Paper Citation Record · LEDGER

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition

As of 4 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2605.02364.

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

pith.paper-citation-record.v1
2605.02364 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:45:52.380042Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T21:10:10.548489Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T21:17:24.096157Z

Reference resolution

50 of 50 outbound references displayed

verified exact6
verified fuzzy36
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94c0a1a3-a34c-4f18-a4a3-81a8f870df79 · outbound

This paper cites Scaling Learning Algorithms Towards.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Scaling Learning Algorithms Towards

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.396697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e706b4a9-17e4-4b06-8541-f3de161a9435 · outbound

This paper cites and Osindero, Simon and Teh, Yee Whye , journal =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition and Osindero, Simon and Teh, Yee Whye , journal =

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.385586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c3faed76-48c7-4728-ad91-051d775fdd2a · outbound

This paper cites 2016 , publisher=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2016 , publisher=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.363426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fcb5fc7c-5c94-4441-9b3f-1e4749fa75ac · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.382075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1f84c3a7-b28d-4796-888d-e8bd434ec562 · outbound

This paper cites 2023 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2023 , eprint=

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.356618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c262169c-c1ec-40d1-bc5c-bbf51cbe0c29 · outbound

This paper cites Few-shot Learning with Multilingual Generative Language Models.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Few-shot Learning with Multilingual Generative Language Models

Reference 6

Resolution: verified exact; doi, observed 2026-05-08T18:49:24.763775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 88200595-8201-41ce-917b-9adfe763c10a · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.374978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 44b984cd-fac3-4ef1-9d4c-c793f5a72728 · outbound

This paper cites Transactions on Machine Learning Research , issn=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Transactions on Machine Learning Research , issn=

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.320265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1f3303ee-e963-43a5-945a-a993ad73e00c · outbound

This paper cites 2023 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2023 , eprint=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.390092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 47c0fd26-1584-4619-80e2-0856cc32a1db · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , articleno =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Proceedings of the 41st International Conference on Machine Learning , articleno =

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.345413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 659719f8-3c27-4233-8a22-7bf220eeeb17 · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.393209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1e33909b-8832-4e07-944f-eaabc9316599 · outbound

This paper cites Scaling Laws for Neural Language Models.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Scaling Laws for Neural Language Models

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-05-09T06:15:37.209432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5fd30999-ffed-4b0e-bebd-75152f9ad89d · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.330041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d0c4fe74-7c8b-4624-b75e-2a27fd6bc27e · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.326675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9713b7ac-116a-4289-b17e-fe8879fa51ce · outbound

This paper cites 2023 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2023 , eprint=

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.389519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 073be856-1375-4dff-b1ed-443c6991b39d · outbound

This paper cites and Sifre, Laurent , title =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition and Sifre, Laurent , title =

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.378818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d3956e38-917f-4e6b-8a33-7641ef34fb70 · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.393008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cafc5f2a-c80b-4fed-8925-55742b1c98d5 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , articleno =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Proceedings of the 41st International Conference on Machine Learning , articleno =

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.370493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4f87c13c-ca9c-44c9-b6c2-0cdc351bb17b · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.371553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b46eca6f-8557-43f1-a480-55312c00c5e7 · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.327100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 84f28b52-ac7b-4b9a-ac98-3c3d73c68d57 · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.308014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 63f11eea-1e93-4808-bdca-b5502167c992 · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.313061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d983311e-82c2-423f-9f76-c1a8d6d4815d · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.304451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0ffebefa-a16f-4c4f-8d1c-f45787aedbac · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.357701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a590d4f8-86a3-42d1-bdf8-3df67d78325a · outbound

This paper cites and Barak, Boaz and Le Scao, Teven and Piktus, Aleksandra and Tazi, Nouamane and Pyysalo, Sampo and Wolf, Thomas and Raffel, Colin , title =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition and Barak, Boaz and Le Scao, Teven and Piktus, Aleksandra and Tazi, Nouamane and Pyysalo, Sampo and Wolf, Thomas and Raffel, Colin , title =

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.383667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cbd4f636-075a-4e01-b5ac-c390aa82af32 · outbound

This paper cites 2022 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2022 , eprint=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.396381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cb6ef5e6-38c1-4783-accc-67670304de54 · outbound

This paper cites Deduplicating Training Data Makes Language Models Better.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Deduplicating Training Data Makes Language Models Better

Reference 27

Resolution: verified exact; doi, observed 2026-05-08T18:49:25.464214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a19ae7db-ef35-4c00-8331-239a0679cfe6 · outbound

This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T23:57:11.134962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1f525ac2-bf3c-4768-aefe-6b2da1622f63 · outbound

This paper cites 2025 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2025 , eprint=

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.360602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c150fcac-23b5-4f74-a24b-2a7647603359 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Deep Learning Scaling is Predictable, Empirically

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T04:01:58.491474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39142426-38ac-4710-a00c-0b180ec0084b · outbound

This paper cites 2024 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2024 , eprint=

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.347681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 140bedea-67ec-4e37-8888-eb67179131da · outbound

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

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:13:40.454049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8d3a83b8-4359-49be-9583-5fe1435f0e42 · outbound

This paper cites Language Models are Few-Shot Learners , url =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Language Models are Few-Shot Learners , url =

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.338732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c120ddd6-9746-4470-b320-b27336f6c371 · outbound

This paper cites Language Models are Unsupervised Multitask Learners , url =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Language Models are Unsupervised Multitask Learners , url =

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.374156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1a9b33bd-b65f-48a5-9d70-f98d3689cb7c · outbound

This paper cites title =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition title =

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.349096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fa3cd43b-1746-4991-b602-74afb2637979 · outbound

This paper cites booktitle =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition booktitle =

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.366872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e195405d-9dd7-446f-9abe-44aea9a7aa05 · outbound

This paper cites Attention is All you Need , url =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Attention is All you Need , url =

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.284073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ee1e290e-31e2-4da9-abfe-55d74c0d2e78 · outbound

This paper cites RoFormer: Enhanced transformer with Rotary Position Embedding , journal =.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition RoFormer: Enhanced transformer with Rotary Position Embedding , journal =

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-08T18:49:25.446433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f78e4d51-2b34-446e-9017-a2a8ccee330e · outbound

This paper cites 2020 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2020 , eprint=

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.360279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e17b215d-5d03-43fc-adb3-45ed150e63e9 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T06:08:06.759074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 60cbf363-5845-4a46-b395-5a261572d9ac · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Language models scale reliably with over-training and on downstream tasks

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:15:37.244820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 810dd919-b834-4c7b-aa5e-a33660d5f189 · outbound

This paper cites QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:15:37.219533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8abad15e-4670-4cfd-b2c4-b81eae569004 · outbound

This paper cites 2018 , eprint=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition 2018 , eprint=

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.377339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26a2614c-d487-436c-b7ab-c9e0c382aa2e · outbound

This paper cites Measuring Massive Multitask Language Understanding.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Measuring Massive Multitask Language Understanding

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.334332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation eb1eaa39-5ee9-436b-b82a-a5e67b0dde7e · outbound

This paper cites TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension

Reference 45

Resolution: verified exact; doi, observed 2026-05-08T18:49:24.755652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation caa0ee54-30c7-47e8-9583-f65d2283c90c · outbound

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

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition URL https:// doi.org/10.18653/v1/p19-1472

Reference 46

Resolution: metadata mismatch; doi, observed 2026-05-08T18:49:24.759595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7de3fc29-33bb-40d2-8d76-1d6fee50c1df · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Advances in Neural Information Processing Systems , volume=

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.363956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9e7d1f0a-44e0-42cb-82f6-574c6dd46af9 · outbound

This paper cites Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:15:37.238284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ef85db39-dadc-4467-a575-73fab92bf041 · outbound

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

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition RegMix: Data Mixture as Regression for Language Model Pre-training

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:15:37.272265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3508e138-148c-48f4-8c5d-50aed512980c · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition Advances in Neural Information Processing Systems , volume=

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:07:19.380780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 823ca312-4935-4b5a-a72d-dabc32919ab4 · inbound

DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition

Reference 174

Resolution: verified exact; local_arxiv, observed 2026-07-01T15:45:47.700401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0794ea55-ab27-4175-99b4-29d30f256602 · inbound

DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition

Reference 174

Resolution: verified exact; local_arxiv, observed 2026-07-02T21:17:24.099070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.