Paper Citation Record · LEDGER

Scaling Inference-Efficient Language Models

As of 13 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 4 inbound Pith citation observations for arXiv:2501.18107.

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

pith.paper-citation-record.v1
2501.18107 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:43:29.577850Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:08:06.751426Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T08:15:31.617791Z

Reference resolution

60 of 60 outbound references displayed

verified exact1
verified fuzzy6
unresolved53
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08438660-135f-4f83-8318-11db118c05cf · outbound

This paper cites Results over vLLM In this section, we first evaluate the inference efficiency of open-source large language models over vLLM using NVIDIA Tesla A100 Ampere 40 GB GPU.

Scaling Inference-Efficient Language Models Results over vLLM In this section, we first evaluate the inference efficiency of open-source large language models over vLLM using NVIDIA Tesla A100 Ampere 40 GB GPU

Reference 1

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Observation 2b1402f3-23ec-4a99-b4c5-b2d7da5810f7 · outbound

This paper cites Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models.

Scaling Inference-Efficient Language Models Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

Reference 2

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Observation e99b4b80-e0aa-44e4-bb15-535bb83e9156 · outbound

This paper cites dmodel is the hidden size, fsize is the intermediate size,n layers is the number of layers, andn heads is the number of attention heads.

Scaling Inference-Efficient Language Models dmodel is the hidden size, fsize is the intermediate size,n layers is the number of layers, andn heads is the number of attention heads

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:43:30.521770Z

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Observation f8420ad8-50cf-478b-8687-4f560a193fe0 · outbound

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

Scaling Inference-Efficient Language Models D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 7

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Observation a6c22c8c-5962-4fbf-8814-178c017b4dad · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Scaling Inference-Efficient Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 8

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Observation 41086873-bfa6-4052-9826-7283fb07b62a · outbound

This paper cites The evaluated models include LLaMA (Touvron et al., 2023a), Qwen (Yang et al., 2024), Gemma (Team et al., 2024a;b), and MiniCPM (Hu et al., 2024).

Scaling Inference-Efficient Language Models The evaluated models include LLaMA (Touvron et al., 2023a), Qwen (Yang et al., 2024), Gemma (Team et al., 2024a;b), and MiniCPM (Hu et al., 2024)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:43:30.510314Z

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Observation 9acfc0b0-95f6-439f-97a1-f5df976f7a35 · outbound

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

Scaling Inference-Efficient Language Models Language models scale reliably with over-training and on downstream tasks

Reference 12

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Observation 8a484819-c134-4763-9cce-a00b0300d799 · outbound

This paper cites SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs.

Scaling Inference-Efficient Language Models SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs

Reference 13

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Observation 034e27fd-824d-48e9-bf03-ea75755be632 · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking.

Scaling Inference-Efficient Language Models rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 14

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Observation cb9179df-6538-4e28-954a-eebf8fb915fa · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Scaling Inference-Efficient Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

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Observation ad7d11a5-4b1c-45e8-946d-01b54697d36b · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Scaling Inference-Efficient Language Models Measuring Massive Multitask Language Understanding

Reference 16

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Observation d53c4378-4f48-474a-af51-15bfe863e5a9 · outbound

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

Scaling Inference-Efficient Language Models Measuring Mathematical Problem Solving With the MATH Dataset

Reference 17

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Observation af18da9b-6d42-4283-9145-de574b0b3f52 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling Inference-Efficient Language Models Training Compute-Optimal Large Language Models

Reference 18

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Observation 54d7b0e2-73b2-4a48-a3b9-f20cc31d373c · outbound

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

Scaling Inference-Efficient Language Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 19

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Observation 5eb28230-aa94-400e-806e-1f4f1ac094ca · outbound

This paper cites OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization.

Scaling Inference-Efficient Language Models OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

Reference 20

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Observation d2582b16-804f-4781-8624-a8375140aac3 · outbound

This paper cites MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention.

Scaling Inference-Efficient Language Models MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

Reference 21

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Observation b79f3d96-6d66-44fc-abbb-039d5df1ac2a · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling Inference-Efficient Language Models Scaling Laws for Neural Language Models

Reference 22

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Observation 743d0cea-2e11-4eb2-99ef-552c3de26c33 · outbound

This paper cites Scaling Laws for Fine-Grained Mixture of Experts.

Scaling Inference-Efficient Language Models Scaling Laws for Fine-Grained Mixture of Experts

Reference 23

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Observation 666f646f-b6b1-47c6-95dd-a7f66d8fcb67 · outbound

This paper cites Scaling Laws for Precision.

Scaling Inference-Efficient Language Models Scaling Laws for Precision

Reference 24

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Observation 779028af-5f12-49e8-b0c4-84a3dd7a88cc · outbound

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

Scaling Inference-Efficient Language Models DataComp-LM: In search of the next generation of training sets for language models

Reference 25

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Observation 9dffec26-ad04-42ff-8152-7cac27223013 · outbound

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

Scaling Inference-Efficient Language Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 26

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Observation 2d390137-7835-4a37-bb3a-ab1eb0fed272 · outbound

This paper cites MLC-LLM, 2023-2025.

Scaling Inference-Efficient Language Models MLC-LLM, 2023-2025

Reference 27

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Observation c39a3c32-edfa-4498-a651-cb7a50cd9876 · outbound

This paper cites TensorFlow-Serving: Flexible, High-Performance ML Serving.

Scaling Inference-Efficient Language Models TensorFlow-Serving: Flexible, High-Performance ML Serving

Reference 28

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Observation d03d123a-ee22-4941-be21-0958ae264f8a · outbound

This paper cites Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws.

Scaling Inference-Efficient Language Models Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

Reference 32

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Observation 2b19d0a3-c7a2-490a-9d9e-b3bd645aac06 · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

Scaling Inference-Efficient Language Models Fast Transformer Decoding: One Write-Head is All You Need

Reference 33

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Observation 8097040e-2e6c-40b2-a1bd-28c807b7bda2 · outbound

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

Scaling Inference-Efficient Language Models Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 34

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Observation 65e47aa4-c5d0-4a68-944d-b62ee373bee7 · outbound

This paper cites Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies.

Scaling Inference-Efficient Language Models Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies

Reference 36

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Observation dc59e599-d3da-4b64-9257-7944773488cd · outbound

This paper cites Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers.

Scaling Inference-Efficient Language Models Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 37

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Observation 8bfdace5-9950-45a0-85f6-edef190d15f8 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Scaling Inference-Efficient Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 38

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Observation 1a995995-f6c7-451f-a4e0-6671d0b5e10b · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Scaling Inference-Efficient Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 39

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Observation 320db702-f16d-48ef-9b86-197de543fb9e · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Scaling Inference-Efficient Language Models HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 40

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Observation e8361014-a9c4-4b52-ae56-22eb6041ae8c · outbound

This paper cites DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads.

Scaling Inference-Efficient Language Models DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads

Reference 42

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Observation 79f4d5c7-acd7-4733-ac4d-20ae0c18214a · outbound

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Scaling Inference-Efficient Language Models Decoding Speculative Decoding

Reference 43

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Observation 130af53b-c465-461f-ad57-d562fce51fa9 · outbound

This paper cites Qwen2.5 Technical Report.

Scaling Inference-Efficient Language Models Qwen2.5 Technical Report

Reference 44

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Observation 6edbb00f-43ea-488c-9d79-e57a745fb3d2 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Scaling Inference-Efficient Language Models MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 45

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This paper cites FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving.

Scaling Inference-Efficient Language Models FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving

Reference 46

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Scaling Inference-Efficient Language Models Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Reference 47

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

Scaling Inference-Efficient Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 48

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Scaling Inference-Efficient Language Models OPT: Open Pre-trained Transformer Language Models

Reference 49

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This paper cites (Center) We indicate the relationship between inference latency and hidden size with the number of layers fixed.

Scaling Inference-Efficient Language Models (Center) We indicate the relationship between inference latency and hidden size with the number of layers fixed

Reference 54

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Observation 92eb9d33-d361-4bba-9201-2c641ead1be5 · outbound

This paper cites The evaluated models include LLaMA (Touvron et al., 2023a), Qwen (Yang et al., 2024), Gemma (Team et al., 2024a;b), and MiniCPM (Hu et al., 2024).

Scaling Inference-Efficient Language Models The evaluated models include LLaMA (Touvron et al., 2023a), Qwen (Yang et al., 2024), Gemma (Team et al., 2024a;b), and MiniCPM (Hu et al., 2024)

Reference 56

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Scaling Inference-Efficient Language Models Unresolved cited work

Reference 57

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Reference 58

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Reference 59

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Reference 60

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Reference 256

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Scaling Inference-Efficient Language Models Efficient Streaming Language Models with Attention Sinks

Reference 1921

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Scaling Inference-Efficient Language Models Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 1961

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This paper cites Observational Scaling Laws and the Predictability of Language Model Performance.

Scaling Inference-Efficient Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 2011

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This paper cites Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers.

Scaling Inference-Efficient Language Models Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers

Reference 2016

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Scaling Inference-Efficient Language Models The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 2017

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Observation c9fc260f-b242-46db-99aa-ed15d558a6b6 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Scaling Inference-Efficient Language Models Training Verifiers to Solve Math Word Problems

Reference 2018

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Observation e3130bf6-75bc-4e61-824d-6417abde6ec9 · outbound

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

Scaling Inference-Efficient Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

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Observation 9fce7d57-3670-412a-b473-88a41959fe5e · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Scaling Inference-Efficient Language Models GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 2020

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Observation f9b70a2b-b9ac-4d21-ad99-c0df5c2c6344 · outbound

This paper cites The Llama 3 Herd of Models.

Scaling Inference-Efficient Language Models The Llama 3 Herd of Models

Reference 2021

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Observation 764af2e5-a84a-46ec-ab84-f40ab30dc6e6 · outbound

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

Scaling Inference-Efficient Language Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2022

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This paper cites Qwen Technical Report.

Scaling Inference-Efficient Language Models Qwen Technical Report

Reference 2023

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This paper cites Phi-4 Technical Report.

Scaling Inference-Efficient Language Models Phi-4 Technical Report

Reference 2024

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Observation 9965cdfd-08b6-47ba-9256-79a8b6f2a577 · outbound

This paper cites CHAI: Clustered Head Attention for Efficient LLM Inference.

Scaling Inference-Efficient Language Models CHAI: Clustered Head Attention for Efficient LLM Inference

Reference 2025

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Observation 8e4fcd9c-f716-4138-b306-c9f8e6b8b30e · outbound

This paper cites an unresolved cited work.

Scaling Inference-Efficient Language Models Unresolved cited work

Reference 2048

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Pith citing papers

Observation 49760f12-0291-4357-a221-0caf1b0379f9 · 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 Scaling Inference-Efficient Language Models

Reference 174

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Observation b109582b-e94e-4858-853b-129ad6b632fc · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scaling Inference-Efficient Language Models

Reference 6

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Observation ad67b25a-8e8c-41f2-9975-9de5ae1c9a41 · inbound

Comprehensive AI governance requires addressing non-model gains cites this paper.

Comprehensive AI governance requires addressing non-model gains Scaling Inference-Efficient Language Models

Reference 14

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Observation 58850515-0fa4-454f-b1c8-916159b0c19b · inbound

Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts cites this paper.

Compute-Optimal Is Not Cluster-Optimal: Systems-Aware Scaling for Sparse Mixture-of-Experts Scaling Inference-Efficient Language Models

Reference 42

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