Paper Citation Record · LEDGER

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks

As of 12 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2604.25967.

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

pith.paper-citation-record.v1
2604.25967 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-07T15:15:36.175289Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:35:40.530304Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact1
verified fuzzy15
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1fc64249-21ea-4c21-ba9e-25b670c86f0d · outbound

This paper cites To- ward 6G networks: Use cases and technologies.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks To- ward 6G networks: Use cases and technologies

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.396655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 034af806-662a-4dcf-90d4-91bef0f797f6 · outbound

This paper cites Joint radar and communication design: Applications, state-of-the-art, and the road ahead.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Joint radar and communication design: Applications, state-of-the-art, and the road ahead

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.350709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2da2d424-76b5-4487-b28e-8d1c7445da76 · outbound

This paper cites Integrated sensing and com- munication for 6G: Recent advances and research challenges.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Integrated sensing and com- munication for 6G: Recent advances and research challenges

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.364745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0dc24150-9f9f-44e5-bb7a-ad43b29048bf · outbound

This paper cites Interference management for integrated sensing and communication systems.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Interference management for integrated sensing and communication systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.368046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation da71d8ab-f239-4e23-a8cf-b77133615a65 · outbound

This paper cites Empowering the 6G cellular architecture with open RAN.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Empowering the 6G cellular architecture with open RAN

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.388986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9a8f6853-6224-4bc9-bfd7-8d5340b47347 · outbound

This paper cites Wireless network intelligence at the edge: Latency, reliability, and scalability.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Wireless network intelligence at the edge: Latency, reliability, and scalability

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.375046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a34c6f20-1ca0-4861-9097-7ec4eb868705 · outbound

This paper cites Optimal resource allocation in wireless sys- tems with delayed state information.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Optimal resource allocation in wireless sys- tems with delayed state information

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.361021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03e8fc21-f5f3-4bb3-a28d-9ae0ca65fe16 · outbound

This paper cites Digital twin-enabled control for wireless networks: Architecture and applications.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Digital twin-enabled control for wireless networks: Architecture and applications

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.392796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 442e1b1d-c663-4932-b69c-b08406c6defa · outbound

This paper cites Joint beamforming and power allocation strategy for NOMA empowered ISAC systems.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Joint beamforming and power allocation strategy for NOMA empowered ISAC systems

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.385589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0620dcb-386f-421c-b470-3e79d7b7ef08 · outbound

This paper cites Joint maneuver and beamforming design for UA V-enabled integrated sensing and communication.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Joint maneuver and beamforming design for UA V-enabled integrated sensing and communication

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.381924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2aaaa750-9b58-4376-9042-3461cfeca6df · outbound

This paper cites Deep reinforcement learning for integrated sensing and communication in RIS- assisted 6G V2X system.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Deep reinforcement learning for integrated sensing and communication in RIS- assisted 6G V2X system

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.357556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7986179-0b76-4954-a585-c710f8a3b303 · outbound

This paper cites DRL-based STAR-RIS-assisted ISAC secure communications.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks DRL-based STAR-RIS-assisted ISAC secure communications

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:26:19.384769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ffdf8d6-7bfa-4c15-ad64-87e35691472a · outbound

This paper cites Wireless network digital twin for 6G: Generative AI as a key enabler.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Wireless network digital twin for 6G: Generative AI as a key enabler

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.371653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a5697fbe-e3aa-412d-b0d1-4f10ae525f69 · outbound

This paper cites Digital twin for O-RAN towards 6G.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks Digital twin for O-RAN towards 6G

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.378591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85602fbe-2cb8-4c32-b326-317b8a8599bc · outbound

This paper cites ORANUS: Latency-tailored orchestration via stochastic network calculus in 6G O-RAN.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks ORANUS: Latency-tailored orchestration via stochastic network calculus in 6G O-RAN

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.354158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 734d8310-aab4-4b3f-a4f5-d8003c1ec838 · outbound

This paper cites MAREA: A delay-aware multi-time-scale radio resource orchestrator for 6G O-RAN.

Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks MAREA: A delay-aware multi-time-scale radio resource orchestrator for 6G O-RAN

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T03:38:47.347353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation d4fa8dfc-6583-481b-80fb-0bd3299850db · inbound

Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South cites this paper.

Sovereign Cognitive Digital Twins: Fusing 6G ISAC, AI-RAN, and Zero-Trust Edge Grids for National Resilience in the Global South Digital Twin-assisted belief-state reinforcement learning for latency-robust ISAC in 6G networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T00:35:40.530304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.