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Paper Citation Record · LEDGER

dots.llm1 Technical Report

As of 10 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 2026-08-09T06:31:02.800959+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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:05.944665Z digest=sha256:ffc9058ec94d298c58b7cd2be3dcf9280d66b9c090791c182b36f9b0b29d6285

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:b58069d6e9c545a3bcb4966c43190a24ba966a54bafe2194f86e329b9c576631

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=sha256:07ad78797f1f51a742ede6920c653f145e341e7e6aaed55101198b75ab8af2e8

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=sha256:953d6a5b598d577b025a708ed0bf8220cf558a10a82cb73fbc987737c5b563d2

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

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source=pdf_text observed=2026-08-07T10:19:06.869670Z digest=sha256:2bf1ce612931c3a6587c99437c93815851f9f389c0a54b7b124535dd02dc977d

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:b49624752397a38ce5b4c089c956641ba70afa3f847c703c8390e03d996f1803

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:07.247635Z digest=sha256:6437234867311b935c06a9c7e0741473ab56683bb3f7cfb3d223efb77eed0cb0

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:d293796dd4791a1fa0abc87840f65388eaf165c430520cb8cac3e61ffcb46dd5

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=sha256:1115d0d00eac0c4b392b6903ff7dc196f3106d603077aec0f1d5b0575461be31

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=sha256:4f32291be212fa475d890be99f440683a4b62f62939b6f99021e879b67a3ae4d

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

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source=pdf_text observed=2026-08-07T10:19:07.787573Z digest=sha256:eb7f92d4fa3d462cdb5b0072ac9bbd840058dd6cb2b2f0ccb4b81467796a071a

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:b2416dbb9c4da5c23e4b2968e9a3961cab5e0205a13ce970b00a0966f37e957a

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=sha256:3274bbcf3779b0b84b5ec94da181e86c49afcfc04263468e09753f8fb2526db3

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:08.097824Z digest=sha256:2ed9be0b9f7208ea33e3df28efc6e6769782fd778fcf045562aacd9506eb2923

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=sha256:5b74e27dd6312894eb903f1ed0549b1ce2cdc6ae0d7f4e0f83bb6510a5143b08

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=sha256:001027b0e8e79561973e36f7cbcc13209e1afee9fd0e5883cddf83769f656be6

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=sha256:43332b9cef7fe709d3b57d74510faa04926913feae9666af9e65f85b072c77bd

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:08.637720Z digest=sha256:4e9a0732576b1404aa59f2912cc0634b1dae5185029f4ef608cf7e5391b33df4

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=sha256:267a9d4bc0ef011c27cb5b55384858e140eeaa5d16b2056aeb9058f3c3b3a7c1

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=sha256:9bb75581a96236ce2e2f459846595b93839af92ffd0c98f01579796676a724c0

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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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:08.996594Z digest=sha256:d7edd46d2aa285064c27d9d88810bda74b53940aefc219aafde41b3a4c77d1cf

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T10:19:09.100923Z digest=sha256:31d77b9478896387447ad8aa9f0eb02e2558a8863f74b28f3ba80dc2153dbe40

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:09.191769Z digest=sha256:872db9f873a408d4aefe654385ec76022d2b22a17611c0e34cce372fac467e54

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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source=pdf_text observed=2026-08-07T10:19:09.404970Z digest=sha256:8fc2cbda2498d077e02c836d2db39919b1e4bd3706573f1abc8dbf4be243d2fe

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=sha256:1061be1d4f997cf3792a3f99fb9c25d35c7beeb304126c38f87e47a90c446719

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=sha256:236b6e9a4b4a3008620fc89db3358a396ebe8006ce7c9d36af629f469829e10a

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:f4056279da1f84a0d56ea87b29496a87200ae2c582e81ed8f3c61d5a796c1028

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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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:c17e0826cf716c2400c5665eb7ae5317bfceaba9105b6755f57af292ea36b1de

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:09.918457Z digest=sha256:0633ff8c714856d5270811f0363f7700a6841eba820313b627764c7ec9159102

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=sha256:94b04b98faf2f904dca86f21fc8bc65ff5f0af9c59ff06af73c48afd55671a2a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:10.133246Z digest=sha256:22ca49c9b0dc8b58119650d3321523a89aa5a935d9aebf1e24ec3bc7d935dc5b

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:ee1a92cebfee6403064f4f3a550805a83ccefd81b254086d911e53d02cb34f7a

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

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source=pdf_text observed=2026-08-07T10:19:10.371013Z digest=sha256:67708efc6aa48e0f320d052639f01d903f1cc7809a7d969b3f2114cebd759072

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T10:19:10.501473Z digest=sha256:3e20ac018c11f0a0093f5076a13e9a917c8379c3c6093b589e93ea4a20726adb

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=sha256:28abec4c743c6bde2228ea93d736710b7b242d7010cdabb14ddf16d6ba1715ca

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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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=sha256:7ce7c5a96211a1c6bc0c878f94a208bf3fa139d91f21d36381029f0253870ad4

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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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:e2905041833a81f46d4166531aa6b2f0d744ec757d359918bc2d148163bbbb74

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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Observation 9949bfc3-4dfc-4637-8b6a-808984fff756 · outbound

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Reference 49

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Observation 875f7de0-8f14-4d20-bc44-a8f2e8d682d7 · outbound

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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.

dots.llm1 Technical Report URL https://doi.org/10.1609/aaai.v34i05.6239

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

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 694b8fdb-094f-4618-9cf5-ffb90a1578b4 · outbound

This paper cites Are We Done with MMLU?.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

This paper cites McEval: Massively Multilingual Code Evaluation.

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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

Observation 7a2fae88-c8b7-441e-a300-6c77883d71e3 · inbound

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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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