Paper Citation Record · LEDGER

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.03621.

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

pith.paper-citation-record.v1
2412.03621 v4

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:32:20.824316Z

measured 37 of 37 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

37 of 37 outbound references displayed

verified exact0
verified fuzzy29
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b04d268d-0a8c-4ba5-9ca3-7ff036145108 · outbound

This paper cites JPPO: Joint power and prompt optimization for accelerated large language model services,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services JPPO: Joint power and prompt optimization for accelerated large language model services,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.094944Z

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.

Observation eb40f94d-e554-4ac1-9270-e9b4414665c4 · outbound

This paper cites Large language models (LLMs) inference offloading and resource allocation in cloud-edge computing: An active inference approach,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Large language models (LLMs) inference offloading and resource allocation in cloud-edge computing: An active inference approach,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.087491Z

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.

Observation 5a6e0143-94ba-4eb2-ad2c-b6707ab0a9ff · outbound

This paper cites A survey on large language models for communication, network, and service management: Application insights, challenges, and future directions,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services A survey on large language models for communication, network, and service management: Application insights, challenges, and future directions,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.740124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3839ae91-21b5-4a19-a885-7d68dcff2690 · outbound

This paper cites EdgeMoE: Empowering sparse large language models on mobile devices,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services EdgeMoE: Empowering sparse large language models on mobile devices,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.076096Z

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.

Observation 81520502-beae-4e73-a125-b79cadc01f66 · outbound

This paper cites Mobile edge intelligence for large language models: A contemporary survey,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Mobile edge intelligence for large language models: A contemporary survey,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.745618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b899b35-c79f-4185-aec4-1605b0c4664a · outbound

This paper cites Indus- trial internet of things with large language models (LLMs): an intelligence-based reinforcement learning approach,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Indus- trial internet of things with large language models (LLMs): an intelligence-based reinforcement learning approach,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.065017Z

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.

Observation bbb71eb0-f25c-4bf5-a2a1-90f400ece007 · outbound

This paper cites LLM-based edge intelligence: A com- prehensive survey on architectures, applications, security and trustworthiness,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services LLM-based edge intelligence: A com- prehensive survey on architectures, applications, security and trustworthiness,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.057816Z

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.

Observation 436f2d5f-6787-40a5-b359-720ad60aa271 · outbound

This paper cites Recent advances in natural language processing via large pre-trained language models: A survey,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Recent advances in natural language processing via large pre-trained language models: A survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.050440Z

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.

Observation 73c123b9-5f1a-46e3-b1bd-d17d191a120c · outbound

This paper cites Large language model enhanced multi-agent systems for 6G communications,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Large language model enhanced multi-agent systems for 6G communications,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.043188Z

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.

Observation 246639d3-823c-4c67-99a6-fe4e5d3c6769 · outbound

This paper cites A review of current trends, techniques, and challenges in large language models (LLMs),.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services A review of current trends, techniques, and challenges in large language models (LLMs),

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.036120Z

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.

Observation 86bcfbbb-c603-446a-a08d-f0bd87b8ec7c · outbound

This paper cites What makes for good tokenizers in vision transformer?.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services What makes for good tokenizers in vision transformer?

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.028372Z

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.

Observation 06bfaab0-543a-49d1-a055-663c3d5339b4 · outbound

This paper cites Long-context LLMs Struggle with Long In-context Learning.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Long-context LLMs Struggle with Long In-context Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.763197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 797619b7-34b8-4c6a-a35e-2be1b8a86acd · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Chain-of-thought prompting elicits reasoning in large language models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.020769Z

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.

Observation 83cc6ca7-016f-4d90-a645-a1dd1d9af0b0 · outbound

This paper cites Efficient prompting for LLM-based generative internet of things,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Efficient prompting for LLM-based generative internet of things,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.013429Z

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.

Observation b607f92f-b588-4621-b494-95256dc716ba · outbound

This paper cites To repeat or not to repeat: Insights from scaling LLM under token-crisis,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services To repeat or not to repeat: Insights from scaling LLM under token-crisis,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:21.005920Z

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.

Observation 451bc9a2-8be9-46c1-a39b-5fa59b2ba18a · outbound

This paper cites LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.773340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fafd3145-8a47-4bef-9cbd-59044a61e353 · outbound

This paper cites LLM-Slice: Dedicated wireless network slicing for large language models,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services LLM-Slice: Dedicated wireless network slicing for large language models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.998732Z

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.

Observation 6b7f6183-aba2-480d-b7c2-095711299a20 · outbound

This paper cites Deeploy: Enabling energy- efficient deployment of small language models on heterogeneous microcontrollers,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Deeploy: Enabling energy- efficient deployment of small language models on heterogeneous microcontrollers,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.991152Z

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.

Observation cc66ff15-b223-474b-a00e-4b925acc2d6b · outbound

This paper cites Denoising diffusion probabilistic models,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Denoising diffusion probabilistic models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.780844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 600a4bff-5760-421a-a73b-db7d1546154c · outbound

This paper cites Wire- lessLLM: Empowering large language models towards wireless intelligence,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Wire- lessLLM: Empowering large language models towards wireless intelligence,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.980116Z

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.

Observation 8bde7fc6-a6b7-4bd5-9a4d-6c2039919702 · outbound

This paper cites Edge intelligence optimization for large language model inference with batching and quantization,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Edge intelligence optimization for large language model inference with batching and quantization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.973215Z

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.

Observation b8fd5ac5-6949-45e0-b850-6e35b0082e44 · outbound

This paper cites Beyond the cloud: Edge inference for generative large language models in wireless networks,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Beyond the cloud: Edge inference for generative large language models in wireless networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.966218Z

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.

Observation ae538a7e-3057-45c5-9635-9e35d3385e52 · outbound

This paper cites Large multi-modal models (LMMs) as universal foundation models for AI-native wireless systems,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Large multi-modal models (LMMs) as universal foundation models for AI-native wireless systems,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.959075Z

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.

Observation bb17ef0f-8616-4b15-97ab-5f7c949948cf · outbound

This paper cites Adapting LLMs for efficient context processing through soft prompt compression,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Adapting LLMs for efficient context processing through soft prompt compression,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.951744Z

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.

Observation 7643b011-894b-4752-942e-37bce2cb04df · outbound

This paper cites Prompt-assisted semantic interference cancelation on moderate interference chan- nels,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Prompt-assisted semantic interference cancelation on moderate interference chan- nels,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.944681Z

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.

Observation 4cc9a607-1c5a-427e-abd3-af2a0bdfbad4 · outbound

This paper cites Cross modal compression with variable rate prompt,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Cross modal compression with variable rate prompt,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.937469Z

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.

Observation 67448533-5779-412e-aeeb-199d1076b55c · outbound

This paper cites Discrete prompt compression with rein- forcement learning,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Discrete prompt compression with rein- forcement learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.929942Z

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.

Observation 5b5500a8-70de-4193-88e0-fda914260452 · outbound

This paper cites Intelligent cloud-edge collaborations for energy-efficient user association and power allocation in space-air-ground integrated networks,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Intelligent cloud-edge collaborations for energy-efficient user association and power allocation in space-air-ground integrated networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.922172Z

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.

Observation d8ecf47e-f24e-4070-b9ac-7e7c1d5cbb53 · outbound

This paper cites Joint resource allocations for energy consumption optimization in HAPS-aided MEC-NOMA systems,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Joint resource allocations for energy consumption optimization in HAPS-aided MEC-NOMA systems,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.914369Z

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.

Observation 6d6ae3d9-edc9-42dc-8a1e-ff16b51cdb74 · outbound

This paper cites Graph neural networks approach for joint wireless power control and spectrum allocation,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Graph neural networks approach for joint wireless power control and spectrum allocation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.906617Z

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.

Observation 4b9f70a3-2eb5-4f73-9b6c-279a70d3c600 · outbound

This paper cites LLMCarbon: Modeling the end-to-end carbon footprint of large language models,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services LLMCarbon: Modeling the end-to-end carbon footprint of large language models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.898733Z

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.

Observation 61a1fba7-34b7-4e4c-82be-f4ae2d44ff3f · outbound

This paper cites Tse and P.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Tse and P

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.890611Z

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.

Observation 2d425dfb-d471-48eb-81fe-d13b98c811a8 · outbound

This paper cites an unresolved cited work.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.814634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ed71506-8d56-4178-bb84-029ae9713d5c · outbound

This paper cites Deep reinforcement learn- ing with double q-learning,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Deep reinforcement learn- ing with double q-learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.879283Z

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.

Observation 6b5707ee-d05b-44d6-9a79-cb6a283ed499 · outbound

This paper cites an unresolved cited work.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:32:20.871804Z

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.

Observation 39c02c51-2435-478e-850f-638579cbe7ad · outbound

This paper cites Meetingbank: A benchmark dataset for meeting summarization,.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services Meetingbank: A benchmark dataset for meeting summarization,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:32:20.864182Z

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.

Observation 5cb6d87a-a4b1-496d-8204-f2f8ae8171d8 · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

JPPO++: Joint Power and Denoising-inspired Prompt Optimization for Mobile LLM Services LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T22:32:20.824316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.