Pith. sign in

Paper Citation Record · LEDGER

Continual Reinforcement Learning for Digital Twin Synchronization Optimization

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.08045.

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

pith.paper-citation-record.v1
2501.08045 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:37:04.168493Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

45 of 45 outbound references displayed

  • verified exact0
  • verified fuzzy40
  • unresolved4
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 636494d6-bd83-44d5-aaac-1b6fc96fd060 · outbound

This paper cites Digital twin networks: A survey,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital twin networks: A survey,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.827634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:03.967419Z digest=sha256:5f957b1092c0e32258f05700e659cb22b6976981f276d3a8ea3b411a11e2f510

Observation 1663a4cb-4dfd-4745-bbd0-cea2a0826412 · outbound

This paper cites Meta- verse for wireless systems: Vision, enablers, architecture, and future directions,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Meta- verse for wireless systems: Vision, enablers, architecture, and future directions,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.814130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:03.972828Z digest=sha256:cf4d0a898f3d535595729a23e676ca89a9f38cfe304f7d47df170f01f61b9184

Observation 7723d7e0-eedc-4f20-b0f7-334c46bad108 · outbound

This paper cites Digital twin for networking: A data-driven performance modeling perspective,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital twin for networking: A data-driven performance modeling perspective,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.799811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:03.977415Z digest=sha256:20a4333b556bdf482bf1c533a5cb3389553964fbb6f55edcece5c784178ea6eb

Observation 097ddd5d-982e-4125-bdb4-04a5bab1ddba · outbound

This paper cites Mobility-aware service provisioning in edge computing via digital twin replica placements,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Mobility-aware service provisioning in edge computing via digital twin replica placements,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.786436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:03.981773Z digest=sha256:cd3145000235f54b799a96295bc09f9799e3872582a49e32e9eee1a9c747425f

Observation 4e2fff57-f489-4e49-b354-020044a48ffa · outbound

This paper cites Toward communication-efficient digital twin via ai-powered transmission and reconstruction,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Toward communication-efficient digital twin via ai-powered transmission and reconstruction,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.772807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:03.986408Z digest=sha256:28fdd19fd8f91c1f3a821f19f5eb9c870e82bcc585621829d39f312b652576ab

Observation 9923824f-6fda-411c-b6f6-70cbc70e789c · outbound

This paper cites End-to-end network sla quality assurance for c-ran: A closed-loop management method based on digital twin network,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization End-to-end network sla quality assurance for c-ran: A closed-loop management method based on digital twin network,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.758894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:03.990970Z digest=sha256:36415a57653f1ee78a2949529a6975f564264ad1be10cecc93c59ffbc4d56276

Observation 541a7a06-384d-4ec0-8fbe-fda87721b93f · outbound

This paper cites Digital-twin-enabled intelligent dis- tributed clock synchronization in industrial IoT systems,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital-twin-enabled intelligent dis- tributed clock synchronization in industrial IoT systems,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.730950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.000134Z digest=sha256:3c2d38807124db60430d92875089c5d091e7d3192e4840927669a1ea08ccddab

Observation ff75700d-c476-4a7e-8637-70c2f28a2441 · outbound

This paper cites Digital twin-empowered network planning for multi-tier computing,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital twin-empowered network planning for multi-tier computing,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.716088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.004688Z digest=sha256:e7ccda1a64547643111281ce2b7e093d7ef973f9ea47617a7e45e70c9715599b

Observation 59e2ef49-9e08-4278-8e65-0721d52a4cce · outbound

This paper cites A federated digital twin framework for uavs-based mobile scenarios,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization A federated digital twin framework for uavs-based mobile scenarios,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.702908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.008985Z digest=sha256:72ce539166b8d66e4a1d16c8fc0ec93720567bac4ccb449d5d0fc05e8ce64752

Observation 99f875d7-b4ce-4aec-b964-c6df19ba721b · outbound

This paper cites Cybertwin assisted wire- less asynchronous federated learning mechanism for edge computing,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Cybertwin assisted wire- less asynchronous federated learning mechanism for edge computing,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.689356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.013176Z digest=sha256:9f3b9094f6c8b30ec9c6dc47fb4a7e52f2a65e3e1741514cda370f1baea516dd

Observation 4952007d-3ae5-4098-b3a6-a2518209f8b0 · outbound

This paper cites Adaptive federated learning and digital twin for industrial internet of things,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Adaptive federated learning and digital twin for industrial internet of things,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.676071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.017658Z digest=sha256:e56ded5bdca04026a7aabde6b8ba01fce55571c8a9ae1982de772d85014145fd

Observation 76cc0291-29c6-4591-9ca2-37a3f7948ded · outbound

This paper cites Adaptive digital twin for vehicular edge computing and networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Adaptive digital twin for vehicular edge computing and networks,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.662207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.022162Z digest=sha256:f6a7a6706ca4b4e076e87e004c00bb8a7ca064efecb8d2eae9d5747c4618ca88

Observation 62113179-0896-4d4e-9e8a-2bdf3988e497 · outbound

This paper cites Digital twin-enhanced deep reinforcement learning for resource management in networks slicing,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital twin-enhanced deep reinforcement learning for resource management in networks slicing,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.648367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.026458Z digest=sha256:de1c143f6f70a339c640a5efef1623b87bc8e2d602bd7aa24292f2313a4c4a71

Observation 30ece679-8804-4954-9867-fcb59b0c590b · outbound

This paper cites Digital twin-driven collaborative scheduling for heterogeneous task and edge-end resource via multi-agent deep reinforcement learning,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital twin-driven collaborative scheduling for heterogeneous task and edge-end resource via multi-agent deep reinforcement learning,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.634356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.030740Z digest=sha256:0efc57c53b00670ae495f1373873ce54786b0e9ae4b29aac4e20b8ee88097b7d

Observation fb138a54-ce63-4a79-90d6-124fb8cc3399 · outbound

This paper cites Blockchain- aided digital twin offloading mechanism in space-air-ground networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Blockchain- aided digital twin offloading mechanism in space-air-ground networks,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.620495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.035001Z digest=sha256:966bfc16d005ed15421cc4eb877d998d9d9c3927ff4718d333439448d2de529a

Observation 4a7a6ec9-2bb3-4726-aed9-0759b39bf1ce · outbound

This paper cites A joint communication and computation framework for digital twin over wireless networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization A joint communication and computation framework for digital twin over wireless networks,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.606556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.039431Z digest=sha256:39a16f2a54b0934f895c56068e0af266a91d0b688c6532a484b960460fd7ca6d

Observation 5fc1a886-aa9f-42bf-b850-71bdf7c544a4 · outbound

This paper cites A dynamic hierarchical framework for IoT-assisted digital twin synchronization in the metaverse,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization A dynamic hierarchical framework for IoT-assisted digital twin synchronization in the metaverse,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.593561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.043710Z digest=sha256:6a28a20c552da7f190085f7a9522afcc7d23925fbf965b3e485ab8f5aecc4471

Observation c69e1fc1-6696-4d0e-b379-ac88224021e9 · outbound

This paper cites Optimizing synchronization delay for digital twin over wireless networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Optimizing synchronization delay for digital twin over wireless networks,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.580421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.048054Z digest=sha256:b5408250d930e18dc087c9f213dd38989b9e545a05737b40142dec22af0ce483

Observation 55251c47-ee06-496b-96f8-8fde3b846251 · outbound

This paper cites Uav- assisted digital twin synchronization with tiny machine learning-based semantic communications,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Uav- assisted digital twin synchronization with tiny machine learning-based semantic communications,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.567036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.052403Z digest=sha256:fd8c1e37b07924f3aa6b68a1491b3139ee16be78fe15fc29beac29bf16a09d3d

Observation c479ae7a-d3ac-422e-a64e-c48f7cb13dbc · outbound

This paper cites Data synchronization in vehicular digital twin network: A game theoretic approach,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Data synchronization in vehicular digital twin network: A game theoretic approach,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.744869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.056781Z digest=sha256:b1c408a2623d03dcf6b9e609204d1c5fc5b3143246d5fb80b10cb539af0b9d0f

Observation 8b01b690-da9e-4e10-aab3-d03d2dc7c1d8 · outbound

This paper cites Deep reinforcement learning for downlink scheduling in 5G and beyond networks: A review,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Deep reinforcement learning for downlink scheduling in 5G and beyond networks: A review,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.553310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.060929Z digest=sha256:2f6a248393a3172ad24aa82e35554073524956e2b562e5b5002b96f9ce1bcbc8

Observation c41a75fc-dfb1-44df-b0b4-cf38ee52a4a1 · outbound

This paper cites Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G hetnet,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G hetnet,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.539625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.065282Z digest=sha256:48b9c6cfa2b2f7bcd232f5076c24095640c0d58c78cd9720ae6528b8a5f0662c

Observation 76800064-cab1-429b-8a1c-c9aed5e615bf · outbound

This paper cites Uplink power control framework based on reinforcement learning for 5G networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Uplink power control framework based on reinforcement learning for 5G networks,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.524665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.069786Z digest=sha256:1c307a48cb60d332c3d8778370e645b03770f9f97d109e5fc28814baa02014f9

Observation d215e0b8-459a-42af-8ea7-38b7b5bf00f3 · outbound

This paper cites Deep reinforcement learning for resource demand prediction and virtual function network migration in digital twin network,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Deep reinforcement learning for resource demand prediction and virtual function network migration in digital twin network,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.510864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.074030Z digest=sha256:9fab623f51146d56b7f3cc30e0dcd7032535d1bbd33e3f561f011e457c41677b

Observation d4f74a4a-a55c-40bf-aad1-e6fb7f67d072 · outbound

This paper cites Adaptive edge association for wireless digital twin networks in 6G,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Adaptive edge association for wireless digital twin networks in 6G,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.496900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.078351Z digest=sha256:f148da8b9ad959ca87e7449eb8f488159f482c2a9b29ab4dc8ec3e725839a17a

Observation b8ec18f6-5f33-4c05-b287-0a289200d858 · outbound

This paper cites Adaptive digital twin and multi- agent deep reinforcement learning for vehicular edge computing and networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Adaptive digital twin and multi- agent deep reinforcement learning for vehicular edge computing and networks,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.483453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.082660Z digest=sha256:25e6413f2ae0dbc9a74dec39d0992ea2afadb35d40d355ec0c969970708f9ab2

Observation 579a57bf-5bf1-4d27-b2c0-9b48fc77a25e · outbound

This paper cites Digital-twin- assisted task assignment in multi-uav systems: A deep reinforcement learning approach,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Digital-twin- assisted task assignment in multi-uav systems: A deep reinforcement learning approach,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.469751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.088166Z digest=sha256:56f2c5c39559f060686755fce7ea81f7ed3d329815fa1404bf96a7b43240047a

Observation 61e24d54-6ea0-4682-b7eb-97ac108f204e · outbound

This paper cites 3GPP TS 23.501: Sys- tem Architecture for the 5G System (5GS),.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization 3GPP TS 23.501: Sys- tem Architecture for the 5G System (5GS),

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.455366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.092487Z digest=sha256:096a6214fa3aefb6da7b48440c6b28236cc851c1b7d3c3bd688b3dd3a6f84fed

Observation 4aa5d1f1-f561-492c-aee8-fa4fc64ed9b8 · outbound

This paper cites A general upper bound to evaluate packet error rate over quasi-static fading channels,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization A general upper bound to evaluate packet error rate over quasi-static fading channels,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.440901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.096704Z digest=sha256:35cf1df150ed892cf2d5662f0ac89b4093db78a9a6676b5b49250dfdf4cf6c97

Observation b39a293e-af9d-4a4b-8f48-789832ca19e5 · outbound

This paper cites The logarithmic nature of qoe and the role of the weber-fechner law in QoE assessment,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization The logarithmic nature of qoe and the role of the weber-fechner law in QoE assessment,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.426449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.100838Z digest=sha256:52785eb87441df953b3a1066c31bf36ced94972ddaf905a65091304f3f9829f0

Observation cb8a5285-877d-4b3a-8bcc-4ca453cf44ee · outbound

This paper cites Multistate constraint multipath-assisted positioning and mismatch alleviation,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Multistate constraint multipath-assisted positioning and mismatch alleviation,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.411320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.105295Z digest=sha256:89a601a19015c77ccd2ce6679f19f3800259f985734a79fba706867caa235c3f

Observation 0bdcfe22-93a2-4413-8462-37468c276a04 · outbound

This paper cites Altman, Constrained Markov decision processes.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Altman, Constrained Markov decision processes

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.397062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.109721Z digest=sha256:8a1f498cca04507361e79d2810b03c66a5fa342259538ca2e6cbdf94c5d7b4b8

Observation 9895103a-5b49-4962-b165-27e1c432aecb · outbound

This paper cites Performance optimization for digital internet-of-things twins over wireless networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Performance optimization for digital internet-of-things twins over wireless networks,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.382896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.114032Z digest=sha256:3f7b6a4e124c32cc1ea640c4045c43208f34fad879b1f66c701d24c55185081b

Observation a6493f11-cd71-40f8-a630-a775af76ae71 · outbound

This paper cites Toward enhanced reinforcement learning-based resource management via dig- ital twin: Opportunities, applications, and challenges,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Toward enhanced reinforcement learning-based resource management via dig- ital twin: Opportunities, applications, and challenges,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.368710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.118444Z digest=sha256:a9d1037c8dbc4bc30342c8751263485e5218744d5d6e1e6274c1e9e016eb32de

Observation e37cc7a5-09f5-448c-b436-1ae03ee72c1c · outbound

This paper cites Continual Reinforcement Learning with Multi-Timescale Replay.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Continual Reinforcement Learning with Multi-Timescale Replay

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T20:37:04.123256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:37:04.123256Z digest=sha256:789c5af4d0f134ab4cc32fc115ec18b81010ac3d581fda8eb64622ce59fd021e

Observation e38fad77-df4c-4aef-baa5-1a414b832de5 · outbound

This paper cites Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-10T20:37:04.127976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:37:04.127976Z digest=sha256:7b9d7bef86b0fcb1586bc9d01d5e66f0fdf0cf81f9c1d3ef2655525a6db6f9d1

Observation 2886e0ef-f333-4c0d-8bb9-a5c263072bac · outbound

This paper cites The age of incorrect in- formation: an enabler of semantics-empowered communication,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization The age of incorrect in- formation: an enabler of semantics-empowered communication,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.354418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.132731Z digest=sha256:520d5b37d703cd02190803d87ce813ea7ced88ce4c4b3b24a072bc787f916971

Observation 809f38a2-ebd0-498c-b7ef-f3c5ad976829 · outbound

This paper cites Last-iterate convergent policy gradient primal-dual methods for constrained MDPs,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Last-iterate convergent policy gradient primal-dual methods for constrained MDPs,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.338637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.137075Z digest=sha256:5095592071bb4c8550ab2994af549cfb3c0d09966adcb50194c43b213f892772

Observation cd9dffde-501c-4e57-bcaa-b6a38bfb97ab · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.322276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.141735Z digest=sha256:767c01e96ba9e0dc6f38147948f062c7e971db501c6c536de999f7acd17c8f92

Observation 2204aa8f-4b50-4e1a-9707-4feab140b14f · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Soft Actor-Critic Algorithms and Applications

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-10T20:37:04.146276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:37:04.146276Z digest=sha256:be00326e243221e5076ce1d654b0099664a833ddf97c0091389a201c5df831d3

Observation 9e69c8a7-d665-4853-95ad-197e63b3fc1e · outbound

This paper cites Invariant Risk Minimization.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Invariant Risk Minimization

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T20:37:04.151013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:37:04.151013Z digest=sha256:7255a381b9a2ee8a577bf26dda41fcde2abad58b344bab7a247323e015f3dad0

Observation 54752ef1-c0e3-4a52-a11a-a29b33144ac0 · outbound

This paper cites Semantic- aware remote state estimation in digital twin with minimizing age of incorrect information,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Semantic- aware remote state estimation in digital twin with minimizing age of incorrect information,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.307673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.155592Z digest=sha256:669309d3d044ef951bf21565a7af1d9e2bbfb82e77951e4c0bea8f7760838c1d

Observation 5110d33d-8c6f-402f-9033-c270dd3d375b · outbound

This paper cites Federated learning based audio semantic communication over wireless networks,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Federated learning based audio semantic communication over wireless networks,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.292875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.160096Z digest=sha256:2de7d8da50a71c7c9b1e508a93c99bd6dc2452e31b0e2257422b01c38f45db34

Observation 78698985-bdec-4229-af6d-66123d58eb28 · outbound

This paper cites Intel berkeley research lab sensor data,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Intel berkeley research lab sensor data,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:37:04.278129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.164097Z digest=sha256:23e5d1046a3beadcf70e04632eb1af254771e40f720cac493134674c84dac537

Observation 47cdc717-ceee-4ffa-ae41-31b05abf341b · outbound

This paper cites Indoor received signal strength data generated from ray- tracing,.

Continual Reinforcement Learning for Digital Twin Synchronization Optimization Indoor received signal strength data generated from ray- tracing,

Reference 46

Resolution
malformed identifier
raw_fallback, observed 2026-08-10T20:37:04.263266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T20:37:04.168493Z digest=sha256:251fbd38b8d22978a51479d13f45be5c83b2a1a677c1c59559c39e745d0681e3

Pith citing papers

No inbound Pith citation observations are available.