Paper Citation Record · LEDGER

When IoT Meet LLMs: Applications and Challenges

As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 4 inbound Pith citation observations for arXiv:2411.17722.

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

pith.paper-citation-record.v1
2411.17722 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:58:52.710891Z

measured 45 of 45 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:30:13.810621Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

41 of 41 outbound references displayed

verified exact3
verified fuzzy10
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 564832be-310a-48b8-b371-3c901a5e14d5 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

When IoT Meet LLMs: Applications and Challenges Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.578960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91c8fddd-e8c6-4d33-98f5-973cbaab9825 · outbound

This paper cites Artificial hallucinations in chatgpt: implications in scientific writing,.

When IoT Meet LLMs: Applications and Challenges Artificial hallucinations in chatgpt: implications in scientific writing,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.142788Z

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 6693103e-e522-48f8-b2ce-82fc46c62a08 · outbound

This paper cites Iot-llm: Enhancing real- world iot task reasoning with large language models,.

When IoT Meet LLMs: Applications and Challenges Iot-llm: Enhancing real- world iot task reasoning with large language models,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.586848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fa03d9b-e6cd-4c67-94b4-6558c9f50c2f · outbound

This paper cites Penetrative ai: Making llms comprehend the physical world,.

When IoT Meet LLMs: Applications and Challenges Penetrative ai: Making llms comprehend the physical world,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.590792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd24b52f-b791-4526-ac04-b6a534445b2d · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

When IoT Meet LLMs: Applications and Challenges A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.594275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e619f1e-24ba-496f-8824-6676a781f802 · outbound

This paper cites Smart home reasoning systems: a systematic literature review,.

When IoT Meet LLMs: Applications and Challenges Smart home reasoning systems: a systematic literature review,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.126550Z

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 bf41684a-1f2d-48a2-939f-677f7066012e · outbound

This paper cites Pllm- cs: Pre-trained large language model (llm) for cyber threat detection in satellite networks,.

When IoT Meet LLMs: Applications and Challenges Pllm- cs: Pre-trained large language model (llm) for cyber threat detection in satellite networks,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.601767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae84cf0-8a98-4bb3-9ac5-bc14493915d7 · outbound

This paper cites Llmind: Orchestrating ai and iot with llm for complex task execution,.

When IoT Meet LLMs: Applications and Challenges Llmind: Orchestrating ai and iot with llm for complex task execution,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.110693Z

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 9402c032-a5c9-4857-976f-5903d5cb6672 · outbound

This paper cites Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,.

When IoT Meet LLMs: Applications and Challenges Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.100524Z

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 279ef132-4236-4d7b-a605-1ccc06d9c15a · outbound

This paper cites DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model.

When IoT Meet LLMs: Applications and Challenges DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.611435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c36e5771-6306-4cd5-a5bb-44fa1910ad70 · outbound

This paper cites PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services.

When IoT Meet LLMs: Applications and Challenges PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.614515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e6e5271-caf8-424c-83ba-8263417f5a3a · outbound

This paper cites Fedbiot: Llm local fine-tuning in federated learning without full model,.

When IoT Meet LLMs: Applications and Challenges Fedbiot: Llm local fine-tuning in federated learning without full model,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.617877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f563a16-64c0-4599-88cd-8c03c3e74ee2 · outbound

This paper cites Attention is all you need in advances in neural information processing systems, 2017,.

When IoT Meet LLMs: Applications and Challenges Attention is all you need in advances in neural information processing systems, 2017,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.085043Z

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 fc899208-20af-4610-a895-0167fb1ec19b · outbound

This paper cites Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models.

When IoT Meet LLMs: Applications and Challenges Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.623237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38cbc901-68df-4b74-8507-cb748231c795 · outbound

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

When IoT Meet LLMs: Applications and Challenges Chain-of-thought prompting elicits reasoning in large language models,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.627000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aecad1ab-d1c1-4672-af05-b18788c32739 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

When IoT Meet LLMs: Applications and Challenges Tree of thoughts: Deliberate problem solving with large language models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.630468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ee4352f-89b7-4481-bb7f-a4f2bc8c6175 · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

When IoT Meet LLMs: Applications and Challenges Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.633510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c3efe67-0749-4dc7-8725-67067b1d6e54 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

When IoT Meet LLMs: Applications and Challenges Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.637084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ed410b4-9154-46af-9762-47d355673912 · outbound

This paper cites Efficient deep learning: A survey on making deep learning models smaller, faster, and better,.

When IoT Meet LLMs: Applications and Challenges Efficient deep learning: A survey on making deep learning models smaller, faster, and better,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.640230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 035642ff-56e6-47e2-9d39-e9cd8ada6820 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

When IoT Meet LLMs: Applications and Challenges Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.643650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8cc886b-3085-4614-9ee6-008bfcdbb538 · outbound

This paper cites Context aware computing for the internet of things: A survey,.

When IoT Meet LLMs: Applications and Challenges Context aware computing for the internet of things: A survey,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.051035Z

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 f3d36d5b-4f4c-41d3-9b5f-bebee8ba97ab · outbound

This paper cites Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A Survey.

When IoT Meet LLMs: Applications and Challenges Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A Survey

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:58:52.852013Z

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 1445dbf9-badd-411a-a8f1-7220bac23c6a · outbound

This paper cites POLCA: Power Oversubscription in LLM Cloud Providers.

When IoT Meet LLMs: Applications and Challenges POLCA: Power Oversubscription in LLM Cloud Providers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.653469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78df1b8c-6772-4fb0-af1b-c1103d2cc36f · outbound

This paper cites Edge-llm: A collaborative framework for large language model serving in edge computing,.

When IoT Meet LLMs: Applications and Challenges Edge-llm: A collaborative framework for large language model serving in edge computing,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.656919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9944860c-d4ae-42f8-bfa0-4ac7fc69473b · outbound

This paper cites EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting.

When IoT Meet LLMs: Applications and Challenges EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.660151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa073323-667b-4ce6-a9b9-a57bb67a161f · outbound

This paper cites Edge and terminal cooper- ation enabled llm deployment optimization in wireless network,.

When IoT Meet LLMs: Applications and Challenges Edge and terminal cooper- ation enabled llm deployment optimization in wireless network,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.663752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df7e531-44eb-45c0-aa53-3b734723631a · outbound

This paper cites Resource allocation for stable llm training in mobile edge computing,.

When IoT Meet LLMs: Applications and Challenges Resource allocation for stable llm training in mobile edge computing,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.029597Z

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 e2f1a88d-b074-4cfd-97ce-83d7cce8e285 · outbound

This paper cites Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications.

When IoT Meet LLMs: Applications and Challenges Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.670590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30c24383-ccef-489d-8d96-8e9af9d580a8 · outbound

This paper cites EdgeShard: Efficient LLM Inference via Collaborative Edge Computing.

When IoT Meet LLMs: Applications and Challenges EdgeShard: Efficient LLM Inference via Collaborative Edge Computing

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.673478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e946f914-4be7-4ae3-be46-170235484918 · outbound

This paper cites IoT-LM: Large Multisensory Language Models for the Internet of Things.

When IoT Meet LLMs: Applications and Challenges IoT-LM: Large Multisensory Language Models for the Internet of Things

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.676416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed63202d-b273-4b6e-ac22-62a5348e135a · outbound

This paper cites Casit: Collective intelligent agent system for internet of things,.

When IoT Meet LLMs: Applications and Challenges Casit: Collective intelligent agent system for internet of things,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.019426Z

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 6fa92e5a-dd34-4d9f-914f-61d3a75f7359 · outbound

This paper cites FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.

When IoT Meet LLMs: Applications and Challenges FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.681964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5dbf462-c192-49a5-8a91-0346f3ee74c2 · outbound

This paper cites Multi-Modal Transformer and Reinforcement Learning-based Beam Management.

When IoT Meet LLMs: Applications and Challenges Multi-Modal Transformer and Reinforcement Learning-based Beam Management

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:58:52.784274Z

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 c6753448-0ee9-4c8d-8807-b8c517a92873 · outbound

This paper cites Efficient Prompting for LLM-based Generative Internet of Things.

When IoT Meet LLMs: Applications and Challenges Efficient Prompting for LLM-based Generative Internet of Things

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.688192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9af3ea3-bede-43d6-b785-e051e927a789 · outbound

This paper cites Safely Learning with Private Data: A Federated Learning Framework for Large Language Model.

When IoT Meet LLMs: Applications and Challenges Safely Learning with Private Data: A Federated Learning Framework for Large Language Model

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.691602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57f71951-67f4-4253-9d57-a136930b1b09 · outbound

This paper cites Cyber Knowledge Completion Using Large Language Models.

When IoT Meet LLMs: Applications and Challenges Cyber Knowledge Completion Using Large Language Models

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:58:52.753712Z

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 e025de10-1d1d-4444-9adf-a543b66ec918 · outbound

This paper cites Detecting command injection vulnerabilities in linux-based embedded firmware with llm-based taint analysis of library functions,.

When IoT Meet LLMs: Applications and Challenges Detecting command injection vulnerabilities in linux-based embedded firmware with llm-based taint analysis of library functions,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:53.009457Z

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 2c122290-09e1-4a41-b209-8800a279d0e4 · outbound

This paper cites Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model.

When IoT Meet LLMs: Applications and Challenges Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.701320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bef0175b-34f6-4b3f-82c8-dcd5bf71be75 · outbound

This paper cites Let’s hide from llms: An adaptive contextual privacy preservation method for time series data,.

When IoT Meet LLMs: Applications and Challenges Let’s hide from llms: An adaptive contextual privacy preservation method for time series data,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:58:52.998288Z

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 6904bfcf-97f7-4e6c-8801-ef95094534ee · outbound

This paper cites Pac-gpt: A novel approach to generating synthetic network traffic with gpt-3,.

When IoT Meet LLMs: Applications and Challenges Pac-gpt: A novel approach to generating synthetic network traffic with gpt-3,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.707840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 592fb387-168a-4b20-90db-a439e4e97031 · outbound

This paper cites Explainable artificial intelligence (xai) for internet of things: a survey,.

When IoT Meet LLMs: Applications and Challenges Explainable artificial intelligence (xai) for internet of things: a survey,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T15:58:52.710891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation ca1d02ff-796a-4e5e-aa19-374bdae07624 · inbound

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection cites this paper.

A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection When IoT Meet LLMs: Applications and Challenges

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T11:30:13.810621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12c4c663-a883-4999-bed2-4246369cb2dc · inbound

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space cites this paper.

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space When IoT Meet LLMs: Applications and Challenges

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:11:18.689798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ad754bf-aa43-445f-b2ed-4386f2d3bd27 · inbound

Talk with the Things: Integrating LLMs into IoT Networks cites this paper.

Talk with the Things: Integrating LLMs into IoT Networks When IoT Meet LLMs: Applications and Challenges

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T14:41:52.601873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfe145aa-3cd1-4372-9206-51e060d4940d · inbound

Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT cites this paper.

Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT When IoT Meet LLMs: Applications and Challenges

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-26T04:58:58.772772Z

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.