Paper Citation Record · LEDGER

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 5 inbound Pith citation observations for arXiv:2501.17888.

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

pith.paper-citation-record.v1
2501.17888 v3

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T10:57:10.658509Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:18:16.585754Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:09:46.513980Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy32
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 356409a4-5df6-47c1-bbf2-8f4db897af3b · outbound

This paper cites Intent-aware radio re- source scheduling in a ran slicing scenario using reinforcement learning,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Intent-aware radio re- source scheduling in a ran slicing scenario using reinforcement learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.318177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7069e245-9f73-4fa0-9eed-15244206ab58 · outbound

This paper cites Gnn-based power allocation and user association in digital twin network for the terahertz band,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Gnn-based power allocation and user association in digital twin network for the terahertz band,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.302938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8cb14f5d-bdd6-4e77-815d-7b3556b6a38e · outbound

This paper cites Spectral and energy efficiency analysis for cognitive radio networks,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Spectral and energy efficiency analysis for cognitive radio networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.286691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 53c07345-b981-4e4b-925e-ae87e5c703ce · outbound

This paper cites Learning tempo- ral–spectral feature fusion representation for radio signal classification,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Learning tempo- ral–spectral feature fusion representation for radio signal classification,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.270808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 79c2da11-1e5e-49a7-9932-7d7d78beac50 · outbound

This paper cites Exploring llm- based multi-agent situation awareness for zero-trust space-air-ground integrated network,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Exploring llm- based multi-agent situation awareness for zero-trust space-air-ground integrated network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.255156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d457cee8-6be2-49e8-a78e-ad2154e02039 · outbound

This paper cites Toward intelligent communications: Large model empowered semantic communications,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Toward intelligent communications: Large model empowered semantic communications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.239073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6b7923c5-ad58-4382-af23-73fd4b805ee0 · outbound

This paper cites Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Adapting Foundation Models for Information Synthesis of Wireless Communication Specifications

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.484604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 514b81e1-5815-470e-b550-06f393d73ed9 · outbound

This paper cites Wirelessllm: Empowering large language models towards wireless intelligence,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Wirelessllm: Empowering large language models towards wireless intelligence,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.224177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76e84bd9-6fe1-43a9-90f7-3867044c4f58 · outbound

This paper cites LLM4WM: Adapting LLM for Wireless Multi-Tasking.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings LLM4WM: Adapting LLM for Wireless Multi-Tasking

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.494549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44dddf71-50e7-4296-8ee3-d8d090a6c7f7 · outbound

This paper cites WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.499376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0db5930-7dfa-4acc-8ed9-a599ad749898 · outbound

This paper cites Time-LLM: Time series forecasting by reprogramming large language models,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Time-LLM: Time series forecasting by reprogramming large language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.208952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4d806e5a-14f2-4887-bd04-e61e361c878e · outbound

This paper cites TEMPO: Prompt-based generative pre-trained transformer for time series forecasting,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings TEMPO: Prompt-based generative pre-trained transformer for time series forecasting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.193487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b900753c-1ce6-4875-9cae-e8bff7d1d669 · outbound

This paper cites Inception transformer,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Inception transformer,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.178236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2350e866-6794-4a81-a07a-fd25471181b6 · outbound

This paper cites Exploring frequency-inspired optimization in transformer for efficient single image super-resolution,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Exploring frequency-inspired optimization in transformer for efficient single image super-resolution,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.163084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cae55922-aaf4-4e75-abab-ddfa7bf152ed · outbound

This paper cites Improving vision transformers by revisiting high-frequency components,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Improving vision transformers by revisiting high-frequency components,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.147136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b3b60c9-44aa-4dd5-ac89-55cb358defcb · outbound

This paper cites High-frequency component helps explain the generalization of convolutional neural networks,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings High-frequency component helps explain the generalization of convolutional neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.132078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d57737e6-7696-4e6a-ba58-38b96e70b85b · outbound

This paper cites Real-time radio technology and modulation classification via an lstm auto-encoder,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Real-time radio technology and modulation classification via an lstm auto-encoder,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.096402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4a01eaf5-14be-4671-bd68-f5d6a7fafe8d · outbound

This paper cites Mclhn: Towards automatic modulation classification via masked contrastive learning with hard negatives,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Mclhn: Towards automatic modulation classification via masked contrastive learning with hard negatives,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.062645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f63b2fd5-bf7f-4ac8-98a6-d4fcf2ce8565 · outbound

This paper cites Better approach for denoising eeg signals,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Better approach for denoising eeg signals,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.047123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6b1733a7-f2ca-4c7c-8702-75d941daaa67 · outbound

This paper cites A generative self-supervised framework for cognitive radio leveraging time-frequency features and attention-based fusion,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings A generative self-supervised framework for cognitive radio leveraging time-frequency features and attention-based fusion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.031175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44921b33-72b0-42ad-8a5e-d5e9480c13bc · outbound

This paper cites S2ip-llm: Semantic space informed prompt learning with llm for time series forecasting,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings S2ip-llm: Semantic space informed prompt learning with llm for time series forecasting,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.015885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c8f2842-363b-4f88-a498-a14756b72040 · outbound

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

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.566640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d632e04b-39c6-4f1f-9edb-f0c263f043dd · outbound

This paper cites Radio machine learning dataset generation with gnu radio,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Radio machine learning dataset generation with gnu radio,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.571437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69f586f5-bf93-4de3-bada-6e28559f10c2 · outbound

This paper cites Convolutional radio mod- ulation recognition networks,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Convolutional radio mod- ulation recognition networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.575952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba5b7c0e-bd3b-4da8-a4e5-8255047e734c · outbound

This paper cites Rml22: Realistic dataset generation for wireless modulation classification,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Rml22: Realistic dataset generation for wireless modulation classification,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.980020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89f4eee1-9c40-402b-a8e3-01b9633b56c6 · outbound

This paper cites Over-the-air deep learning based radio signal classification,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Over-the-air deep learning based radio signal classification,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.584872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a900424-a040-4bed-aec0-cd0d66766dd6 · outbound

This paper cites Large-scale real-world radio signal recognition with deep learning,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Large-scale real-world radio signal recognition with deep learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.955457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 96a910c1-c8c0-4c07-8f40-3907e90c46f7 · outbound

This paper cites Oracle: Optimized radio classification through convo- lutional neural networks,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Oracle: Optimized radio classification through convo- lutional neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.940203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0e2b006-fc2d-40d1-b19a-cde9f65295d2 · outbound

This paper cites A hierarchical classification head based convolutional gated deep neural network for automatic modulation classification,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings A hierarchical classification head based convolutional gated deep neural network for automatic modulation classification,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.924539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e084aee-e66e-40bf-a327-3a8dc82f9ef0 · outbound

This paper cites An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.116157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36f384b7-8aad-41c2-b529-f58b74e49000 · outbound

This paper cites A spatiotemporal multi-channel learning framework for automatic modulation recognition,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings A spatiotemporal multi-channel learning framework for automatic modulation recognition,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.909241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ccdaa8e-7c2e-4c79-a7ed-fcbc6c8fe03c · outbound

This paper cites An efficient specific emitter identification method based on complex- valued neural networks and network compression,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings An efficient specific emitter identification method based on complex- valued neural networks and network compression,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.894026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2e1238e-4b61-4f27-9076-ca860a03d0cd · outbound

This paper cites Resource- constrained specific emitter identification using end-to-end sparse feature selection,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Resource- constrained specific emitter identification using end-to-end sparse feature selection,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.878179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b0b58ce4-3686-4563-ab35-fdad16c7d37a · outbound

This paper cites Cnn-based automatic modulation classification for beyond 5g communications,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Cnn-based automatic modulation classification for beyond 5g communications,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.861144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63958384-b308-4698-a190-2c536560ea9e · outbound

This paper cites A two-stage model based on a complex-valued separate residual network for cross-domain iiot devices identification,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings A two-stage model based on a complex-valued separate residual network for cross-domain iiot devices identification,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.845774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08d7e5a4-684d-47f4-93ea-53e1e3b258dd · outbound

This paper cites A transformer- based contrastive semi-supervised learning framework for automatic modulation recognition,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings A transformer- based contrastive semi-supervised learning framework for automatic modulation recognition,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:11.078403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 744f1a8e-f601-48d1-b7bc-2398c32378b7 · outbound

This paper cites Self-contrastive learning based semi-supervised radio modulation classification,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Self-contrastive learning based semi-supervised radio modulation classification,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.830056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d2a592ff-0455-41e9-8ed4-eda1c0883416 · outbound

This paper cites What is a savitzky-golay filter?[lecture notes],.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings What is a savitzky-golay filter?[lecture notes],

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.814617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec7f205d-f17f-4b95-988b-2939574566e2 · outbound

This paper cites Dncnet: Deep radar signal denoising and recognition,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Dncnet: Deep radar signal denoising and recognition,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:57:10.798710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20e8eee1-5979-45e0-86f1-50ed05bd4422 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.649453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a647ae18-884a-4eae-8579-ee4dcd714202 · outbound

This paper cites Language models are unsupervised multitask learners,.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings Language models are unsupervised multitask learners,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.654129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ad29439-fcbb-4466-b1d6-6e1f0a518d22 · outbound

This paper cites The Llama 3 Herd of Models.

RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings The Llama 3 Herd of Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T10:57:10.658509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 70f9c577-72a2-4ae3-8662-eadfb4f57b4a · inbound

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding cites this paper.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.585754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfd86f00-e447-47dd-9af2-8511b804debd · inbound

BLAST: Blockchain-based LLM-powered Agentic Spectrum Trading cites this paper.

BLAST: Blockchain-based LLM-powered Agentic Spectrum Trading RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:02.299297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2888611-7519-4e1a-8af2-370fb71c8fce · inbound

RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation cites this paper.

RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:26:24.506288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9094631-dd4b-49a1-aa19-753d52252dac · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

Reference 207

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:09:46.515703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1bb934ce-3f8f-439e-976f-6d99244f6cae · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

Reference 207

Resolution: unresolved; no resolver link, observed 2026-08-02T10:27:18.459331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.