Paper Citation Record · LEDGER

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting

As of 3 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.02523.

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

pith.paper-citation-record.v1
2607.02523 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T17:31:15.972423Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy0
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67670063-65f4-4d82-88db-b7b2a73e9733 · outbound

This paper cites O-RAN.WG2.AIML-v01.03: AI/ML Workflow De- scription and Requirements,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting O-RAN.WG2.AIML-v01.03: AI/ML Workflow De- scription and Requirements,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation b110543c-0144-46cc-8b0e-b9a6fcca1ac0 · outbound

This paper cites Multi-access Edge Computing (MEC); Framework and Refer- ence Architecture,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Multi-access Edge Computing (MEC); Framework and Refer- ence Architecture,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 55acd5f7-4403-435c-a7a2-ab2bec3fa0e6 · outbound

This paper cites NVIDIA EGX Platform: Enterprise AI at the Edge,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting NVIDIA EGX Platform: Enterprise AI at the Edge,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Observation 95754c4e-46a7-4af8-9ccd-ef7e7dffbc9f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting LoRA: Low-Rank Adaptation of Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 631d17d6-53c8-491c-a359-7ca6b644d92d · outbound

This paper cites Fine-tuning LLMs Guide,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Fine-tuning LLMs Guide,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation edf0b74b-8d09-45f2-80e1-b7543e86525d · outbound

This paper cites Language Models are Few-Shot Learners,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Language Models are Few-Shot Learners,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 98286267-7d7d-4406-8b37-ad6c97d1613e · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting QLoRA: Efficient Finetuning of Quantized LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation d430f827-c1c0-44d1-8d73-dde4919457a2 · outbound

This paper cites Mastering LLM Techniques: Inference Optimization,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Mastering LLM Techniques: Inference Optimization,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 94b11f64-5e58-4c48-82b6-623e343d0b8c · outbound

This paper cites Available: https://developer.nvidia.com/blog/mastering- llm-techniques-inference-optimization/.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Available: https://developer.nvidia.com/blog/mastering- llm-techniques-inference-optimization/

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36bbb660-1b89-4e5e-9f91-c03772605987 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Observation 8e5f9032-d7cd-4df2-aa58-ef1b88649739 · outbound

This paper cites KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation df274166-ffec-40af-9d3d-3978699cc3a4 · outbound

This paper cites DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db4971cb-75b1-46cc-a5bc-9efe313ef085 · outbound

This paper cites SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 62930509-3921-44f0-9454-25bfe42e389b · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 09a01829-d9fa-4475-8a7a-6e582925aeae · outbound

This paper cites TRL:Transformer Reinforcement Learning Library,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting TRL:Transformer Reinforcement Learning Library,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 6efd6013-4262-4cd2-a0b9-5e200e7455a6 · outbound

This paper cites Chat Templates Documentation,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Chat Templates Documentation,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Observation dc08ea07-ec20-457b-9f91-983d2ba02ec9 · outbound

This paper cites Qwen3 Quickstart and Thinking-Mode Controls,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Qwen3 Quickstart and Thinking-Mode Controls,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 867ecd30-64be-441e-969c-8f3f44ae6591 · outbound

This paper cites Multi-access Edge Computing (MEC); MEC Support for Edge AI/ML,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Multi-access Edge Computing (MEC); MEC Support for Edge AI/ML,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 693a0a36-42ca-423f-b1b2-72e670168412 · outbound

This paper cites Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 0ba44355-8e18-4273-80c7-03b737616d40 · outbound

This paper cites A Joint Learning and Communications Framework for Federated Learning over Wireless Networks,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting A Joint Learning and Communications Framework for Federated Learning over Wireless Networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

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Observation 5d93993a-d0e3-4def-a012-db8639f7f9a5 · outbound

This paper cites Understanding the Performance and Estimating the Cost of LLM Fine-Tuning.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Understanding the Performance and Estimating the Cost of LLM Fine-Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

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Observation 1629d081-15cb-4a7a-86da-5806c9710f1c · outbound

This paper cites Why Reinforcement Learning Beats Supervised Fine-Tuning When Data Is Scarce,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Why Reinforcement Learning Beats Supervised Fine-Tuning When Data Is Scarce,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 6bf0498e-6520-49d3-abd9-c067f7a6c6f6 · outbound

This paper cites Ragas: Automated Evaluation of Retrieval Augmented Generation.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Ragas: Automated Evaluation of Retrieval Augmented Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b612a67c-0f42-4112-91f1-12a67a9ec70e · outbound

This paper cites Qwen2.5: A Party of Foundation Models,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Qwen2.5: A Party of Foundation Models,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Observation bf7c4aab-c497-423b-a99f-64631944c76e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 844e192b-0706-4b0a-87e8-c5a129cc353d · outbound

This paper cites Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecom- munications,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecom- munications,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Observation 7aaa8487-1955-403d-840d-062db1f4d2f9 · outbound

This paper cites The Llama 3 Herd of Models.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting The Llama 3 Herd of Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Observation 3fe4c21e-b431-40d0-bfc1-9affe5c1c163 · outbound

This paper cites Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Lan- guage Models (SLMs) for Automated Telecom Network Troubleshoot- ing,.

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Lan- guage Models (SLMs) for Automated Telecom Network Troubleshoot- ing,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T17:31:15.972423Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.