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Paper Citation Record · LEDGER

Profiling Lightweight Large Language Models

As of 14 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.20806.

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

pith.paper-citation-record.v1
2607.20806 v1

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measured 32 of 32 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-01T09:23:42.381010Z

measured 32 of 32 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

32 of 32 outbound references displayed

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Outbound references

Observation 4ce27c9c-e1c4-4b21-b3a5-9a087de10f8e · outbound

This paper cites Energy and Policy Consid- erations for Deep Learning in NLP,.

Profiling Lightweight Large Language Models Energy and Policy Consid- erations for Deep Learning in NLP,

Reference 1

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Observation 9c0686ea-f937-4d41-8d77-4ff3c82a364b · outbound

This paper cites Green AI,.

Profiling Lightweight Large Language Models Green AI,

Reference 2

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Observation dc799f71-5680-4df6-ab1b-411ab8169dc6 · outbound

This paper cites A Comprehensive Survey on TinyML,.

Profiling Lightweight Large Language Models A Comprehensive Survey on TinyML,

Reference 3

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Observation 39cf0452-bb57-4ec2-ba09-5fee27a122f2 · outbound

This paper cites On-Device Language Models: A Comprehensive Review.

Profiling Lightweight Large Language Models On-Device Language Models: A Comprehensive Review

Reference 4

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Observation bf974530-5a73-4566-9c8d-48fa17fdffed · outbound

This paper cites Privacy issues in Large Language Models: A survey,.

Profiling Lightweight Large Language Models Privacy issues in Large Language Models: A survey,

Reference 5

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Observation ecc4429a-53ca-4b8d-9c27-d7b76249b71c · outbound

This paper cites Edge Computing: Vision and Challenges,.

Profiling Lightweight Large Language Models Edge Computing: Vision and Challenges,

Reference 6

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Observation 6f4278e1-418e-4cdd-9f09-208323dbb198 · outbound

This paper cites The Emergence of Edge Computing,.

Profiling Lightweight Large Language Models The Emergence of Edge Computing,

Reference 7

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Observation aa17519f-ea10-492d-8650-3a96aa932d1d · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale,.

Profiling Lightweight Large Language Models LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale,

Reference 8

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Observation cfd23771-0cf9-4978-a238-069e5dbbc6c0 · outbound

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

Profiling Lightweight Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers,

Reference 9

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Observation ec26b72c-c720-4360-8177-916ef54cf57f · outbound

This paper cites LLM-Pruner: On the Structural Pruning of Large Language Models,.

Profiling Lightweight Large Language Models LLM-Pruner: On the Structural Pruning of Large Language Models,

Reference 10

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Observation e319cc9e-99c4-4428-89e0-f03b8a7e64bf · outbound

This paper cites A Review on Edge Large Language Models: Design, Execution, and Applications,.

Profiling Lightweight Large Language Models A Review on Edge Large Language Models: Design, Execution, and Applications,

Reference 11

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Observation ed8413f8-a6af-42fe-bfbd-2fab07e4a2ef · outbound

This paper cites 1.1 Computing’s energy problem (and what we can do about it),.

Profiling Lightweight Large Language Models 1.1 Computing’s energy problem (and what we can do about it),

Reference 12

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Observation 3b846143-a7ff-4e90-a942-05407142c857 · outbound

This paper cites Energy-aware metaheuristics,.

Profiling Lightweight Large Language Models Energy-aware metaheuristics,

Reference 13

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Observation 87e88f19-0df9-4367-8231-fd75ac1042e6 · outbound

This paper cites Green optimization: Energy-aware design of metaheuristics by using machine learning surrogates to cope with real problems,.

Profiling Lightweight Large Language Models Green optimization: Energy-aware design of metaheuristics by using machine learning surrogates to cope with real problems,

Reference 14

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Observation bcd8d9bd-d44d-4ba2-a1c8-e351bb9bb5d8 · outbound

This paper cites White-box execution refactoring of transformers for lower energy,.

Profiling Lightweight Large Language Models White-box execution refactoring of transformers for lower energy,

Reference 15

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Observation f3c94add-8ecb-4f79-8c7c-47d17a12455d · outbound

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

Profiling Lightweight Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 16

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Observation 4a55202f-cfc4-47df-a0f2-cf8bc23fb327 · outbound

This paper cites Mistral 7B.

Profiling Lightweight Large Language Models Mistral 7B

Reference 17

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Observation b07029ab-eb29-4c55-b042-cfb8b6e56b39 · outbound

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

Profiling Lightweight Large Language Models TinyLlama: An Open-Source Small Language Model

Reference 18

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Observation ec215d32-32cf-4d7c-9cf4-9cfc83fe10c0 · outbound

This paper cites Qwen2.5 Technical Report.

Profiling Lightweight Large Language Models Qwen2.5 Technical Report

Reference 19

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Observation 5c04f705-3ca3-4b6b-86c4-378bf932b5be · outbound

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

Profiling Lightweight Large Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 20

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Observation 8ade16ce-5e15-4ea8-91aa-245f2143dbb9 · outbound

This paper cites Phi-2: The Surprising Power of Small Language Models,.

Profiling Lightweight Large Language Models Phi-2: The Surprising Power of Small Language Models,

Reference 21

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Observation 5b93593a-efe8-4fbc-ae49-ff9c270b4ea3 · outbound

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

Profiling Lightweight Large Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 22

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Observation 73070ab2-5fbe-462f-8ec6-70533d73ed66 · outbound

This paper cites Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning,.

Profiling Lightweight Large Language Models Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning,

Reference 23

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Observation 414e3ec8-e1d0-41d7-b89f-9527c16db861 · outbound

This paper cites Evaluating Large Language Models Trained on Code,.

Profiling Lightweight Large Language Models Evaluating Large Language Models Trained on Code,

Reference 24

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Observation d3820b28-7d62-4573-9ab6-9e3ca640a184 · outbound

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Profiling Lightweight Large Language Models Training Verifiers to Solve Math Word Problems

Reference 25

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Observation b4c2d04a-0ad0-4d22-82a3-02d81d6edef5 · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark,.

Profiling Lightweight Large Language Models MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark,

Reference 26

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Observation a682c2cd-1856-48ae-9ec9-28faa55e543c · outbound

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Profiling Lightweight Large Language Models Measuring Massive Multitask Language Understanding,

Reference 27

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Observation ba1bee03-4950-4680-a13e-8565d3becbc6 · outbound

This paper cites A Survey on Edge Performance Benchmarking,.

Profiling Lightweight Large Language Models A Survey on Edge Performance Benchmarking,

Reference 28

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Observation 5f47a7d5-3cc8-4455-8835-ea4372125a0f · outbound

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Profiling Lightweight Large Language Models Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications,

Reference 29

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Observation f616e81b-9816-454c-84fd-ad93c6503de8 · outbound

This paper cites tinybenchmarks: evaluating LLMs with fewer examples,.

Profiling Lightweight Large Language Models tinybenchmarks: evaluating LLMs with fewer examples,

Reference 30

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Observation 2d7bac58-3aa4-4f61-85ec-51e4cb9ab218 · outbound

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Profiling Lightweight Large Language Models THOP: PyTorch-OpCounter,

Reference 31

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Observation 093516f6-33f5-4f74-831d-345fc2626f43 · outbound

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

Profiling Lightweight Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 2023

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Pith citing papers

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