Paper Citation Record · LEDGER

Densing Law of LLMs

As of 21 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 9 inbound Pith citation observations for arXiv:2412.04315.

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

pith.paper-citation-record.v1
2412.04315 v2

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:40:05.617004Z

measured 67 of 67 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:10:21.963779Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:26:05.711057Z

Reference resolution

58 of 58 outbound references displayed

verified exact0
verified fuzzy12
unresolved46
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da815800-213f-4718-a4e1-23d69723e1cf · outbound

This paper cites write newline.

Densing Law of LLMs write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.343096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3e6901-a07d-493d-879a-684ff3fae5e6 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Densing Law of LLMs Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.349605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f33735a8-fa32-4596-a7e2-d41ba59d70e3 · outbound

This paper cites Gqa: Training generalized multi-query transformer models from multi-head checkpoints.

Densing Law of LLMs Gqa: Training generalized multi-query transformer models from multi-head checkpoints

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.583704Z

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.

Observation 0315bbad-310e-4bb9-8946-150ff04b91b7 · outbound

This paper cites The Falcon Series of Open Language Models.

Densing Law of LLMs The Falcon Series of Open Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.360703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2128bf0c-1989-4b2c-8e3f-d036e5ad3c91 · outbound

This paper cites Welcome to llmflation – llm inference cost is going down fast.

Densing Law of LLMs Welcome to llmflation – llm inference cost is going down fast

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.568851Z

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.

Observation fd50fc07-b58d-4723-9bb3-5bc9f1656b7e · outbound

This paper cites Program Synthesis with Large Language Models.

Densing Law of LLMs Program Synthesis with Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.370802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a95284a-301d-4196-b024-f622450fd23a · outbound

This paper cites Stable LM 2 1.6B Technical Report.

Densing Law of LLMs Stable LM 2 1.6B Technical Report

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.376053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26e1fa71-b080-48cc-b64a-ddfcd204a169 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Densing Law of LLMs On the Opportunities and Risks of Foundation Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.381515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2feeb6e-f33f-42d7-b28c-4bcca7b5c424 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Densing Law of LLMs Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.386806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adbd9491-2df6-409f-84e0-cb0536ddfbfd · outbound

This paper cites an unresolved cited work.

Densing Law of LLMs Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:40:06.553643Z

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.

Observation 71982829-2659-428e-92a5-f57fc2dd5e35 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Densing Law of LLMs Evaluating Large Language Models Trained on Code

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.396760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f8cbbfb-0728-4e1d-8ed9-e6f47ae12ade · outbound

This paper cites Palm: Scaling language modeling with pathways.

Densing Law of LLMs Palm: Scaling language modeling with pathways

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.401657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09b5b24a-c94a-43a1-b906-57b7bc6bd9da · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Densing Law of LLMs FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.405948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3b50cb4-942c-4b6b-acdb-603ebfb6ca48 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Densing Law of LLMs Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.410253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8fa1931-994c-4158-b9c9-8c7df8f5dc14 · outbound

This paper cites Training on the Test Task Confounds Evaluation and Emergence.

Densing Law of LLMs Training on the Test Task Confounds Evaluation and Emergence

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.414736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c397a604-83d8-4a44-a909-bc3a33b17e41 · outbound

This paper cites The Llama 3 Herd of Models.

Densing Law of LLMs The Llama 3 Herd of Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.419237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7789df8-cdf0-4bff-97d8-f83e088ad4de · outbound

This paper cites Textbooks Are All You Need.

Densing Law of LLMs Textbooks Are All You Need

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.423469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bff893b-5b0e-4c7e-93dd-625f9f5dceb3 · outbound

This paper cites Apple intelligence foundation language models.

Densing Law of LLMs Apple intelligence foundation language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.428177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf67b272-e18c-4d9a-ba0c-b67da04938cf · outbound

This paper cites Pre-trained models: Past, present and future.

Densing Law of LLMs Pre-trained models: Past, present and future

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.517084Z

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.

Observation f8add9ff-2197-4293-8d2a-dddad06ceccc · outbound

This paper cites Measuring massive multitask language understanding.

Densing Law of LLMs Measuring massive multitask language understanding

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.437642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 277b439c-d78f-42e8-8a1f-a64ade3be7cf · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

Densing Law of LLMs Measuring mathematical problem solving with the math dataset

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.442303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0393296-5016-4508-8cf3-0d1f5b1b2c56 · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Densing Law of LLMs Scaling Laws for Autoregressive Generative Modeling

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.446835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13b41fea-cda9-4c65-b521-7f70e0434576 · outbound

This paper cites Trends in machine learning hardware, 2023.

Densing Law of LLMs Trends in machine learning hardware, 2023

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.481136Z

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.

Observation dddeaacd-ed54-4d55-bac4-8a938890a972 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Densing Law of LLMs Training Compute-Optimal Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.456239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e156f295-1b67-4efb-8024-606f312c4194 · outbound

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

Densing Law of LLMs MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.461217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c97a9ba-93e9-40fa-9807-071823c56551 · outbound

This paper cites Mistral 7B.

Densing Law of LLMs Mistral 7B

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.465939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e51276e9-248d-4b27-9afa-77673185cf98 · outbound

This paper cites Scaling Laws for Neural Language Models.

Densing Law of LLMs Scaling Laws for Neural Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.471109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15135d31-e378-486b-b33b-5322fb171f73 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

Densing Law of LLMs Efficient memory management for large language model serving with pagedattention

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.475932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a94945bb-c980-4fb3-aa7a-29db01f8bcee · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Densing Law of LLMs u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.480407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4fdf972-6ba1-46bd-9771-70b45c1bc496 · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

Densing Law of LLMs Textbooks Are All You Need II: phi-1.5 technical report

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.484778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde65092-a518-4d48-a95b-10d45c8d403d · outbound

This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation.

Densing Law of LLMs Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.489748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e46d9c3-77cb-4f4e-939a-39fc3dcc35bb · outbound

This paper cites Deja vu: Contextual sparsity for efficient llms at inference time.

Densing Law of LLMs Deja vu: Contextual sparsity for efficient llms at inference time

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.434160Z

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.

Observation f85576e0-1b33-427e-8485-5b831ac520b2 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

Densing Law of LLMs Llm-pruner: On the structural pruning of large language models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.498859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8753a28f-de96-48d8-b7d7-16a6efa15459 · outbound

This paper cites Cramming more components onto integrated circuits.

Densing Law of LLMs Cramming more components onto integrated circuits

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.407074Z

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.

Observation 81ecaeb3-a8c0-47b7-9947-b988fb4ea3cd · outbound

This paper cites Compact Language Models via Pruning and Knowledge Distillation.

Densing Law of LLMs Compact Language Models via Pruning and Knowledge Distillation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.508305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b037a556-6406-4c78-a49d-f4ea44fbd61f · outbound

This paper cites GPT-4 Technical Report.

Densing Law of LLMs GPT-4 Technical Report

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.513532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 906fc973-70e5-4375-8966-0209c11fcce7 · outbound

This paper cites Learning to reason with llms.

Densing Law of LLMs Learning to reason with llms

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.391612Z

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.

Observation 3a57189d-ed86-4cbd-aec8-4551ec5c36a4 · outbound

This paper cites Gpt-4o mini: advancing cost-efficient intelligence.

Densing Law of LLMs Gpt-4o mini: advancing cost-efficient intelligence

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.366486Z

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.

Observation dd422771-1486-452b-91e1-864c03232ce2 · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.

Densing Law of LLMs Opencompass: A universal evaluation platform for foundation models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.349440Z

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.

Observation f6f30f30-9146-4978-bdb5-c916036927f4 · outbound

This paper cites Pre-trained Models for Natural Language Processing: A Survey.

Densing Law of LLMs Pre-trained Models for Natural Language Processing: A Survey

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.532012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14ddd2d3-6fdf-46c1-b43d-05a6145cc980 · outbound

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

Densing Law of LLMs Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.536977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43739e82-513c-47d8-83fb-80e12feb533c · outbound

This paper cites Beyond chinchilla-optimal: Accounting for inference in language model scaling laws.

Densing Law of LLMs Beyond chinchilla-optimal: Accounting for inference in language model scaling laws

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.333480Z

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.

Observation 1f2068ef-7cbc-4f8b-b51b-80fcb518e0b2 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Densing Law of LLMs Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.545721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 739bc385-8ffc-4d4a-b0f4-7bce56ebc486 · outbound

This paper cites PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU.

Densing Law of LLMs PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.550244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28bb2574-2d8f-4200-9141-4496369f863b · outbound

This paper cites A simple and effective pruning approach for large language models.

Densing Law of LLMs A simple and effective pruning approach for large language models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.554953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67077d4f-697b-4848-a4f5-70c56b5acc8d · outbound

This paper cites Challenging big-bench tasks and whether chain-of-thought can solve them.

Densing Law of LLMs Challenging big-bench tasks and whether chain-of-thought can solve them

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.559393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c77d0e9b-9b1b-4aa6-9437-4a179224d15d · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Densing Law of LLMs Gemma 2: Improving Open Language Models at a Practical Size

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.563496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e87ca8d0-2273-4dd9-ad3f-347e05be160c · outbound

This paper cites Introducing mpt-30b: Raising the bar for open-source foundation models, 2023.

Densing Law of LLMs Introducing mpt-30b: Raising the bar for open-source foundation models, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.297869Z

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.

Observation 39be9f42-f49f-4e47-9a58-9519499e9ee3 · outbound

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

Densing Law of LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.573355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d604a72e-01d5-4994-a7a6-5e6efcfbed9a · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Densing Law of LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.578119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e9f1bc9-ee11-4809-b3b5-c28db56625b0 · outbound

This paper cites Emergent abilities of large language models.

Densing Law of LLMs Emergent abilities of large language models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.582950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2d3848b-55c6-4466-ab6c-b3bbaab5ee25 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Densing Law of LLMs Chain-of-thought prompting elicits reasoning in large language models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.587721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57bee0cb-a9ce-4477-8cf0-cb81f17bc279 · outbound

This paper cites Skywork: A More Open Bilingual Foundation Model.

Densing Law of LLMs Skywork: A More Open Bilingual Foundation Model

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.592486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d97e941f-8c21-4bcc-8bba-a58ab25cbc85 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Densing Law of LLMs A Survey on Knowledge Distillation of Large Language Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.597293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0aac06a-5260-43f4-bdb5-24caff9e8358 · outbound

This paper cites PowerInfer-2: Fast Large Language Model Inference on a Smartphone.

Densing Law of LLMs PowerInfer-2: Fast Large Language Model Inference on a Smartphone

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.602148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f89b80e-1e14-4aad-9577-4ed75f5ce4f1 · outbound

This paper cites Survey on knowledge distillation for large language models: Methods, evaluation, and application.

Densing Law of LLMs Survey on knowledge distillation for large language models: Methods, evaluation, and application

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:40:06.260657Z

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.

Observation 40428787-b2ee-440b-9530-3fcf9cf18a3c · outbound

This paper cites Toward Inference-optimal Mixture-of-Expert Large Language Models.

Densing Law of LLMs Toward Inference-optimal Mixture-of-Expert Large Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.611932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a448a4c6-5a05-4d96-bce1-d6da9f37e3b0 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Densing Law of LLMs TinyLlama: An Open-Source Small Language Model

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T21:40:05.617004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e0bbb01a-c49d-4979-80ab-0756b7d0bca7 · inbound

Language Games as the Pathway to Artificial Superhuman Intelligence cites this paper.

Language Games as the Pathway to Artificial Superhuman Intelligence Densing Law of LLMs

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T22:01:12.450093Z

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Observation 0b813fa8-2309-47ac-8c0a-071a4190360c · inbound

Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data cites this paper.

Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data Densing Law of LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:10:21.963779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0a3e955-ccce-438d-8ef1-b83837d38bb2 · inbound

Seed1.5-VL Technical Report cites this paper.

Seed1.5-VL Technical Report Densing Law of LLMs

Reference 152

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:26:05.714751Z

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.

Observation 5fe31107-6777-4e53-8041-b8fc08558648 · inbound

Semantic Retention and Extreme Compression in LLMs: Can We Have Both? cites this paper.

Semantic Retention and Extreme Compression in LLMs: Can We Have Both? Densing Law of LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:56.021042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9902f502-2188-40e4-ad7d-855b05649d8e · inbound

Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding cites this paper.

Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding Densing Law of LLMs

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:32:42.812379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9db46184-a4d4-4d3b-b225-9c60b5f86560 · inbound

MiniCPM4: Ultra-Efficient LLMs on End Devices cites this paper.

MiniCPM4: Ultra-Efficient LLMs on End Devices Densing Law of LLMs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:31:21.920986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7786d76e-84fb-4402-a578-d68742c0b14c · inbound

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search cites this paper.

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search Densing Law of LLMs

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T05:07:39.739462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038897af-0a81-4a76-a9b9-303ed4d95674 · inbound

Know What, Know Why: Semantic Hazard Communication for Intelligent V2X Systems cites this paper.

Know What, Know Why: Semantic Hazard Communication for Intelligent V2X Systems Densing Law of LLMs

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T11:39:07.798950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a72f6df-2388-4440-99c5-eb54d538c2d1 · inbound

Predicting LLM Reasoning Performance with Small Proxy Model cites this paper.

Predicting LLM Reasoning Performance with Small Proxy Model Densing Law of LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T15:52:00.787352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.