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Paper Citation Record · LEDGER

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation

As of 9 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2607.23738.

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

pith.paper-citation-record.v1
2607.23738 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T14:29:39.970212Z

measured 14 of 14 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 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

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d9b5ff81-9021-4b4d-a7d9-fb76e06b793a · outbound

This paper cites WirelessGPT: A generative pre- trained multi-task learning framework for wireless communication,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation WirelessGPT: A generative pre- trained multi-task learning framework for wireless communication,

Reference 1

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no resolver link, observed 2026-07-30T14:29:39.906729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.906729Z digest=sha256:647d1e9857a655b4a59e44d55e0d486833c36120493c716a23a5a1ecda1b2349

Observation ee140b3f-436f-4e75-b331-d83afb883bd2 · outbound

This paper cites Large Wireless Model (LWM): A Foundation Model for Wireless Channels.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Large Wireless Model (LWM): A Foundation Model for Wireless Channels

Reference 2

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no resolver link, observed 2026-07-30T14:29:39.912355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.912355Z digest=sha256:f799844627ce994dcfbde220af35604b892b915f14762cb08a1b6f0f0c0d028e

Observation 5646f63c-51c8-41a7-8fad-ca1271f5c34e · outbound

This paper cites Large language model-empowered channel prediction and predictive beamforming for leo satellite communications,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Large language model-empowered channel prediction and predictive beamforming for leo satellite communications,

Reference 3

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no resolver link, observed 2026-07-30T14:29:39.917647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.917647Z digest=sha256:93b0972fec454832536f4659b710f2108a02acd85eba65e239cd4f4ddeee49eb

Observation cdd71a45-e753-4f37-b42a-8c05cd075116 · outbound

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

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation LVM4CSI: Enabling Direct Application of Pre-Trained Large Vision Models for Wireless Channel Tasks

Reference 4

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no resolver link, observed 2026-07-30T14:29:39.922126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.922126Z digest=sha256:ae16d552d745d83dcae4cb6567d91d3cb28fc4dc37447fefd7bb3570e007d179

Observation 7a5df8e8-0496-4831-b997-757aeb1df7ae · outbound

This paper cites A Wireless Foundation Model for Multi-Task Prediction.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation A Wireless Foundation Model for Multi-Task Prediction

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.927419Z digest=sha256:ece90a5e65981bc52e5ad2bb81e00e242adcf7a7ff54f8276762cf664afeb69d

Observation 552f702f-624c-4918-af2b-acf7a473df61 · outbound

This paper cites 6G-oriented CSI-based multi-modal pre- training and downstream task adaptation paradigm,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation 6G-oriented CSI-based multi-modal pre- training and downstream task adaptation paradigm,

Reference 6

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no resolver link, observed 2026-07-30T14:29:39.932418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.932418Z digest=sha256:ad3ca5da0116a81e62f68125f15f40fddb1d54240403985df20e30bb0c192d24

Observation da3008e0-af2e-40c8-a22d-87b7ce8f9411 · outbound

This paper cites Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems,

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.937680Z digest=sha256:9f074e4206f3db68805b8e792fa7c669e0ab157d05fdaaa99df2f3b6eadab58c

Observation 0e7b8807-07a1-4ca6-aaa5-9919cc586894 · outbound

This paper cites An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,

Reference 8

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unresolved
no resolver link, observed 2026-07-30T14:29:39.942420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.942420Z digest=sha256:c3112416788458e3bfd1300b3e50e2a1a49b7cfb5e0e816b3cae75a277992f14

Observation 005013b5-971f-43c3-9f95-86ebfe445515 · outbound

This paper cites A size-generalizable graph neural network for learning multi-user multi-stream MIMO precoding,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation A size-generalizable graph neural network for learning multi-user multi-stream MIMO precoding,

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.946952Z digest=sha256:ae313a0c1e89fafab3735efed353ca4cf6e528127a0f2ccea6735af8d95e65b7

Observation e5613ffd-fb3b-4dd1-aca6-c5647242f705 · outbound

This paper cites Recursive GNNs for learning precoding policies with size- generalizability,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Recursive GNNs for learning precoding policies with size- generalizability,

Reference 10

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source=pdf_text observed=2026-07-30T14:29:39.951623Z digest=sha256:02c007ed551bc4edd38f94454a464ac5b71c169819c7af3ac87d3d567fe94a35

Observation bac4985c-0309-4919-92ec-e7af73d55066 · outbound

This paper cites Low-complexity joint beamforming for RIS-assisted MU-MISO systems based on model-driven deep learn- ing,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Low-complexity joint beamforming for RIS-assisted MU-MISO systems based on model-driven deep learn- ing,

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.956129Z digest=sha256:e4b1550a73fb87aa531c0aaca40e5e2b4526928cbb02c1967e177ffc128113d1

Observation 3b11595b-6840-4954-905c-5584fd48a523 · outbound

This paper cites When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.960509Z digest=sha256:cf950419bb37488a3ca6ef24a094102853e5e363bac20e5f1c06b7fea8332730

Observation 695ed5e5-b344-40c3-a543-296e80f98268 · outbound

This paper cites Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks,

Reference 13

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unresolved
no resolver link, observed 2026-07-30T14:29:39.965883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.965883Z digest=sha256:60c99747d271c3cafb594c0f96e4f523a22d50975316146454dbba350802249d

Observation c9ad24c0-8a82-421e-b1a1-363f09226300 · outbound

This paper cites Multidimensional graph neural networks for wireless communications,.

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation Multidimensional graph neural networks for wireless communications,

Reference 14

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no resolver link, observed 2026-07-30T14:29:39.970212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T14:29:39.970212Z digest=sha256:0a7030b59686f10c36517ca46b768c7f2fc921d067317587ada3bd6ccbd4f259

Pith citing papers

No inbound Pith citation observations are available.