Paper Citation Record · LEDGER

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 2 inbound Pith citation observations for arXiv:2506.22469.

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

pith.paper-citation-record.v1
2506.22469 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:40.027669Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T20:38:28.755740Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:35:47.088766Z

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy22
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8958c4e3-cbb9-4986-85a1-bfbc8735f27f · outbound

This paper cites Draft New Recommendation ITU-R M. [IMT. Frame- work for 2030 and Beyond],.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Draft New Recommendation ITU-R M. [IMT. Frame- work for 2030 and Beyond],

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:36.959402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01a24287-2758-4c11-bd7c-bc645a2d7d90 · outbound

This paper cites Integrated sensing and communications: Toward dual-functional wire- less networks for 6G and beyond,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Integrated sensing and communications: Toward dual-functional wire- less networks for 6G and beyond,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:37.024799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bd45619-519d-452b-8701-553acc52fa0d · outbound

This paper cites V2X functional and performance test report: Test procedures and results,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks V2X functional and performance test report: Test procedures and results,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:43.506190Z

Source-reported events for the cited work

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Observation 8c5bdcde-0a42-4950-8d33-022f1e48395b · outbound

This paper cites Toward ISAC-empowered vehicular networks: Framework, Advances, and Opportunities,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Toward ISAC-empowered vehicular networks: Framework, Advances, and Opportunities,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:43.384136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a42ef997-c2d5-41cc-ae66-7ed8c6811787 · outbound

This paper cites On the fundamental tradeoff of integrated sensing and communications under gaussian channels,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks On the fundamental tradeoff of integrated sensing and communications under gaussian channels,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:37.241678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 499b436f-a27d-4491-a7ef-79407ef7c646 · outbound

This paper cites Real time object detection using lidar and camera fusion for autonomous driving,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Real time object detection using lidar and camera fusion for autonomous driving,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:43.264990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d8d6049c-575c-444e-9804-a618a6ad1896 · outbound

This paper cites Intelligent multi-modal sensing-communication integration: Synesthesia of machines,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Intelligent multi-modal sensing-communication integration: Synesthesia of machines,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:43.140668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d8605cac-fcd0-4c6c-8773-4a980f11550f · outbound

This paper cites Multimodal deep learning empowered millimeter-wave beam prediction,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Multimodal deep learning empowered millimeter-wave beam prediction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:43.034544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 99a36490-0b14-444d-a953-478c75c3a6fa · outbound

This paper cites Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:37.654270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03bddced-811f-4f04-a219-bbabea8bed68 · outbound

This paper cites Advancing multi-modal beam prediction with multipath-like data augmentation and efficient fusion mechanism.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Advancing multi-modal beam prediction with multipath-like data augmentation and efficient fusion mechanism

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:49:40.605139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0d38b102-9625-406d-989d-8c5beb30c43c · outbound

This paper cites Advancing multi-modal beam prediction with cross-modal feature enhancement and dynamic fusion mechanism,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Advancing multi-modal beam prediction with cross-modal feature enhancement and dynamic fusion mechanism,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.882853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 28b3dcef-e01c-4d4a-a945-3a7fffe9d96c · outbound

This paper cites Deep quantum-transformer networks for multimodal beam prediction in isac systems,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Deep quantum-transformer networks for multimodal beam prediction in isac systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.771165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 369e2a3f-c2e4-4516-a116-e4144bb6642e · outbound

This paper cites Multi-modality sensing in mmwave beamforming for connected vehicles using deep learning,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Multi-modality sensing in mmwave beamforming for connected vehicles using deep learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.657814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation de63f621-2ce8-4dcf-b305-8473f16b861f · outbound

This paper cites Multi-modal transformer and reinforcement learning-based beam man- agement,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Multi-modal transformer and reinforcement learning-based beam man- agement,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.541412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b217f1d7-a595-4f09-93d4-5aae89cb881b · outbound

This paper cites Trans- fuser: Imitation with transformer-based sensor fusion for autonomous driving,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Trans- fuser: Imitation with transformer-based sensor fusion for autonomous driving,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.423469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5bb3720e-0ed4-49b2-b186-95fa939ae3bb · outbound

This paper cites Attention is all you need,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Attention is all you need,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:38.335006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65e935b0-edc8-49ff-acde-896cccb3d200 · outbound

This paper cites Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:49:40.281477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b13b7ef3-1ee7-4a90-b66e-227ddbff1e25 · outbound

This paper cites Deepsense itu multi modal beam prediction challenge 2022 – deepsense,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Deepsense itu multi modal beam prediction challenge 2022 – deepsense,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.283455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e52c66f-0c0e-477f-b943-ab3473523934 · outbound

This paper cites Cross-entropy loss functions: Theoretical analysis and applications,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Cross-entropy loss functions: Theoretical analysis and applications,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.156679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3875b67c-8f88-41ef-99ac-d0f85bc2116f · outbound

This paper cites Focal loss for dense object detection,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Focal loss for dense object detection,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:38.746589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e4bc4be-926e-4625-8eb1-67934e308eae · outbound

This paper cites Multi-Modal Beam Prediction Challenge 2022: Towards Generalization.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Multi-Modal Beam Prediction Challenge 2022: Towards Generalization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:38.836334Z

Source-reported events for the cited work

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Observation a696704d-d935-4abf-aeeb-f0753308c018 · outbound

This paper cites Deep residual learning for image recognition,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Deep residual learning for image recognition,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:38.926296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63382e0b-37e7-4a2a-8820-cd78b8c8d11d · outbound

This paper cites GPT-4 Technical Report,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks GPT-4 Technical Report,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:42.017180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6dd304b-5457-4504-8f3c-8ffd94241e92 · outbound

This paper cites A survey on vision transformer,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks A survey on vision transformer,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.877912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c80e60d0-e5ea-4aa2-9488-82812c2b742e · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.745760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c49e8a6b-7c0a-48ec-8255-00c372a07ca8 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Distilling the Knowledge in a Neural Network

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:39.298534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e22bb3d-fb54-413d-ae66-e3649f46c19a · outbound

This paper cites an unresolved cited work.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:39.387724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b7430f-8280-4a07-94c8-60ec5252d12c · outbound

This paper cites Adaptive head pruning for attention mechanism in the maritime domain,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Adaptive head pruning for attention mechanism in the maritime domain,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.638322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e4982bb0-95e4-4c63-be62-eb412cddcda0 · outbound

This paper cites Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.452027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3586bde9-660e-44d6-8cbe-2297fb2f386e · outbound

This paper cites Auto-encoding variational bayes,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Auto-encoding variational bayes,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.308721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4e938e0f-bec2-43f5-bff8-aa54be705ae1 · outbound

This paper cites Generative adversarial nets,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Generative adversarial nets,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:39.649266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85e3094f-2ea3-43b4-af28-34974d9eccb2 · outbound

This paper cites Diffusion models in vision: A survey,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Diffusion models in vision: A survey,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:39.700053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e02d769f-b210-47f3-a1d0-a62c0f96dfb2 · outbound

This paper cites An introduction to variational autoen- coders,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks An introduction to variational autoen- coders,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.176065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c183553b-457c-4e24-a5e0-f98773ec2046 · outbound

This paper cites Learning structured output representation using deep conditional generative models,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Learning structured output representation using deep conditional generative models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:41.033071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6ee01280-3257-4121-85f0-5963cc8445eb · outbound

This paper cites Semi-supervised learning with deep generative models,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Semi-supervised learning with deep generative models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:40.882811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 42d601c9-27cc-447f-9ffe-a6f1d3a88a0e · outbound

This paper cites Deepsense 6G: A large-scale real-world multi-modal sensing and communication dataset,.

Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks Deepsense 6G: A large-scale real-world multi-modal sensing and communication dataset,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:40.741985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation c4c5a7db-3fb4-4931-9f56-a6392da40738 · inbound

Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks cites this paper.

Transformer Architecture with Minimal Inference Latency for Multi-Modal Wireless Networks Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-09T01:19:36.681641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c9d6d390-538a-4294-bd83-e897a8165209 · inbound

Multimodal Learning for MIMO Beam Prediction Based on Variational Inference cites this paper.

Multimodal Learning for MIMO Beam Prediction Based on Variational Inference Multi-Modal Beamforming with Model Compression and Modality Generation for V2X Networks

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-09T01:19:36.681641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.