Paper Citation Record · LEDGER

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection

As of 18 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2505.07293.

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

pith.paper-citation-record.v1
2505.07293 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:23:36.717737Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T12:57:26.458109Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T06:07:41.034120Z

Reference resolution

58 of 58 outbound references displayed

verified exact2
verified fuzzy5
unresolved49
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 15052427-48b9-41a9-8b25-c77efce244c3 · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.342892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 569a43c7-96b9-4512-8f85-cdd643740b12 · outbound

This paper cites Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.349667Z

Source-reported events for the cited work

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Observation b7ade84b-4b54-4018-a42e-bee1c430bb16 · outbound

This paper cites Smollm-corpus,.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Smollm-corpus,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.355299Z

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Unavailable: canonical work link unavailable.

Observation 2c76a108-f35f-40b1-903f-9d77feff11d3 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Piqa: Reasoning about physical commonsense in natural language

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.368664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aed61845-245e-47aa-98eb-f335b73c0347 · outbound

This paper cites Towards monoseman- ticity: Decomposing language models with dictionary learning.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Towards monoseman- ticity: Decomposing language models with dictionary learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:23:37.690423Z

Source-reported events for the cited work

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Observation 3cf91f5d-d6c8-42ce-925d-e88747df4240 · outbound

This paper cites Efficient Intent Detection with Dual Sentence Encoders.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Efficient Intent Detection with Dual Sentence Encoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.382927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1baea1ab-b71e-4cca-8a05-cd76f6d155e2 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Evaluating Large Language Models Trained on Code

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.471433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6c97e77-ca05-401c-85fe-9ce465d75f56 · outbound

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

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.565205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c64a20fe-1d9e-4398-9eef-1b2ae8502e15 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Training Verifiers to Solve Math Word Problems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.679161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a00cb4c-a9d6-484b-9a20-5c363045ed9d · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.686363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b36fbfd1-88ab-4571-8ea4-5434f4336146 · outbound

This paper cites Not all heads matter: A head-level kv cache compression method with integrated retrieval and reasoning.arXiv preprint arXiv:2410.19258, 2024.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Not all heads matter: A head-level kv cache compression method with integrated retrieval and reasoning.arXiv preprint arXiv:2410.19258, 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.691752Z

Source-reported events for the cited work

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Observation 6a40318b-0c9f-4437-9189-f6318105ea54 · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Transformer Feed-Forward Layers Are Key-Value Memories

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.696873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7acfe777-81cb-4bb6-a786-6e249ad97c33 · outbound

This paper cites The Llama 3 Herd of Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection The Llama 3 Herd of Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.701654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a88aba2a-ac26-4803-9af0-f023b5bd3126 · outbound

This paper cites Optimizing Pretraining Data Mixtures with LLM-Estimated Utility.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Optimizing Pretraining Data Mixtures with LLM-Estimated Utility

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.706278Z

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Unavailable: canonical work link unavailable.

Observation 536a8b14-8458-4a31-87c7-0ef0a3cb05b5 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Measuring Massive Multitask Language Understanding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.755937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d03563a-249c-4f5d-b769-cf0bf78000ac · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Measuring Mathematical Problem Solving With the MATH Dataset

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.865394Z

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Unavailable: canonical work link unavailable.

Observation bdc98545-8281-4dcb-b2a6-0c9efa88a7f3 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Distilling the Knowledge in a Neural Network

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.972358Z

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Observation 5cbba9c2-fc87-44cc-907f-07727ec3d6d6 · outbound

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

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.976986Z

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Unavailable: canonical work link unavailable.

Observation 1306cc60-deb5-40fb-b1bd-9a2b326454ae · outbound

This paper cites C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.981579Z

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Unavailable: canonical work link unavailable.

Observation 7624a982-d77e-4261-b3ba-4d94b28278ca · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.985894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d18e8cd2-ecdc-4426-a0e5-248831889a75 · outbound

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

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection FastText.zip: Compressing text classification models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.991918Z

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Observation 9bce7833-f631-49c4-98fb-4c6689a22566 · outbound

This paper cites The mirrored influence hypothesis: Efficient data influence estimation by harnessing forward passes.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection The mirrored influence hypothesis: Efficient data influence estimation by harnessing forward passes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:23:37.667541Z

Source-reported events for the cited work

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Observation 305aafe7-e1ab-470a-8712-979f547e8a11 · outbound

This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.000905Z

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Observation 5dbcc96b-45f7-4856-9eec-336deb053f64 · outbound

This paper cites Datacomp-lm: In search of the next generation of training sets for language models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Datacomp-lm: In search of the next generation of training sets for language models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.004896Z

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Observation 4bc5fe5b-f274-4d54-b097-8cebf7207d18 · outbound

This paper cites ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.012927Z

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Observation bf3bc0d5-060e-4834-b6ee-e68a1b4bf6a3 · outbound

This paper cites Rho-1: Not All Tokens Are What You Need.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Rho-1: Not All Tokens Are What You Need

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.017738Z

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Observation 163f28b4-f51b-4165-a90c-3ea13af6f64c · outbound

This paper cites RegMix: Data Mixture as Regression for Language Model Pre-training.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection RegMix: Data Mixture as Regression for Language Model Pre-training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.073298Z

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Observation de5d1edb-5f33-423e-9b9f-477cb03c0a84 · outbound

This paper cites Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Interpreting Key Mechanisms of Factual Recall in Transformer-Based Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.181667Z

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Observation d43b5d7a-c0cd-4cb6-b09f-f7d93bb999d9 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.303495Z

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Observation d5fc0da0-896e-4e02-8ebf-acdffd5c36c4 · outbound

This paper cites 2 OLMo 2 Furious.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection 2 OLMo 2 Furious

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.309882Z

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Observation d7c0278b-4f75-47d2-bd93-13c13b5b2a1a · outbound

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AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection In-context Learning and Induction Heads

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.314555Z

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Observation 549d502c-fe53-4c79-8ba9-790de5786f38 · outbound

This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.Advancesin Neural Information Processing Systems, 37:30811–30849, 2024.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection The fineweb datasets: Decanting the web for the finest text data at scale.Advancesin Neural Information Processing Systems, 37:30811–30849, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.318551Z

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Observation 8e477ee4-2df2-4ae2-93ce-7c1326bd5a14 · outbound

This paper cites DataMan: Data Manager for Pre-training Large Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection DataMan: Data Manager for Pre-training Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.323015Z

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Observation eccd86e7-1eeb-4d3e-a5d7-efc6425c456b · outbound

This paper cites CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:23:37.109571Z

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Observation d9e3d4bc-8ec3-44a2-81f5-a5fe463174d9 · outbound

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

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 35

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Observation b1a710f5-84fa-4872-b11f-228ddd6ecf9a · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.335197Z

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Observation e4f4725c-4c7c-4f97-a2d8-ab229eb7892a · outbound

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

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.433043Z

Source-reported events for the cited work

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Observation d00461b2-4989-4b1b-bdf6-2e1ae719bfb0 · outbound

This paper cites Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.485660Z

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Observation 1215aaac-4870-451d-8d1f-5006b754c1fa · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.539919Z

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Observation 27bba49a-c6ab-4884-8771-3e1f26187e7e · outbound

This paper cites Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.545689Z

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Observation 0e8b2492-d185-4735-80b9-9012088498c6 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.551402Z

Source-reported events for the cited work

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Observation 27ee5e9c-59fb-450c-ae64-9de5435ad704 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.556605Z

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Observation 8038c5c9-bb5e-49fb-8d72-70ff9035f7cf · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection LLaMA: Open and Efficient Foundation Language Models

Reference 43

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Observation da7a92d6-6bd0-4504-8c55-5d2b3b2cf846 · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:23:37.631020Z

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Observation f5069de9-453b-47c6-9d16-d3e7e0ac01d1 · outbound

This paper cites QuRating: Selecting High-Quality Data for Training Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection QuRating: Selecting High-Quality Data for Training Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.569987Z

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Observation 1648229f-8a32-450f-8ed0-2c1d03f64a6c · outbound

This paper cites Organize the Web: Constructing Domains Enhances Pre-Training Data Curation.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Organize the Web: Constructing Domains Enhances Pre-Training Data Curation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.576037Z

Source-reported events for the cited work

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Observation 7d1e7c72-9bd7-49a3-8403-c1dcfea521f5 · outbound

This paper cites Retrieval Head Mechanistically Explains Long-Context Factuality.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Retrieval Head Mechanistically Explains Long-Context Factuality

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.581532Z

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Observation c1106b25-412c-4878-b6d7-a6f0c0abe522 · outbound

This paper cites Doremi: Optimizing data mixtures speeds up language model pretraining.Advances in Neural Information Processing Systems, 36:69798–69818, 2023.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Doremi: Optimizing data mixtures speeds up language model pretraining.Advances in Neural Information Processing Systems, 36:69798–69818, 2023

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.585807Z

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Observation 2105e29b-e248-4728-8d25-f58d390110a2 · outbound

This paper cites Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.590686Z

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Observation 9088e316-d5e9-46c9-b83e-f793a793c9f1 · outbound

This paper cites Mates: Model-aware data selection for efficient pretraining with data influence models.Advances in Neural Information Processing Systems, 37:108735–108759, 2024.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Mates: Model-aware data selection for efficient pretraining with data influence models.Advances in Neural Information Processing Systems, 37:108735–108759, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:23:37.608675Z

Source-reported events for the cited work

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Observation 727e857c-c636-488d-a5c8-3c70e7a52234 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.600908Z

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Observation 67b5ae53-835e-4bfe-bd07-633422466e4b · outbound

This paper cites DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:23:36.894507Z

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Observation e409344f-83e1-4c8e-b326-ab841ba8882b · outbound

This paper cites Attention Heads of Large Language Models: A Survey.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Attention Heads of Large Language Models: A Survey

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.611340Z

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Observation 37fdae11-9ece-4c3e-95b9-a05642bae739 · outbound

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

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:36.616305Z

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Observation 8c57fe57-fa0d-4e2a-afdb-205bbbc40cc4 · outbound

This paper cites Focus Directions Make Your Language Models Pay More Attention to Relevant Contexts.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Focus Directions Make Your Language Models Pay More Attention to Relevant Contexts

Reference 55

Resolution: malformed identifier; no resolver link, observed 2026-08-15T22:23:36.648696Z

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Observation d61d900d-6071-44fc-825a-f19e204938d7 · outbound

This paper cites "This is a test string.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection "This is a test string

Reference 57

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T22:23:36.832878Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c016396b-fa74-44e1-822b-b2eef5aba8ad · outbound

This paper cites 26 Figure 18 The cloud maps of the data selected by AttentionInfluence and FineWeb-Edu Classifier, respectively.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection 26 Figure 18 The cloud maps of the data selected by AttentionInfluence and FineWeb-Edu Classifier, respectively

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:23:37.593637Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6fea8a0a-3d62-4e62-9dbb-15a270189090 · outbound

This paper cites an unresolved cited work.

AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection Unresolved cited work

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T22:23:35.361541Z

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Pith citing papers

Observation 2ea950b2-81b0-4595-bed0-f55952963bc9 · inbound

Hubs or Fringes: Pretraining Data Selection via Web Graph Centrality cites this paper.

Hubs or Fringes: Pretraining Data Selection via Web Graph Centrality AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T06:07:41.035631Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.