Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs | Paper Citation Record

Observation 1da1a75f-503e-45d4-8765-a892a0188f69 · outbound

This paper cites Phi-4 Technical Report.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Phi-4 Technical Report

Reference 1

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

Reference 2

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Observation bdc0747a-befe-4b43-9c4a-b213055b852e · outbound

This paper cites GPT-4 Technical Report.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs GPT-4 Technical Report

Reference 3

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This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 4

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This paper cites Program Synthesis with Large Language Models.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Program Synthesis with Large Language Models

Reference 5

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

This paper cites Scaling Inference-Efficient Language Models.

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

Reference 6

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 7

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Observation e5d40e9f-ebb8-4d07-be6e-17c8a44c3b17 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901

Reference 8

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This paper cites Exploring Diffusion Transformer Designs via Grafting.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Exploring Diffusion Transformer Designs via Grafting

Reference 9

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Observation 83b1f712-d611-498f-9b56-7fe78a8936e6 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Evaluating Large Language Models Trained on Code

Reference 10

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Observation 29230e97-6ab5-4f76-b4cd-0ae41a7c2b4f · outbound

This paper cites Scaling Law for Quantization-Aware Training.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scaling Law for Quantization-Aware Training

Reference 11

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Observation 34b87522-9e74-4c47-b8f5-bfbac89ef4f7 · outbound

This paper cites Reducing the carbon impact of generative ai inference (today and in 2035).

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Reducing the carbon impact of generative ai inference (today and in 2035)

Reference 12

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 13

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Observation ff32e271-e228-46d2-9303-efb746bd19f2 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Training Verifiers to Solve Math Word Problems

Reference 14

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This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 15

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Observation e85a2c3f-e98e-4305-be55-f6fefa8f9f0c · outbound

This paper cites The Llama 3 Herd of Models.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs The Llama 3 Herd of Models

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.031768Z

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Observation e5b57a8b-8ff2-4038-9194-3c93348bc488 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Language models scale reliably with over-training and on downstream tasks

Reference 17

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This paper cites He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs He, B., Yin, L., Zhen, H.-L., Liu, S., Wu, H., Zhang, X., Yuan, M., and Ma, C

Reference 18

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Observation 2b12d6ce-7fe1-40c5-baf4-25f423b5945b · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 19

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Observation d14a1da3-bbd1-4d8c-b2d8-95c2ae267dc2 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

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Observation 26c4dd41-d428-496f-94a5-2159be8a4e24 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Measuring Mathematical Problem Solving With the MATH Dataset

Reference 21

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This paper cites Training Compute-Optimal Large Language Models.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Training Compute-Optimal Large Language Models

Reference 22

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Observation f662aaaf-2814-40c7-9a9f-294ddf7af60d · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 23

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Observation 87954259-ef6d-4d24-a4b2-7bf3539ab4e2 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

Reference 24

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Observation 830e0122-ba17-4433-8bad-3fd466451386 · outbound

This paper cites Scaling Laws for Neural Language Models.

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

Reference 25

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Observation 8e35aa42-8db1-4f09-a7d3-54e90d3459ab · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scaling Laws for Fine-Grained Mixture of Experts

Reference 26

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Observation 77a1a9f5-b51f-4a18-b02d-d7fb973e58e6 · outbound

This paper cites Scaling Laws for Precision.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scaling Laws for Precision

Reference 27

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Observation e24042eb-c1cc-4cb0-bca9-681c1e1b0b19 · outbound

This paper cites DeepSeek-V3 Technical Report.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs DeepSeek-V3 Technical Report

Reference 28

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 29

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This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 30

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This paper cites The Impact of Depth on Compositional Generalization in Transformer Language Models.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 31

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers

Reference 32

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Observation f272d83c-a6b2-4c07-b2f3-06e2045b63b0 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Observational Scaling Laws and the Predictability of Language Model Performance

Reference 33

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Observation 64973a38-ee69-4354-a109-9d52931c3803 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

Reference 34

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Observation 988fb2ff-4019-4f6b-9cd8-5ddf3e42bffa · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 35

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Observation 02ad5651-0047-4b35-8e23-3585d10ce9f5 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 36

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Observation e7ebcbc1-7631-4364-8326-91ff1f732d3c · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 37

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Observation 4a349a68-e32e-4bc8-9752-804a537431a2 · outbound

This paper cites Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Reference 38

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Observation ad6a4eb1-b724-46c8-9c8d-59507364db17 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 39

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Observation b10b1dce-e4e7-4b07-b21d-a88466423d0d · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Gemma: Open Models Based on Gemini Research and Technology

Reference 40

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Observation 693b4fc7-6cb5-4544-bafe-17a44bd934c4 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.142851Z

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Observation 5d0bb1cb-a0b8-4006-b2cc-121d4b7df4e1 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 42

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Observation a32cf55a-0359-4b57-b055-0f8711cce19d · outbound

This paper cites Emergent Abilities of Large Language Models.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Emergent Abilities of Large Language Models

Reference 43

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Observation 808db350-a29a-451b-8c6a-d6ad43d91182 · outbound

This paper cites Crowdsourcing Multiple Choice Science Questions.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Crowdsourcing Multiple Choice Science Questions

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.108024Z

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Observation 3dc8d93e-5ac6-4414-b19f-b16be7165640 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Efficient Streaming Language Models with Attention Sinks

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.015207Z

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Observation 3da75dd6-ac2e-46ec-b23c-ecce9857d078 · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:49:16.947404Z

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Observation 72fca583-44f3-4719-980d-b715d56f87f3 · outbound

This paper cites Qwen3 Technical Report.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Qwen3 Technical Report

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.085018Z

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Observation 366e5d4d-bf24-4113-9445-f6f00f570e4d · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.023596Z

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Observation 817612f6-1772-48df-8973-acaaf0cd85c5 · outbound

This paper cites Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.027469Z

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Observation 3e18282a-a525-43b9-8ffc-343806a7f11b · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.007029Z

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Observation c338478b-de5f-4260-961b-ead6fc97eb4e · outbound

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

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs TinyLlama: An Open-Source Small Language Model

Reference 51

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Observation 85388c2f-c1de-4033-879f-dc63fd0b9bcc · outbound

This paper cites It was not used to generate research ideas.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs It was not used to generate research ideas

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:30:55.447361Z

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

Observation 5fc007a8-5807-40d6-89ee-8db1be66b5e0 · outbound

This paper cites Across varying batch sizes and model scales, larger hidden sizes yield higher inference throughput under a fixed parameter budget.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Across varying batch sizes and model scales, larger hidden sizes yield higher inference throughput under a fixed parameter budget

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:30:55.449858Z

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Observation 2b9ec17b-1782-4749-8eca-43101dfe8541 · outbound

This paper cites Moreover, while multiplicative and additive calibrations differ in formulation, their MSE and Spearman values remain nearly identical.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Moreover, while multiplicative and additive calibrations differ in formulation, their MSE and Spearman values remain nearly identical

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:30:55.445040Z

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Observation 8cfb120f-2eff-4147-9a34-e2b24c757cf4 · outbound

This paper cites an unresolved cited work.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-18T05:30:55.439819Z

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

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