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Paper Citation Record · LEDGER

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G

As of 22 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2506.14288.

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

pith.paper-citation-record.v1
2506.14288 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:22:25.854759Z

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 2026-06-30T20:32:17.658231Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T20:35:02.502384Z

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 202e76bd-2d9b-4b90-b079-ecae681a354d · outbound

This paper cites A tutorial on fluid antenna system for 6G networks: Encompassing communication theory, optimization methods and hard- ware designs,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G A tutorial on fluid antenna system for 6G networks: Encompassing communication theory, optimization methods and hard- ware designs,

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:23.622922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:23.622922Z digest=sha256:18b9349e6e321684693ed27611ae1cf36525219e52c3d1ab0a80d1c30553b350

Observation 244b2286-3950-45bc-bb07-e7769ed55995 · outbound

This paper cites Virtual FAS by learning-based imaginary antennas,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Virtual FAS by learning-based imaginary antennas,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:29.168307Z

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.

source=pdf_text observed=2026-08-07T00:22:23.854900Z digest=sha256:17be3f764be0d559bcb8d77f74e9bfd672b0cc922c66d2699c9f54f83844061e

Observation eed40649-b0e4-4bdc-b05c-4394a50c6f17 · outbound

This paper cites Fluid antenna system liberating multiuser MIMO for ISAC via deep reinforcement learning,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Fluid antenna system liberating multiuser MIMO for ISAC via deep reinforcement learning,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:28.874749Z

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.

source=pdf_text observed=2026-08-07T00:22:24.013246Z digest=sha256:c358abfc87724008aef78cf236ae45af2f94f25a2b5f9c83d743cd5dbe1bfc77

Observation c6ed6b5d-5fa2-4fde-a114-ef1a4a9329c6 · outbound

This paper cites AI-empowered fluid antenna systems: Opportunities, challenges, and future directions,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G AI-empowered fluid antenna systems: Opportunities, challenges, and future directions,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:28.624909Z

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.

source=pdf_text observed=2026-08-07T00:22:24.084838Z digest=sha256:8e5cffd6c4666a65c496aa123950cf486132b5ce06539eb05d208c5b1ea36638

Observation 50838da0-af10-41f6-bd1a-4e9c44100ed4 · outbound

This paper cites Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:24.170671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:24.170671Z digest=sha256:89850f19eab33d6f2b6059ebea14c55b373951bc1c453cb6fec1dea8bfe23522

Observation f2f06628-0fdb-457d-a867-da6d372b8316 · outbound

This paper cites Generative AI agents with large language model for satellite networks via a mixture of experts transmission,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Generative AI agents with large language model for satellite networks via a mixture of experts transmission,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:28.295963Z

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.

source=pdf_text observed=2026-08-07T00:22:24.274737Z digest=sha256:45d2f9d4f860e1431d1a7479981987225da543b28a11e3af04cff25351e736e9

Observation fbedcce2-97a7-4adc-b662-ad56f3781c96 · outbound

This paper cites FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:24.504827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:24.504827Z digest=sha256:9ead836fe21fc759cc1b8eb8abc3c52be659060e3c6193687cc2ee4c7d0fc5cd

Observation d76bd04e-48be-4fd1-a9d4-55d9d28af5a5 · outbound

This paper cites Port-LLM: A Port Prediction Method for Fluid Antenna based on Large Language Models.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Port-LLM: A Port Prediction Method for Fluid Antenna based on Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:24.744753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:24.744753Z digest=sha256:1388d3c54a6a1964722eb29592764c9b12fc0692a519dcdebb71309612bb6c71

Observation cbd627f5-19dc-49fb-8f63-7d1c1de6ddd7 · outbound

This paper cites Channel knowledge map aided channel prediction with measurements-based evaluation,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Channel knowledge map aided channel prediction with measurements-based evaluation,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:28.064775Z

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.

source=pdf_text observed=2026-08-07T00:22:24.904835Z digest=sha256:2e8311079d7acb12f7c2ee588ced077845fc3de3c990b764f8ebec31b999ee61

Observation a515bb84-2da3-4549-a36e-0dfdcd90649f · outbound

This paper cites Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:25.044749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:25.044749Z digest=sha256:a4d50f4c4ee3d69aa6c85255d26412f201dd89023e7b70785f8ccaa0a2d86064

Observation 8fae5bc5-e8ea-48a1-9e82-7a66ef7d9d82 · outbound

This paper cites A Survey on Multimodal Large Language Models.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G A Survey on Multimodal Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:25.204974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:25.204974Z digest=sha256:dbf234d60176529b12c33a27cf8ea0bc4ce3ff79dff31ca6a51a8f814a8d863d

Observation 2b3ab284-8eff-409c-bc90-793de91723bf · outbound

This paper cites Benchmarking large language models in retrieval-augmented generation,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Benchmarking large language models in retrieval-augmented generation,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:27.832121Z

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.

source=pdf_text observed=2026-08-07T00:22:25.402036Z digest=sha256:e96a4f3e06ff40de93fd50f44bdfc6d9ac4aa5771f39555fbc75e50c24ab30c9

Observation 3ea6e156-97c4-4f59-8b7f-1e7245ef7f9c · outbound

This paper cites Using large language models for hyperparameter optimization,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Using large language models for hyperparameter optimization,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:27.554956Z

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.

source=pdf_text observed=2026-08-07T00:22:25.564895Z digest=sha256:544223a5b69d2dea610c8177d8a9bb8b24ec5d63163668229c9b469845cdd387

Observation 833b4167-e039-4e5e-9ef7-ac693f504493 · outbound

This paper cites Llm4cp: Adapting large language models for channel prediction,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Llm4cp: Adapting large language models for channel prediction,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:27.270458Z

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.

source=pdf_text observed=2026-08-07T00:22:25.756225Z digest=sha256:7eb3afb0ef703391369fa2642e385be0dcece2ecbb5ad13f540596e755649a44

Observation 475545c1-ca7c-4ed3-955e-fd698265ccfc · outbound

This paper cites Reevo: Large language models as hyper-heuristics with reflective evolution,.

Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G Reevo: Large language models as hyper-heuristics with reflective evolution,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:22:26.905262Z

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.

source=pdf_text observed=2026-08-07T00:22:25.854759Z digest=sha256:6eb66a545611dbedfb3c7667adc4432f5c900afe1728803d48793c5e25b0142b

Pith citing papers

Observation 12107696-cc6c-4921-9f0e-9f6d97752d07 · inbound

LLM-Enabled Automated Algorithm Design for Multiuser Fluid Antenna Communications cites this paper.

LLM-Enabled Automated Algorithm Design for Multiuser Fluid Antenna Communications Large Language Model Empowered Design of Fluid Antenna Systems: Challenges, Frameworks, and Case Studies for 6G

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-06-30T20:35:02.504202Z

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.

source=pdf_text observed=2026-06-30T20:32:17.658231Z digest=sha256:846b0e5624a1c48aeb4f755eb9fd130f326c3b06699ff229585cdb454a9efa9c