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Paper Citation Record · LEDGER

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

As of 21 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 2026-08-21T06:32:19.484+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:b23afcaa3c19cefdfbe3ff1693746f5502352210575fdf6f4c3de40ec635b87d

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=sha256:01678aa6e2558fed549ec5300c6ec7b8a02dd4e1356c2218b4879d43664165cd

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=sha256:076b17c03d2fb7bab7ec198d452c3f7c7c498a478959f29e10153a555399866a

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:a95c6526422b5b14d085159f554d6777ba57a804f3567daee45d214fa0775b90

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=sha256:51f3f007ce3042065067eaf0b611da41c188962952d9100c1e980a96bd1d5b01

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=sha256:6e003222b109876c6c4dc151bea87dbbf01f08b01f869d31729790fa35e025f8

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=sha256:8cd91072f59ef7b6536e0930eaa0754e9c7c30aa5ef1f2c1b796847372dbcea9

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=sha256:7e091c608d039d5333ed79d026e9583ae66cbdf69fc0780b04111f27dbaf3dfe

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=sha256:30870bc32c43ec6851322874305e986d54dab24cae649eabb22fba9babacc1d4

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:ba9cdaf074aa32567476cfdb226b6716fddd66e9d9638c822043725d80f0f2f1

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:beb503815c893850b8f6e7ccb7d0d734b9a01146f6302156b381fda383459666

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=sha256:8ae1660bc888369ec44dab89ed75b489642c62d1b4b701fb9dc1e126cd67bd6d

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=sha256:0cbdbfa16d6053b3d3b4814f3fcc7d1a538d9e333dcb7f80d975d748df37dc48

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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source=pdf_text observed=2026-08-06T20:58:55.359470Z digest=sha256:4df66814c98c9b7a8768c1c82ac22518d22113003a72792f0bdc8e28cefb5bdf

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:a7c1ce4892166cce9c81b90c62c0f6869828479bd8828116b15e54b10b417a7f

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:aa4c0174345b565e95c450a523339cb3dc0bcd0e8aa7856873596d88ca9da773

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:c04e5945e6c4ef2338b622c426cc6f4fbb532b43bda215d37d0358a999a66477

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=sha256:3dfe86f5e862b02997d77ab366db7ba592fa86b4844944d2898de70a4d3a96f3

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:a4079f22166036a9f03dea3b937f84fe42d5285016b4a06c03c4e84ddc226248

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=sha256:89667bd014bf08ca6d57746786e663428d076390f21255895c6ed283c0fb83b1

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=sha256:5d86bb19f941107a6d05ef5f453fff653f5a107887a40e165a12db81662c3090

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:ff6d39c1bd484ebbed1b51654f971d1454150b2e2812fbabc8696988ddeaa107

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:af7d62ac7c31176e8870bf8893130790d451e984e633c7f1e75073683bd020f7

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:d500bd2ad935d52bf1161a7323ff92b2c5f01de158ae16663121a56cf891f5ab

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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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=sha256:25e05b5f16f3552bb4bc6fa2d33e2ef54b2ac8f1daf8abeae17d97b8685bfe86

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=sha256:652c8157253f147b74dd8d448edb05a46d25e1cc6ccaf460a00fbf7abbeb4c29

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=sha256:58018cf4e074371143ea955f8c7f0298d3aba0305f50fa3057eeea0403b1b8d4

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:f6ad9489a920f8f1b6c9d32f8730d450c34e2fa2595e4e06c725a5fbf3942f4e

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=sha256:990288d148281418c7210643dccc857814356cdf4d6c27973e1c62b873ef3c15

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:c032a72d2fea7cd19feb52bce02ec63866fd1a68afba97e41d215cca31c598ea

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:e4d28ac971a34d1a507189fb442cd077cdb35e0b22f8f49ff0c5a3da34bd2c5a

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=sha256:875cda94a6bc95a2b85ac30d48de4e477b6206f7f93faa225f597d15f8933da5

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:c2794f3654aea6d06085225769910d196e416ca829338f0a4c77ae834cf1789d

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:a4595f02f452babf3cbb5acd0b70db0ba49dff927abe01a90335fd3bf91bc5a2

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=sha256:75bae3ce638e686a0cb1f25711046c7f8026b13af547fc512be052b5723eea8e

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:cf481272118aeb42d7adc2e5c777ef2d61f1687ae909f8fc8711ba32ed251a8e

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=sha256:2258a0cb09a48b8d6bcdda6dc0e3dc089bea4440ec67dd2e72bc4e988445eca3

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:b0080b1bfa3cafffd7783f1535895f5feabed2a0b0b8fd3d1dd534b796bc3f1b

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=sha256:99b3cc830c884abdc97a0275f6e6d6b8aba328744ff31ffb64760c2faa62b04f

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:d5c0fffd8fe27b66d801d85adc78d83447e13be081b2d475313eb1dbbd9fef39

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:f2b4c9872cb7755f4bf7176fbb1f866f4b82ee188aa32bd93fa3504297ad3cfe

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=sha256:9c384f82746b7e5026d61dfa498a147d7de7ab6ebbfd522214b99454279d4692

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:b97e8bd9dbfefb608d134dfb2984950742fa09cb3fba77acda04899365a91717

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=sha256:8938e8816a3ba0d7f4d567b839e2166de64123f59130221732c278b958c41697

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=sha256:1fda388d8c02789e06c4081801a3e7147a086fb111a819d8e12fa3f83ebcf5a5

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=sha256:6ff7ae4c560e04b378e50d36c7b1ca06e982d2f56eaed8178759f92e5fb08cdf

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:e45327ac6abe92a67010fedbc8edb18757fcdc73de38e2b28d1f68db0dde5f9a

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=sha256:1f8fa92f878a771d6388635d26443f2b9eca53e1e057d8ce0a3af3300832b7d0

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:c21621b0b87ebd9121cacfd9e968190a27cbd43e22d866318f99833a91a915b3

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:fa950caeb2ea1605a1cd3ba793af6c9fe7ccf51eb8748ff6666e35bd491c5bad

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=sha256:37aebd3e295f7885fa11f1c06d177b19a960b0392848dc5d9c947d6903e2e8a2

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:58:59.733300Z digest=sha256:beea1429360a592d9b7620f38a765ad30f422f457c7503d7b31c27983f1eb508

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=sha256:93a8613927e1185189cf8c194b95865a179dc97f0eeda2e50b505f90cf4d040b

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:d5b9c7380f1c513833b283e8695a4fb4f974c485650db05d03093ad545716e61

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=sha256:7d775193ed85a3335884816eb0b67ff11aabc1ab8b04e300ee352ea42367907c

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=sha256:7f5b71250e2ece1b074d6f4ae2b878292497e844e4caa47b11150cc199246165

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:a6d438e8899280657e530a99f5e030d45321e52d651c776b90d4ac0e584691b3

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=sha256:89e8611367823d6e9fab4c8b8acb56079b9c6cd08bd0d8c138ff24571afdc086

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=sha256:5ee77d471efcff667f626bb4eca56200e0ae923bbe40ec4b2a9fbc206cdad1de

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:a0829513e30ffd96d89f9f131ee894c8543b28b4b9b612f852bf237ebde2a1b8

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:00.723435Z digest=sha256:30bab9a76e3b639ce73e033976d50048b4d31de50178424948e016a18818b210

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:db3076fb1f1d61cffd180d2f5b9cc20adc0836621d8f325310316e8907d10dfd

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:00.490704Z digest=sha256:bf74f5ab4bbde11e90c8d3bc40d69a423a14cc18ba25a274daaf8bb7621b5f07

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=sha256:2d75dba7577bff5eef53f92913935f99fa151efa5e2429f2f2bbc74690270ce2

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:01.085886Z digest=sha256:d6f92fcdb73fd7d86545b4f72f4ed1ad22a8a35b3922fdc52ad8e5a236a8abe6

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:01.176909Z digest=sha256:882cd6304673e618dccb331492700bbd71175cacd1bfa6e9d151dd42f397fa5c

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=sha256:39116ff6608d7ab930ded99da09d4981fcd3d17b2164932cc4d1a87e247bfba8

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=sha256:80ff6f9890691b4385d4ab563efb8edfd67d701a56db5281c35b38da4c029409

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:c2b04baf584b855470790c84307b64a39492a4e43ae31a957615b5e06bc3d53f

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

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source=pdf_text observed=2026-08-06T20:59:01.514175Z digest=sha256:ecbda6f415365ff98480c756ce86197718861f567deb217573ef8c008dc75600

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

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source=pdf_text observed=2026-08-06T20:59:01.586931Z digest=sha256:e5403d4dcea1b2d62977ce53e9a954de9dbc38d8860caa9ca1bce9ccf6b9e8c3

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:fe0ddb49349c43cac475bca6c805e9bafde6c9f62dac84f2d961ec7cd251a8e2

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:01.750031Z digest=sha256:8af733fb822fd9b052ac3d3361501b449edbf9d7640a12d7c6ac064ccfd7fd53

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:01.384286Z digest=sha256:db8578d912dfff79d7294e23b5fd43638e64fd94eaaada74ede69f44ee590061

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=sha256:6d0c66bc4f8c6507568a1e05c1b4ff20342809b2903b09f82b797916315a15d7

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:01.989191Z digest=sha256:d4c6b643966b34b299d623329938a1aefa90c7df2f96b7ba595288863e9eeb80

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:02.062110Z digest=sha256:a2866d4131513582f60bbbee2e61845e04a5283d29e6268324155999a08caa71

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:01.654628Z digest=sha256:240880d6b03542ac6fce9ce513f05c83ded363b83e55ff59dae9e7b3b36ecd8a

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

Resolution
unresolved
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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:02.240139Z digest=sha256:2a8a293ddcf73e65b5af5444d1943f46bb83cf0480c992740911d248d3895cec

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=sha256:0e7bfd12f2f59d02de2d2e943f33ae05884d4e3db50eca1ed672bd05cd357f6e

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:02.480244Z digest=sha256:49819fe1db03af6a087ad4970721e729e60c7f23a0018ff9a02405063d8042b8

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

Resolution
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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:02.569226Z digest=sha256:e45206ff80938bffb0b17b4810b4d87b3c69a96182013f4e9ce74da67a6499c4

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

Resolution
unresolved
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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:02.646600Z digest=sha256:1a58f627837e8710d23513d3dfe62ba6c3b6980e9f9063133567e783802f1a3d

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:02.104052Z digest=sha256:6f3f151dd69450e00a4573fcd0f7f3eafa0d61ea2a589b94c9d20c5d3af2b174

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:02.848131Z digest=sha256:fd6fd698f4bdb18cf1280afb52987c3721e6ea588b1b3318d6c25312c0584412

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:02.917733Z digest=sha256:8d528fedb9273ec18af7233d166070fb754ed4b5921693ba4a9462d4f52fe3e3

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

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:02.976420Z digest=sha256:ef0e041ce9c39ce38186b9ed22c5e94c7a95111b72e36e8347e625b8a0a35119

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

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

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:02.389027Z digest=sha256:7f93ece1b4a2d6c3dcfc8c20923629a74e8f1e5c634d726dafa6ef73a69b7605

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:03.200266Z digest=sha256:2e281d9b83bce80a07a4961f581f36313e4fa01887b07be82b2fb4820a727bd4

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

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:03.268870Z digest=sha256:da8f948efb1037baf3c696f49c4998c81b74bbe839913256eba728734cd9bd99

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:03.315877Z digest=sha256:3c8a445b9752c42dae9324329c69557d4d3adcabeacb71af633e92d7f9d61745

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

Resolution
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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:02.739736Z digest=sha256:0d9eff9b4796db14bdc0d70226c43735189da87331d4df8f361995cb3b1b34e3

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

Resolution
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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=sha256:65b33a96caecd6e6b6c38fe438040f5e63829d107442de5407e0035f38e911f5

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=sha256:8698a7c656568b08190db437e515fb19ce248466a45b41e77ee9c9dc617a8985

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
unresolved
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=sha256:8f165df6b6adad5024afea6dcd1fd000309f61400b646bb805d7e68727a725f1

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:03.063732Z digest=sha256:953c16b2162846dd92fcf1b2fb15a7bac36a9ac14a53d180cc11fc385b31b3be

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 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T20:59:03.380449Z digest=sha256:ad79e6b4506e77361f2385469824ed1d766a4de05b89c3bd30e9a58d2fbc7145

Pith citing papers

No inbound Pith citation observations are available.