Paper Citation Record · LEDGER

Position: AI Scaling: From Up to Down and Out

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2502.01677.

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

pith.paper-citation-record.v1
2502.01677 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:19:21.084613Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

35 of 35 outbound references displayed

verified exact0
verified fuzzy4
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3ef38a17-7e32-4a08-bd20-04c5231e5091 · outbound

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

Position: AI Scaling: From Up to Down and Out Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.874601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38d747c1-50d9-43d6-98fb-a73c7b5d59c0 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for lan- guage understanding.

Position: AI Scaling: From Up to Down and Out Bert: Pre-training of deep bidirectional transformers for lan- guage understanding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:19:21.761430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bacbac8-8723-4fe2-a504-c81632d13bb1 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Position: AI Scaling: From Up to Down and Out GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.939550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac3541a4-1752-453e-bf58-69a36938e927 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Position: AI Scaling: From Up to Down and Out Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.952392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b13aa8-9b47-4c80-bf7c-715f21dd0067 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Position: AI Scaling: From Up to Down and Out Distilling the Knowledge in a Neural Network

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.958325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29a3e7c6-1f50-453c-b517-917272a5fe9a · outbound

This paper cites Mistral 7B.

Position: AI Scaling: From Up to Down and Out Mistral 7B

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.971155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25be5821-29d9-4f11-8314-212fdccb067e · outbound

This paper cites Mixtral of Experts.

Position: AI Scaling: From Up to Down and Out Mixtral of Experts

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.977199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d560141-60cb-4ab4-a62b-c65467e756af · outbound

This paper cites Crafting papers on machine learning.

Position: AI Scaling: From Up to Down and Out Crafting papers on machine learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.989996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06a253ca-fcb5-41ee-a8b5-37398ceccd27 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Position: AI Scaling: From Up to Down and Out Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.004163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 377ae90a-b793-43db-9794-e3700844ad21 · outbound

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

Position: AI Scaling: From Up to Down and Out DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.016100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 863380b9-935d-4b31-9411-9eb9c8e9256b · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Position: AI Scaling: From Up to Down and Out Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.021961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ed2f0e2-9307-4a4f-a90d-0755c556733b · outbound

This paper cites vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention.

Position: AI Scaling: From Up to Down and Out vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.033324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a8b6b91-d9c0-415a-8502-86df283c6403 · outbound

This paper cites N., Kingsbury, B., Sindhwani, V ., Arisoy, E., and Ramabhadran, B.

Position: AI Scaling: From Up to Down and Out N., Kingsbury, B., Sindhwani, V ., Arisoy, E., and Ramabhadran, B

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:19:21.726335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 581e6e7c-22f6-47f3-bc3d-16255db61ef1 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Position: AI Scaling: From Up to Down and Out Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.044686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3e598cc-fa12-42ae-a2d8-eb751f614ab1 · outbound

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

Position: AI Scaling: From Up to Down and Out Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.051050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 400f7717-c21e-4bfe-8bde-5fbae6450ba1 · outbound

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

Position: AI Scaling: From Up to Down and Out Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.056758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d863f8-f5af-43bf-9c29-5701bb2beb3c · outbound

This paper cites Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.

Position: AI Scaling: From Up to Down and Out Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.062115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ecb65e-bba3-4d8a-9e94-f669345a0964 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Position: AI Scaling: From Up to Down and Out Linformer: Self-Attention with Linear Complexity

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.067464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc540b7-a3dc-4cb0-a402-a5062d3d5b0a · outbound

This paper cites Qwen2.5 Technical Report.

Position: AI Scaling: From Up to Down and Out Qwen2.5 Technical Report

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.072698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eb3be5e-11d3-4841-81b2-118a23a91f86 · outbound

This paper cites Building Cooperative Embodied Agents Modularly with Large Language Models.

Position: AI Scaling: From Up to Down and Out Building Cooperative Embodied Agents Modularly with Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.078764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d150633-9484-4bdf-a423-7d4ff00c1141 · outbound

This paper cites H2o: Heavy-hitter oracle for efficient generative inference of large language models.

Position: AI Scaling: From Up to Down and Out H2o: Heavy-hitter oracle for efficient generative inference of large language models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:19:21.703431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57aec9ad-3c28-42b5-adc6-24dd36243098 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Position: AI Scaling: From Up to Down and Out GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.996636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bebd6bc2-70aa-4a84-9f5c-fa8055282e08 · outbound

This paper cites Scaling Laws for Neural Language Models.

Position: AI Scaling: From Up to Down and Out Scaling Laws for Neural Language Models

Reference 1994

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.983646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e95b3db-334a-4fad-ba9f-b8c6779a42da · outbound

This paper cites Textbooks Are All You Need.

Position: AI Scaling: From Up to Down and Out Textbooks Are All You Need

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.946350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0664e562-cfce-45cc-b606-29bba954991a · outbound

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

Position: AI Scaling: From Up to Down and Out DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.896363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b929ace7-0282-4ccc-949c-08dcc83878a0 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Position: AI Scaling: From Up to Down and Out D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.903249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de86a116-0904-45d5-88d3-b74cd333efb0 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Position: AI Scaling: From Up to Down and Out LoRA: Low-Rank Adaptation of Large Language Models

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.964540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e5dc8eb-a371-49fb-980e-a8fc1a478bf4 · outbound

This paper cites Crosslingual Generalization through Multitask Finetuning.

Position: AI Scaling: From Up to Down and Out Crosslingual Generalization through Multitask Finetuning

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.027559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d20ba0c3-02b2-4270-93d4-509ec297f707 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Position: AI Scaling: From Up to Down and Out DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.914751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39505420-256b-4744-baff-f642fd6b4f3e · outbound

This paper cites Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges.

Position: AI Scaling: From Up to Down and Out Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.927285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6bb4560-33b7-4d5e-ba11-cc98ec44644f · outbound

This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Position: AI Scaling: From Up to Down and Out Accelerating Large Language Model Decoding with Speculative Sampling

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.908955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37b4c954-adb7-4c57-8e89-cc1ea506640b · outbound

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

Position: AI Scaling: From Up to Down and Out Textbooks Are All You Need II: phi-1.5 technical report

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:21.010351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0966d0a9-e78f-40b9-9156-fba11bb8aafa · outbound

This paper cites A Review of Sparse Expert Models in Deep Learning.

Position: AI Scaling: From Up to Down and Out A Review of Sparse Expert Models in Deep Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.933334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 589ee48c-b617-4566-bd1b-7ac8a8ba98d7 · outbound

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

Position: AI Scaling: From Up to Down and Out Gqa: Training generalized multi-query transformer models from multi-head check- points

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:19:21.793717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cc7e1a5b-f8b6-40d7-b1a2-d5f8edfdaad0 · outbound

This paper cites GPT-4 Technical Report.

Position: AI Scaling: From Up to Down and Out GPT-4 Technical Report

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T18:19:20.884222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.