Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:52:06.233462Z
Paper Citation Record · LEDGER
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network
As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2509.10478.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-13T17:37:00.586612Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T17:38:02.500906Z
Reference resolution
27 of 27 outbound references displayed
- verified exact1
- verified fuzzy20
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1cb3621a-d56d-4838-8c12-9f3839a7f7d3 · outbound
This paper cites A survey on open radio access networks: Challenges, research directions, and open source approaches.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network A survey on open radio access networks: Challenges, research directions, and open source approaches
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.734800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9828ce4c-896b-4229-8c98-32c7b51d505a · outbound
This paper cites Tm forum introductory guide autonomous networks technical architecture.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Tm forum introductory guide autonomous networks technical architecture
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.716585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 612fa887-dc19-4dfa-8b38-5839da4b2366 · outbound
This paper cites Intent driven management.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Intent driven management
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.698507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 76e9fc38-9782-4808-8360-beab36cb2992 · outbound
This paper cites Intent-Based Network for RAN Management with Large Language Models.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Intent-Based Network for RAN Management with Large Language Models
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T16:52:06.355133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 19e1a27b-28df-42d4-bd87-07d03817ea46 · outbound
This paper cites Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:52:06.114470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e53575bc-71f2-4eb3-ae9d-5d95eb622972 · outbound
This paper cites Aira technologies demonstrates rangpt, the world’s first llm- based utility for ran query and control.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Aira technologies demonstrates rangpt, the world’s first llm- based utility for ran query and control
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.681800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7f84a845-80bf-4e13-a7c9-5865a80ed5f8 · outbound
This paper cites WiLLM: an Open Framework for LLM Services over Wireless Systems.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network WiLLM: an Open Framework for LLM Services over Wireless Systems
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:52:06.126895Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87cd1301-86e4-4faa-b027-8f422fd7a6bc · outbound
This paper cites Llm-xapp: A large language model empowered radio resource management xapp for 5g o-ran.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Llm-xapp: A large language model empowered radio resource management xapp for 5g o-ran
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.662989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ae17b928-8869-47be-a1af-dc493515c7a3 · outbound
This paper cites an unresolved cited work.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T16:52:06.646532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 76f6b236-720c-4730-a937-fed89f8e681e · outbound
This paper cites Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:52:06.143096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd8af19f-bc21-4766-b29d-eb88a79162bf · outbound
This paper cites Clemm, L.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Clemm, L
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.627431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 31f8452f-78f9-4367-bdfa-62ee5cb07625 · outbound
This paper cites What is agentic architecture? https://www.ibm.com/think/topics/ agentic-architecture#:~:text=Advancements%20in%20machine%20learning%20, agents%20to%20complete%20complex%20tasks.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network What is agentic architecture? https://www.ibm.com/think/topics/ agentic-architecture#:~:text=Advancements%20in%20machine%20learning%20, agents%20to%20complete%20complex%20tasks
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.608868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ee8d8844-930d-49f0-aad8-91844de0e038 · outbound
This paper cites Toward standardization of genai-driven agentic architectures for radio access networks.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Toward standardization of genai-driven agentic architectures for radio access networks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.591475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2163cd76-d8e9-44a2-bb88-1da437b222df · outbound
This paper cites React: Synergizing reasoning and acting in language models.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network React: Synergizing reasoning and acting in language models
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.574731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d396532a-b7f6-4ec4-8ccf-88c8114b6d3e · outbound
This paper cites Toolformer: Language models that teach themselves to use tools.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Toolformer: Language models that teach themselves to use tools
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.557629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 42bb52d8-953e-47ef-bbe7-951fb77dbbbc · outbound
This paper cites LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:52:06.174319Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73694bb0-c5ce-430e-ab2b-5f50d6da390d · outbound
This paper cites Research report on cross-domain ai.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Research report on cross-domain ai
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.541745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5f06a22b-2568-49c0-95e6-556ec648bf41 · outbound
This paper cites Reddi Sashank, and Kumar Sanjiv.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Reddi Sashank, and Kumar Sanjiv
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.525303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 392bff6a-c9de-4f62-a3c2-05faf7e0c955 · outbound
This paper cites Multilayer feedforward networks are universal approximators.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Multilayer feedforward networks are universal approximators
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.509056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f2dd93fd-d913-44a0-af28-3b88aa8a4029 · outbound
This paper cites multi-domain network digital twins.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network multi-domain network digital twins
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.491367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3698ee7e-1e5f-4fdc-bd67-a4215fa1e3bf · outbound
This paper cites Specifically, [18] proved that a Transformer with sufficient capacity can approximate any continuous, permutation-equivariant sequence-to-sequence function.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Specifically, [18] proved that a Transformer with sufficient capacity can approximate any continuous, permutation-equivariant sequence-to-sequence function
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.475272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 83ca4b3c-e29e-4efd-bd48-94b0ca09fa09 · outbound
This paper cites prompt context.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network prompt context
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.458793Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d6be5cc0-3632-48b9-953b-294458e31f00 · outbound
This paper cites This is precisely the class of problems that Transformers are proven to be able to approximate [18].
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network This is precisely the class of problems that Transformers are proven to be able to approximate [18]
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.441075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4b5e4327-831a-45fb-8106-43225a890766 · outbound
This paper cites do-nothing.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network do-nothing
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.424342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0828daf5-90a4-4ba6-87c0-95d6f70d5a50 · outbound
This paper cites This means thatat must yield a utility that is greater than or equal to the utility produced by any other possible actiona′∈A.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network This means thatat must yield a utility that is greater than or equal to the utility produced by any other possible actiona′∈A
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.407514Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 558e7ead-2fb6-46da-8161-31d06f5bd5f2 · outbound
This paper cites an unresolved cited work.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network Unresolved cited work
Reference 26
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T16:52:06.390300Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 73d8f050-fadc-4e02-9644-7e0da0468940 · outbound
This paper cites operational distance.
The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network operational distance
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:52:06.372881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
Observation df455e50-a179-4f62-a70c-81f934e6287d · inbound
When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks cites this paper.
When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T17:38:02.502763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.