046ffd2520c0543cbb3a62f3cec3

Observation 3e815e8f-b7d8-4f4f-a5e3-437ea48638f0 · outbound

This paper cites Multi-modal retrieval for large language model based speech recognition.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Multi-modal retrieval for large language model based speech recognition

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T04:20:01.864939Z digest=sha256:0e3a54a35570ac8de7fe593e0f284473ebf7d51e3e790f0f84a84996c75dba0b

Observation b01983a9-bd29-40e3-9935-155b55572131 · outbound

This paper cites The Llama 3 Herd of Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models The Llama 3 Herd of Models

Reference 10

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source=pdf_text observed=2026-08-07T04:20:01.974493Z digest=sha256:31a444930d376ec6e767cc80d52d43b1b4d49694b7fa9fc176615257e476ec7f

Observation 77259df9-ea19-4f21-ac1c-ae6023676458 · outbound

This paper cites Gossip learning as a decentralized alternative to federated learning.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Gossip learning as a decentralized alternative to federated learning

Reference 11

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T04:20:02.148062Z digest=sha256:65f96cc6ea19e85cb225a15e2feec92f93f87751a7951960ee23d3588b52ca61

Observation 3374d221-5816-47ef-83dd-ecb16684645c · outbound

This paper cites INTELLECT-1 Technical Report.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models INTELLECT-1 Technical Report

Reference 12

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source=pdf_text observed=2026-08-07T04:20:02.210179Z digest=sha256:580b41317434fa6698b09878321efa72a0b497402153eb10afe1c5a5d32f6381

Observation 3c9ed00b-a904-4091-89e0-3e9c0ab20b07 · outbound

This paper cites Eager Updates For Overlapped Communication and Computation in DiLoCo.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Eager Updates For Overlapped Communication and Computation in DiLoCo

Reference 13

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source=pdf_text observed=2026-08-07T04:20:02.348645Z digest=sha256:faa6170bcaf49c8f7f62f0e1bcbebf9f4974c9974b7ccd38d33d13ddfc2da348

Observation b4b1e656-90ed-4aec-8020-cb5f269abe2c · outbound

This paper cites Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

Reference 14

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source=pdf_text observed=2026-08-07T04:20:02.441768Z digest=sha256:759aac1293235c33f57dad0fb6ef5e77fd18a5d107587f5a041e716fc34e3441

Observation 10e37934-bee7-490a-a7d6-0280c0aea2cc · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 16

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source=pdf_text observed=2026-08-07T04:20:02.634075Z digest=sha256:63533b85d6bd91369101cc0288751002ffea15e5acb0db95cedab8f21c7b8cf4

Observation 530c52cb-cc8e-42ee-8693-b14682ecb157 · outbound

This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 17

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source=pdf_text observed=2026-08-07T04:20:02.772956Z digest=sha256:832fad1c5a727301d5e7304787af3ee1e654257b20d006bd4ffb9e1474863c26

Observation a4cc0a39-b71e-4342-829e-77b1049da533 · outbound

This paper cites Voxtlm: Unified decoder-only models for consolidating speech recognition, synthesis and speech, text continuation tasks.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Voxtlm: Unified decoder-only models for consolidating speech recognition, synthesis and speech, text continuation tasks

Reference 18

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raw_fallback, observed 2026-08-07T04:20:05.196952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T04:20:02.891618Z digest=sha256:441232e9f2dd153b83f9228772a744862aa7f243e426c8a515ed40f8930c33b4

Observation aa96d7c7-d788-4ba7-87a7-e7b9fa5ab439 · outbound

This paper cites Decoupled momentum optimization.arXiv preprint arXiv:2411.19870,.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Decoupled momentum optimization.arXiv preprint arXiv:2411.19870,

Reference 19

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source=pdf_text observed=2026-08-07T04:20:02.977721Z digest=sha256:176d8b182d0104db9e5ae41207f9bb281fd5bb7b825a592fa3a6c1132e42c88e

Observation 95c78d5c-61c1-4da5-ab96-fd94ef911029 · outbound

This paper cites AudioPaLM: A Large Language Model That Can Speak and Listen.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models AudioPaLM: A Large Language Model That Can Speak and Listen

Reference 20

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source=pdf_text observed=2026-08-07T04:20:03.098045Z digest=sha256:5881b4d465b050d5d1db79d0c5912a1a27a0919ab03401a18dd635afd1efad84

Observation 32e693ef-cd80-4b41-b600-8517c3f41108 · outbound

This paper cites Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training

Reference 23

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source=pdf_text observed=2026-08-07T04:20:03.399755Z digest=sha256:a9dd106b91d3e89463598dfdbf5434e39d34d55ac9bcf1eceb86e5717b6e2d8f

Observation cd3950f5-67fb-440c-b0d9-34d6f7f6243a · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Gemini: A Family of Highly Capable Multimodal Models

Reference 24

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source=pdf_text observed=2026-08-07T04:20:03.463865Z digest=sha256:9f5d303ad3a088800e16353ec3f1917a9c1420e56b795593f71f46278a271e08

Observation 7bd30d2f-f0d0-4fb0-8a71-1d82af263cdc · outbound

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

NoLoCo: No-all-reduce Low Communication Training Method for Large Models LLaMA: Open and Efficient Foundation Language Models

Reference 25

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source=pdf_text observed=2026-08-07T04:20:03.596714Z digest=sha256:1fd2426f654d41bd2b93784d06aadff2ad03430c9dd57351a368a1c3963a0c4d

Observation acc50d72-f732-4238-9b91-c6dec2b411bf · outbound

This paper cites Understanding Short-Horizon Bias in Stochastic Meta-Optimization.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Understanding Short-Horizon Bias in Stochastic Meta-Optimization

Reference 26

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source=pdf_text observed=2026-08-07T04:20:03.712956Z digest=sha256:213daa20f6e5a365128d0f16d575701a518d040c12730caf4ddbb12ba0f04ac4

Observation 220f8bba-cf8e-446a-b0d3-556a1f5f6b26 · outbound

This paper cites LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

Reference 28

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source=pdf_text observed=2026-08-07T04:20:03.894658Z digest=sha256:2755567429f07730af2ba6d492f6371e4ce29e86135fcf484f03de3060a130b2

Observation 690eda80-e49a-436d-882c-c9a5acbceded · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models OPT: Open Pre-trained Transformer Language Models

Reference 29

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source=pdf_text observed=2026-08-07T04:20:04.008649Z digest=sha256:18be2528fcd4ce85a2baa7773e33e5bf48fd1737d153d203d95dbe14003fb497

Observation f07824be-df69-48a6-93ae-a5650d60d4c5 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 30

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source=pdf_text observed=2026-08-07T04:20:04.131435Z digest=sha256:734bd06d46845aa265e125c5665f69b52c4e45efbb11404dd19f681c2b00f1f7

Observation 53121814-be8b-4886-b40d-58a300d2c5a5 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 2013

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source=pdf_text observed=2026-08-07T04:20:03.212684Z digest=sha256:14471523b37573e405ffc5e64f94da01b160b95d1c8ff2e658e5eaeb33f1f834

Observation 98f1ddbe-3a72-4627-8ce1-11e6c3bbf1d3 · outbound

This paper cites Qwen2.5-Omni Technical Report.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Qwen2.5-Omni Technical Report

Reference 2018

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source=pdf_text observed=2026-08-07T04:20:03.825593Z digest=sha256:5b165033a60d9f06cb7bdd9824cb4de960b86da9dfddcf19eff43eb44f680db2

Observation 447711e1-fe9b-42e6-9541-dab3815cd531 · outbound

This paper cites Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters

Reference 2019

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source=pdf_text observed=2026-08-07T04:20:03.307804Z digest=sha256:ab5532dd8578991808d96ee4daf4146781c1e949362859b53acbbe5d2518ca84

Observation 55ee83f6-9f03-4f0c-a4d7-a8b5b4219cc9 · outbound

This paper cites Boosting Asynchronous Decentralized Learning with Model Fragmentation.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Boosting Asynchronous Decentralized Learning with Model Fragmentation

Reference 2020

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local_arxiv, observed 2026-08-07T04:20:04.862629Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T04:20:00.935672Z digest=sha256:810628f01deaf474bca79fb6ac7a4d76f81dee2b31fe88b77cbfb97665540242

Observation 407c224f-5d82-43e0-9a22-a0ba775b36cf · outbound

This paper cites DiLoCo: Distributed Low-Communication Training of Language Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models DiLoCo: Distributed Low-Communication Training of Language Models

Reference 2021

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source=pdf_text observed=2026-08-07T04:20:01.338312Z digest=sha256:0e1e843727321443681d1c2485bfc12954afe229dca1260e9db16789beaa73fa

Observation 1faccbb5-2426-49f5-9ba6-a16d4417fef3 · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 2022

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source=pdf_text observed=2026-08-07T04:20:02.518775Z digest=sha256:cbf59212b0e74f74f9601513e525386bf29e577abc6a51d6f4e7f6c69e326b71

Observation 4d229f63-2d0b-43a6-bd9c-1b00d23b3a15 · outbound

This paper cites DiPaCo: Distributed Path Composition.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models DiPaCo: Distributed Path Composition

Reference 2023

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source=pdf_text observed=2026-08-07T04:20:01.483869Z digest=sha256:b90f50cb446e3e5db2bbdfbc6729997fb98af4fe84de1e120156bfaadf5869af

Observation 280f9081-1285-4bea-b77f-4aa7a991a3ec · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models A Survey on Mixture of Experts in Large Language Models

Reference 2024

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source=pdf_text observed=2026-08-07T04:20:01.009446Z digest=sha256:36f62bf87e4f1abf2e837cda6d05c2bcb4f1e30dd4d1439308c6d6b947b1bbe6

Observation a13ce9fc-8aa6-4add-9d92-7032a549a2ea · outbound

This paper cites Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.

NoLoCo: No-all-reduce Low Communication Training Method for Large Models Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Reference 2025

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source=pdf_text observed=2026-08-07T04:20:01.226611Z digest=sha256:4e7086f96b19eb77b75f866f14b48e002d32798f09eb018fe05eb1f4a7104e17

Pith citing papers

Observation 9f4ddb48-f020-4438-8ec9-11deb9df7c3c · inbound

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning cites this paper.

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning NoLoCo: No-all-reduce Low Communication Training Method for Large Models

Reference 41

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arxiv_id, observed 2026-05-19T05:42:06.077358Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-19T05:39:53.088948Z digest=sha256:cff5ec2700f39de7e45f7548616fbc8a87202c5e9df3eca529e4d5a882f2a290

Observation d5a3b6dc-44df-48ab-b0e6-d5b776525c35 · inbound

Decoupled DiLoCo for Resilient Distributed Pre-training cites this paper.

Decoupled DiLoCo for Resilient Distributed Pre-training NoLoCo: No-all-reduce Low Communication Training Method for Large Models

Reference 12

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arxiv_id, observed 2026-05-09T22:49:15.635560Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-09T22:20:21.090246Z digest=sha256:5b82ddf0691b87bd027bfe53a3a1fdf06ec98e78b7308c42068ba25d1aed3d8c

Observation 1369cf40-bbcb-47e1-8b3d-2acc0ba81afd · inbound

HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity cites this paper.

HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity NoLoCo: No-all-reduce Low Communication Training Method for Large Models

Reference 9

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arxiv_id, observed 2026-06-28T20:52:37.872136Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-28T20:45:29.941331Z digest=sha256:fbc4a645b8bb28e80616d6ff0203c43ab705817a24273f916b11ddc5b5cec99f

Observation 4ee2c74a-bab3-43c8-8200-9d4ed470519e · inbound

Unifying Local Communications and Local Updates for LLM Pretraining cites this paper.

Unifying Local Communications and Local Updates for LLM Pretraining NoLoCo: No-all-reduce Low Communication Training Method for Large Models

Reference 16

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arxiv_id, observed 2026-07-03T04:07:37.155683Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-27T14:07:42.062805Z digest=sha256:57d395263a67215deab11e7768405fe94fe3ed5c49a31048d85d8d4a94ecb560

Observation 20fcc947-712b-4d19-80f8-a8be10b42200 · inbound

Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure cites this paper.

Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure NoLoCo: No-all-reduce Low Communication Training Method for Large Models

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T12:16:10.904456Z digest=sha256:733e0714799dc4edb0fd914abe48828d2a1f2b5b14f5fc452866343d897e1e82