Paper Citation Record · LEDGER

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2507.02427.

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

pith.paper-citation-record.v1
2507.02427 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:35:33.964387Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-30T14:29:39.960509Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy35
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a3da820-e719-4fdb-b08b-4ddccff61436 · outbound

This paper cites GPT-4 Technical Report.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:31.877761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d2fb915-8175-4d57-9c0a-edfed3ec5ade · outbound

This paper cites Transformer-empowered 6G intelligent networks: From massive MIMO processing to semantic communication,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Transformer-empowered 6G intelligent networks: From massive MIMO processing to semantic communication,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:39.502557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25ec0a6d-1917-448a-879a-5ae97349f8c6 · outbound

This paper cites Large language model enhanced multi-agent systems for 6G communications,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Large language model enhanced multi-agent systems for 6G communications,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:39.391627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 987d0832-e07b-43d3-a01f-ffa0dfe754ee · outbound

This paper cites Attention is all you need,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Attention is all you need,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:39.250084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 90542f52-8a5a-420c-bebf-e95a0c9cfb9d · outbound

This paper cites Parallel attention-based transformer for channel estimation in RIS-aided 6G wireless communications,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Parallel attention-based transformer for channel estimation in RIS-aided 6G wireless communications,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:39.126406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a86bb7c5-8d26-457f-9367-b85d8283b457 · outbound

This paper cites Pay less but get more: A dual-attention- based channel estimation network for massive MIMO systems with low-density pilots,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Pay less but get more: A dual-attention- based channel estimation network for massive MIMO systems with low-density pilots,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:39.012898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c228f8f7-3054-4798-8260-ad06e33ce10e · outbound

This paper cites Transformer network based channel prediction for CSI feedback enhancement in AI-native air interface,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Transformer network based channel prediction for CSI feedback enhancement in AI-native air interface,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:38.927072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf737aef-371c-43b8-b9a3-f7e07f6148bb · outbound

This paper cites Transformer-based channel prediction for rate-splitting multiple access-enabled vehicle-to-everything communication,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Transformer-based channel prediction for rate-splitting multiple access-enabled vehicle-to-everything communication,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:38.764443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47c1a78d-81d2-4a24-b8fb-8767a30abd64 · outbound

This paper cites HPE Transformer: Learning to optimize multi-group multicast beamforming under nonconvex QoS constraints,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? HPE Transformer: Learning to optimize multi-group multicast beamforming under nonconvex QoS constraints,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:38.564869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54b65301-f87c-42c6-a9ac-422619fbcd29 · outbound

This paper cites Transformer-based power optimization for max-min fairness in cell-free massive MIMO,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Transformer-based power optimization for max-min fairness in cell-free massive MIMO,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:38.424402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4f1af340-49bd-4125-8c6a-3bd061298ec0 · outbound

This paper cites Learning Precoding in Multi-user Multi-antenna Systems: Transformer or Graph Transformer?.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Learning Precoding in Multi-user Multi-antenna Systems: Transformer or Graph Transformer?

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:35:34.164157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d34c7222-0d1b-4ac3-b145-60df0cafc578 · outbound

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

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Multidimensional graph neural networks for wireless communications,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:38.218875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0513b9b0-7b23-408a-92c1-10944e0e41c3 · outbound

This paper cites Improving learning efficiency for wireless resource allocation with symmetric prior,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Improving learning efficiency for wireless resource allocation with symmetric prior,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:38.024101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65a57f05-5c93-40b9-9a78-ddb6fce3e94c · outbound

This paper cites Understanding the performance of learning precoding policies with graph and convolutional neural networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Understanding the performance of learning precoding policies with graph and convolutional neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:37.874845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ace03604-43c8-4a45-b775-bcf467cda1ad · outbound

This paper cites Optimal wireless resource allocation with random edge graph neural networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Optimal wireless resource allocation with random edge graph neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:37.732631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b6e70a9-4c04-4f49-81a5-10f20971031e · outbound

This paper cites Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:37.579386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3b31304d-ea78-4e32-b631-3851ba3010b0 · outbound

This paper cites Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:37.434772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 44a2c46b-2364-4b30-8410-cfc5aecee274 · outbound

This paper cites Distributed graph-based learning for user association and beamforming design in multi-ris multi-cell networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Distributed graph-based learning for user association and beamforming design in multi-ris multi-cell networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:37.285401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c76c000-58f0-40c7-936d-949cf3699fb1 · outbound

This paper cites Graph embedding-based wireless link scheduling with few training samples,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Graph embedding-based wireless link scheduling with few training samples,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:37.139636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ca96904-1d6f-4a97-bfd7-71a0a17b1c2e · outbound

This paper cites Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural network,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural network,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:36.992543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 69812aa7-5319-451d-b826-a4e33f3d39b9 · outbound

This paper cites Graph neural network aided power control in partially connected cell-free massive MIMO,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Graph neural network aided power control in partially connected cell-free massive MIMO,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:36.869842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77681241-dcae-415a-9519-38b62a5048a7 · outbound

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

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Recursive GNNs for learning precoding policies with size-generalizability,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:36.705839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aed9646f-7f2c-4b43-b67b-2b168ad9dbbc · outbound

This paper cites GNN-based beamforming for sum-rate maximization in MU-MISO networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? GNN-based beamforming for sum-rate maximization in MU-MISO networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:36.504925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da590850-73a1-4185-80f3-5540610e1a68 · outbound

This paper cites ENGNN: A general edge-update empowered GNN architecture for radio resource management in wireless networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? ENGNN: A general edge-update empowered GNN architecture for radio resource management in wireless networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:36.302570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e9c95d5-8f0d-48d4-99a9-cfd882c6749d · outbound

This paper cites Equivariance through parameter-sharing,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Equivariance through parameter-sharing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:36.165179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c6bfd5d7-bce1-42df-bf2c-17eac5c658a4 · outbound

This paper cites Deep sets,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Deep sets,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.970785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ba3252e-3390-40de-b750-6d627a99f0f9 · outbound

This paper cites Structure of deep neural networks with a priori information in wireless tasks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Structure of deep neural networks with a priori information in wireless tasks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.808199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ebb410d-f36f-44b2-99d7-576413f439f2 · outbound

This paper cites Beamforming design and association scheme for multi-RIS multi-user mmwave systems through graph neural networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Beamforming design and association scheme for multi-RIS multi-user mmwave systems through graph neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.671709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7f1431a-7528-4c2a-a60d-53f5d0a25947 · outbound

This paper cites A bipartite graph neural network approach for scalable beamforming optimization,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? A bipartite graph neural network approach for scalable beamforming optimization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.544684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae45ce96-4ff7-4013-9ed2-c8f9770b3ee0 · outbound

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

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? A size-generalizable graph neural network for learning multi-user multi-stream MIMO precoding,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.466832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c1801ff9-15ae-455f-b2f7-8b98980efee9 · outbound

This paper cites Joint spectrum, precoding, and phase shifts design for RIS-aided multiuser MIMO THz systems,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Joint spectrum, precoding, and phase shifts design for RIS-aided multiuser MIMO THz systems,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.393815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f79318c-eedc-46cd-bc53-5be06effaaf3 · outbound

This paper cites Graph attention networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Graph attention networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.259462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30c97a12-ecb9-46bf-90d2-a86a67d489ca · outbound

This paper cites Graph attention network-based precoding for reconfigurable intelligent surfaces aided wireless communication systems,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Graph attention network-based precoding for reconfigurable intelligent surfaces aided wireless communication systems,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:35.095759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 801e7085-e343-4ac6-98d0-d7fc494b8cf8 · outbound

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

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Weighted sum-rate maximization for reconfigurable intelligent surface aided wireless networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:34.947868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ccf669b8-5906-4e79-bdff-dc7f92446039 · outbound

This paper cites Universal approximations of permutation invariant/equivariant functions by deep neural networks.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Universal approximations of permutation invariant/equivariant functions by deep neural networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:33.732858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d471085c-699f-482a-abbc-4984a98e3916 · outbound

This paper cites an unresolved cited work.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Unresolved cited work

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:33.798577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db1c2192-228e-4a90-9528-22d719ea0c21 · outbound

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

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:34.787319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b77c4a82-8820-4419-9cb5-2aaee68f137d · outbound

This paper cites an unresolved cited work.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:35:34.629538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe7e4994-b23e-4655-8d75-9c7e03069844 · outbound

This paper cites Transformers are graph neural networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Transformers are graph neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:34.413481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cfa592d4-406b-44d2-a3c6-622f2d94aa94 · outbound

This paper cites Learning beamforming for RIS-aided systems with permutation equivariant graph neural networks,.

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently? Learning beamforming for RIS-aided systems with permutation equivariant graph neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:35:34.281946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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

Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation cites this paper.

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

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T14:29:39.960509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.