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Paper Citation Record · LEDGER

Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2501.10630.

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

pith.paper-citation-record.v1
2501.10630 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:44:03.391437Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:46:09.792436Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9e8192e2-ad3a-4f3a-a1c6-d1096c387f2d · inbound

LLM4SG: Adapting Large Language Model for Scatterer Generation via Synesthesia of Machines cites this paper.

LLM4SG: Adapting Large Language Model for Scatterer Generation via Synesthesia of Machines Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T14:44:03.391437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:44:03.391437Z digest=sha256:3b31edb45c11192cd94eb4bbe0c130b2394243fe7048ef13ae7bde8992335e41

Observation 331dc49f-3656-496b-aba0-4cb325305c80 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-07T05:33:55.689365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:33:55.689365Z digest=sha256:9a2268b0179fa1bfda36a016d11d296acd56d0c8253171b1be9d2ad04f9b76e4

Observation e44b21ee-0c5d-467e-a1b0-f15045aae727 · inbound

LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks cites this paper.

LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T19:37:33.202262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:37:33.202262Z digest=sha256:e2febf11aff133b7f3966213740cfdea5a934cda4908e21410d4ec333347e429

Observation 63e83ebe-c812-46ec-87a0-25116c77ff55 · inbound

MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction cites this paper.

MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:46:09.794396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T13:59:33.879990Z digest=sha256:f77095f8eef7683ddbc8a3e99f6c1dd786b892290ab2ca06ef033e70ae1ce957

Observation 16c8890e-3ff0-4d91-83f9-ae5b787e69e3 · inbound

AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G cites this paper.

AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G Exploring the Potential of Large Language Models for Massive MIMO CSI Feedback

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:01:49.035323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T07:00:52.738443Z digest=sha256:d93d4b279ea03daa697064e20c1972bedf2a5e1b7dde64aa06c11ba4ed685cab