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Paper Citation Record · LEDGER

dots.llm1 Technical Report

As of 19 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 5 inbound Pith citation observations for arXiv:2506.05767.

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

pith.paper-citation-record.v1
2506.05767 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:19:11.474503Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:55:09.763377Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T20:40:36.526819Z

Reference resolution

52 of 52 outbound references displayed

  • verified exact1
  • verified fuzzy6
  • unresolved44
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef6c8e10-a1ae-4d1c-96dd-93af39aa0d70 · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

dots.llm1 Technical Report GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 1

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no resolver link, observed 2026-08-07T10:19:05.944665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:05.944665Z digest=sha256:ee119598c3dc41b696b48c78353de2efe185189b20e4a0c4e08c46903c36b1fe

Observation 874c6d55-412e-44e8-837b-22792e1024e3 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

dots.llm1 Technical Report Evaluating Large Language Models Trained on Code

Reference 6

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source=pdf_text observed=2026-08-07T10:19:06.505142Z digest=sha256:10d60a0f3f09c80d2a40dd201e2360d6b18c379bb0cf549cf670d9b7694e3511

Observation a929a264-7db0-4d92-973b-318edfa6424a · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

dots.llm1 Technical Report Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

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no resolver link, observed 2026-08-07T10:19:06.589140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:06.589140Z digest=sha256:5cf8f10b77f1f0fea650489d1069a66b3787a5717861688e5fe30aa2a918d4cb

Observation 4de6955d-0759-439f-88d1-fa8474b325bf · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

dots.llm1 Technical Report DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:06.770176Z digest=sha256:7e63084faedbee950f0ddd6fa6bd28f529c950f14bce422dab7b342adef81230

Observation 7dda731e-f33b-44d3-8d76-0bfb6f1de269 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

dots.llm1 Technical Report DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:06.869670Z digest=sha256:c52040735623c0d29cad1710c4152fbf98065b9e6a4fbdd9108f52b613452883

Observation 466d1906-528a-4036-a4db-93f0a6e31ad2 · outbound

This paper cites DeepSeek-V3 Technical Report.

dots.llm1 Technical Report DeepSeek-V3 Technical Report

Reference 11

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no resolver link, observed 2026-08-07T10:19:06.966756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:06.966756Z digest=sha256:ae759b7d5a2e6ec237cdafb11ecce2fac76e9c20fef44faa0ed4796287d2f3be

Observation 70df57b9-3c64-48b7-86be-03d5b1a13380 · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

dots.llm1 Technical Report Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 14

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no resolver link, observed 2026-08-07T10:19:07.247635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.247635Z digest=sha256:3a75cdf301a18b90f197cb1c5518b58b5f6b2d67f72eb816e125739e2a67a286

Observation 2b325499-623b-42c7-a976-6ef01c17d7ee · outbound

This paper cites Are We Done with MMLU?.

dots.llm1 Technical Report Are We Done with MMLU?

Reference 16

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:19:07.465034Z digest=sha256:bedebbe4a9fbf88f801a8c30e8643c19b147988799dbde0d00a79a2d69cc6df6

Observation 9fdb3c03-4061-4d7a-9a7e-936015379f53 · outbound

This paper cites Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation.

dots.llm1 Technical Report Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation

Reference 17

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no resolver link, observed 2026-08-07T10:19:07.566146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.566146Z digest=sha256:3b4a6b04f774e8ba40a6964b8378f6da1ea4f927a73f01d8def2aeb3df214840

Observation a16ed031-f900-4199-8887-d64ba2551741 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

dots.llm1 Technical Report RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.663478Z digest=sha256:cc4488ac91f452958576f421bd2f83195a4f49246aa1ea60c19859b805e5a6b8

Observation 3fd57e70-35bd-4eca-a815-f60b6b26379c · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

dots.llm1 Technical Report MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 19

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no resolver link, observed 2026-08-07T10:19:07.787573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.787573Z digest=sha256:3a19c44ffe0c68620747b6aedb59f2bd6f3f51e5870e9356f453acb39d6e9aab

Observation 2d0cfd26-094f-4b46-89d4-b83703665267 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

dots.llm1 Technical Report LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.880725Z digest=sha256:dd756b701733695d2ae6a530507bc073a36e14f7ea9494a8b63f1518f43c4619

Observation 4698fee8-303f-49a7-8c53-e933f36d40fe · outbound

This paper cites Style Mixture of Experts for Expressive Text-To-Speech Synthesis.

dots.llm1 Technical Report Style Mixture of Experts for Expressive Text-To-Speech Synthesis

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.960917Z digest=sha256:a6eb6205383cfe5fb64433947137dbd0f9d6330b09f2f984ff92bdd7426ba372

Observation f86bd160-19ff-4530-b1d6-2534e72d62a6 · outbound

This paper cites Mixtral of Experts.

dots.llm1 Technical Report Mixtral of Experts

Reference 22

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no resolver link, observed 2026-08-07T10:19:08.097824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.097824Z digest=sha256:2e9302933b6eda9e28aacb6b5f67659fa21e443a9770f11989ea473a7f134e02

Observation 5a3de6e9-767f-451e-a3a5-718ee58beeb3 · outbound

This paper cites URLhttps://doi.org/10.1162/tacl a 00276.

dots.llm1 Technical Report URLhttps://doi.org/10.1162/tacl a 00276

Reference 24

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no resolver link, observed 2026-08-07T10:19:08.319176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.319176Z digest=sha256:b0b4d21f69c9000ab1b93e1a6df1a97017849ec270224cd0d32561d8e177669c

Observation 2c9352a6-200d-43b2-8bf1-6ccd7310f589 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

dots.llm1 Technical Report CMMLU: Measuring massive multitask language understanding in Chinese

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.400230Z digest=sha256:986c621a1a11923775f04165829fc83a197a13d787b0e01ac486b25e850c72fd

Observation ea5670b8-aafe-413c-89de-390a86bd6947 · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

dots.llm1 Technical Report From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.514551Z digest=sha256:7fbf27f2e8ce9bc698d38468168e2145baccfb1985758db34ccf3699b67905d8

Observation b125ddbb-6f4d-44d5-8f15-a1a3eb7d7ef8 · outbound

This paper cites A coordinated tiling and batching framework for efficient GEMM on gpus.

dots.llm1 Technical Report A coordinated tiling and batching framework for efficient GEMM on gpus

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-07T10:19:14.410380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:19:08.637720Z digest=sha256:f59585e0da560fa878a827658834db22039a7cdd85fa5cbdd184b61aa4b5ebc8

Observation 15cae609-7370-44d3-b397-353c30ac1352 · outbound

This paper cites URLhttps://doi.org/10.1145/3293883.3295734.

dots.llm1 Technical Report URLhttps://doi.org/10.1145/3293883.3295734

Reference 28

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no resolver link, observed 2026-08-07T10:19:08.722169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.722169Z digest=sha256:b87e5cf777bfd8900d044e55e5dcd887055d550984e4d659e4755ae82036c1c0

Observation 8527008f-023f-43a9-bbdc-fe0330356534 · outbound

This paper cites Evaluating Language Models for Efficient Code Generation.

dots.llm1 Technical Report Evaluating Language Models for Efficient Code Generation

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.859635Z digest=sha256:b9ae0c0b1347f1d2e58ab18a78f07ea575acddb97198b1eaefb982f2d37b154c

Observation 9a816d02-a227-46da-b469-98334aa45ca4 · outbound

This paper cites American invitational mathematics examination - aime.

dots.llm1 Technical Report American invitational mathematics examination - aime

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-07T10:19:14.138644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:19:08.996594Z digest=sha256:72538d5e3e6ef46ad822eab681fb6ecee140b66fb359c14f0834364a93700ac0

Observation de4206f7-58c5-4b9b-881c-f4cbb172ddd2 · outbound

This paper cites 2 OLMo 2 Furious.

dots.llm1 Technical Report 2 OLMo 2 Furious

Reference 31

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no resolver link, observed 2026-08-07T10:19:09.100923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.100923Z digest=sha256:5cac4c4c31655fb960bb4f5e071d11b9a554abcf4427fac17019670d9b96e4b1

Observation 2c1639d7-5a84-4d64-984c-650507aab5d5 · outbound

This paper cites an unresolved cited work.

dots.llm1 Technical Report Unresolved cited work

Reference 32

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raw_fallback, observed 2026-08-07T10:19:13.844193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:19:09.191769Z digest=sha256:c1baeaeb8e1780f2683edb418ce5fa1eb13b6130c82aa52aebba7752787a0745

Observation 199f002f-1d8a-42ba-ad1c-186077619a80 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

dots.llm1 Technical Report Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.404970Z digest=sha256:53fe382df672cdad974f7b7f5303816ecb954e3df0e814d658d349ad264efc15

Observation 08225807-ecd9-41ec-ac8d-8bc2997c788b · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

dots.llm1 Technical Report GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 35

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no resolver link, observed 2026-08-07T10:19:09.508961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.508961Z digest=sha256:cdd7bf0e81863a5752dabf0c69a47c1e531dd015a169517ccbfb0ea90606d501

Observation 22c20e23-6217-4f49-9916-af3bbdd70b5e · outbound

This paper cites GLU Variants Improve Transformer.

dots.llm1 Technical Report GLU Variants Improve Transformer

Reference 36

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no resolver link, observed 2026-08-07T10:19:09.613113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.613113Z digest=sha256:47774880cedf716756ea0732b015fbfcd1b6fa0701b3f1611a2feb30ddb56aac

Observation 8e802fe5-4bee-4ec8-8294-0adb838c78f0 · outbound

This paper cites Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

dots.llm1 Technical Report Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 37

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no resolver link, observed 2026-08-07T10:19:09.708715Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.708715Z digest=sha256:87064a61e2514ce77c33d5f861c2914f5559412757bf9e14439b97c78ccaeb11

Observation bf8e0a40-cfe6-4325-a704-9ce7aabcf641 · outbound

This paper cites SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines.

dots.llm1 Technical Report SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Reference 38

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unresolved
no resolver link, observed 2026-08-07T10:19:09.796174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.796174Z digest=sha256:f99ce66921764fb2dbe1a7d3c227e332b61b680a5fcf93cdeea905b1ee0c6306

Observation 55ab1894-7d2e-4258-b98e-5c51dfda6149 · outbound

This paper cites Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts.

dots.llm1 Technical Report Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Reference 39

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no resolver link, observed 2026-08-07T10:19:09.918457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.918457Z digest=sha256:57bbd50a4a84f9cc2e0e553eab81ee458f0d784c8814ce8ebce8cbe641a69c6a

Observation 7e91f080-f5b3-4684-b3c8-6d239717794d · outbound

This paper cites Small-scale proxies for large-scale Transformer training instabilities.

dots.llm1 Technical Report Small-scale proxies for large-scale Transformer training instabilities

Reference 40

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no resolver link, observed 2026-08-07T10:19:10.005263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:10.005263Z digest=sha256:ac64dec68d2266ac92e2e0f08e4e00cf4c56f2679e7c964c0aeeb1e8c47a0dd6

Observation f6f260a1-5374-492f-8e19-a98e7802c536 · outbound

This paper cites an unresolved cited work.

dots.llm1 Technical Report Unresolved cited work

Reference 41

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unresolved
raw_fallback, observed 2026-08-07T10:19:13.557576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:19:10.133246Z digest=sha256:427435bd6f0f7b0f4bce43adb016916a1d1ce860a1c4b93a7b6866a18d4619e7

Observation 4b98e07d-91b7-4e71-9c15-9aaaa0bc664a · outbound

This paper cites Qwen2.5 Technical Report.

dots.llm1 Technical Report Qwen2.5 Technical Report

Reference 42

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unresolved
no resolver link, observed 2026-08-07T10:19:10.230548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:10.230548Z digest=sha256:a543fbe6de5b635497984bf8c92423b6cee1a094e274490c8090f1417e2f8046

Observation b12a93e3-f33a-4404-8e35-4a89b11d0ae5 · outbound

This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

dots.llm1 Technical Report Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 43

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no resolver link, observed 2026-08-07T10:19:10.371013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:10.371013Z digest=sha256:eda9063476c5334c72da0137cb138101e555ced12d0e3d9a9f106a132c45855c

Observation a7fda504-eadb-42b7-8e76-ddf51d6378c0 · outbound

This paper cites URL https://doi.org/10.1 007/s11227-022-04336-3.

dots.llm1 Technical Report URL https://doi.org/10.1 007/s11227-022-04336-3

Reference 44

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verified exact
doi, observed 2026-08-07T10:19:11.727351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:19:10.501473Z digest=sha256:35acc04cf79d3ef76524b59cf8376471dd69e9d679dcc7fb2e0f49c5a9d530d3

Observation 331151e7-c28b-4bb6-8c64-409cd28eb5db · outbound

This paper cites URLhttps://doi.org/10.18653/v1/p19-1472.

dots.llm1 Technical Report URLhttps://doi.org/10.18653/v1/p19-1472

Reference 45

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no resolver link, observed 2026-08-07T10:19:10.610267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:10.610267Z digest=sha256:e464d8fe13765eb953ca64c149b3b2a46e3c9bc737f258c0951a732a5efa6edc

Observation d32dcfcc-79c2-42d9-be34-be6939a940d3 · outbound

This paper cites URL https://doi.org/10.110 9/HPCC-DSS-SmartCity-DependSys57074.2022.00143.

dots.llm1 Technical Report URL https://doi.org/10.110 9/HPCC-DSS-SmartCity-DependSys57074.2022.00143

Reference 46

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unresolved
no resolver link, observed 2026-08-07T10:19:10.726181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:10.726181Z digest=sha256:de1e58daae5c70cc526411a70416da0d3b89ee8b05abd31770bd8c30951f8d6d

Observation 075b5f16-5f97-4571-9558-5dd63cd38c43 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

dots.llm1 Technical Report AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 47

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unresolved
no resolver link, observed 2026-08-07T10:19:10.836071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:10.836071Z digest=sha256:d7c0a2e337a3ffa88e46065929fa75b07e49ec8ca81fb3870fc0901c5f7dde46

Observation 36f3b8fb-9001-4088-a469-8b4a76f468b4 · outbound

This paper cites BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions.

dots.llm1 Technical Report BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Reference 48

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unresolved
no resolver link, observed 2026-08-07T10:19:10.967462Z

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Unavailable: canonical work link unavailable.

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Reference 49

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Reference 50

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Observation 4d45c2e5-1bde-4045-b290-efc0c8da941a · outbound

This paper cites The process involves the following steps: First, we apply the same text standardization as in the Identity Removal step.

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Reference 51

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Observation c14e087c-596b-4339-8e96-85cebb7c55f8 · outbound

This paper cites Quality ModelThe quality model performs comprehensive multidimensional analysis to evaluate and score training samples (Qwen, 2024a; Penedo et al., 2024).

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Reference 52

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Observation d0d39a29-5d2a-4235-a516-9c5f3ff028cd · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

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Reference 2016

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Observation 07f4b476-928e-4ab7-affc-42a1d274c0a0 · outbound

This paper cites FastText.zip: Compressing text classification models.

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Reference 2017

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Reference 2018

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Observation 4d21e61d-1cd5-4a46-a56e-4df876624cdc · outbound

This paper cites The Llama 3 Herd of Models.

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Reference 2019

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Observation 7e046010-1d3e-4fb3-9e18-9fc4de538bc2 · outbound

This paper cites URL https://doi.org/10.1609/aaai.v34i05.6239.

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Reference 2020

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Observation 9968483b-4e90-42a4-b346-dbdfc6958140 · outbound

This paper cites Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi.

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Reference 2021

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Source-reported events for the cited work

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Reference 2022

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Observation 014d4ab6-34bd-4b6c-bb88-9cc54102f850 · outbound

This paper cites DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs.

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Reference 2023

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d6f1e018-cf6d-4687-a980-ffa9bfeed1e7 · outbound

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Reference 2024

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Observation 3381bbcd-06f1-4942-a951-f60497195e1f · outbound

This paper cites Program Synthesis with Large Language Models.

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Reference 2025

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Pith citing papers

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Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts cites this paper.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts dots.llm1 Technical Report

Reference 21

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Observation f339dd7c-1b5f-4c73-b747-c9875d60c6f5 · inbound

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning cites this paper.

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning dots.llm1 Technical Report

Reference 19

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Observation 7e67ca73-fa02-476a-bdb4-53b3e93a009e · inbound

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts cites this paper.

Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts dots.llm1 Technical Report

Reference 8

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arxiv_id, observed 2026-05-21T20:40:36.529155Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 475a8df5-5fba-425a-be89-411437f1e31e · inbound

PolicyLong: Towards On-Policy Context Extension cites this paper.

PolicyLong: Towards On-Policy Context Extension dots.llm1 Technical Report

Reference 6

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arxiv_id, observed 2026-05-11T06:55:59.972876Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1dc3dcc8-afca-4cf8-a372-b9febde852be · inbound

CuraWeb: Joint Optimization of Quality, Redundancy, and Diversity for Web-Scale Pretraining Data cites this paper.

CuraWeb: Joint Optimization of Quality, Redundancy, and Diversity for Web-Scale Pretraining Data dots.llm1 Technical Report

Reference 13

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