Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities | Paper Citation Record

Observation e872bdb5-5124-45db-a663-0c4bec173868 · outbound

This paper cites Splitwiser: Efficient LM inference with constrained resources.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Splitwiser: Efficient LM inference with constrained resources

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.405665Z

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Observation 8bd69898-d6fc-4bd3-88cb-2dbe1fbcac68 · outbound

This paper cites Phi-4 Technical Report.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Phi-4 Technical Report

Reference 2

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 3

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This paper cites GPT-4 Technical Report.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities GPT-4 Technical Report

Reference 4

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 6

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 7

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 8

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Observation 0e8bfe49-e1ce-4cdb-acb7-fc3c7fd67785 · outbound

This paper cites Program Synthesis with Large Language Models.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Program Synthesis with Large Language Models

Reference 9

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Observation cf5a809b-9aa4-471f-9f38-38d911cc4597 · outbound

This paper cites Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

Reference 10

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 11

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 12

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 13

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 14

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This paper cites Reinhardt, Ali Saidi, Arkaprava Basu, Joel Hestness, Derek R.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Reinhardt, Ali Saidi, Arkaprava Basu, Joel Hestness, Derek R

Reference 15

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 16

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 17

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 18

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This paper cites DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization

Reference 19

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 20

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Observation 5ceffb50-e36a-46d9-a0ae-e334e6e8b49f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Evaluating Large Language Models Trained on Code

Reference 21

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 22

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Observation bf82dbdd-e2f9-4219-a521-8b71e6949b37 · outbound

This paper cites Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence

Reference 23

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Observation c2d561bb-330f-4594-8a91-bfe5239e9fce · outbound

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 24

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 25

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This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 26

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

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 27

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Observation 25f7156e-c329-4bb8-8bac-e0751c380682 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Training Verifiers to Solve Math Word Problems

Reference 28

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This paper cites SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.393173Z

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 30

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This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 31

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 32

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 33

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 34

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 35

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This paper cites A Survey on In-context Learning.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities A Survey on In-context Learning

Reference 36

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 37

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 38

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This paper cites Learned Step Size Quantization.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Learned Step Size Quantization

Reference 39

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 40

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 41

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 42

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 43

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 45

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 46

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 47

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Observation ce5c8a57-b983-4c09-9428-39b512f06059 · outbound

This paper cites Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.254185Z

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Observation 818e14f1-aae7-44fb-828c-684dec3aa460 · outbound

This paper cites Mask-Predict: Parallel Decoding of Conditional Masked Language Models.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Mask-Predict: Parallel Decoding of Conditional Masked Language Models

Reference 49

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Observation 855a05e1-166a-43af-bcc6-a02c4936a87f · outbound

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 50

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Observation 67baaf38-74cd-4ccc-baff-300bb6000970 · outbound

This paper cites TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:16:10.476352Z

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 52

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Observation b034079b-1959-4eb3-a35a-e5cd1ea85ddb · outbound

This paper cites Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.554055Z

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Observation 187f7736-0996-4e40-8a2c-1279959f92d5 · outbound

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 54

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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 55

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Observation 94ab65aa-515b-40d7-9190-1b11512d84e6 · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:16:10.217361Z

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Observation 5ef99ea2-2c9f-4fec-a8db-5088f1ecdd4f · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.360876Z

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

Observation 7b382ea9-2c91-419c-ad6d-a96f9da1d219 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.507718Z

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Observation a3253717-4ff2-49a4-8345-1af39188c8db · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.137417Z

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Observation c15515e7-8350-4c04-8488-0e3bbe790797 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.143516Z

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Observation ea5f8022-845c-4304-82d9-cf43e2ea1d04 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.165781Z

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Observation 7ff8f9ae-20f2-4271-a8a6-6609c845d7f8 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.086654Z

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Observation 4ef7c4a7-c1c9-47d1-9264-6d6edde21ef8 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.253224Z

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

Observation 61b831f4-fcd0-47ff-adc3-18b849d53a70 · outbound

This paper cites DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.210742Z

Source-reported events for the cited work

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

Observation e2ba28d7-9b2c-4bf3-9bb3-e753026a5fc8 · outbound

This paper cites semi-PD: Towards Efficient LLM Serving via Phase-Wise Disaggregated Computation and Unified Storage.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities semi-PD: Towards Efficient LLM Serving via Phase-Wise Disaggregated Computation and Unified Storage

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.262035Z

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

Observation 6bfbd1b0-46fe-4eb7-8e25-d394ae49869f · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.504771Z

Source-reported events for the cited work

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

Observation e7cd867a-47f6-448a-bb11-85941de7199f · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.414523Z

Source-reported events for the cited work

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

Observation b10e7059-25db-469d-af91-2429b76e253d · outbound

This paper cites Beerel, et al.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Beerel, et al

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:47:59.540999Z

Source-reported events for the cited work

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

Observation 7bacd580-cb66-4e4e-8e5f-19faea510a60 · outbound

This paper cites Pipeline Parallelism for Inference on Heterogeneous Edge Computing.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Pipeline Parallelism for Inference on Heterogeneous Edge Computing

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.418868Z

Source-reported events for the cited work

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

Observation 51f2121a-7c45-4c66-b7d1-0f6ed96a43f2 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.551866Z

Source-reported events for the cited work

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

Observation 8f829e8d-7d4a-488d-a5ae-556d7907eb13 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.544526Z

Source-reported events for the cited work

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

Observation bb2cdca7-87c3-4d82-b709-2ba892fd838f · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.567582Z

Source-reported events for the cited work

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

Observation 21ac28e4-317c-468d-af14-98fe0f236e7e · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.532125Z

Source-reported events for the cited work

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

Observation 64ee13b0-8589-407d-92a7-05ea07c5c323 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Qwen2.5-Coder Technical Report

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:16:10.452943Z

Source-reported events for the cited work

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

Observation 2bb97dd9-038b-45a2-b9c8-7a58087c20d5 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.534956Z

Source-reported events for the cited work

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

Observation b5c2b0ef-d645-4ccd-a48b-d686b7ed56cd · outbound

This paper cites Mistral 7B.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Mistral 7B

Reference 76

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T20:16:10.540313Z

Source-reported events for the cited work

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

Observation e1cd307b-041c-40d6-992b-5b7ba6a162d7 · outbound

This paper cites Mixtral of Experts.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Mixtral of Experts

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:16:10.411353Z

Source-reported events for the cited work

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

Observation 014ae315-e287-4f6d-8774-a13586751f2d · outbound

This paper cites NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.221722Z

Source-reported events for the cited work

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

Observation 3ca446b1-0817-4e67-91a7-bd882fbde0a3 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.555186Z

Source-reported events for the cited work

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

Observation 88c497ea-01ba-4c29-a58a-09c700ca049c · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.525733Z

Source-reported events for the cited work

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

Observation 16d738a7-7593-408d-b3bb-463e39467121 · outbound

This paper cites Large Language Model Partitioning for Low-Latency Inference at the Edge.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Large Language Model Partitioning for Low-Latency Inference at the Edge

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.529259Z

Source-reported events for the cited work

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

Observation 6a3586dd-7a0c-4259-90d9-413ffcda75ad · outbound

This paper cites Scaling Laws for Neural Language Models.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Scaling Laws for Neural Language Models

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:16:10.396954Z

Source-reported events for the cited work

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

Observation ae3063bf-425c-40c1-8a58-d912d123478d · outbound

This paper cites A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.494626Z

Source-reported events for the cited work

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

Observation 3b934fc4-e386-4114-86d8-39091814c3ef · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.326755Z

Source-reported events for the cited work

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

Observation 468b79bb-6791-4473-857b-8d22e7b250b5 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.501361Z

Source-reported events for the cited work

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

Observation 0d04f59e-c139-4530-b991-732dc4f8fb03 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.528754Z

Source-reported events for the cited work

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

Observation d53288ac-5709-4b1d-ac6c-a2b6dd50dd96 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-05-13T12:26:58.164513Z

Source-reported events for the cited work

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

Observation c4ac1b1f-aeb1-46ff-bb6f-7468353d8042 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.498232Z

Source-reported events for the cited work

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

Observation 45cad72a-bbfe-4859-89a3-e799aa2b82ad · outbound

This paper cites Larsson, Ove Edfors, Fredrik Tufvesson, and Tho mas L.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Larsson, Ove Edfors, Fredrik Tufvesson, and Tho mas L

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T15:47:59.491435Z

Source-reported events for the cited work

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

Observation e0965329-eca0-4a82-b1bc-4ea478d0a999 · outbound

This paper cites Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.449209Z

Source-reported events for the cited work

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

Observation 0f639cd5-cec4-4f9c-bd3f-20fcd49c478f · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.508055Z

Source-reported events for the cited work

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

Observation 52b05ca3-b4b5-4424-9836-d60d8ec8319e · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.454377Z

Source-reported events for the cited work

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

Observation 5e77c088-5647-4f8f-b62a-fad5b1f30a78 · outbound

This paper cites A Survey on Large Language Model Acceleration based on KV Cache Management.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities A Survey on Large Language Model Acceleration based on KV Cache Management

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.280024Z

Source-reported events for the cited work

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

Observation 75c719aa-47bc-47ac-be6b-1cf8627ff0ab · outbound

This paper cites CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.229507Z

Source-reported events for the cited work

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

Observation 7455507a-1109-4751-9cc9-6177fe8660d9 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.460396Z

Source-reported events for the cited work

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

Observation a810e497-b01d-45fa-9b42-a86b8c879930 · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.442867Z

Source-reported events for the cited work

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

Observation 092006a3-8121-4200-89b3-a474a5bf846d · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 97

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.513407Z

Source-reported events for the cited work

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

Observation 99ee0257-767e-4135-a67c-b62e6ef3d08e · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.445564Z

Source-reported events for the cited work

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

Observation 233769eb-2a8d-4bf0-921e-77db5376cdbe · outbound

This paper cites DeepSeek-V3 Technical Report.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities DeepSeek-V3 Technical Report

Reference 99

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T20:16:10.204099Z

Source-reported events for the cited work

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

Observation 98f971d8-e1bf-40c5-a8e4-ecf7b0486f7b · outbound

This paper cites an unresolved cited work.

Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-05-26T15:47:59.432621Z

Source-reported events for the cited work

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

Coverage vector

Reference resolution

Outbound references

Pith citing papers