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Paper Citation Record · LEDGER

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices

As of 13 August 2026, this Paper Citation Record lists 100 of 109 outbound references and 0 inbound Pith citation observations for arXiv:2507.01438.

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

pith.paper-citation-record.v1
2507.01438 v1

Coverage vector

measured 100 of 109 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:59:03.657419Z

measured 100 of 100 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 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

100 of 109 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved96
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 155a4a9a-86c1-4f36-8549-c55b3f5a1e80 · outbound

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

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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source=pdf_text observed=2026-08-06T20:58:54.216719Z digest=sha256:de356d2b362d6d3bcfc0d97ca553fe4956558ad0fc56a033e3817b4c5f2cdcec

Observation 9733a787-e914-40ae-9428-03732befad00 · outbound

This paper cites Towards a Human-like Open-Domain Chatbot.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Towards a Human-like Open-Domain Chatbot

Reference 2

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source=pdf_text observed=2026-08-06T20:58:54.275247Z digest=sha256:6b79189bd91f2a2dfc8f2af4559916d1e6b21afa35ffccf38ccb01cc6a38bd54

Observation 5c7af1b7-b74f-4ab7-877b-defe0adecebc · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-06T20:58:54.350465Z digest=sha256:5407a6529f5537c7f82debdb37e566621a85f5a088921833ae2468562dd473a2

Observation 5fa49fe0-f239-4c90-b32c-817b60a264f0 · outbound

This paper cites Qwen Technical Report.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Qwen Technical Report

Reference 4

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source=pdf_text observed=2026-08-06T20:58:54.458351Z digest=sha256:cf7cc86a8c8b3c3f6b41681a26dbe22c5c1059f06274c65ba8b2c9626f1b2ae6

Observation 4d43623f-5c82-4dd4-a34d-e46eaa4c9d66 · outbound

This paper cites LoRA Learns Less and Forgets Less.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LoRA Learns Less and Forgets Less

Reference 5

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source=pdf_text observed=2026-08-06T20:58:54.519614Z digest=sha256:5df562322f3cd72441ae89e10dbf94d19e7682f5ecbc0624440bc9f611cc6775

Observation 1798d89a-d376-4af4-8b38-9992595268c0 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-06T20:58:54.626582Z digest=sha256:fc605505e3f8f2b254f1a5a3dab92b578ca61b2019d74a0539f949e2db1db7f9

Observation 5c3fd7f8-5c47-47e4-bbc8-e51daa8b4c32 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-06T20:58:54.713582Z digest=sha256:6b513f0fcdd2e9da1a8bc6410782669c832d9a785079cd4fba8349e1157438a2

Observation 1a229013-a723-48df-a265-7b52cfae57f2 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-06T20:58:54.780646Z digest=sha256:d8787071b0f211eaa17c1f5c4b913664e80826363314f29d796f4ecb912f8bde

Observation e69981a7-2ec7-4c7c-8a6d-c1bc12034ac9 · outbound

This paper cites Language Models are Few-Shot Learners.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Language Models are Few-Shot Learners

Reference 9

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source=pdf_text observed=2026-08-06T20:58:54.891242Z digest=sha256:a56c863119e7d7bfbc2d29700a20bc4c0043c913019331507826c61815371932

Observation 05cbaa60-6614-46c1-b161-e736e89aa65a · outbound

This paper cites Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead

Reference 10

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source=pdf_text observed=2026-08-06T20:58:54.973490Z digest=sha256:646df0777ae84ace12d59f1db16399ba6a28300b608366387780b2636b048073

Observation 18998b76-9630-4786-95ba-8b0c1b63f62d · outbound

This paper cites PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices PipeInfer: Accelerating LLM Inference using Asynchronous Pipelined Speculation

Reference 11

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local_arxiv, observed 2026-08-06T20:59:04.413079Z

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source=pdf_text observed=2026-08-06T20:58:55.063428Z digest=sha256:c4d72a764872eb948eb70ec90ff38ab9cb60dca1befd011cce807ba3fe685eeb

Observation ec579f46-5a3a-426f-8c43-d7b01002b4ca · outbound

This paper cites One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 12

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source=pdf_text observed=2026-08-06T20:58:55.188112Z digest=sha256:fad1326bf4e03b01c6a115cf941cb1f8431444c7d7b7f66651da418fe623fd37

Observation e6150b3d-d79a-4eb4-b573-6f1538b83ca7 · outbound

This paper cites Punica: Multi-Tenant LoRA Serving.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Punica: Multi-Tenant LoRA Serving

Reference 13

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source=pdf_text observed=2026-08-06T20:58:55.287915Z digest=sha256:4bbdf323f41e9f1530afa4184b9167c1c9578a51dc7bdeff375839006d776c1b

Observation 38107059-deff-4489-b49d-3d9c4e133fdc · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 14

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Observation 41c65450-5ac0-49ab-9744-0a9f172974f8 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 15

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source=pdf_text observed=2026-08-06T20:58:55.425627Z digest=sha256:ddf36326b564a056255c91089aa7deb40fdb8a8b87411552d64658539e57d27b

Observation e3715fe2-8cca-438e-8358-2809d9854531 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-06T20:58:55.526114Z digest=sha256:96342476a9eaec63001efab21b3e7d8db6069fe380e9a1e23bd5b0c48d955c60

Observation 544b5aa5-0369-48d7-8cd4-8f1081a0ce08 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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source=pdf_text observed=2026-08-06T20:58:55.572004Z digest=sha256:5806bfb462a5935ae12b0863c0fb4ebdc3bea9f34afac92528c84abb8f12db5b

Observation 9e373ff4-4a20-4486-ae7a-7ae8ded09fab · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-06T20:58:55.675551Z digest=sha256:df22bda176e66d84aa300f4012e194e4535a861add4ada15fe23eb52878c76b6

Observation e7001b7d-2f6e-4c04-84bc-b03df58cb9b4 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-06T20:58:55.806165Z digest=sha256:f60a2bf0b926292671d0c06f9f6dc5e11030ecc43831f0f2f68b6d5c645115cd

Observation e76af152-e7de-4a3a-bd2b-e8419b7fb046 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-06T20:58:55.961761Z digest=sha256:2b94bfb46d8d23f7dc1711c3078d50ded659a4d8d948b1ac6a55b2d2f5b632f3

Observation 3ceaa933-54b8-4bfe-8f52-71b75092b9c5 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-06T20:58:56.020158Z digest=sha256:bed92aa807362729dde763c5bf5036405e2c8f1c16436a166e4b19b5d6b1babf

Observation 82f1dc8e-f75a-41ec-a563-6ac4bb7191fc · outbound

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

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 22

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Observation f2c6b3aa-806b-4b6b-9e05-fc585e927ca1 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-06T20:58:56.260496Z digest=sha256:994eb7f6b1629d41e9f9fd510cf19f489f24ee295685bdc19b422e1883edeced

Observation eee4ed60-70f2-4687-8696-449efbefd1a7 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-06T20:58:56.375295Z digest=sha256:e0bc15be43a051410d3f9b3b3cbcd92c1321b04b3631827791f843ba1ee2d5ec

Observation 4cd7b491-ea86-4dea-b954-9282756ee8d1 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-06T20:58:56.495120Z digest=sha256:f6358e696f573e19c05a4ab69b769b310affdbf4ad7a25843c8cf3414943acc5

Observation dc773c05-af7f-483e-8f26-a00cc3dd7477 · outbound

This paper cites MultiModal-GPT: A Vision and Language Model for Dialogue with Humans.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices MultiModal-GPT: A Vision and Language Model for Dialogue with Humans

Reference 26

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source=pdf_text observed=2026-08-06T20:58:56.603817Z digest=sha256:6bbb826752ae551286fa4c8618ae75ac4e44d054dc9ff9ee0f1385b8f4acdfc3

Observation 8115d255-c229-447b-8901-002a575d3def · outbound

This paper cites The Llama 3 Herd of Models.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices The Llama 3 Herd of Models

Reference 27

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Observation c72e5ab9-4a99-4369-9e96-6c2059d651df · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 28

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source=pdf_text observed=2026-08-06T20:58:56.882898Z digest=sha256:23faee73a830d3fa94c9febded2c58c73079813ac7d167158d64c7aac3631ec4

Observation f36efb7b-2958-4216-b7d7-52231ece2967 · outbound

This paper cites InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

Reference 29

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source=pdf_text observed=2026-08-06T20:58:57.015007Z digest=sha256:92711eccf0de988305161203791408e2c0cb9fbfea279f82f57078f41041f041

Observation fe27a31d-715e-433e-bc60-a1b3c65db538 · outbound

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

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Measuring Mathematical Problem Solving With the MATH Dataset

Reference 30

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source=pdf_text observed=2026-08-06T20:58:57.147917Z digest=sha256:6755d08189bbb8bf52e705fc9d5a62384db6b91430453b14ab5791216d6e3430

Observation 4b72408a-2209-47e6-95be-27fddf4cd7a7 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 31

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source=pdf_text observed=2026-08-06T20:58:57.273544Z digest=sha256:e9472079c548e7c04ebf3d0da80acab2bd15f56a61030c7d2648ff5d218839d8

Observation efe6693e-acff-498a-a6f2-9b4374b46997 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 32

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source=pdf_text observed=2026-08-06T20:58:57.419710Z digest=sha256:678810ab5dbdf07c97e25daa8d43f808716de7921a4f99361d846247dc97b1c7

Observation 5849ed9a-0349-495f-8b30-558199302020 · outbound

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

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LoRA: Low-Rank Adaptation of Large Language Models

Reference 33

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source=pdf_text observed=2026-08-06T20:58:57.503162Z digest=sha256:e1712095a58116d8b3597b2de30cbebea8c897167b5666d23c9dd365ddb0151d

Observation b1ddc4d3-654a-4657-85ab-b212e8f835ab · outbound

This paper cites Mixtral of Experts.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Mixtral of Experts

Reference 34

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source=pdf_text observed=2026-08-06T20:58:57.574171Z digest=sha256:6f3a5b72323aa77515375db634d529fb19b2cd42b3fed14428af1a947a2bc1b0

Observation 7d13410d-f661-4ceb-8aa7-169602a8454e · outbound

This paper cites Lion: Adversarial Distillation of Proprietary Large Language Models.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Lion: Adversarial Distillation of Proprietary Large Language Models

Reference 35

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source=pdf_text observed=2026-08-06T20:58:57.671927Z digest=sha256:9aef655779d2a4a79074a58047ba2f3f85a5d0595d5d79a156f4a137d7d5a13e

Observation e21fba61-d5ed-461a-a18d-45cc7bd48ac6 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 36

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source=pdf_text observed=2026-08-06T20:58:57.748879Z digest=sha256:71ec5f9405b50d116a945578cc44961995ee6a5ee47f0d05479367cb22b2bc73

Observation a1585906-a585-4c36-ba80-a4a35718b074 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-06T20:58:57.809900Z digest=sha256:2f5d3c355f37af73d0786e4acbe16ccba59124ce039224170fca0288e1a29e26

Observation 95e5cbd2-ae0f-435f-967b-061f9696e90e · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 38

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source=pdf_text observed=2026-08-06T20:58:57.889013Z digest=sha256:687ba122e66a550e7df068e30c541ae0b6353845bcf7fcbb6afacc07fee883b4

Observation 438bb62b-d495-4a96-bf0c-6cda7f240d2e · outbound

This paper cites 2024.{InfiniGen}: Efficient generative inference of large language models with dynamic{KV} cache management.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices 2024.{InfiniGen}: Efficient generative inference of large language models with dynamic{KV} cache management

Reference 39

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source=pdf_text observed=2026-08-06T20:58:57.985900Z digest=sha256:0405a5c0051860f24f8f6ecf19f60da8acf04d787c7b2f172b88dbd38a32df2e

Observation edd55d40-b4e5-4816-a4b8-c636f39e2d4f · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 41

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source=pdf_text observed=2026-08-06T20:58:58.181720Z digest=sha256:01c9924aec7ef0bab099713e0f06623fbee6c539bfb7860e5848787a8134e644

Observation 62002a72-a1a7-4d74-844f-99082af9aa95 · outbound

This paper cites CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference

Reference 42

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source=pdf_text observed=2026-08-06T20:58:58.276719Z digest=sha256:7b78504fd686c2c6dce8fcd229844cf23e9f0f2b2ccf4cf06f31e9dab555e8ba

Observation 8ea84793-3076-4305-b2fe-9514efb41ddd · outbound

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

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 43

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source=pdf_text observed=2026-08-06T20:58:58.399360Z digest=sha256:562a971381e0464d40f60dcd59472ff3e70d50ebde9d8d379e925225695b39a0

Observation 55d7d7a6-14ce-4031-ad9f-c63106a2d612 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 44

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source=pdf_text observed=2026-08-06T20:58:58.506160Z digest=sha256:d1bfb583bd0b733f630c1b46c89ed01500b0a5acf3cdbb8c0d5c8f9944fef11c

Observation ee5dda58-e335-4e86-a82c-1e4fe8db64e6 · outbound

This paper cites Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Reference 45

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source=pdf_text observed=2026-08-06T20:58:58.725171Z digest=sha256:208ed7580105844b167e7fe2fda09d811b58ebf43b11ddfd260953400b92ac70

Observation 1b40480a-7ea3-4213-81db-144480da9d31 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 46

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source=pdf_text observed=2026-08-06T20:58:58.821144Z digest=sha256:a3417f23cd2e5d60de68683c5f933fdff9c1b0e54d2068cf7c1ebb975666eaf2

Observation 6755b5cc-b0c7-4c82-aec5-3069666138e5 · outbound

This paper cites LLM-grounded Video Diffusion Models.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LLM-grounded Video Diffusion Models

Reference 47

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source=pdf_text observed=2026-08-06T20:58:58.891881Z digest=sha256:6203cbbd03e76e814fe390f95bb7bfbec342469b62189fc6424d1d9173c16126

Observation 406f14ba-feeb-476d-8a50-0296be30b8cb · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 48

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source=pdf_text observed=2026-08-06T20:58:58.970079Z digest=sha256:0e99e23b5909ac9d7141abaa8e3ef42df6d14ad26c1cece142ab7255f49a6a1f

Observation 81319f9c-33c6-4cb6-8776-f0a7163ed375 · outbound

This paper cites Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks

Reference 49

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source=pdf_text observed=2026-08-06T20:58:59.066310Z digest=sha256:0e0a901cc929c6319e96abe044b55abad7f4942103b6084ad1d88641bf7ab5e6

Observation 571983aa-256e-4f56-b96c-68a11dfe7ae9 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 50

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source=pdf_text observed=2026-08-06T20:58:59.143355Z digest=sha256:a33dbf960c926f87e2b2c2b217431559b9ad0421f92b6035ef40dd75321a899c

Observation c9cec157-f1d6-4b48-9140-2eb43236a41c · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 51

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source=pdf_text observed=2026-08-06T20:58:59.223878Z digest=sha256:5c551bd0d6406caf8a020999bd98145912ef901293a01d97b7a776d83115e9d7

Observation 1606c9ea-83a9-4a69-8a6e-0b73634c7ee4 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 52

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source=pdf_text observed=2026-08-06T20:58:59.275626Z digest=sha256:81a5a91955cb6f747b494f5cca423c37b8f203d13495fa611cb1ad094a2b041c

Observation 45f82f12-538a-49ed-8601-d97a8e587845 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 53

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source=pdf_text observed=2026-08-06T20:58:59.383703Z digest=sha256:420b00b45a12a18c9a75a3adaa22094b076613dbb4203f322ab78f4aa2132351

Observation 0eae0b1a-b32d-45bb-a672-f07a29daff20 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 54

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source=pdf_text observed=2026-08-06T20:58:59.504517Z digest=sha256:93329c436f07e2670045f88b1c12c2ef1f52d86f439bc968cc8ac627620f0caa

Observation 3af90498-5a8a-482a-8ae5-6955c5773bf9 · outbound

This paper cites OpenELM: An Efficient Language Model Family with Open Training and Inference Framework.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

Reference 55

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source=pdf_text observed=2026-08-06T20:58:59.597356Z digest=sha256:3511fefc119ac788e36b5b9d4f914d26698c6359e11cd6043ef34878d14b3e97

Observation 258b5983-38aa-4e5e-bd76-b87dabb5a8b8 · outbound

This paper cites Empower Vision Applications with LoRA LMM.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Empower Vision Applications with LoRA LMM

Reference 56

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verified exact
local_arxiv, observed 2026-08-06T20:59:04.162207Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:58:59.733300Z digest=sha256:eb233580454bc6dc44eb1c7ea5951a5c89497178f896467667babff115dde3da

Observation 13006e3a-7a8f-4ee6-8f21-75a1b91ab756 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 57

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source=pdf_text observed=2026-08-06T20:58:59.820038Z digest=sha256:ba07150c9940028cfdeb1aead5eef06bd02adedca23f44f281b52d74b8b14fa8

Observation 4a22c84d-7f65-4d9e-823f-580bd864ff0a · outbound

This paper cites 2023-2025.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices 2023-2025

Reference 58

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source=pdf_text observed=2026-08-06T20:58:59.972053Z digest=sha256:f44cc024b6c49a0143ba77554acf2e79d62f26e63f77a2f4c89e50c64e7c96a7

Observation 10f8ed8f-07b3-40ee-867d-f7a6094ee9f4 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 59

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source=pdf_text observed=2026-08-06T20:59:00.102564Z digest=sha256:8cdd5ccd29b7d6b810aacddfb0b28399db5dbc1ec21c66a239a42eb46d61cc6e

Observation 13037068-1147-4ec9-b577-6d44952ada5d · outbound

This paper cites Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

Reference 60

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source=pdf_text observed=2026-08-06T20:59:00.217057Z digest=sha256:46bf5a2ecd2b6411e158d4ee71fd25ca2e4f759a92bdf31cac576767beb4d524

Observation a4ca4d0d-7009-4dc3-ad21-0b9ba0ee494c · outbound

This paper cites GPT-4 Technical Report.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices GPT-4 Technical Report

Reference 61

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source=pdf_text observed=2026-08-06T20:59:00.276568Z digest=sha256:220296a493e9a815f6ca47f011741a80f03a533755ca6a33fcf56e166c396482

Observation 55f0130c-498d-4e35-be18-df2c4fcdec97 · outbound

This paper cites Zhang, Mark Harman, and Meng Wang.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Zhang, Mark Harman, and Meng Wang

Reference 62

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source=pdf_text observed=2026-08-06T20:59:00.400932Z digest=sha256:a6ae3fdfed30488f18e27fc745829b1fd25fbde17296046343bb5754500832a1

Observation 617744f1-b7c5-4193-8285-d9b8a8c1e0e1 · outbound

This paper cites Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers

Reference 63

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source=pdf_text observed=2026-08-06T20:59:00.178162Z digest=sha256:77adf6be619334da1977f8770937a93483358cd46e0de86068fa9242dd9f0f61

Observation 5d0d6566-cfa8-492e-bf01-5a959911f2ec · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 64

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source=pdf_text observed=2026-08-06T20:59:00.566750Z digest=sha256:81521987e194f45af6dc53513262f2093b130858a8f9291110006809586a7bce

Observation c2f2d3a9-42af-4563-9be1-6aff78b6f5e1 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 65

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raw_fallback, observed 2026-08-06T20:59:05.947418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:00.723435Z digest=sha256:cebb69894215d5bd0c4edde4f1c30060411fc80cc86123ba424cafb07a166c5a

Observation de4af2ae-759e-410b-a46a-d88c424af820 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 66

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source=pdf_text observed=2026-08-06T20:59:00.806714Z digest=sha256:c2bce5dd916757fef66b656c7c843e5a70dc8f169fcde3bea0bc379a7cda8517

Observation 6fbaf9a2-be3d-4cb9-9c08-c5618b31e11e · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 67

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raw_fallback, observed 2026-08-06T20:59:06.047196Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:00.490704Z digest=sha256:73fdb82ab1879cca7adb7253836822535d8e1fad49cda504b9b073b996ccc6ab

Observation 1269a875-dcdb-4f9f-89f1-f425a63affff · outbound

This paper cites Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

Reference 68

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source=pdf_text observed=2026-08-06T20:59:00.992772Z digest=sha256:560983757a30310a5e66560bdff2cb90ebdbd67fe1966e77da60e49cc4ca9d73

Observation baef3bd7-a0fa-4cc6-a9f9-3330b9945417 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 69

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raw_fallback, observed 2026-08-06T20:59:05.840603Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:01.085886Z digest=sha256:b40a106dfc24b720ea08c0bde589408219fc616061bd8a27446a32ca549649f1

Observation 855648a5-7807-46ce-a69d-bafa5bab2e8e · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 70

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raw_fallback, observed 2026-08-06T20:59:05.728278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:01.176909Z digest=sha256:d0e739479c41f8cad864df58538cff7c05c479b52935a93fe2a1b20eb79c4101

Observation a5b6eea1-bc57-44ca-9cab-ee12f7df98f1 · outbound

This paper cites LLaSM: Large Language and Speech Model.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LLaSM: Large Language and Speech Model

Reference 71

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source=pdf_text observed=2026-08-06T20:59:01.315222Z digest=sha256:ccce7d72b52f0a0cb9808145a7b11f76d5a505437eb9af81750493965403cdcd

Observation ed9e4cf3-a4d7-43b7-9502-f11478102146 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Code Llama: Open Foundation Models for Code

Reference 72

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source=pdf_text observed=2026-08-06T20:59:00.896996Z digest=sha256:1721c438d7f3bc432789fdefa66a28a1c196f37fa40c1b190ad416b24071db89

Observation 19b3b77a-05de-49b5-bdc2-eff859c31c48 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 73

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source=pdf_text observed=2026-08-06T20:59:01.441707Z digest=sha256:e01f93905a87f9c04670859da7f570dc02d94c7a40e0d71e35eac7def9cfe4bf

Observation d580ae2e-c08a-4398-9254-a1a722f9e353 · outbound

This paper cites Llumnix: Dynamic Scheduling for Large Language Model Serving.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Llumnix: Dynamic Scheduling for Large Language Model Serving

Reference 74

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no resolver link, observed 2026-08-06T20:59:01.514175Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:01.514175Z digest=sha256:38a7369ccdd6c64d4c3269a7f32ec7e42131abb097ce2c0bfaa7c4447034881f

Observation ea9eaf75-cb4a-44b5-a548-0e5743f88284 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 75

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malformed identifier
no resolver link, observed 2026-08-06T20:59:01.586931Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:01.586931Z digest=sha256:0aeeeed83909a8e105b6a3c33b1f42b47824e8020f35681427c0441a10410c6e

Observation 99c10f8b-f9d6-470e-b478-4f57d1c909d4 · outbound

This paper cites S-LoRA: Serving Thousands of Concurrent LoRA Adapters.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Reference 76

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source=pdf_text observed=2026-08-06T20:59:01.255365Z digest=sha256:b9fcc52c1ee507226e9321dd572848ed2458b0457c50d2aca7bb807955401395

Observation 1f7a281f-9fed-41cb-b0d0-0793ef5cc9aa · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 77

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unresolved
raw_fallback, observed 2026-08-06T20:59:05.481014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:01.750031Z digest=sha256:2c856398a8fbb00dde5d60531f89a0ba3b6fe8002881f25e5313f99800dba1ce

Observation 28de88fc-46bb-42ff-940c-1ae6e1dfb9e5 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 78

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raw_fallback, observed 2026-08-06T20:59:05.611027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:01.384286Z digest=sha256:f27fc27f237a1051b5fb237b309c58afa79a8a4e9e5aa38f07b0cdc46071ce34

Observation edf136a0-c492-4b58-9058-bc9d47281df1 · outbound

This paper cites OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

Reference 79

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no resolver link, observed 2026-08-06T20:59:01.912987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:01.912987Z digest=sha256:baa7794e6006e7a4462db90fe87c5264042a2eec13a32cff69ba7f04fb16f385

Observation 0018ca47-847d-4f9b-ac9a-6a7f46d8e7be · outbound

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

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LLaMA: Open and Efficient Foundation Language Models

Reference 80

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source=pdf_text observed=2026-08-06T20:59:01.989191Z digest=sha256:c32f3b03d5c4dab8ba29e5af8ac2b752fa4468ccde7aa038570ccc2a8c7cb4e7

Observation 3e159eb2-5cf8-49a5-89ac-a2603f76b23c · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 81

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no resolver link, observed 2026-08-06T20:59:02.062110Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:02.062110Z digest=sha256:5dbe5e04fcad54b17500fb7a2b19dcd3c347d776bbe6b2c4449e2c71b79b8623

Observation 43045b74-7746-4ac7-b03f-8e40d37c2796 · outbound

This paper cites MathScale: Scaling Instruction Tuning for Mathematical Reasoning.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices MathScale: Scaling Instruction Tuning for Mathematical Reasoning

Reference 82

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no resolver link, observed 2026-08-06T20:59:01.654628Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:01.654628Z digest=sha256:18cfbbb1b9e5ba9c692f6494eb46668bcba88bd75a9307afafbc8ad98c8091ab

Observation 55d8775e-f13c-457a-aa20-3a4da6df49f1 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 83

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raw_fallback, observed 2026-08-06T20:59:05.251565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:02.240139Z digest=sha256:0bfabb4fb88430619f6bef19de66933985def26b14fe6beb56d368c80eeb7258

Observation d0242b78-1758-43a1-b7a2-0571493f61ee · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Gemini: A Family of Highly Capable Multimodal Models

Reference 84

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no resolver link, observed 2026-08-06T20:59:01.835977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:01.835977Z digest=sha256:94fc08993ef48ac8b99676e2b80d12737893494edcec2f2032571b183bcfbb41

Observation 23ff07f6-ccb0-43f3-beb1-d05a180084db · outbound

This paper cites FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Reference 85

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no resolver link, observed 2026-08-06T20:59:02.480244Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:02.480244Z digest=sha256:54e344db47943016e0ba317eb9a108621739e84392cfcb2df914940d2aedb831

Observation fcadc5fe-ec4b-4d4d-8a9f-2026e928eeb7 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 86

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unresolved
raw_fallback, observed 2026-08-06T20:59:05.145731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:02.569226Z digest=sha256:cf9cd9a9f8121282bcbbd5838218087fbbb9b8a694229ae5c1e1f155603cd082

Observation 49873064-557f-4ee1-962f-ff87032bc9c1 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 87

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raw_fallback, observed 2026-08-06T20:59:05.026959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:02.646600Z digest=sha256:e43fc8306a233ff42eee11a9b2fa898baa3c5b4a3a776b609eca161ce54293cd

Observation 1c18de58-1efa-4ba3-9f79-c0959f7fe3db · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 88

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no resolver link, observed 2026-08-06T20:59:02.104052Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:02.104052Z digest=sha256:4c6af18f3e92fb71ef56201f6f8ec7b9fe7e70eb4a3a6d431e18b5464c6563e8

Observation e57df223-99ed-4b89-8ea7-69d3d8776c19 · outbound

This paper cites Structured Pruning Learns Compact and Accurate Models.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Structured Pruning Learns Compact and Accurate Models

Reference 89

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no resolver link, observed 2026-08-06T20:59:02.848131Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:02.848131Z digest=sha256:7c34e6320e840419da1e63800a5d583640d214ab5fd8a5fa3f22029b71ce4180

Observation c018eddb-9598-44e8-b9ca-8f52602e4682 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 90

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no resolver link, observed 2026-08-06T20:59:02.917733Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:02.917733Z digest=sha256:a1acf2a14a0ca5279d12b8919e8abd15f6786549d3e47bd98d2eb1d569f5565a

Observation b6dcbe22-7055-4b0b-85f0-19202e4bc1ba · outbound

This paper cites LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention

Reference 91

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no resolver link, observed 2026-08-06T20:59:02.976420Z

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source=pdf_text observed=2026-08-06T20:59:02.976420Z digest=sha256:91f859012be4e51e3ef515fd2d5c8594010388d760a541f2e31eef155923879f

Observation 58ee5fe9-b9a1-47fb-842b-0053e92d9dce · outbound

This paper cites CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

Reference 92

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no resolver link, observed 2026-08-06T20:59:02.389027Z

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source=pdf_text observed=2026-08-06T20:59:02.389027Z digest=sha256:b64633f8689ab173c58f8fc5ff4264f1906e44bbc8b3e21afa99d37f4778fb79

Observation 7f8b2880-ed33-413e-8938-fa173daa2f0b · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 93

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no resolver link, observed 2026-08-06T20:59:03.200266Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:03.200266Z digest=sha256:7efcf04fca646a34c25a954a4f58ed02809bb60c46c6208437168a3f0cd33656

Observation f3df008a-d87d-4b2d-94f5-ec0652849643 · outbound

This paper cites DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

Reference 94

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no resolver link, observed 2026-08-06T20:59:03.268870Z

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source=pdf_text observed=2026-08-06T20:59:03.268870Z digest=sha256:1c0b3c1c10a773977ef24d79c3ee3b9a1fe2cb6340b51bc35142a1f2815015ef

Observation 61b0dd77-698a-4aa1-9c60-a48d7b6b694d · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 95

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:59:04.576782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:03.315877Z digest=sha256:47d6574a206d4b5fa585ea97aa6e153737bd251c0a42b21d64fe66856610e661

Observation c1ecc4ec-eb6c-4bc6-8114-cf945b7e36e4 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 96

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unresolved
raw_fallback, observed 2026-08-06T20:59:04.924207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:02.739736Z digest=sha256:ad6001e86bb695e65aaad93dc22090dfe8d1c1d1418437063bc24af9f3ef2e75

Observation cef7367c-e1f3-4999-a3b4-17401fa30d44 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 97

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no resolver link, observed 2026-08-06T20:59:03.487001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:03.487001Z digest=sha256:1ad0884d1b6b3349cce02dfc0ba1386c896a2d18ce45b6de5f22520354bd0c3f

Observation ed55f536-5a76-4541-ba7a-ddc55df54962 · outbound

This paper cites Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-06T20:59:03.569899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:03.569899Z digest=sha256:51829a48b7761ee8341fdbd444c4cd3452473cef5c41d7056e42d73e95298376

Observation 8f9b1b4e-d052-4374-aecc-bd9166737427 · outbound

This paper cites SGLang: Efficient Execution of Structured Language Model Programs.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices SGLang: Efficient Execution of Structured Language Model Programs

Reference 99

Resolution
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no resolver link, observed 2026-08-06T20:59:03.657419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:03.657419Z digest=sha256:9303fd4199d86b3a1f9b04d7ee8226c383915c5a071f5c2f3c12bd24bcefaa85

Observation 7a03938a-dce3-4993-8929-29a903a25a35 · outbound

This paper cites an unresolved cited work.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Unresolved cited work

Reference 100

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:59:04.831377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:03.063732Z digest=sha256:1431deb39f7fa4963210497e79296de3fc7042ad87e81997e74bb4a958d8d3d4

Observation 1a9c4c75-b88a-4423-885a-8d01ccd3e7d9 · outbound

This paper cites SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation.

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation

Reference 105

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:59:03.882214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-06T20:59:03.380449Z digest=sha256:dad63bde8c47d4b576b0b2732b5417233e45472f219998638d3447a5b5f965b2

Pith citing papers

No inbound Pith citation observations are available.