Paper Citation Record · LEDGER

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models

As of 23 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2412.10107.

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

pith.paper-citation-record.v1
2412.10107 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:23:57.260385Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T01:56:01.451833Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T10:46:32.446279Z

Reference resolution

15 of 15 outbound references displayed

verified exact1
verified fuzzy6
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b9fcd3e-6518-4978-809e-5095291030bd · outbound

This paper cites Leveraging large language models for intelligent control of 6G integrated TN-NTN with iot service,.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Leveraging large language models for intelligent control of 6G integrated TN-NTN with iot service,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:23:57.439967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 28a6d54d-e999-45b0-9ef5-dd9153336f4a · outbound

This paper cites At the dawn of generative ai era: A tutorial- cum-survey on new frontiers in 6g wireless intelligence,.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models At the dawn of generative ai era: A tutorial- cum-survey on new frontiers in 6g wireless intelligence,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:23:57.428701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7a6edaa4-9e06-4255-a0c8-d778fe56f4ba · outbound

This paper cites Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.214264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b685523-35ef-43e1-96c5-dd958452c8c9 · outbound

This paper cites Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.218728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3d901a1-d74b-43f9-9379-17c54a4b0816 · outbound

This paper cites Large language models empowered autonomous edge AI for connected intelligence,.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Large language models empowered autonomous edge AI for connected intelligence,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:23:57.417390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07ac89ae-e008-48f6-ad7a-5bb352a10a7b · outbound

This paper cites Large generative ai models for telecom: The next big thing?.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Large generative ai models for telecom: The next big thing?

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:23:57.406526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 177fe275-001c-4b33-b18e-134d8dd48840 · outbound

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

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Large language model enhanced multi-agent systems for 6g communications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.230434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 750111b0-15ac-4606-a4de-c49518dff2b7 · outbound

This paper cites WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.233876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5f61671-4d6a-47c0-96d2-49639fb4de9f · outbound

This paper cites AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.237586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14876fa7-5a94-4a0d-a94d-c8ca0f1e6753 · outbound

This paper cites When large language model agents meet 6G networks: Perception, grounding, and alignment,.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models When large language model agents meet 6G networks: Perception, grounding, and alignment,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:23:57.388912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 194d3b9f-7c66-436a-a944-5b46e66ada9b · outbound

This paper cites TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-11T16:23:57.328371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9bb9c53-8f35-441f-8eaf-b4100da52f1b · outbound

This paper cites TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.248704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9343b7-60fa-4b0d-888d-4215fd520031 · outbound

This paper cites TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.252585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 856471b7-bf0d-4fe8-8435-8faca56255a0 · outbound

This paper cites ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:57.256635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d6ebc19-6f2a-49a6-8c0c-40afd049db46 · outbound

This paper cites Deep learning power allocation in massive mimo,.

NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models Deep learning power allocation in massive mimo,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:23:57.378155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 7d7b3484-8ef0-4900-96e7-96c78f5024b1 · inbound

Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management cites this paper.

Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:46:32.451643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.