Paper Citation Record · LEDGER

JEPA for AI-Native 6G: Predictive Representations and Open Challenges

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.09798.

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

pith.paper-citation-record.v1
2607.09798 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T15:32:53.282167Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

20 of 20 outbound references displayed

verified exact0
verified fuzzy0
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a0f7988-98d5-435b-ac0c-440c6fca0f02 · outbound

This paper cites The roadmap to 6G: AI empowered wireless networks,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges The roadmap to 6G: AI empowered wireless networks,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daf93151-1f20-48d7-a98a-c84789367ee3 · outbound

This paper cites Enhancing network data analytics functions: Integrating AIaaS with ML model provisioning,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Enhancing network data analytics functions: Integrating AIaaS with ML model provisioning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21f20639-b89c-45b7-8efc-5bb91405c933 · outbound

This paper cites Study on artificial intelligence (AI)/machine learning (ML) for NR air interface,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Study on artificial intelligence (AI)/machine learning (ML) for NR air interface,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71625e49-3b66-47f6-a1c9-6213aec27e4e · outbound

This paper cites Towards AI-native 6G systems: Standards enablers for 6G network automation,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Towards AI-native 6G systems: Standards enablers for 6G network automation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11494f9e-64e2-4263-82e9-7a54350aaed7 · outbound

This paper cites A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1207b5f0-9643-4c7f-bc09-92b94e9cf62a · outbound

This paper cites Deep learning for modulation recognition: A survey with a demonstration,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Deep learning for modulation recognition: A survey with a demonstration,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbabd5a2-cc7d-46cd-bd71-03573861cb5f · outbound

This paper cites Revolutionizing wireless networks with self-supervised learning: A pathway to intelligent communications,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Revolutionizing wireless networks with self-supervised learning: A pathway to intelligent communications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53dd2421-53d6-4ea7-b6ed-5470551a7f23 · outbound

This paper cites A multi-task foundation model for wireless channel representation using contrastive and masked autoencoder learning,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges A multi-task foundation model for wireless channel representation using contrastive and masked autoencoder learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd9faf04-4764-4453-9077-ac65f85aa9d2 · outbound

This paper cites PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb8d8613-4083-4a36-9095-f18e6f9564dc · outbound

This paper cites A tutorial-cum-survey on self-supervised learning for Wi-Fi sensing: Trends, challenges, and outlook,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges A tutorial-cum-survey on self-supervised learning for Wi-Fi sensing: Trends, challenges, and outlook,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef40e51d-2d47-4a91-8578-a7a3a1588089 · outbound

This paper cites Self-supervised learning from images with a joint- embedding predictive architecture,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Self-supervised learning from images with a joint- embedding predictive architecture,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd26cc29-3d1e-480b-97e9-878ea36fd357 · outbound

This paper cites WirelessJEPA: A multi-antenna foundation model using spatio-temporal wireless latent predictions,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges WirelessJEPA: A multi-antenna foundation model using spatio-temporal wireless latent predictions,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63cc7196-5b0c-43ce-9b39-8850e67fb74f · outbound

This paper cites Understanding collapse in non-contrastive siamese representation learning,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Understanding collapse in non-contrastive siamese representation learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c3eab99-d0e2-47f9-a5e6-04eae20799dd · outbound

This paper cites Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

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Observation 38e69d18-9c63-4543-87a6-591adf6518da · outbound

This paper cites A cross-layer survey on secure and low-latency communications in next-generation IoT,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges A cross-layer survey on secure and low-latency communications in next-generation IoT,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

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Observation 0f544c7c-7887-4f46-bc47-9a3caf61a813 · outbound

This paper cites Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0d6c516-1146-4506-a30e-1739af0445f6 · outbound

This paper cites Lifecycle management of trustworthy AI models in 6G networks: The REASON approach,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Lifecycle management of trustworthy AI models in 6G networks: The REASON approach,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0663855-3534-4f92-b0cc-deee7ecdca56 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges A simple framework for contrastive learning of visual representations,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4730aab-0ed9-4f4b-b4e7-bc8f4fef7f0c · outbound

This paper cites Masked autoencoders are scalable vision learners,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges Masked autoencoders are scalable vision learners,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99552618-8da1-4666-9353-ce2e7221b7eb · outbound

This paper cites A path towards autonomous machine intelligence,.

JEPA for AI-Native 6G: Predictive Representations and Open Challenges A path towards autonomous machine intelligence,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T15:32:53.282167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.