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Paper Citation Record · LEDGER

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers

As of 16 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 3 inbound Pith citation observations for arXiv:2412.15304.

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

pith.paper-citation-record.v1
2412.15304 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:58:04.394490Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:55:06.601276Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:24:19.337232Z

Reference resolution

65 of 65 outbound references displayed

  • verified exact0
  • verified fuzzy51
  • unresolved13
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a276736-2978-4893-a2dc-b91c2e33cb6e · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone, 2024

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.534913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.067598Z digest=sha256:88680c1b0b4232f5dd9a37cde3647ff04bb40c23c7065f9176fe009d0ca36444

Observation 5f1e5727-c9ab-4089-8dbd-82e0b3dbd510 · outbound

This paper cites The Claude 3 Model Family: Opus, Sonnet, Haiku, 2023.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers The Claude 3 Model Family: Opus, Sonnet, Haiku, 2023

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.516084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 9c2d31e8-1482-4082-8d69-9319615cb6d9 · outbound

This paper cites Beelink EQ13 N100, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Beelink EQ13 N100, 2024

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.497328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.079115Z digest=sha256:8967dfb85975b7690e24e84b08c08b54f4ccd501feca4f5957dd0485fba655d3

Observation 6848b6cc-c3a4-4d3b-8ca3-2ab920db5d05 · outbound

This paper cites an unresolved cited work.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:58:05.478337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.084739Z digest=sha256:89f01acbebbd53c8243ba64c96092201a52bacf9461aa33dc3759016374ba523

Observation 3d76385a-3619-41ab-9a04-4fbce6526c92 · outbound

This paper cites Swimming style recognition and lap counting using a smartwatch and deep learning.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Swimming style recognition and lap counting using a smartwatch and deep learning

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.459560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.090445Z digest=sha256:82af8630ff5d7d9d00e020cd06e396aad858a8d16faafec42f589b7691b755ed

Observation 14eab856-5468-4e38-94f2-d6ddae5f8053 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4, 2023.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Sparks of Artificial General Intelligence: Early experiments with GPT-4, 2023

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.430598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.101890Z digest=sha256:26cf5dfdaa9f107b2f3620b027f39927080fc50e97f50bdcc46f18bd3c42f93f

Observation 08f3c6f9-6d70-486a-ab38-b3abb9093838 · outbound

This paper cites DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T11:58:04.107792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.107792Z digest=sha256:9bee395e34ab801d1137eb5cbe4ee48416c593ecb0cba0e3bfa9d376170f9b93

Observation 32408ced-a369-473d-bc2d-1e56662a8dae · outbound

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

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T11:58:04.113336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.113336Z digest=sha256:19d8e6c5e81c80b6b854681055be3ce2e5bd1205f34f6e0c1782079627782382

Observation 215d5d15-0a84-4868-87ab-cdb03696598d · outbound

This paper cites Speech-transformer: A no- recurrence sequence-to-sequence model for speech recognition.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Speech-transformer: A no- recurrence sequence-to-sequence model for speech recognition

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.408725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.118865Z digest=sha256:f2de5f57a939e6a8b20e5c46b1f6e8c38c2a735ab753793e79755a12db9b3665

Observation f903effe-f5a8-4438-8ce9-b9c32c990c2b · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale, 2021.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers An image is worth 16x16 words: Transformers for image recognition at scale, 2021

Reference 10

Resolution
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no resolver link, observed 2026-08-11T11:58:04.124026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.124026Z digest=sha256:a567ee1d2d57295ecad0cdceff39dfe60bad641a5b339756dfeb6ce006d7c2da

Observation c9595f34-139e-4cd8-b47f-c13ca62f8373 · outbound

This paper cites The llama 3 herd of models, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers The llama 3 herd of models, 2024

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.376751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.130223Z digest=sha256:a4711d145379724b817161ed0d92052dbbcacbc4c186aeb93faf07b3561ebf0c

Observation 2f2cf982-6e68-4d31-9888-150533aa9fa5 · outbound

This paper cites AdamW Optimizer.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers AdamW Optimizer

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.357670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.135909Z digest=sha256:16823491cdf668251efb41b040d6c65353820db05bb1d5e1b7f4d400dfeee9ce

Observation 2ea7b96a-92df-4c39-8b34-526a520a7f0f · outbound

This paper cites Codebert: A pre-trained model for programming and natural languages, 2020.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Codebert: A pre-trained model for programming and natural languages, 2020

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T11:58:04.141091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.141091Z digest=sha256:572df31d4f89ba95a54811bfd60614bda039865a44a6dd7df28ca6f6f434abfd

Observation ac968cf6-0949-4f33-90f6-b41af0725d59 · outbound

This paper cites Model Inversion Attacks that Exploit Confidence Information and Basic Countermea- sures.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Model Inversion Attacks that Exploit Confidence Information and Basic Countermea- sures

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.327362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.146521Z digest=sha256:c9b144d829107a7fd42b1e5452fdf95991949d4aaddb76f01d6cab469e44d76c

Observation 76e259b7-0898-4a0b-9633-18ecf4ad7ea7 · outbound

This paper cites Description of Quantization Types in llama.cpp.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Description of Quantization Types in llama.cpp

Reference 15

Resolution
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raw_fallback, observed 2026-08-11T11:58:05.309979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.151356Z digest=sha256:f21c10287080bf941a4807128ed1f475afe346f51ad2801ac4633e1888bcb7fc

Observation 98de512a-1909-4617-92bc-5398d02ae1ea · outbound

This paper cites Llama.cpp.https://github.com/ggerganov/llama.cpp,.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Llama.cpp.https://github.com/ggerganov/llama.cpp,

Reference 16

Resolution
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raw_fallback, observed 2026-08-11T11:58:05.291197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.156256Z digest=sha256:46bc8b5e58c7b165730c0d868412c71b9aa76bea875a493a12bfab63466f1c4b

Observation 5d8bde02-c335-4cce-b6cc-82a2e1ae4ab2 · outbound

This paper cites Mahoney, and Kurt Keutzer.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Mahoney, and Kurt Keutzer

Reference 17

Resolution
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raw_fallback, observed 2026-08-11T11:58:05.254889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.166028Z digest=sha256:9208fa67eaf79223cd36f9a6b65ab950013611938dd36b91cdf8f8c7000c0ff7

Observation fa967c5a-d1af-4af9-bed0-4da368b4081a · outbound

This paper cites The university of sussex-huawei locomotion and transportation dataset for multimodal analytics with mobile devices.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers The university of sussex-huawei locomotion and transportation dataset for multimodal analytics with mobile devices

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.237748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.170917Z digest=sha256:0d0323a8fba7e92b20bb447bdf28cefdd6022e9e77747ad6b3d854e8d49c19cf

Observation a5d3730c-67b9-44b7-a97a-2705cfc0d70a · outbound

This paper cites Rae, Oriol Vinyals, and Laurent Sifre.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Rae, Oriol Vinyals, and Laurent Sifre

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.220009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.175603Z digest=sha256:b3ffe0af51c30e331568dfe052ca7928ca62dc63bb99ad2911f3900b968ebde1

Observation 119cf40d-d83d-4a5d-819f-fadb7d5a5055 · outbound

This paper cites Eval- uating large language models as virtual annotators for time-series physical sensing data, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Eval- uating large language models as virtual annotators for time-series physical sensing data, 2024

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.202844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.180813Z digest=sha256:7016476bf41f54cabe0ec4e3f3eac8fd3f06b8029f1e9ed2ccea1421c213bd42

Observation 40775770-9d7a-482d-8148-32a01b28c8a9 · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.185477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.185694Z digest=sha256:386750fa58ecb284f13c11bcaa0158e21c2219d1b8bc64379741e4612df10daf

Observation 4a046c11-ece1-49f5-9bf0-18847c9f59fe · outbound

This paper cites Dai, Matthew D.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Dai, Matthew D

Reference 22

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raw_fallback, observed 2026-08-11T11:58:05.167004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.190615Z digest=sha256:5b51a9786f988694d4e91f15391afe61463f7aee82fc62ff7366ec1541f930ca

Observation fadf4895-68de-476b-af52-86f7d4cf4f45 · outbound

This paper cites Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bam- ford, and other.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bam- ford, and other

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.148327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.195734Z digest=sha256:e44bd0673ed4683060ee4de5848251485a863d7703a5a500a3fa4b1453719f82

Observation dcd3e917-e56b-43e3-9de8-a1c4e9b23f66 · outbound

This paper cites Brown, Ben- jamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Brown, Ben- jamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.129202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.200745Z digest=sha256:ffea649a2310164b66490dd58b76ece532420dbfb028cd8fc086bca19fa6e4f3

Observation c8681899-49d9-499f-9aeb-3146bec836a4 · outbound

This paper cites Mahoney, and Kurt Keutzer.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Mahoney, and Kurt Keutzer

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.111804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.205844Z digest=sha256:45bebd214a7e06179bcf03a362af55ccdd47798d56bf2526f532053ad956469d

Observation 29c98342-8314-4a82-b547-a604dcc05670 · outbound

This paper cites Lattepanda sigma, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Lattepanda sigma, 2024

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.094784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.211071Z digest=sha256:f02d87549888be1f76e562a95f6e500c5c49e9ed1fef17fae4785e86bbbe02d9

Observation ce28f562-0996-408a-9ce6-72ab3f7377a9 · outbound

This paper cites Starcoder: may the source be with you!, 2023.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Starcoder: may the source be with you!, 2023

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.077734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.216447Z digest=sha256:22b03d654ace9d009d0db74e519b64a35b839cde56e4a9b232a1b87d779a0cf2

Observation 3c379d57-aa79-4dc1-ae11-72d0b325eafe · outbound

This paper cites Large language models are few-shot health learners, 13 Conference’17, July 2017, Washington, DC, USA Viswanadh and Ambuj 2023.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Large language models are few-shot health learners, 13 Conference’17, July 2017, Washington, DC, USA Viswanadh and Ambuj 2023

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.059806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.221144Z digest=sha256:24d12ef2d70080533745a06e810a2dfb2f6e1f0d67786a7a6d01476fc0a62cb8

Observation 328f6048-5bb6-42ef-9743-2b7831a1e304 · outbound

This paper cites The era of 1-bit llms: All large language models are in 1.58 bits, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers The era of 1-bit llms: All large language models are in 1.58 bits, 2024

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-11T11:58:04.226813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.226813Z digest=sha256:044197336b108bbd28ac777aebb4bf3e5dad8bb6de58995ad34e1297627b18cb

Observation be98ab17-6d3e-4f10-86c0-c683d3f97bef · outbound

This paper cites Mankowitz et al.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Mankowitz et al

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.029514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.231859Z digest=sha256:bfd8c81c820c370343f127b9052eab6ac7a6b8225b6a01702e30fefd0c0fea76

Observation 1a98d0af-fee3-44f6-a1ad-135fbec5c4f0 · outbound

This paper cites On faithfulness and factuality in abstractive summarization.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers On faithfulness and factuality in abstractive summarization

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:05.011599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.236995Z digest=sha256:9b093b724937f719129ef8058dbc589c976d72683091778e08ffcff605187ff0

Observation 8e93e6a6-e510-4c26-95b6-640d75068983 · outbound

This paper cites Meta large language model compiler: Foundation models of compiler optimization, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Meta large language model compiler: Foundation models of compiler optimization, 2024

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:04.989903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.242033Z digest=sha256:695369551756cdc600e924b7ec43268956584b0e93dd7cb4b5cd89d79aaef2f2

Observation 61a2f8a7-9679-44bf-9cea-3413e35e73d4 · outbound

This paper cites Codestral: Hello, world!, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Codestral: Hello, world!, 2024

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:58:04.970118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.246871Z digest=sha256:d14fc0b66e3025791f5fd561dbbf04ba784b36cbaa9d5dcf1adf34710c1f333e

Observation 0da16cbe-c8c5-464e-84e5-f4c72391b7fb · outbound

This paper cites Iot-lm: Large multisensory language models for the internet of things, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Iot-lm: Large multisensory language models for the internet of things, 2024

Reference 34

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.251942Z digest=sha256:28997c85c2e94c42fbbdb570c53475a2a81f146257d9ebfa06bd0f62924bde3d

Observation 2b9f6b53-93f2-4f92-9705-abe6f90f23e9 · outbound

This paper cites ChatGPT (June 2024 version).

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers ChatGPT (June 2024 version)

Reference 35

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.256711Z digest=sha256:d4ea20675a098dbe732e82ca31c156ceffc4ec3db7e11d9a2c9744813832bb79

Observation b7baa7e8-7d9b-4b2a-b5ab-d7f537cfbc38 · outbound

This paper cites OpenAI Model Pricing, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers OpenAI Model Pricing, 2024

Reference 36

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raw_fallback, observed 2026-08-11T11:58:04.917879Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.261381Z digest=sha256:845b5d1585a99ca2399cb008c5112f73a0e10ceb9bb3ebdc0cb0f5f1fa97a1e8

Observation 34cd47c5-fa08-42ba-82cb-ab7f3b160e0a · outbound

This paper cites Gpt-4 technical report, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Gpt-4 technical report, 2024

Reference 37

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.266014Z digest=sha256:c409e3416f924fb174c367b8cb9a399158dc04c3288c5e956aa07af7769f65e2

Observation 90995e9a-14c4-4d06-8779-61f5d2fadbe9 · outbound

This paper cites Carbon Emissions and Large Neural Network Training, 2021.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Carbon Emissions and Large Neural Network Training, 2021

Reference 38

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source=pdf_text observed=2026-08-11T11:58:04.270714Z digest=sha256:753559310594f9e70757fed4b79c113d90fa22a4449e6200b0cab4c66bec8578

Observation 81798711-e5ff-41db-8b12-e0c106770302 · outbound

This paper cites FineWeb, 04 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers FineWeb, 04 2024

Reference 39

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.275513Z digest=sha256:91b86ca5fdadb8fe742098f109402d66de0ffc92be2122922e91b134a2c81a56

Observation d2f0a4b4-4152-4b21-9e78-14b736f175b1 · outbound

This paper cites Puccinelli and M.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Puccinelli and M

Reference 40

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.280438Z digest=sha256:4d75eba231c66107a6a6ffd1414126c50bbca51750be4ed62a516342469f0dbe

Observation 4a4c215a-3cb3-44d1-a02c-d2707d48be1c · outbound

This paper cites Language Models are Unsupervised Multitask Learners.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Language Models are Unsupervised Multitask Learners

Reference 41

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raw_fallback, observed 2026-08-11T11:58:04.841585Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.285296Z digest=sha256:6ca7dfb296f8f3384bb754324273d99e9fd2226a97f811a26a9f70492a419664

Observation 25f1d49b-fd8e-4943-bd4e-cca7e9eba1ff · outbound

This paper cites Raspberry Pi, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Raspberry Pi, 2024

Reference 42

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.290603Z digest=sha256:e32c642f5bc568b8e644570e9cd341f54f87c8380b2b8ed6de430bc487f629fa

Observation eeade378-7a77-4a5e-8fc4-b52b82a2afda · outbound

This paper cites Phi-2: The Surprising Power of Small Language Models.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Phi-2: The Surprising Power of Small Language Models

Reference 43

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raw_fallback, observed 2026-08-11T11:58:04.808327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.295497Z digest=sha256:6a607d9714fbd31f71977914992fc0fcbca92956751e42c1aa63dcae1251efb5

Observation 08bd89c1-f411-4e8f-adce-2dd0dfd37845 · outbound

This paper cites High-resolution image synthesis with latent diffu- sion models.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers High-resolution image synthesis with latent diffu- sion models

Reference 44

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no resolver link, observed 2026-08-11T11:58:04.300480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.300480Z digest=sha256:2d718eb1049fd6b03115703f8ff43ae4c0de714637ff164aaab6fb351d755fb8

Observation 1161d9bd-6ed9-4a9b-9e80-baaadd2ee4d0 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Code Llama: Open Foundation Models for Code

Reference 45

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no resolver link, observed 2026-08-11T11:58:04.305662Z

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source=pdf_text observed=2026-08-11T11:58:04.305662Z digest=sha256:3246237a5b43e2c49e1503b9a8623f61c908f5e8713029184c1d2d01fce9f8b3

Observation 399f9c6e-3547-4138-bc50-f4379522d028 · outbound

This paper cites Grandmaster-level chess without search, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Grandmaster-level chess without search, 2024

Reference 46

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raw_fallback, observed 2026-08-11T11:58:04.780766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.310974Z digest=sha256:ef7f97395b851e5e702e39666c4bc7a7fee7aabec15463ba7c59d6e2742845db

Observation 55d35ca3-6ae9-452a-941a-e17de982fea9 · outbound

This paper cites From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference

Reference 47

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raw_fallback, observed 2026-08-11T11:58:04.763438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.316578Z digest=sha256:06a0a4fdf6877536bbea91b70d52fd730dba3d1ede2c64a3eb4f99184c868712

Observation 74a3ef2f-f507-47ec-9d34-b88870329ed7 · outbound

This paper cites Orange Pi 5, 2022.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Orange Pi 5, 2022

Reference 48

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raw_fallback, observed 2026-08-11T11:58:04.747528Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.321342Z digest=sha256:54f947a63992602cb467296d11431e57abdf7a4796296886558fa37e793558ad

Observation 0398875b-d27c-46d6-a859-d25639dc658b · outbound

This paper cites Orange Pi Zero 2W, 2022.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Orange Pi Zero 2W, 2022

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-11T11:58:04.731353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.326261Z digest=sha256:ade00ffd8be7aec4879da3cc048c94b57c1f053427ac6169a8db58380a9343f1

Observation 53f77eb3-2559-4873-bde7-86c4b5a8519e · outbound

This paper cites Knowledge boosting during low-latency inference, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Knowledge boosting during low-latency inference, 2024

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-11T11:58:04.712690Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.331493Z digest=sha256:e3a354d0acf5045b17f3f96439e6fe98be66700c483d72fb20f73d1c0110feec

Observation 3e648f86-11af-4e62-9dd5-fa4c3e7249bc · outbound

This paper cites Hashimoto.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Hashimoto

Reference 51

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verified fuzzy
raw_fallback, observed 2026-08-11T11:58:04.694332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.336330Z digest=sha256:b6294528510c17651c7f167082da00c5ee9f7566fb116618758d74872a313bf8

Observation addb9801-74c3-4169-85d0-2f3305c09a40 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Gemini: A Family of Highly Capable Multimodal Models, 2024

Reference 52

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raw_fallback, observed 2026-08-11T11:58:04.676978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.341096Z digest=sha256:e058f5ca0915d9428ad5ad5b60aae526906eeddecec78ac856045134c450314f

Observation 4b47462d-af38-4aff-b7d8-555f8cc746aa · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Gemma: Open Models Based on Gemini Research and Technology, 2024

Reference 53

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raw_fallback, observed 2026-08-11T11:58:04.660383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.346113Z digest=sha256:7422587cbed93f259c13c714739f290fce179af732e6e49bdf8c90e3a9731b7a

Observation 7bad7ead-4c0b-4878-8a15-0f672500b64f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 54

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no resolver link, observed 2026-08-11T11:58:04.351506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.351506Z digest=sha256:7a5b347d23b6ccbf9619a67c5345ae17dc2330a294c04b6e670984442ddc6385

Observation b0383d7d-9c72-4f42-9bfd-63f47f679ea6 · outbound

This paper cites Recognizing de- tailed human context in the wild from smartphones and smartwatches.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Recognizing de- tailed human context in the wild from smartphones and smartwatches

Reference 55

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raw_fallback, observed 2026-08-11T11:58:04.643277Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.356391Z digest=sha256:03aa17b0d037175c865a740fbce3cd77ac530ba5fc2fbd41d21d96090ab636d4

Observation d3a6c806-dd7f-4857-8514-1bc78d5676e7 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 56

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.360890Z digest=sha256:c0979775c36642526dcf7c3088f23cee3fa25e48e922f66f2318fa3874a23896

Observation ffa2153e-670c-40a7-8ed5-f83e77484e00 · outbound

This paper cites Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M

Reference 57

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T11:58:04.365431Z digest=sha256:1b4c6e5c9f25ab70ce9a6f49f117462969709e1036554887063889f5c64d30ab

Observation 433a5467-8e02-4584-bc28-1ece9e899495 · outbound

This paper cites Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus

Reference 58

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raw_fallback, observed 2026-08-11T11:58:04.596896Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.370491Z digest=sha256:93184ec21956c32e3878a783db3cd93dd494a5e626337d98d1df319bbf445a58

Observation f45b09bd-0a26-4aed-ad4a-b045dbb3ddd0 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 59

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raw_fallback, observed 2026-08-11T11:58:04.578211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.375127Z digest=sha256:cbae48833faedbabc1c8750637b6a148e16a06055f86af7e23acfe40b5d5c039

Observation 70f04b59-ee70-4dca-8dc8-5e54fc5557bc · outbound

This paper cites Pene- trative ai: Making llms comprehend the physical world.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Pene- trative ai: Making llms comprehend the physical world

Reference 60

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.379923Z digest=sha256:9031829ba700e2c3ba6773120c528c580d876f9d913157364a47ce9aaafd9978

Observation d163bbad-651d-4eb6-95a4-70fb49185fed · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Hallucination is Inevitable: An Innate Limitation of Large Language Models, 2024

Reference 61

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raw_fallback, observed 2026-08-11T11:58:04.540972Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.384631Z digest=sha256:8e99ae8973bf82328b17e3184c73c32ce5086b4478a0c80411d1fddc33247648

Observation ab878af9-f70d-4cf9-b470-b42123c3941c · outbound

This paper cites Mobile foundation model as firmware.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Mobile foundation model as firmware

Reference 62

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raw_fallback, observed 2026-08-11T11:58:04.524835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.389625Z digest=sha256:e6bef07bcf00344abb8e242540a5722e66406e4ca1d6205ed4b033ddb74a4b74

Observation e4fadee3-4351-440d-9ede-a7bb613de5ff · outbound

This paper cites A Survey on Efficient Inference for Large Language Models, 2024.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers A Survey on Efficient Inference for Large Language Models, 2024

Reference 63

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raw_fallback, observed 2026-08-11T11:58:04.507944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.394490Z digest=sha256:b797ab25f5c8bef752cd0b7129b7167b4c3d5a65f6d0ac9d4facb7daba39fd98

Observation 9266a7ce-aedf-4c46-8179-3f87ed2ca6f1 · outbound

This paper cites an unresolved cited work.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Unresolved cited work

Reference 2019

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no resolver link, observed 2026-08-11T11:58:04.095994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:58:04.095994Z digest=sha256:af082c198e06ab95830148999203e8ad8147fb7c219e6176f99a5d571d3a74ed

Observation 48c56d81-9985-4412-887a-c9a89d8e36c8 · outbound

This paper cites an unresolved cited work.

TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers Unresolved cited work

Reference 2024

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parse uncertain
raw_fallback, observed 2026-08-11T11:58:05.273411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T11:58:04.161232Z digest=sha256:f70e260e453ae54a61379f460cbdc4ea299414ca1e9d2b1f3a340bb6eceb349d

Pith citing papers

Observation 2026bde9-e74d-4f90-89e3-5b2eac1b236f · inbound

Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission cites this paper.

Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:06.601276Z digest=sha256:887aa89ff24f7dfac1bfba83e9f4d0c0c028c5698b2dae1a2359e5aa1debc161

Observation c07ea990-c21b-4fc3-a972-751a27ca755b · inbound

Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction cites this paper.

Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers

Reference 67

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arxiv_id, observed 2026-05-19T20:02:44.877533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-19T19:59:48.949689Z digest=sha256:9eec0dbcd06f8098380bb939fb4626834d8acec1d4dc097393c130f049ad7406

Observation 48007148-96b7-46f1-9cd7-49f44d3cc8e1 · inbound

Little Brains, Big Feats: Exploring Compact Language Models cites this paper.

Little Brains, Big Feats: Exploring Compact Language Models TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers

Reference 15

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metadata mismatch
arxiv_id, observed 2026-06-30T06:24:19.338688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T06:18:11.792566Z digest=sha256:8f923d386c4f0161ddf87826dadb0f38bd6dbab79d316dbce043567450ae4beb