Paper Citation Record · LEDGER

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

As of 22 August 2026, this Paper Citation Record lists 100 of 177 outbound references and 6 inbound Pith citation observations for arXiv:2507.00672.

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

pith.paper-citation-record.v1
2507.00672 v1

Coverage vector

measured 100 of 177 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:16:14.844486Z

measured 106 of 106 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:12:28.940425Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T15:43:32.793691Z

Reference resolution

100 of 177 outbound references displayed

verified exact8
verified fuzzy0
unresolved92
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efc39e6c-2146-45f1-ad91-b06cfc5cf20b · outbound

This paper cites Espd-lp: Edge service pre- deployment based on location prediction in mec,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Espd-lp: Edge service pre- deployment based on location prediction in mec,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.614576Z

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Observation 69945a45-c37d-4428-bcd2-a1ce311ff2a6 · outbound

This paper cites Towards edge general intelligence via large language models: Opportunities and challenges,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Towards edge general intelligence via large language models: Opportunities and challenges,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.617977Z

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Observation 3c995fd9-a6ad-402d-a58e-b5cac5f2619e · outbound

This paper cites Edge intelligence: The confluence of edge computing and artificial intelligence,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Edge intelligence: The confluence of edge computing and artificial intelligence,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.621442Z

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Observation b6a0cef3-83df-4b7b-a1a1-4c0866999820 · outbound

This paper cites Generative ai in cybersecurity: A comprehensive review of llm applications and vulnerabilities,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Generative ai in cybersecurity: A comprehensive review of llm applications and vulnerabilities,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.623966Z

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Observation e46c0bca-75ba-4d57-9d00-e09625d524a7 · outbound

This paper cites Generative ai-driven semantic communication networks: Architecture, technologies and applications,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Generative ai-driven semantic communication networks: Architecture, technologies and applications,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.626205Z

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Observation 6860f24b-372c-44a4-a042-1101aa69425b · outbound

This paper cites A survey on applications of large language model-driven digital twins for intelligent network optimization,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A survey on applications of large language model-driven digital twins for intelligent network optimization,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.628521Z

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Observation 8aea3ad2-08f7-43ed-b283-c52df6e895ef · outbound

This paper cites A survey on evaluation of large language models,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A survey on evaluation of large language models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.631034Z

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Observation f2f90f24-0392-47f2-9955-cc1ba26ceea4 · outbound

This paper cites Generative ai for secure physical layer communications: A survey,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Generative ai for secure physical layer communications: A survey,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.632860Z

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Observation 56a1513b-46d2-4e47-b450-17ff6e3064fd · outbound

This paper cites Generative ai-enabled vehicular networks: Fundamentals, framework, and case study,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Generative ai-enabled vehicular networks: Fundamentals, framework, and case study,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.634620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 814f1322-bed4-477a-a036-0421920b41d3 · outbound

This paper cites The road toward general edge intelligence: Standing on the shoulders of foundation models,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration The road toward general edge intelligence: Standing on the shoulders of foundation models,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.636486Z

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Observation ee36dd3e-a979-4dfc-9b00-4a6fe57a2a52 · outbound

This paper cites Embodied ai-enhanced vehicular networks: An integrated large language models and reinforcement learning method,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Embodied ai-enhanced vehicular networks: An integrated large language models and reinforcement learning method,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.638499Z

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Observation 759c929d-8059-4ad9-9378-39e4ac998146 · outbound

This paper cites Edge Large AI Models: Collaborative Deployment and IoT Applications.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Edge Large AI Models: Collaborative Deployment and IoT Applications

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.958338Z

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 c276db98-1f46-41c8-ae8f-6c48e9114a70 · outbound

This paper cites Toward democratized generative ai in next-generation mobile edge networks,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Toward democratized generative ai in next-generation mobile edge networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.642440Z

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Observation 3c0d070d-e8d8-4715-9a32-9e953b627f74 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Llm-pruner: On the structural pruning of large language models,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.644202Z

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Observation 6d06f119-9f9e-45a8-ae89-3dadb60cdb62 · outbound

This paper cites Efficient llms for edge devices: Pruning, quantization, and distillation techniques,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Efficient llms for edge devices: Pruning, quantization, and distillation techniques,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.646313Z

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Observation 975f136d-50d0-43ae-a57e-0af6e2fc6d64 · outbound

This paper cites Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.948938Z

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 ad993da5-8d5e-40f6-a882-c03bf04be0cf · outbound

This paper cites TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.651510Z

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Observation 31976f3b-2395-441e-b2a1-0d4efd41ba74 · outbound

This paper cites An edge-cloud collaboration framework for generative ai service provision with synergetic big cloud model and small edge models,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration An edge-cloud collaboration framework for generative ai service provision with synergetic big cloud model and small edge models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.654154Z

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Observation 68d4207f-068e-44d4-a34b-cedfe178d609 · outbound

This paper cites Knowledge Fusion of Large Language Models.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Knowledge Fusion of Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.656057Z

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Observation e1b01388-7899-4b3a-b75a-55dfd63d41eb · outbound

This paper cites A Multi-LLM Debiasing Framework.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A Multi-LLM Debiasing Framework

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.658645Z

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Observation e62ff7a9-b2d9-4b66-a5b1-c9a254e21b07 · outbound

This paper cites Easy2hard- bench: Standardized difficulty labels for profiling llm performance and generalization,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Easy2hard- bench: Standardized difficulty labels for profiling llm performance and generalization,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.662061Z

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Observation d5725a42-33f6-463d-b443-bd8451669ffc · outbound

This paper cites Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.921322Z

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 e672e52e-ced3-43f9-b5dd-8b719b253c37 · outbound

This paper cites Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.667359Z

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Observation b7e56115-5338-41f9-b348-96fb38c5d6a4 · outbound

This paper cites Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.671173Z

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Observation e645b681-f8d5-4868-9005-3bcffac7dec8 · outbound

This paper cites Learning to Decode Collaboratively with Multiple Language Models.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Learning to Decode Collaboratively with Multiple Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.673666Z

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Observation e3ba201a-f360-46e8-abc2-d80dbed1fe46 · outbound

This paper cites Performance analysis on the applications of large language models: A case for elderly care,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Performance analysis on the applications of large language models: A case for elderly care,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.676420Z

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Observation b5bc9217-449d-49b0-bc5a-4946428c57e2 · outbound

This paper cites A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network

Reference 27

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Observation b609d046-fa6b-4fd9-aa3a-2825aaeb5d84 · outbound

This paper cites Levels of AI Agents: from Rules to Large Language Models.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Levels of AI Agents: from Rules to Large Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.891910Z

Source-reported events for the cited work

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Observation c6ca3c48-7187-4960-908b-39d507823f3c · outbound

This paper cites Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:16:15.882907Z

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 0a5efcc3-41e6-4b40-87ef-6d5a393a0570 · outbound

This paper cites A survey on the integration and optimization of large language models in edge computing envi- ronments,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A survey on the integration and optimization of large language models in edge computing envi- ronments,

Reference 30

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Observation 0d9d4e41-f2fb-4fcf-ba21-78d82f8d9d4c · outbound

This paper cites A review on edge large language models: Design, execution, and applications,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A review on edge large language models: Design, execution, and applications,

Reference 31

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Observation e4a630a4-c6fb-493a-9627-b11f20c76ae9 · outbound

This paper cites Empowering large language models to edge intelligence: A survey of edge efficient llms and techniques,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Empowering large language models to edge intelligence: A survey of edge efficient llms and techniques,

Reference 32

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Observation 7eae4355-fbc1-4657-87ab-502798eba035 · outbound

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

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Mobile edge intelligence for large language models: A contemporary survey,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.692904Z

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Observation d2fd1a91-05f3-4df1-8daf-0006f43c4a60 · outbound

This paper cites Language models at the edge: A survey on techniques, challenges, and applica- tions,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Language models at the edge: A survey on techniques, challenges, and applica- tions,

Reference 34

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Observation 68a018e9-adca-4875-8ee1-20f5ccecb305 · outbound

This paper cites Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

Reference 35

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Observation 5a914080-b8e5-4007-9ae5-e7f8b3c7975e · outbound

This paper cites Harnessing Multiple Large Language Models: A Survey on LLM Ensemble.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Harnessing Multiple Large Language Models: A Survey on LLM Ensemble

Reference 36

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Observation 1beae76d-4245-43c6-b881-0e26cb334450 · outbound

This paper cites When One LLM Drools, Multi-LLM Collaboration Rules.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration When One LLM Drools, Multi-LLM Collaboration Rules

Reference 37

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Observation 19791be3-e3e5-496c-81f5-37d155521da5 · outbound

This paper cites What is the Role of Small Models in the LLM Era: A Survey.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration What is the Role of Small Models in the LLM Era: A Survey

Reference 38

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Observation f352bd03-71e4-4c75-8f9d-68e1b66e91ab · outbound

This paper cites A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case

Reference 39

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Observation 23944b62-c4d5-4df6-a7d5-771ddacf0dca · outbound

This paper cites Cost-Effective Online Multi-LLM Selection with Versatile Reward Models.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Cost-Effective Online Multi-LLM Selection with Versatile Reward Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.711497Z

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Observation 787dd9df-3281-49db-8d70-4b188e629697 · outbound

This paper cites Enhancing supermarket robot interac- tion: an equitable multi-level llm conversational interface for handling diverse customer intents,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Enhancing supermarket robot interac- tion: an equitable multi-level llm conversational interface for handling diverse customer intents,

Reference 41

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Observation 0d986450-4dfb-430f-a11a-36d2e1872e7b · outbound

This paper cites A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A Case Study of Scalable Content Annotation Using Multi-LLM Consensus and Human Review

Reference 42

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Observation 9f4a48b4-048c-446b-96d8-7640b84fe55e · outbound

This paper cites GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments

Reference 43

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Observation f2ae86fa-fc0e-4e73-8e38-5b65b51f70fe · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

Reference 44

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This paper cites SocraSynth: Multi-LLM Reasoning with Conditional Statistics.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 45

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Multi-LLM Text Summarization

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.727238Z

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Observation 4e34e0a7-344c-4e0e-ba3b-5a1b6f812db4 · outbound

This paper cites Mlsdet: Multi-llm statisti- cal deep ensemble for chinese ai-generated text detection,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Mlsdet: Multi-llm statisti- cal deep ensemble for chinese ai-generated text detection,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.730047Z

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Observation 97dfd605-77d7-4623-b699-e30971073d1b · outbound

This paper cites Improving the End-to-End Efficiency of Offline Inference for Multi-LLM Applications Based on Sampling and Simulation.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Improving the End-to-End Efficiency of Offline Inference for Multi-LLM Applications Based on Sampling and Simulation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.732186Z

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Observation 90ca3e33-4867-4701-9250-82bd48cd3020 · outbound

This paper cites Multi-Agent Collaboration Mechanisms: A Survey of LLMs.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Multi-Agent Collaboration Mechanisms: A Survey of LLMs

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.734583Z

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Observation ea30ef75-203a-48b7-b22c-abc05e0caa82 · outbound

This paper cites Minimizing hallucinations and communication costs: Adversarial debate and voting mechanisms in llm-based multi-agents,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Minimizing hallucinations and communication costs: Adversarial debate and voting mechanisms in llm-based multi-agents,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.737653Z

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Adversarial multi-agent evaluation of large language models through iterative debates,

Reference 51

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Reference 52

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration An energy efficient ecg ventricular ectopic beat classifier using binarized cnn for edge ai devices,

Reference 53

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Reference 54

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Reference 55

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Data-centric artificial intelligence: A survey,

Reference 56

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This paper cites On the Reasoning Capacity of AI Models and How to Quantify It.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration On the Reasoning Capacity of AI Models and How to Quantify It

Reference 57

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This paper cites The pipeline for the continuous development of artificial intelligence models—current state of research and practice,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration The pipeline for the continuous development of artificial intelligence models—current state of research and practice,

Reference 58

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This paper cites Edge ai: a survey,.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Edge ai: a survey,

Reference 59

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Megalodon: Efficient llm pretraining and inference with unlimited context length,

Reference 60

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Large language models for networking: Applications, enabling techniques, and challenges,

Reference 61

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Gia: Llm-enabled generative intent abstraction to enhance adaptability for intent-driven networks,

Reference 62

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Observation 05f5f6fa-5602-4fa8-9e9f-36c1b32e7e7b · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Generative ai for space-air-ground integrated networks,

Reference 63

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Reference 64

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Large language model (llm) for telecommu- nications: A comprehensive survey on principles, key techniques, and opportunities,

Reference 65

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Beyond the cloud: Edge inference for generative large language models in wireless networks,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.775301Z

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Observation 68b8aeb1-eac3-4060-8cd8-bf85c034fe93 · outbound

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Reference 67

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Reference 68

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Distill-vq: Learning retrieval oriented vector quantization by distilling knowledge from dense embeddings,

Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 74

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Reference 75

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Observation 65356ccd-5144-4c94-a62d-e06d59b5f5ba · outbound

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Reference 76

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Reference 77

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Reference 78

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Reference 79

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Reference 80

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Multi-agent ai system for adaptive cognitive training in elderly care,

Reference 81

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration A comprehen- sive survey on artificial intelligence empowered edge computing on consumer electronics,

Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Chatgpt and other large language models for cybersecurity of smart grid applications,

Reference 87

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Observation e730caa1-acaa-4be3-a92e-479b2c3618b0 · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Review of the opportunities and challenges to accelerate mass-scale application of smart grids with large-language models,

Reference 88

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Reference 89

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Reference 90

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Reference 91

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Observation 7f4a9525-6f46-4271-b4d2-2ad548cdefbe · outbound

This paper cites Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Reference 92

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Observation f1c88c2d-0990-4fa6-841c-d1638498f507 · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration The universal fog proxy: A third-party authentication solution for federated fog systems with multiple protocols,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.830074Z

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Observation e3cbd681-6bc8-4643-af81-f95f5d7e9a48 · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems

Reference 94

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Observation 4d15908d-adaa-4a81-8aad-861b94a3b347 · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Integrating llms with its: Recent advances, potentials, challenges, and future directions,

Reference 95

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Observation 9d029e36-2ff3-4de6-ba2a-afb3749ea980 · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Federated learning assisted intelligent iov mobile edge computing,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.835930Z

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Observation 965e4f0d-8d57-4cc0-8c5a-9a5fed7671b1 · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Potential game based distributed iov service offloading with graph attention networks in mobile edge computing,

Reference 97

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Observation 3bc28279-0281-4cc1-ae9c-f7e15201355b · outbound

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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration Esia: An efficient and stable identity authentication for internet of vehicles,

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:14.840474Z

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Observation 527b27dc-c4a0-4785-a4b3-b1156b98ef48 · outbound

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Reference 99

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Observation 85def907-79b7-4cfd-ae54-0f6db3f5c7cf · outbound

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Reference 100

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Pith citing papers

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Balancing Information Accuracy and Response Timeliness in Networked LLMs Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

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Observation 0eee49b5-95f4-4bb9-891b-23a334a1571f · inbound

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

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Observation 47383e80-40df-4961-9b92-f192318c668c · inbound

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey cites this paper.

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T15:26:33.458218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea8fc53c-f0a6-4109-b3d0-164b45088b5b · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

Reference 167

Resolution: unresolved; no resolver link, observed 2026-08-05T04:50:32.137852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15113123-a3ae-4bd0-8849-1038d1c8bf6d · inbound

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives cites this paper.

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:34:23.482981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc769164-e17e-4dec-8338-18755e2436d9 · inbound

Autonomic Federated-Market Orchestration for the Edge-Cloud Continuum cites this paper.

Autonomic Federated-Market Orchestration for the Edge-Cloud Continuum Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-29T15:43:32.795301Z

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.