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Paper Citation Record · LEDGER

Motif 3: Technical Report

As of 21 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 0 inbound Pith citation observations for arXiv:2608.09119.

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

pith.paper-citation-record.v1
2608.09119 v1

Coverage vector

measured 80 of 80 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:12:10.257064Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

80 of 80 outbound references displayed

  • verified exact2
  • verified fuzzy25
  • unresolved53
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96f7824b-af72-45ca-94c4-b066ab27a3ba · outbound

This paper cites Open-swe-traces: Advancing dual-mode multilingual distillation for software engineering agents.arXiv preprint arXiv:2606.16038, 2026.

Motif 3: Technical Report Open-swe-traces: Advancing dual-mode multilingual distillation for software engineering agents.arXiv preprint arXiv:2606.16038, 2026

Reference 1

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source=pdf_text observed=2026-08-11T23:12:09.825707Z digest=sha256:9e4a1df644d219361ef4cc1f283b44f9942f8bbf2275a52e3b8bdd8e5c9da4b3

Observation 352dba50-6749-42e0-9915-6d0e5411b031 · outbound

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

Motif 3: Technical Report GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2

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source=pdf_text observed=2026-08-11T23:12:09.831671Z digest=sha256:ac7672f5810e078182925815cb59bb984e988ca8dd56c04596b93efa20c22d81

Observation 841138b4-2724-4e69-8baf-5a5130e03212 · outbound

This paper cites Artificial analysis long context reasoning benchmark (AA-LCR).

Motif 3: Technical Report Artificial analysis long context reasoning benchmark (AA-LCR)

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.727269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.837121Z digest=sha256:650868a1e41f11c2cba3c1e2faf1f5ff52d0fec4317dd4531e014a64c4a15c1f

Observation 40aa0b26-a862-4a1c-a28d-1b757effdcea · outbound

This paper cites GDPval-AA v2 leaderboard.

Motif 3: Technical Report GDPval-AA v2 leaderboard

Reference 4

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raw_fallback, observed 2026-08-11T23:12:12.706703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.842271Z digest=sha256:cf4e5cd3cffb7f11aa4e3726aa430b0cf42c1b7ed433fb6b6939ce85b1a842c5

Observation b6625f1d-cf36-4563-9c2b-fc35cddbc030 · outbound

This paper cites ITBench-AA benchmark leaderboard.

Motif 3: Technical Report ITBench-AA benchmark leaderboard

Reference 5

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raw_fallback, observed 2026-08-11T23:12:12.686022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.847341Z digest=sha256:c4e63a915a1f302b6fbfd9b4e87312696cb998ffa764fecc1ef1d8cbf68b7a54

Observation fa898f5c-a7e0-46f9-9761-2f37619f2734 · outbound

This paper cites Online benchmark and leaderboard, 2026.

Motif 3: Technical Report Online benchmark and leaderboard, 2026

Reference 6

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raw_fallback, observed 2026-08-11T23:12:12.669642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.852533Z digest=sha256:a2b84ccbc33e893e7fbe62eb4824c27d64104f69e791129cca50358380481b06

Observation 5185f0e5-841c-4aa7-b863-672c442b734a · outbound

This paper cites Program Synthesis with Large Language Models.

Motif 3: Technical Report Program Synthesis with Large Language Models

Reference 7

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:09.857957Z digest=sha256:85486a5c2680315f51feac0302e216c916f29d8b78a7d4fdbf1b1f9442f543f6

Observation 6f0ef21b-96ac-4424-ab24-8974ed0a099e · outbound

This paper cites $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment.

Motif 3: Technical Report $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Reference 8

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source=pdf_text observed=2026-08-11T23:12:09.863007Z digest=sha256:aa053f480bbabdb9764d96b2c33094f0fd22486b32a16e3206a66a01aad710c6

Observation e50f4243-4533-4305-aadb-db1254c99409 · outbound

This paper cites PIQA: Reasoning about physical commonsense in natural language.Proceedings of the AAAI Conference on Artificial Intelligence, 2020.

Motif 3: Technical Report PIQA: Reasoning about physical commonsense in natural language.Proceedings of the AAAI Conference on Artificial Intelligence, 2020

Reference 9

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raw_fallback, observed 2026-08-11T23:12:12.651150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.868468Z digest=sha256:058aa3f3ebb319f0dc35eeb63314777c62ee36f249f290bef00869824434e86f

Observation 199e88d6-ca7d-46ff-9779-2cd69ff64e3f · outbound

This paper cites Striped Attention: Faster Ring Attention for Causal Transformers.

Motif 3: Technical Report Striped Attention: Faster Ring Attention for Causal Transformers

Reference 10

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:09.873268Z digest=sha256:6d637928509ea6cf8c7b36c38a10638b69a78524b5076b48185e7b2eccb7f433

Observation 58dd115d-3f9b-4094-9d29-0a3d1c046c96 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Motif 3: Technical Report Evaluating Large Language Models Trained on Code

Reference 11

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source=pdf_text observed=2026-08-11T23:12:09.879247Z digest=sha256:d1111a69a9d0887e1544ebd3b7a4a82dcaaf0acd88bb448b6ddb492bb225fe59

Observation a00acdfe-bf4a-4e3e-a746-186b50672570 · outbound

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

Motif 3: Technical Report Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 12

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source=pdf_text observed=2026-08-11T23:12:09.884723Z digest=sha256:9dbe5474918519250d9e1355333dc84ec9e4ef076a53190d241f09e2d159c990

Observation d348bc73-30ba-441d-9d03-714343bc4085 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Motif 3: Technical Report Training Verifiers to Solve Math Word Problems

Reference 13

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source=pdf_text observed=2026-08-11T23:12:09.890228Z digest=sha256:f079882fd132624cf7f1cd65b21a77c72458d08ddd70521813791f0944a5034f

Observation e0858f7e-9ac6-49bd-b227-d4ba0c732763 · outbound

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

Motif 3: Technical Report DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 14

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source=pdf_text observed=2026-08-11T23:12:09.896042Z digest=sha256:9bb2d985d55203ae08a8ef70dc17be947459664bb6435d688a5b5efc4a6450af

Observation d6f21ccd-3edc-476e-8f72-bf4d16546030 · outbound

This paper cites DeepSeek-V3 Technical Report.

Motif 3: Technical Report DeepSeek-V3 Technical Report

Reference 15

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source=pdf_text observed=2026-08-11T23:12:09.900991Z digest=sha256:f8a547a4d1252b067353d72430bcc72df9b106168c143b262d6789434fa376ac

Observation 03229335-269f-4c32-8f16-8163414fc04a · outbound

This paper cites DeepEP: Hybrid expert-parallel communication.

Motif 3: Technical Report DeepEP: Hybrid expert-parallel communication

Reference 16

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raw_fallback, observed 2026-08-11T23:12:12.631570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.906139Z digest=sha256:7c25fc2d274c4c34c066bd34ed9bd95c913090964dcfde1a753f692abb2a2a91

Observation 8124b7f4-17dc-49ef-802c-09147d7e1253 · outbound

This paper cites DeepSeek-V4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, June 2026.

Motif 3: Technical Report DeepSeek-V4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, June 2026

Reference 17

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:09.911684Z digest=sha256:08d94b826d249e0b6ca49aac69d98adcf48a4590547340beba69e1ccb6be8916

Observation 9cea9e0b-9a49-4487-83cd-920769eed99c · outbound

This paper cites Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs.

Motif 3: Technical Report Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs

Reference 18

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:09.916832Z digest=sha256:763ac65a99c19283434af80aa98eb2d8d3a016dc1065ba261a636b357e7d0105

Observation 381efd72-9d36-46f9-82e4-76da54536f5d · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

Motif 3: Technical Report GLM-5: from Vibe Coding to Agentic Engineering

Reference 19

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:09.921673Z digest=sha256:ad9adc7762e2daef9484c7218b10c7464e9088d4e6c54ad0f95317e9e5424f50

Observation 3588dac7-b590-4ab2-abd8-4a4a8e694ebb · outbound

This paper cites Measuring massive multitask language understanding.International Conference on Learning Representations, 2021.

Motif 3: Technical Report Measuring massive multitask language understanding.International Conference on Learning Representations, 2021

Reference 20

Resolution
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raw_fallback, observed 2026-08-11T23:12:12.605477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.926792Z digest=sha256:56ebbc35892f723bd086ca7b403a2b6c5a0fd3f03949c73abf8c2282d115c176

Observation 1858c407-b6bc-47fe-b373-97f9a0ca6b0e · outbound

This paper cites Measuring mathematical problem solving with the MATH dataset.NeurIPS Datasets and Benchmarks, 2021.

Motif 3: Technical Report Measuring mathematical problem solving with the MATH dataset.NeurIPS Datasets and Benchmarks, 2021

Reference 21

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source=pdf_text observed=2026-08-11T23:12:09.931747Z digest=sha256:540d9fecedb62ed9f3b22e15088aef24a83d16a874359e6fe753bf963fd17403

Observation 233b03fc-430f-4ebc-8bd8-207738f4ba10 · outbound

This paper cites Liger Kernel: Efficient Triton Kernels for LLM Training.

Motif 3: Technical Report Liger Kernel: Efficient Triton Kernels for LLM Training

Reference 22

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source=pdf_text observed=2026-08-11T23:12:09.937202Z digest=sha256:eaab658969f99caa0ff3c022b1be991bbf597e8cbd56f3156db182725c0c0cfc

Observation 38b03e6b-efa1-419c-b898-0a2ac5fecc36 · outbound

This paper cites Aa-omniscience: Evaluat- ing cross-domain knowledge reliability in large language models.arXiv preprint arXiv:2511.13029, 2025.

Motif 3: Technical Report Aa-omniscience: Evaluat- ing cross-domain knowledge reliability in large language models.arXiv preprint arXiv:2511.13029, 2025

Reference 23

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source=pdf_text observed=2026-08-11T23:12:09.942934Z digest=sha256:a9efcb77c87c0a94ae92dc4ab1f467e6da1ff6752e1c4f589b141c4d017cd122

Observation 5d5cfcbc-cbdb-41b1-9bc2-94807788e6d4 · outbound

This paper cites DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models.

Motif 3: Technical Report DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

Reference 24

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source=pdf_text observed=2026-08-11T23:12:09.947925Z digest=sha256:a5f92e5000218caeff6a54163fd0e07d611ae63af659f1e3977eeb20333f5a30

Observation 07b4d204-c61b-4404-a55a-7273f46e0ce1 · outbound

This paper cites ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks.

Motif 3: Technical Report ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks

Reference 25

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:09.952843Z digest=sha256:893039e5b73a407e0b966ce2aa1df3d9027d68c1ba6f4e2709ae5d1465f7c4d4

Observation 4d05a1ea-4f01-4880-adce-36b5a6f98733 · outbound

This paper cites DCP: Addressing input dynamism in long-context training via dynamic context parallelism.

Motif 3: Technical Report DCP: Addressing input dynamism in long-context training via dynamic context parallelism

Reference 26

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source=pdf_text observed=2026-08-11T23:12:09.957692Z digest=sha256:9ca4ea0131e3fc0dd6595725f649b13259a82c26314fe49f304db3551f37296c

Observation 608cb4a4-1909-4c77-a336-02bdd30fdb8a · outbound

This paper cites Swe-bench: Can language models resolve real-world github issues? InInternational Conference on Learning Representations, volume 2024, pages 54107–54157, 2024.

Motif 3: Technical Report Swe-bench: Can language models resolve real-world github issues? InInternational Conference on Learning Representations, volume 2024, pages 54107–54157, 2024

Reference 27

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source=pdf_text observed=2026-08-11T23:12:09.962235Z digest=sha256:8fe4ad5e1e48affdc0544be84bd9c6913ceb386dc2145f26f503bb7ea2c3b139

Observation 5c2e2b74-fd1e-488c-90a6-2d3ddfe1ccba · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks.

Motif 3: Technical Report Muon: An optimizer for hidden layers in neural networks

Reference 28

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raw_fallback, observed 2026-08-11T23:12:12.562956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:09.967546Z digest=sha256:c234797778873b3f842c3e3141c8bbee134dd2af00fe2fc70ef097edd4601186

Observation 03783b4a-ceea-4732-92b8-3b051137d883 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

Motif 3: Technical Report Kimi K2: Open Agentic Intelligence

Reference 29

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source=pdf_text observed=2026-08-11T23:12:09.972456Z digest=sha256:9bae244a3ffb0b46485eac28552db73b6274c7603dc1c13e3a32fff73cf82524

Observation 0e065b7b-3567-4d23-a895-c6ee4e169766 · outbound

This paper cites Kimi K2.5: Visual Agentic Intelligence.

Motif 3: Technical Report Kimi K2.5: Visual Agentic Intelligence

Reference 30

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source=pdf_text observed=2026-08-11T23:12:09.977432Z digest=sha256:172a1398cc039723085d4b50b541e572a5baace8852adebafd565b0022e8fb7f

Observation 0071cf32-e991-49d1-bede-73c3b89a0af7 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Motif 3: Technical Report Gonzalez, Hao Zhang, and Ion Stoica

Reference 31

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source=pdf_text observed=2026-08-11T23:12:09.982830Z digest=sha256:992ce306e8692251a9a37d0f1e8f56a00ad8fc172174bbb6d1d76b724819d774

Observation 8ca5d116-f7fb-4b0d-90d6-2ac85455ca3e · outbound

This paper cites TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training.

Motif 3: Technical Report TorchTitan: One-stop PyTorch native solution for production ready LLM pre-training

Reference 32

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source=pdf_text observed=2026-08-11T23:12:09.988368Z digest=sha256:73dfba4a84b568b0b37aa4802a39650fc66f737aed72ba0b16617344c110c0d1

Observation 4e3c003a-1cc6-4aa6-bfa8-fc59fbd83a1d · outbound

This paper cites Motif 2 12.7b technical report.arXiv preprint arXiv:2511.07464, 2025.

Motif 3: Technical Report Motif 2 12.7b technical report.arXiv preprint arXiv:2511.07464, 2025

Reference 33

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source=pdf_text observed=2026-08-11T23:12:09.993766Z digest=sha256:5f399077c87d6c9b1f7092e17e53abbf0482fc22ef92225f73c4e5d4a2cffcae

Observation 01144cd2-f180-4711-8d7e-67887affa96e · outbound

This paper cites Every step evolves: Scaling reinforcement learning for trillion-scale thinking model.arXiv preprint arXiv:2510.18855, 2025.

Motif 3: Technical Report Every step evolves: Scaling reinforcement learning for trillion-scale thinking model.arXiv preprint arXiv:2510.18855, 2025

Reference 34

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source=pdf_text observed=2026-08-11T23:12:09.998809Z digest=sha256:f1c7d70f7cf74a93086cc5ad35ac810b6a51cb6fc02e9dddb79e1a4125d0be5a

Observation 194d245f-806a-4e4b-aa45-421f97a75a4c · outbound

This paper cites SuperBPE: Space Travel for Language Models.

Motif 3: Technical Report SuperBPE: Space Travel for Language Models

Reference 35

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source=pdf_text observed=2026-08-11T23:12:10.004036Z digest=sha256:8580ea46457e3667a392e6e83c6cfe0533450731443ae41f4e2285d1d2ec7924

Observation 62623cfa-e887-45df-be92-d580fb9462ee · outbound

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

Motif 3: Technical Report Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 36

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source=pdf_text observed=2026-08-11T23:12:10.009526Z digest=sha256:9324e36fd736676f1d1e65da3347994b5c306d33249f0ebafe19ed3a29c17cdc

Observation cb7fa046-b61b-45ad-9634-2c906667a580 · outbound

This paper cites Muon is Scalable for LLM Training.

Motif 3: Technical Report Muon is Scalable for LLM Training

Reference 37

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source=pdf_text observed=2026-08-11T23:12:10.014334Z digest=sha256:f9a266a4a89868832c22dca6ab875ac6cf72356b35bde794555674611b21c3e5

Observation cd137cd5-4779-4fa2-9e77-d3ebc0ac8e99 · outbound

This paper cites Towards robust mathematical reasoning.

Motif 3: Technical Report Towards robust mathematical reasoning

Reference 38

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source=pdf_text observed=2026-08-11T23:12:10.019631Z digest=sha256:cb6dff032903f52f835a2b06dcfecf5759dd950f52ce1e9990c5c99235f53021

Observation 638ea282-a2b2-4fbd-8cc9-111502ca2e14 · outbound

This paper cites Terminal-bench: Benchmarking agents on hard, realistic tasks in command line interfaces.

Motif 3: Technical Report Terminal-bench: Benchmarking agents on hard, realistic tasks in command line interfaces

Reference 39

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raw_fallback, observed 2026-08-11T23:12:12.523344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.024237Z digest=sha256:ff70b1094428e47a660e0fd4f42662b8d6c6fe56674a845758c1ea635071b17d

Observation b5f5423a-0c9f-4548-9faa-4ef49be4751d · outbound

This paper cites Recipes for Pre-training LLMs with MXFP8.

Motif 3: Technical Report Recipes for Pre-training LLMs with MXFP8

Reference 40

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source=pdf_text observed=2026-08-11T23:12:10.029200Z digest=sha256:f56236a4c2807b6392bf1299a5698ff21551d120af0ac993011d7571ba18f27b

Observation c9449845-72c5-4ff1-a580-2de88c5682c9 · outbound

This paper cites Jordan, and Ion Stoica.

Motif 3: Technical Report Jordan, and Ion Stoica

Reference 41

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raw_fallback, observed 2026-08-11T23:12:12.504267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.034216Z digest=sha256:4cc8ad8a08a67d41e3d6347603e66b504efeeb3e354fec2d967605e2a2a3d93d

Observation 65642ad7-ac59-48e1-bfc5-1ace938c1f21 · outbound

This paper cites Motif 3 training example.

Motif 3: Technical Report Motif 3 training example

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.482443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.039368Z digest=sha256:f58af1a8aa5254642227d20f7602fd2486b5f84adff89b27061b717d614a4f5f

Observation 2f79ac33-eac1-4e0d-893a-0f71b3cbd299 · outbound

This paper cites Scalable training of mixture-of-experts models with megatron core.arXiv preprint arXiv:2603.07685, 2026.

Motif 3: Technical Report Scalable training of mixture-of-experts models with megatron core.arXiv preprint arXiv:2603.07685, 2026

Reference 43

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source=pdf_text observed=2026-08-11T23:12:10.044081Z digest=sha256:06a452f79d8b2483653401f7d2da7aa19d7082296360b47f3d0dc21e7bc64264

Observation c69914e2-01a5-4b36-9d00-c8d3757c2d83 · outbound

This paper cites Nemotron post-training v3.

Motif 3: Technical Report Nemotron post-training v3

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.462590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.048895Z digest=sha256:30fc388a80104d890e4c9c58de2c6aaa808e469721eb32214ba29cdb15928d51

Observation bf27e2ef-21f7-4b22-b3ca-33a0ed3e5e65 · outbound

This paper cites Nemotron pre-training datasets.

Motif 3: Technical Report Nemotron pre-training datasets

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.443282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.053750Z digest=sha256:941e45f45c4d28f12e03dcce76b8ea9933b1f766a34804e50781fa29bd7741fa

Observation f06151ac-f6c9-46e6-88f2-f881a7c2196c · outbound

This paper cites NeMo Gym: Environments for evaluating and improving models and agents.

Motif 3: Technical Report NeMo Gym: Environments for evaluating and improving models and agents

Reference 46

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.425302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.058846Z digest=sha256:553d7360f6e20b3ad543d0b241dca9a1d8e629c54db04d8ed0ad16fb6593b609

Observation 6ee8c42a-f812-4fe4-a905-c26b76c62e5c · outbound

This paper cites NeMo RL: A scalable and efficient post-training library.

Motif 3: Technical Report NeMo RL: A scalable and efficient post-training library

Reference 47

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.406166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.063668Z digest=sha256:879a0e7fcb334dc4dc84d79723a516ed5b01eb950f4938f278dbfd19af144b21

Observation 4f0b6eab-fa09-42d0-8eee-4c506f806ed5 · outbound

This paper cites Nemotron 3 Ultra: Open, efficient mixture-of-experts hybrid mamba-transformer model for agentic reasoning.arXiv preprint arXiv:2606.15007, 2026.

Motif 3: Technical Report Nemotron 3 Ultra: Open, efficient mixture-of-experts hybrid mamba-transformer model for agentic reasoning.arXiv preprint arXiv:2606.15007, 2026

Reference 48

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doi, observed 2026-08-11T23:12:10.634258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.068239Z digest=sha256:9042b50c0eb08175b14c925dbdb33876dbc4c0c2631c7b0103de111069aabc66

Observation cce1f40d-086c-48e6-b247-19cfc96c0975 · outbound

This paper cites gpt-oss: Reference implementations for gpt-oss-120b and gpt-oss-20b.

Motif 3: Technical Report gpt-oss: Reference implementations for gpt-oss-120b and gpt-oss-20b

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.386399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.073048Z digest=sha256:7bec36c1eeac4f59ce19769d96dd59552901a5f16338bdd39d1cdd580ef935ab

Observation 0e720d45-4fbf-4372-ba4a-9280a982f235 · outbound

This paper cites TorchAO: PyTorch-native training-to-serving model optimization.ICML Workshop on Championing Open-source Development, 2025.

Motif 3: Technical Report TorchAO: PyTorch-native training-to-serving model optimization.ICML Workshop on Championing Open-source Development, 2025

Reference 50

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raw_fallback, observed 2026-08-11T23:12:12.348690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.088862Z digest=sha256:e008004054cc9a351972265e05e3fe07609a29bf3bd218167c92bf2db5f2b8c8

Observation 5bd55e93-7cab-4b2f-9add-de6afb2e3fe8 · outbound

This paper cites Gdpval: Evaluating ai model performance on real-world economically valuable tasks.

Motif 3: Technical Report Gdpval: Evaluating ai model performance on real-world economically valuable tasks

Reference 51

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.331577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.095075Z digest=sha256:131b3f2e0cf5bd778b2326aa05a9a4e86d5fd69d6f7837290f0bab1326a63813

Observation 8d7537e0-6840-4ccc-b39f-3756a90d6595 · outbound

This paper cites Humanity's Last Exam.

Motif 3: Technical Report Humanity's Last Exam

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.100397Z digest=sha256:fe0a55631fce595c32e5e6c4d54eefca27224c341d47fd9c7b5e0fb3131966e2

Observation 658b672c-b141-44bc-8c72-634020f74e42 · outbound

This paper cites On data engineering for scaling llm terminal capabilities, 2026.

Motif 3: Technical Report On data engineering for scaling llm terminal capabilities, 2026

Reference 53

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:10.105901Z digest=sha256:5a1ba7a20f1da4ddf587deecfd876f47566841d1f5123b01d46ab6ab2ac4b76b

Observation 130bbe81-6b5c-4f1b-82c2-3a8874152cdf · outbound

This paper cites Generalizing verifiable instruction following.Advances in Neural Information Processing Systems, 38, 2025.

Motif 3: Technical Report Generalizing verifiable instruction following.Advances in Neural Information Processing Systems, 38, 2025

Reference 54

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raw_fallback, observed 2026-08-11T23:12:12.315765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.110791Z digest=sha256:5701fca7073b804f768cb62f9a2633cd415186edc0ddd0b9fa8cf54c78d22695

Observation 9f3b0e17-5fb2-4950-8600-2c48208ebd01 · outbound

This paper cites Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free.

Motif 3: Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 55

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.115800Z digest=sha256:d32db9fbec9437c0f5998570192ac1a07ed4fe7d5c1e6139933e8d4740db7fb3

Observation 590e99df-d25f-49e7-84fc-51ee9d765e66 · outbound

This paper cites A unified view of attention and residual sinks: Outlier-driven rescaling is essential for transformer training.arXiv preprint arXiv:2601.22966, 2026.

Motif 3: Technical Report A unified view of attention and residual sinks: Outlier-driven rescaling is essential for transformer training.arXiv preprint arXiv:2601.22966, 2026

Reference 56

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:10.128680Z digest=sha256:41a515b1b16a97cd18552664f8e6b608d208593a5208f99030a2f0f5ce597dc3

Observation fc397ad8-5560-48ee-88ca-4c6fcab0ce3c · outbound

This paper cites Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free.

Motif 3: Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 57

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no resolver link, observed 2026-08-11T23:12:10.121953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.121953Z digest=sha256:05747306d118c403087ac0c8e1b52e39ab018f1b172f691c6854dc8a5fa9e57b

Observation 01c20e77-eb16-4dc3-a761-18865c2bea1d · outbound

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

Motif 3: Technical Report GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 58

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:10.138847Z digest=sha256:16bc5d011c138164a0f35c85dbb04140508da1e7b032ce0529fec982cef7f667

Observation 07f3b8d8-1fb7-46bb-8a01-8fd87e5e148a · outbound

This paper cites Qwen3-Next: Towards ultimate training and inference efficiency.https://qwen.ai/ blog?id=4074cca80393150c248e508aa62983f9cb7d27cd, September 2025.

Motif 3: Technical Report Qwen3-Next: Towards ultimate training and inference efficiency.https://qwen.ai/ blog?id=4074cca80393150c248e508aa62983f9cb7d27cd, September 2025

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.299158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.133972Z digest=sha256:ab727a9101eb9d4b5e2aabd6f3a0dc09abd1b3a7197d4132540d82bea57659dd

Observation 90de544c-c905-4a0a-bdc2-63e3b7083dfd · outbound

This paper cites WinoGrande: An adver- sarial winograd schema challenge at scale.Proceedings of the AAAI Conference on Artificial Intelligence, 2020.

Motif 3: Technical Report WinoGrande: An adver- sarial winograd schema challenge at scale.Proceedings of the AAAI Conference on Artificial Intelligence, 2020

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.281957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.149664Z digest=sha256:6df43866d51d18172a69c5e2cd2784e2750b7bf184690704cecfad9b8fceef83

Observation 88f16c11-468e-465d-97e0-d854675b5a6f · outbound

This paper cites Microscaling Data Formats for Deep Learning.

Motif 3: Technical Report Microscaling Data Formats for Deep Learning

Reference 61

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.143929Z digest=sha256:4727cc8270470c0bc95e97c19e8cbf6dbb5a3e73da196dee4ba151769f062286

Observation 565090d2-b164-4bfe-b451-899deedc8326 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Motif 3: Technical Report RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 62

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.165525Z digest=sha256:a3ee1b2c1370628004acd69d6753f31a8c709680411cdc2e714549f090ba41fd

Observation 48333d04-8b1c-4600-a18e-41b7daa79b4e · outbound

This paper cites τ-knowledge: Evaluating conversational agents over unstructured knowledge.arXiv preprint arXiv:2603.04370,.

Motif 3: Technical Report τ-knowledge: Evaluating conversational agents over unstructured knowledge.arXiv preprint arXiv:2603.04370,

Reference 63

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.154236Z digest=sha256:f95f1b8ecab8cc26a6b8a05bfece6c34ba3df730bf21830c6259969eb2cf9fe7

Observation 3e4ebf87-4881-4c90-a692-b24580d2b981 · outbound

This paper cites Scicode: A research coding benchmark curated by scientists.

Motif 3: Technical Report Scicode: A research coding benchmark curated by scientists

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.263876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.177476Z digest=sha256:2a6f81f521dd935cca979461368edb7dbf7970cad0d5afcaa1b1414ec7d51355

Observation 04c7a0ca-2322-4b10-8f51-e40767904f10 · outbound

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

Motif 3: Technical Report Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Reference 65

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T23:12:10.183916Z digest=sha256:18dfa379a03554080f41cd308164550df238cc6d6225e1d6d9243fcc4a71480d

Observation 51eec268-e592-44dc-924e-ce6a116d184f · outbound

This paper cites Grouped differential attention.arXiv preprint arXiv:2510.06949, 2025.

Motif 3: Technical Report Grouped differential attention.arXiv preprint arXiv:2510.06949, 2025

Reference 66

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.171334Z digest=sha256:210381bdcd07d866e71f79cb6caf2166d47d1700eefcff93e8a4cd3ce7de5d76

Observation 5a88bcdc-bf30-4dce-bd24-8813af3a57db · outbound

This paper cites MiMo-V2-Flash Technical Report.

Motif 3: Technical Report MiMo-V2-Flash Technical Report

Reference 67

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no resolver link, observed 2026-08-11T23:12:10.194310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.194310Z digest=sha256:2d4af0ee61db7dbea8cc85ac251f2e4668c692c7e05c29f063e07defccd570c5

Observation 56d9fe3b-f351-46a0-98fe-19a3e70a3d33 · outbound

This paper cites mHC: Manifold-Constrained Hyper-Connections.

Motif 3: Technical Report mHC: Manifold-Constrained Hyper-Connections

Reference 68

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no resolver link, observed 2026-08-11T23:12:10.200467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.200467Z digest=sha256:6a92c4b0aeb9570f276877e98c8bbaeea9054aa65af205e5e44b9e0fd6ce56bd

Observation 4283f41e-ee27-4cf0-a41e-cf74fe4107e5 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Motif 3: Technical Report MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 69

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no resolver link, observed 2026-08-11T23:12:10.189128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.189128Z digest=sha256:e44622d0482130b9aab63646ccd65c3a076946351accb9f29bc6a64dec16ae99

Observation ffd0b996-51c0-41a8-8970-e8ca0bcbc285 · outbound

This paper cites Differential Transformer.

Motif 3: Technical Report Differential Transformer

Reference 70

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no resolver link, observed 2026-08-11T23:12:10.212539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.212539Z digest=sha256:a6d8047b19d2faa90b4de953a8a5cd2b97e149ae01c11b7646839e6a94561a5a

Observation cb82c6f1-1220-44de-9e27-cfd765db5d69 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Motif 3: Technical Report DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 71

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no resolver link, observed 2026-08-11T23:12:10.219549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.219549Z digest=sha256:e89ee5d03a6a146240619fae565410d7cfe36092df952a303c3fc3c6cf7f6762

Observation 45f55783-e7a0-4d88-ac15-aff26dbe55d6 · outbound

This paper cites Qwen3 Technical Report.

Motif 3: Technical Report Qwen3 Technical Report

Reference 72

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.206551Z digest=sha256:60dabf4b6cb4b7f0598b73f05b75ad624378e3526c6d82b12b69b9a1563c4c36

Observation c6ae2597-b3ff-49f5-8d85-f660c0340cbf · outbound

This paper cites HellaSwag: Can a machine really finish your sentence?Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.

Motif 3: Technical Report HellaSwag: Can a machine really finish your sentence?Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

Reference 73

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verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.247609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.232283Z digest=sha256:7fadb7ea278f0c7552f55da40040729a3245886618642d341b0b466f652cecac

Observation ce6ea7e8-7939-4eb5-aabb-4d35bab167b3 · outbound

This paper cites Root Mean Square Layer Normalization.

Motif 3: Technical Report Root Mean Square Layer Normalization

Reference 74

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.237953Z digest=sha256:e5a60e78578f84b494e2cd0289c4ede3651e3f7435c7a3ae133a63f3e68b25b9

Observation be72b3b2-2e39-439b-a457-401f577ca6fc · outbound

This paper cites FlashAttention-4: Algorithm and kernel pipelining co-design for asymmetric hardware scaling.

Motif 3: Technical Report FlashAttention-4: Algorithm and kernel pipelining co-design for asymmetric hardware scaling

Reference 75

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.226491Z digest=sha256:68423674a51ec11ef43496a34ef84e25986de09f468536f06589b98689e3cd57

Observation ddf7eaa0-a3fb-4bf7-a84c-d0a9f0a7c522 · outbound

This paper cites Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark.

Motif 3: Technical Report Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-11T23:12:10.251313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:12:10.251313Z digest=sha256:e91892fa893bf37919c9f58e542a2f22fb33b6ec88368b81646749a2ad85dbd6

Observation 742f5b50-d7ae-4c5e-941d-5c718e20fb1f · outbound

This paper cites [ˆ\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+.

Motif 3: Technical Report [ˆ\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.213039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.257064Z digest=sha256:a7bfcf1ea36aa1a4a65fa636ddf42a099f5687ba94ad5c4a24e3d99bbb3539b4

Observation 090c824f-4a9c-472a-92ff-8136d99e22bb · outbound

This paper cites Gonzalez, Clark Barrett, and Ying Sheng.

Motif 3: Technical Report Gonzalez, Clark Barrett, and Ying Sheng

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:12:12.229861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.244630Z digest=sha256:5710ca5a76a4f99ef762d92c510782a16279d768b5220c61807bf091070e4490

Observation e207c589-2198-4d99-bb29-b737b23e5835 · outbound

This paper cites an unresolved cited work.

Motif 3: Technical Report Unresolved cited work

Reference 2025

Resolution
unresolved
raw_fallback, observed 2026-08-11T23:12:12.365678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.078240Z digest=sha256:c5d4c3befec7bfa1a87ec87d7a672146ce99186ad193359534d1ba1260d481b7

Observation c73b670a-145d-4993-b16a-88cb5e224723 · outbound

This paper cites an unresolved cited work.

Motif 3: Technical Report Unresolved cited work

Reference 2026

Resolution
verified exact
doi, observed 2026-08-11T23:12:10.523657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T23:12:10.159635Z digest=sha256:adafc1b3d3e5a035881ae66b5bd0247426f3618992469306fa185c3172cf251a

Pith citing papers

No inbound Pith citation observations are available.