Paper Citation Record · LEDGER

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting

As of 19 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2511.00651.

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

pith.paper-citation-record.v1
2511.00651 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:32:59.222170Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

19 of 19 outbound references displayed

verified exact1
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7b59e6cc-3ffe-4205-b53d-1ed5bdb0c854 · outbound

This paper cites Telecom Foundation Models: Applications, Challenges, and Future Trends.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Telecom Foundation Models: Applications, Challenges, and Future Trends

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:57.549861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e7a6d1d-0442-4bf6-ab36-440639f6375a · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting On the Opportunities and Risks of Foundation Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:57.702349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f8d5b1a-4d05-4745-94d9-8f732183d3ea · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:57.780328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8178901b-424f-4c2d-b98c-8554e283a23e · outbound

This paper cites A Survey on Knowledge-Oriented Retrieval-Augmented Generation.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting A Survey on Knowledge-Oriented Retrieval-Augmented Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:57.843214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df4ea8bf-0ae7-4ec0-8d82-cc89f4991bc5 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting ReAct: Synergizing Reasoning and Acting in Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:57.978867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 112433da-d3ac-49da-9ffb-6d02e04b04fb · outbound

This paper cites Small Language Models are the Future of Agentic AI.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Small Language Models are the Future of Agentic AI

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.078712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c90a6df4-4813-412d-aa4e-28c0943e8b8b · outbound

This paper cites Network troubleshoot- ing: Survey, taxonomy and challenges,.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Network troubleshoot- ing: Survey, taxonomy and challenges,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.145957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13647ef7-7bb1-46a8-82c4-1a14ee1f65a6 · outbound

This paper cites Harnessing Machine Learning for Predictive Trou- bleshooting in Telecom Networks,.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Harnessing Machine Learning for Predictive Trou- bleshooting in Telecom Networks,

Reference 9

Resolution: verified exact; doi, observed 2026-08-04T00:34:01.219581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69155dd6-9fd0-4573-b2d4-e278c0c037be · outbound

This paper cites Generative AI.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Generative AI

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.336134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1605a30f-c234-4160-a30c-b91913784991 · outbound

This paper cites Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.432919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49c8543f-b0dc-4af5-8a71-2e4fa83364a3 · outbound

This paper cites Hypha: A distributed application framework for large- scale data management and AI model serving,.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Hypha: A distributed application framework for large- scale data management and AI model serving,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.576255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe8db3a-ea09-4aa8-8851-79444d3347da · outbound

This paper cites BioImage.IO Chat- bot: a community-driven AI assistant for integrative computational bioimaging,.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting BioImage.IO Chat- bot: a community-driven AI assistant for integrative computational bioimaging,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.654260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a665810-6f09-450a-9d59-93f65d1ae87a · outbound

This paper cites From RAG to Memory: Non-Parametric Continual Learning for Large Language Models.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.731227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64d26c45-036a-4f63-bf10-74fed4402085 · outbound

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

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.810397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91c3ed04-8613-45e9-bef5-9f8ce13c9ed3 · outbound

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

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.992599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1206566c-01ab-4184-9194-5b8dfc1176da · outbound

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

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Ragas: Automated Evaluation of Retrieval Augmented Generation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:59.068869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3adeb3-c206-4c93-968a-fdab6c5e6fd4 · outbound

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

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting Think Less, Label Bet- ter: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:59.155084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96c860f3-749d-4c7e-b3fe-c07fb6338f4e · outbound

This paper cites ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:59.222170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0417183-c86e-4c09-87eb-6311747506b2 · outbound

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

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T00:32:58.919802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.