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Paper Citation Record · LEDGER

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G

As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2505.01841.

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

pith.paper-citation-record.v1
2505.01841 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:56.212310Z

measured 43 of 43 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

43 of 43 outbound references displayed

  • verified exact0
  • verified fuzzy27
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e936964f-0f1e-4670-9602-b25e433c054f · outbound

This paper cites Management and Orchestration; Intent Driven Man- agement Services for Mobile Networks,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Management and Orchestration; Intent Driven Man- agement Services for Mobile Networks,

Reference 1

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

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Observation 59afe1c8-33c9-454a-80d5-e843a64ae483 · outbound

This paper cites Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks,

Reference 2

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

source=pdf_text observed=2026-08-16T04:12:56.013293Z digest=sha256:51efd7c8da50b08c0990155d1ca07db047078d01bc851c1c836fe99f5b96bc30

Observation 913b0edd-b48a-4b06-bcc7-2b46a361e775 · outbound

This paper cites SMART Intent-Driven Network Management,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G SMART Intent-Driven Network Management,

Reference 3

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

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Observation 595a2ae7-8b75-4afb-900b-69408d64188d · outbound

This paper cites LLM-Based Policy Generation for Intent-Based Management of Applications,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G LLM-Based Policy Generation for Intent-Based Management of Applications,

Reference 4

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

source=pdf_text observed=2026-08-16T04:12:56.023990Z digest=sha256:9cfa21161c31abad6b715a1cff18dd1fba5e4b70b25d013cfe39fa3107e6f430

Observation 9bbeb8e3-e55c-42aa-8e3d-8dedfe53e14f · outbound

This paper cites NLP Powered Intent Based Network Management for Private 5G Networks,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G NLP Powered Intent Based Network Management for Private 5G Networks,

Reference 5

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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=2026-08-16T04:12:56.029086Z digest=sha256:cc873e58cf96de715d878efd5b0802a12762a188dcb07cb694a37c4069b24a6f

Observation 5f697dee-447f-409a-a042-d8de123af6b5 · outbound

This paper cites Network Meets ChatGPT: Intent Autonomous Man- agement, Control and Operation,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Network Meets ChatGPT: Intent Autonomous Man- agement, Control and Operation,

Reference 6

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

source=pdf_text observed=2026-08-16T04:12:56.034253Z digest=sha256:181c1847c5aee6672ed4d02c03cbe2ae51b7083ea3a75e95c117ad77b3f708cf

Observation 62e6190e-83f5-4eb2-bf4f-fd4e78e6253d · outbound

This paper cites ColO- RAN: Developing Machine Learning-Based xApps for Open RAN Closed-Loop Control on Programmable Experimental Platforms,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G ColO- RAN: Developing Machine Learning-Based xApps for Open RAN Closed-Loop Control on Programmable Experimental Platforms,

Reference 7

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

source=pdf_text observed=2026-08-16T04:12:56.039487Z digest=sha256:3bc2f3552bc6b7ea60916fea088cee9bc548c54b9dcf32f9a518aa21bdc2d17c

Observation d6fbd46c-73d9-437d-9edb-08c446175f1b · outbound

This paper cites Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning,

Reference 8

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

source=pdf_text observed=2026-08-16T04:12:56.044038Z digest=sha256:277ecb4c6a4b66ddc112fbbaf99435381ac839d4c09611c3580062c47620c4f3

Observation 8452af1f-b288-4291-b448-37f44b4fcb0e · outbound

This paper cites LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-16T04:12:56.048533Z digest=sha256:135be4ed309037183711dd9a1fe665bb679b6222eaedacec5b3163e0df27dcc5

Observation f0d4354d-32bb-4523-83b8-9c5fea7dda0e · outbound

This paper cites Attention Is All You Need.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Attention Is All You Need

Reference 10

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source=pdf_text observed=2026-08-16T04:12:56.053333Z digest=sha256:0ab5f5ababd2b77b54f86d7d9ca550cf880d9721f247c7c98dcc0772c05a44f7

Observation b1e9bf01-f337-40bb-a351-b3d901f46468 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G QLoRA: Efficient Finetuning of Quantized LLMs

Reference 11

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source=pdf_text observed=2026-08-16T04:12:56.058098Z digest=sha256:5ee9591b4d21239d594acd04de7d4f2ddc031d4adfb25f9cbdfd17f460ac2344

Observation c9443455-4d14-4f0d-8200-c8f148768e4e · outbound

This paper cites Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 13

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source=pdf_text observed=2026-08-16T04:12:56.067525Z digest=sha256:02fb1389948081785d9102357cc07b2fe1282a5b237019bb24db8423f144e19d

Observation 331febfd-83f2-46fc-9637-3cb1ec2cb863 · outbound

This paper cites Decision Transformer: Reinforcement Learning via Sequence Modeling.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Decision Transformer: Reinforcement Learning via Sequence Modeling

Reference 14

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source=pdf_text observed=2026-08-16T04:12:56.072139Z digest=sha256:d6d6ed0f0734edddf975e4ef6a6b907c15a7dbbd84d703285a053005e1713f0c

Observation c66715f9-65f2-4e83-9b8c-df4e7cffdcf8 · outbound

This paper cites FeUdal Networks for Hierarchical Reinforcement Learning.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G FeUdal Networks for Hierarchical Reinforcement Learning

Reference 15

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source=pdf_text observed=2026-08-16T04:12:56.077172Z digest=sha256:6b82ba01b07e1969a53fd3e99eceeacd888a7415d7dd35075fe1bfa256177735

Observation 720888df-414a-4ac9-a90b-ad048c90a801 · outbound

This paper cites Intent Assurance using LLMs guided by Intent Drift,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Intent Assurance using LLMs guided by Intent Drift,

Reference 16

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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=2026-08-16T04:12:56.081498Z digest=sha256:4c1c51b2b7943605c083d7fc048c7e4ad1122ef5ed73000af118fdc82fb08aae

Observation 3a52eb91-555d-4be0-83a7-04866d84533c · outbound

This paper cites OrchestRAN: Orches- trating Network Intelligence in the Open RAN,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G OrchestRAN: Orches- trating Network Intelligence in the Open RAN,

Reference 17

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

source=pdf_text observed=2026-08-16T04:12:56.085729Z digest=sha256:b46c60a0415295f4db66f26af44048a0f59963487f895a34d7f9dc1bea3c1a09

Observation 62c525aa-c356-4eab-9d28-d849ee4afdd3 · outbound

This paper cites Op- timizing Energy Saving for Wireless Networks Via Offline Decision Transformer,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Op- timizing Energy Saving for Wireless Networks Via Offline Decision Transformer,

Reference 18

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Observation e36dcb0c-c879-48c2-9e9d-7546ca5a2476 · outbound

This paper cites Decision Transformers for Wireless Communications: A New Paradigm of Resource Management.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Decision Transformers for Wireless Communications: A New Paradigm of Resource Management

Reference 19

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source=pdf_text observed=2026-08-16T04:12:56.094449Z digest=sha256:e66e1b4e04e38a119f62eabbbc5f982d88af79e54943049ff4e7b44f7cf96895

Observation df4c5e70-3e1c-48d2-9094-a46f2605c619 · outbound

This paper cites Study on channel model for frequencies from 0.5 to 100 ghz,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Study on channel model for frequencies from 0.5 to 100 ghz,

Reference 20

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

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Observation 9322968e-8670-4082-870d-9b5e8eeb72a9 · outbound

This paper cites Deep Learning Predictive Band Switching in Wireless Networks,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Deep Learning Predictive Band Switching in Wireless Networks,

Reference 21

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

source=pdf_text observed=2026-08-16T04:12:56.103771Z digest=sha256:f17f6a38389de445fb2959e8d0b1fe1828ee986fe6273f154763ada655e82fba

Observation 7f29ec94-5f24-41b5-9ecb-3724e34f41c8 · outbound

This paper cites Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Reference 22

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source=pdf_text observed=2026-08-16T04:12:56.108147Z digest=sha256:7f24ed18ee9db3bc881bf3b2c95478774a52c1e0e959456925b276183b52c77e

Observation 700f1749-b27b-445f-9c5a-0f56af6e975c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G LoRA: Low-Rank Adaptation of Large Language Models

Reference 23

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source=pdf_text observed=2026-08-16T04:12:56.112520Z digest=sha256:a2e874cccc7a143e72e694d0ea741a7ebfafe7b35796c7b0e7d3e7204f0ac2d9

Observation 36870d0a-da2e-4229-9327-b44466665569 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 24

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source=pdf_text observed=2026-08-16T04:12:56.116809Z digest=sha256:1649a2514b6b32ab463dcbb618919bd5d7a90c0288013aafe7f3b2f8cb8af709

Observation 3c07011b-18f0-4f40-9c7e-917a30c37b69 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G LLaMA: Open and Efficient Foundation Language Models

Reference 25

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source=pdf_text observed=2026-08-16T04:12:56.120960Z digest=sha256:a45a64f4dd385ab616e92e75f8853b2b45ba83e5bf1c66596c434c82353399bc

Observation 0fa2c8e4-932e-405c-ab0e-b0c050601e0e · outbound

This paper cites Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN

Reference 26

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source=pdf_text observed=2026-08-16T04:12:56.125907Z digest=sha256:29c408808908da41027a66bfc5a23d5924c351e0b90cdd2adf04a20a0fca510f

Observation 1d19fc04-de27-4b42-9948-7b460535dd07 · outbound

This paper cites Traffic Steering for 5G Multi-RAT Deployments Using Deep Reinforcement Learning,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Traffic Steering for 5G Multi-RAT Deployments Using Deep Reinforcement Learning,

Reference 27

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

source=pdf_text observed=2026-08-16T04:12:56.130943Z digest=sha256:cfca24327d8a1c3d8261ea6d3fd9c90002ce9b51e8eff4bd94aa4ce14f903ef7

Observation 36f862b0-bafb-46f1-83f2-b186f6a3b8ff · outbound

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

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Cooperative Hierarchical Deep Reinforcement Learning Based Joint Sleep and Power Control in RIS-Aided Energy-Efficient RAN,

Reference 28

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

source=pdf_text observed=2026-08-16T04:12:56.135306Z digest=sha256:ee0cb2020a0d49d6598b1bbe076e65e53ad1435e0f0c7b6dc4a93f0ba3b63fec

Observation 0ed818e6-fb7b-4334-a8c5-1255152627be · outbound

This paper cites Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN),.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN),

Reference 29

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

source=pdf_text observed=2026-08-16T04:12:56.139631Z digest=sha256:bb3dff6d9e49527f1cba67cd4ac81a26da83f506f84edbb665f14403c30172e3

Observation 103acacb-8835-487b-a8cc-12d7f879d1ed · outbound

This paper cites Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination,

Reference 30

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

source=pdf_text observed=2026-08-16T04:12:56.143893Z digest=sha256:7812b37a2c01d94440addfa28406d02cbfe422ba83c6f07f2ff0fbad38e747d8

Observation 79bcf6f1-ba12-4e12-895e-448c6a429b5e · outbound

This paper cites Handover Decision Making for Dense HetNets: A Reinforcement Learning Approach,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Handover Decision Making for Dense HetNets: A Reinforcement Learning Approach,

Reference 31

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

source=pdf_text observed=2026-08-16T04:12:56.148341Z digest=sha256:4c087d9ac6e512f8c9f85760c1ba2b8b2bce7f43e6f55358124e6460459fb628

Observation a3ea49a4-dbdf-4b88-b073-45ab5f1c3111 · outbound

This paper cites A Survey on 5G Usage Scenarios and Traffic Models,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G A Survey on 5G Usage Scenarios and Traffic Models,

Reference 32

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

source=pdf_text observed=2026-08-16T04:12:56.152748Z digest=sha256:5cf9e3f701de11790b843f29d0ac1265d041e0d830028baad33c2771f1123179

Observation faadc636-119f-41d2-86b4-7d5b1b50b553 · outbound

This paper cites Which Statistical Distribution Best Characterizes Modern Cellular Traffic and What Factors Could Predict Its Spatiotemporal Variability?.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Which Statistical Distribution Best Characterizes Modern Cellular Traffic and What Factors Could Predict Its Spatiotemporal Variability?

Reference 33

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

source=pdf_text observed=2026-08-16T04:12:56.157353Z digest=sha256:99221316be281c44c528b76660227a1b70e5083046810ea34370b8c23fdcc45e

Observation d4ce92a3-f9cb-4227-82da-920c71471ac6 · outbound

This paper cites AI-Enabled Radio Resource Allo- cation in 5G for URLLC and eMBB Users,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G AI-Enabled Radio Resource Allo- cation in 5G for URLLC and eMBB Users,

Reference 34

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raw_fallback, observed 2026-08-16T04:12:56.562179Z

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=2026-08-16T04:12:56.167622Z digest=sha256:1a963ba37a3abb91144e312edc738b8e9952b0e27a19099fa5f785d235d486a7

Observation 3cec1752-f661-4351-af64-b6d1f84cde71 · outbound

This paper cites Dahlman, S.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Dahlman, S

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:56.547081Z

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=2026-08-16T04:12:56.172406Z digest=sha256:ac605d3b1288d3f559177824d561321c90815543e06c4d7c0f45b0ac82599e23

Observation 58eae7fc-89cc-49be-b55c-ab7676db067c · outbound

This paper cites Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:56.533327Z

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=2026-08-16T04:12:56.176584Z digest=sha256:fb27f0f366ae420b03185d097d39f98306b70703c69ebfc9ca4b3479d876aa4e

Observation e24a1043-7070-4f89-96f9-76112484c530 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G BERTScore: Evaluating Text Generation with BERT

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T04:12:56.180553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:56.180553Z digest=sha256:7eb4a7717017c1c953234cb8ff16bb239e89478ca35d881ec07102c87ae33d86

Observation 814aea8f-ac95-4888-a641-57f82b42b38c · outbound

This paper cites Meteor: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Meteor: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:56.517444Z

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=2026-08-16T04:12:56.185193Z digest=sha256:ce791c4eeda1828661ec437b3441fd40f9c734ca3e0683ea4611616854fd86cd

Observation 5ee38793-6654-4506-996a-a8eaa7009770 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T04:12:56.189697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:56.189697Z digest=sha256:b8dfa947c89313dd95473e49663fb9ed3658d58694412a015662de8e170d062e

Observation b3f82be8-0002-41d8-836c-d1dac0728e14 · outbound

This paper cites Mobile Traffic Prediction from Raw Data Using LSTM Networks,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Mobile Traffic Prediction from Raw Data Using LSTM Networks,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:56.501346Z

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=2026-08-16T04:12:56.194193Z digest=sha256:b88a91a63eb3e0b9e6d4fc7aad0d9d485b473c619c6caf99223a8e0abd9b4799

Observation 8f5ab90f-0672-4116-9cf8-2ccbb66594f8 · outbound

This paper cites Language Models are Unsupervised Multitask Learners,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Language Models are Unsupervised Multitask Learners,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:56.486603Z

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=2026-08-16T04:12:56.198889Z digest=sha256:4196f2aa17af9b8d50542c09f61c57ed7294be622f38e43afcc4a9543fdb603a

Observation 0a4a5e8e-9d57-416d-9133-70fa4f94f6c3 · outbound

This paper cites an unresolved cited work.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-16T04:12:56.471365Z

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=2026-08-16T04:12:56.203081Z digest=sha256:5b9c75aeb47c2253ba1b79303ca168dc213d66540acbfa9343f502214c69e468

Observation 3da6a77a-ef16-4b6d-bfac-b270e60e184e · outbound

This paper cites an unresolved cited work.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-16T04:12:56.457451Z

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=2026-08-16T04:12:56.207877Z digest=sha256:b5941d8404b1a641f9710169aeeb820d83a8f34ded338b92272aff7da98528b0

Observation 89eb9be9-83d9-468e-b60d-e521e7ab14e6 · outbound

This paper cites Increase throughput by 10%,.

Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G Increase throughput by 10%,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:12:56.443123Z

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=2026-08-16T04:12:56.212310Z digest=sha256:d13e740dbc91f6826564be744b2e10f1bf5a926e2504219280da58223512713b

Pith citing papers

No inbound Pith citation observations are available.