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Paper Citation Record · LEDGER

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

As of 7 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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:20:04.267992Z digest=sha256:f029c239172969a035674fa941e4844b1677ea66cca27b1d08ae83283750aa95

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=sha256:7ac5b140fe99858e571462c49e1e4298a608e08371709bd10e4d5bd14d33c199

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=sha256:210e78924bdf9b86391db947c90cdd9a6b47c1f319b5fa06f63c727f37da7a4d

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=sha256:7024460ae4b083e72cbb4210b1cb48db73a532115ba420d0dc078d8f88727d64

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

source=pdf_text observed=2026-08-07T04:20:01.864939Z digest=sha256:5f4c8027896d70c345e30cd3e0ad82b1deff5bad5301df8967256ec0d9f34b4f

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=sha256:83f356c01facea3a6d272ebe7ae9cc7ef955d179cff7328b70406c7119d915b7

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

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T04:20:02.148062Z digest=sha256:d3cd1e1485bbc53866ae50429f904ec3847139ba7ea8cde5ae539dcf3433dcf7

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:d71acb197f81ba7eaad7df072ca5cfbfce00fa8f373938544de921b44931cd9e

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=sha256:89769fe03d720b58abd723dbe74d9a791bfa668d35a92e24e3ea3141f4bc3307

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=sha256:0610bf4e33e8534d7965298ed862a0ffa4cd2ee166eec7f3e6abeea680f54497

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=sha256:98274203c7c24855d82519c33370e6d82197046c2a04a2327695f15d870d011f

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=sha256:9d74e16037f377a711a5b6cb2327ac36d8f9d17ff77854202cf7290e56bd7e2a

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

source=pdf_text observed=2026-08-07T04:20:02.891618Z digest=sha256:ab2474e2a9844c5a679d85fee6e8c5991b7ef058e04b34d43a46f2e34d08ea8d

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=sha256:764c77e618efa42c04641a2f2b8ee62cb3d73d3fb0f40e8757e0b298f667db24

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=sha256:1743ec9360d3255ae1b9df5438cb3871c4609d4ecb4f16ff9bbb7ce6de4b6081

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=sha256:51a758b3e210f5915b0932c166685e559b144cfa34aa67a2da53fe7f640c7e7a

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:e2d6a1e2ffd77dde170d025b5213ebd2ca2cde4ad522fbbd0051d9469dfa3765

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=sha256:03dd72230a7b75b2ee6db707903fbad33d24ef9e754013a7f71462b5ff698dbc

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=sha256:946f851f1f1b742eeef8e2350042676aedb1c87983433050bb32225a037bfbb3

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=sha256:2e79483ce0ad4adc02b11bd9692847a92a89981c2cb6d3540874a93312aeffa9

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=sha256:2dcfa5e0c886863de82e8ed205e0587c9be0c4f1d764cd6a1ff499fe37f10a23

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=sha256:67f8e38784d2ec895b0a663e837b098ffaa573e672d94b19ada7f8feb0392133

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=sha256:355b9c9ca60d95f81f9129e6c3d0d1363607ab41f02b4dcdfb8645bb43bf8ad8

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=sha256:1797471cc9cadae7ecfbbe8aab6427f85d21e210f197a787e167a0538ebc84d5

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:affeb244f6f64149020b258cfb31c1b72e3cd3043948ee498f144466b342cc87

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

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=2026-08-07T04:20:00.935672Z digest=sha256:2f82287a18bf38a4d44cb2e8c416db24abf59969c3bb0c8cfd8181b56b39750a

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=sha256:4213be8aa370fba51e567f65002c78c2a378daa3872cd677fd409e5682b8b763

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=sha256:66c0c47b99d24c59a6247c937e46203d84e6c8d5aa15ebd1633b9dfee0d3abf6

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=sha256:8ed37b572b5439046cff2e243033824d89ac9ddbee15495ac38387b61ca8905d

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:cf58d178943821ea33ba09c8fc4ee03340d5e41a09bfd259563f25784b455b57

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:aadd4b459b6c5038b137d46d72cb4c0a241aa1a7b60a484206934bf0d0196737

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

source=pdf_text observed=2026-05-19T05:39:53.088948Z digest=sha256:4be39cc98f2f946702f3641d5a784e2541b2d87bebc5348dba262adc151ecb9e

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

source=pdf_text observed=2026-05-09T22:20:21.090246Z digest=sha256:9153d07ba09dbde6d531c63632ebfc5a46cde01e96cdabed04857e1b6132eb49

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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source=arxiv_source observed=2026-06-28T20:45:29.941331Z digest=sha256:c89ba8f5013055af451c6cb41852a5dea9fbb2fa2b3423eeecfd93f801e81888

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

source=pdf_text observed=2026-06-27T14:07:42.062805Z digest=sha256:e05688a4d8e464f1737cbeb76133ff2bde013bb5e6e4fb993c08e2f0e07f8151

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=sha256:8d5ce6788cb916a95a1fd970deda690991174dd9404209b1b640fefc4bc8234e