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Paper Citation Record · LEDGER

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication

As of 21 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2506.09855.

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

pith.paper-citation-record.v1
2506.09855 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:45:46.057515Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

19 of 19 outbound references displayed

  • verified exact1
  • verified fuzzy12
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e032ed03-94f1-40d7-980e-96f5a60663af · outbound

This paper cites Cooperative Hierarchical Deep Reinforcement Learning Based Joint Sleep and Power Control in RIS-Aided Energy-Efficient RAN,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Cooperative Hierarchical Deep Reinforcement Learning Based Joint Sleep and Power Control in RIS-Aided Energy-Efficient RAN,

Reference 1

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raw_fallback, observed 2026-08-07T04:45:46.226058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation f0580f5c-3957-496e-8058-b86fc508bf5a · outbound

This paper cites Channel estimation for RIS-aided multiuser millimeter-wave sys- tems,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Channel estimation for RIS-aided multiuser millimeter-wave sys- tems,

Reference 2

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raw_fallback, observed 2026-08-07T04:45:46.218494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation ad04f132-79fe-422a-91f8-b99b127b2f64 · outbound

This paper cites Digital Twin Aided RIS Communi- cation: Robust Beamforming and Interference Management,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Digital Twin Aided RIS Communi- cation: Robust Beamforming and Interference Management,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.016002Z digest=sha256:1bcca5a999b8604e1aa51cd31820215b55c7006aa0b373c6e446ab1e6600e4fb

Observation 5238f3ff-ce3b-48c6-b8d7-790d0a2c9162 · outbound

This paper cites Sum Rate Enhancement using Machine Learning for Semi-Self Sensing Hybrid RIS-Enabled ISAC in THz Bands.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Sum Rate Enhancement using Machine Learning for Semi-Self Sensing Hybrid RIS-Enabled ISAC in THz Bands

Reference 4

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verified exact
local_arxiv, observed 2026-08-07T04:45:46.132641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.018628Z digest=sha256:20c3dc6fdedf5bd9fea2c20d33937cd98ef7cd63b30401d50526226222cdc76c

Observation 5f52d1a0-152c-42cf-82d9-8bb9027abb56 · outbound

This paper cites Hierar- chical Codebook-Based Beam Training for RIS-Assisted mmWave Communication Systems,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Hierar- chical Codebook-Based Beam Training for RIS-Assisted mmWave Communication Systems,

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-07T04:45:46.204198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.021710Z digest=sha256:361ee95fd6fd6a7f305054beae79363f4806d1d0f5c3b7c18cbee7e1ba85e6c9

Observation 936aecbe-ed79-4069-ad41-f8e697a96012 · outbound

This paper cites DRL-based Joint Beamforming and BS-RIS-UE Association Design for RIS- Assisted mmWave Networks,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication DRL-based Joint Beamforming and BS-RIS-UE Association Design for RIS- Assisted mmWave Networks,

Reference 6

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raw_fallback, observed 2026-08-07T04:45:46.196942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.024439Z digest=sha256:f38ed7ad5bf36274474ddabdf00984310c63ee663f596110f64a6d32138c7a89

Observation 9e605d30-5c24-4957-a049-6fb2ee0ceb03 · outbound

This paper cites Multi- Agent Deep Reinforcement Learning for Beam Codebook Design in RIS-Aided Systems,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Multi- Agent Deep Reinforcement Learning for Beam Codebook Design in RIS-Aided Systems,

Reference 7

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raw_fallback, observed 2026-08-07T04:45:46.189786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.027233Z digest=sha256:bb9c4c4e7c73ae07c6797a2e58159474f21b5f64fe2dc12d83d6ac3156988da5

Observation 4bad716a-32e5-42b9-ab2a-1ef3e60a6442 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 8

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no resolver link, observed 2026-08-07T04:45:46.029561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:45:46.029561Z digest=sha256:e87bb2ee2a0106c6d5af648f0baca4a3c76bddf4edcde543b1edf1ece8bd60e5

Observation 07105355-dade-4608-b6e8-8b5c2f14b5b5 · outbound

This paper cites Large Wireless Model (LWM): A Foundation Model for Wireless Channels.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Large Wireless Model (LWM): A Foundation Model for Wireless Channels

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1adeff8f-0508-4e18-9a9c-eac611a941e1 · outbound

This paper cites Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels,

Reference 10

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raw_fallback, observed 2026-08-07T04:45:46.181713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.035177Z digest=sha256:d97188e0af228d2abe4915782a54ba90430a58d585eae1c2b87d922f61da69d4

Observation 4fdf3f2f-8d8d-4e6c-9fbe-030ee38e1a60 · outbound

This paper cites Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:45:46.037524Z digest=sha256:4c4ac6fbb405694a5ad32dc44564efc1783fce6e64b18c166d6a5ab9f03c5a09

Observation 32ef94da-25e2-41ff-a677-5f5245401a66 · outbound

This paper cites RIS-Aided Cell-Free Massive MIMO System: Joint Design of Transmit Beamforming and Phase Shifts,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication RIS-Aided Cell-Free Massive MIMO System: Joint Design of Transmit Beamforming and Phase Shifts,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:45:46.173672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.040172Z digest=sha256:4d2b90993d3db0665bcaad72258fc1b92df54f5d57020a4e2bad7bef07d52e39

Observation fce88eaa-a5af-47c9-8f1e-5f411f41f566 · outbound

This paper cites Enabling Efficient Blockage-Aware Handover in RIS-Assisted mmWave Cel- lular Networks,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Enabling Efficient Blockage-Aware Handover in RIS-Assisted mmWave Cel- lular Networks,

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T04:45:46.165830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.042692Z digest=sha256:f6b47d224dc6f840951138ffc382310eb07b6dec495b1b0acb6607b012f84368

Observation db8b7eef-589c-4198-8825-039390936ae6 · outbound

This paper cites Joint Cross Layer Radio Downlink Beamforming and RIS Configuration via Deep Reinforcement Learning in RIS-Aided MISO Video Commu- nications,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Joint Cross Layer Radio Downlink Beamforming and RIS Configuration via Deep Reinforcement Learning in RIS-Aided MISO Video Commu- nications,

Reference 14

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raw_fallback, observed 2026-08-07T04:45:46.158026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.045019Z digest=sha256:06de9bc28ba0c8af42f7fd6b79e400ccbbf3c576cd3335758cd0323dfd8c9725

Observation 4fe78afa-022d-47ff-ac8c-4831efd3a1c0 · outbound

This paper cites Deep reinforcement learning for energy-efficient networking with recon- figurable intelligent surfaces,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Deep reinforcement learning for energy-efficient networking with recon- figurable intelligent surfaces,

Reference 15

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raw_fallback, observed 2026-08-07T04:45:46.149977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.047358Z digest=sha256:abb8a6e5922fbd75399ed5a77da5848ea06a13c068898a77113c79ae70717ed3

Observation fb1507b8-6927-406f-a181-df168991b4b7 · outbound

This paper cites WiFo: Wireless Foundation Model for Channel Prediction.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication WiFo: Wireless Foundation Model for Channel Prediction

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:45:46.049675Z digest=sha256:4dead77c45077eaf702b1682fbddfe60f0399c85dd5c77c89bf149c0a7c9b142

Observation ecd91ec7-df22-4977-a31e-1ddc9e060f5d · outbound

This paper cites Multi-Modal Transformer and Reinforcement Learning- Based Beam Management,.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Multi-Modal Transformer and Reinforcement Learning- Based Beam Management,

Reference 17

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raw_fallback, observed 2026-08-07T04:45:46.141923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-07T04:45:46.052392Z digest=sha256:fe8df2e444398f13e3d4bf1e14933c98e9c35cfaecf85a9f3ba6164bbd4560b9

Observation 886bd6ef-feb1-4d40-a650-5a08c220fd74 · outbound

This paper cites Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:45:46.054736Z digest=sha256:e97ae2b9e86dc66a5dfdc7a917ba46f88e31720098ba4f02aaedaa03e27c77b6

Observation f82ca02e-229a-40f9-965d-5f946bf7cc16 · outbound

This paper cites DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications.

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.