Paper Citation Record · LEDGER

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

As of 20 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 3 inbound Pith citation observations for arXiv:2501.05079.

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

pith.paper-citation-record.v1
2501.05079 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:23:30.693816Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:27:16.677739Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T21:35:04.144073Z

Reference resolution

49 of 49 outbound references displayed

verified exact1
verified fuzzy35
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f6c995de-7b96-4ce6-b43a-e7fc075f0e26 · outbound

This paper cites Visual Instruction Tuning,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Visual Instruction Tuning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.727822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7cadb73a-e53f-4759-b341-8bd72551ba5b · outbound

This paper cites ELEV ATOR: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization ELEV ATOR: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.606599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 19fa2abf-ebc8-4967-a235-8ec049e22fe2 · outbound

This paper cites Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.432222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7beec386-f07b-4d46-a1e9-bb87ac4e9cf3 · outbound

This paper cites RegionCLIP: Region-based Language-Image Pretraining,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization RegionCLIP: Region-based Language-Image Pretraining,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.414033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ad4dc11e-445f-4d56-bacd-4a9cfc2e4456 · outbound

This paper cites Language-driven Semantic Segmentation,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Language-driven Semantic Segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.398208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bcc32b46-d780-4a68-8aab-5d484ac6d8aa · outbound

This paper cites High- Resolution Image Synthesis with Latent Diffusion Models,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization High- Resolution Image Synthesis with Latent Diffusion Models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.377785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5bbc544d-107f-4d98-a1c9-03fe4497b531 · outbound

This paper cites GPT-4 Technical Report.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization GPT-4 Technical Report

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:29.511811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28ac21e8-5b8d-464d-bf7d-303d5344f7df · outbound

This paper cites DeBERTa: Decoding-Enhanced BERT with Disentangled Attention,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization DeBERTa: Decoding-Enhanced BERT with Disentangled Attention,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.360141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52c2863e-3b46-46ba-a55a-d2eeb40cc82c · outbound

This paper cites Meta-in-Context Learning in Large Language Models,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Meta-in-Context Learning in Large Language Models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.342899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation db9c0a42-5c06-4ba6-a8e2-defc1a8ebfd4 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Improved Baselines with Visual Instruction Tuning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.324493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 498818c5-9af4-4ca2-8be0-1107b1eaed24 · outbound

This paper cites Large Language Models: A Survey.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Large Language Models: A Survey

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54f45f3-bd6a-493c-82c6-fa759f917a24 · outbound

This paper cites GNSS Jamming Classification via CNN, Transfer Learning & the Novel Concatenation of Signal Repre- sentations,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization GNSS Jamming Classification via CNN, Transfer Learning & the Novel Concatenation of Signal Repre- sentations,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.302095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 184966fd-02ac-4c37-8373-ada802d0fe2e · outbound

This paper cites Jammer Classification in GNSS Bands via Machine Learning Algorithms,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Jammer Classification in GNSS Bands via Machine Learning Algorithms,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.284420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c23802d8-d3e7-4416-ac2e-f42b1c14ff29 · outbound

This paper cites A Real-time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization A Real-time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.149595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bf71faff-c4b3-410b-b5a3-d9a56469c795 · outbound

This paper cites GPS Interference Signal Recognition Based on Machine Learning,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization GPS Interference Signal Recognition Based on Machine Learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:32.023054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eea4a0e1-2e15-47ca-96ef-8abbd6ea0bae · outbound

This paper cites 1 GNSS Interference Identification Beyond Jammer Classification,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization 1 GNSS Interference Identification Beyond Jammer Classification,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.999760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1fb0ef0f-070a-4889-9db9-16499780b54f · outbound

This paper cites GNSS Spoofing, Jamming, and Multi- path Interference Classification Using a Maximum-Likelihood Multi-tap Multipath Estimator,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization GNSS Spoofing, Jamming, and Multi- path Interference Classification Using a Maximum-Likelihood Multi-tap Multipath Estimator,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.977438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5f551f93-484d-4586-aa71-229149bffe5b · outbound

This paper cites Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:29.722356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06ae33e0-5d01-4fb0-aaec-d98d99253af2 · outbound

This paper cites NA VISP Asks ChatGPT About the PNT Trends,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization NA VISP Asks ChatGPT About the PNT Trends,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.953662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e82d3391-256c-4bc5-977b-b764ec9dd085 · outbound

This paper cites LLM + GNSS,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization LLM + GNSS,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.933012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9cf819d-8642-4580-a267-0b15903b4f0f · outbound

This paper cites Machine Learning- assisted GNSS Interference Monitoring Through Crowdsourcing,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Machine Learning- assisted GNSS Interference Monitoring Through Crowdsourcing,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:29.893062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a421efd0-4c27-4c4b-9be5-1eef83e2d110 · outbound

This paper cites Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.900100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2362e39b-5f6d-4a2d-8093-d869d26b8bc2 · outbound

This paper cites Research Avenues for GNSS Interfer- ence Classification Robustness: Domain Adaptation, Continual Learning & Federated Learning,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Research Avenues for GNSS Interfer- ence Classification Robustness: Domain Adaptation, Continual Learning & Federated Learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.880480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 37da913f-6195-4559-b8f4-1d59303759e1 · outbound

This paper cites Robust Design of a Machine Learning-based GNSS NLOS Detector with Multi- Frequency Features,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Robust Design of a Machine Learning-based GNSS NLOS Detector with Multi- Frequency Features,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.858033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70dad641-b0dd-449a-9afa-196f066ff220 · outbound

This paper cites Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.835874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 59877c03-3c55-45bc-8cce-b0d3498849a6 · outbound

This paper cites Multimodal Learning for Reliable Interference Classification in GNSS Signals,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Multimodal Learning for Reliable Interference Classification in GNSS Signals,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.718064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2be8fd3e-63c7-4b05-ad44-12cf35fd6cec · outbound

This paper cites Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.511145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87f787cc-4b0f-4d5e-beb1-9a9cac42bd57 · outbound

This paper cites GNSS Interference Monitoring and Detection Based on the Swedish CORS Network SWEPOS,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization GNSS Interference Monitoring and Detection Based on the Swedish CORS Network SWEPOS,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.492343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b7b715f8-47c9-482f-bea6-6aa62cbd25d9 · outbound

This paper cites Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:23:30.961884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5034846c-1bd3-4c80-ad31-f367fc67f192 · outbound

This paper cites A Modeling Language to Support the Interoperability of Global Navigation Satellite Systems,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization A Modeling Language to Support the Interoperability of Global Navigation Satellite Systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.473769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a99a49e9-aead-4e07-a5d9-4806da08bf8c · outbound

This paper cites Towards Signal Processing In Large Language Models.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Towards Signal Processing In Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.132822Z

Source-reported events for the cited work

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Observation 7d3c510a-b3be-467b-967a-75a1e28d21fb · outbound

This paper cites Large language models in 6G security: challenges and opportunities.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Large language models in 6G security: challenges and opportunities

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.138762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f093103b-05cb-4354-8e79-958169e03d40 · outbound

This paper cites Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.145253Z

Source-reported events for the cited work

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Observation 14633582-cdc6-4184-8cc4-4022ae68d846 · outbound

This paper cites Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.454992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd9e3983-8336-4470-8675-323b929cae20 · outbound

This paper cites A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.336120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7aa126b7-83df-4a59-a08a-012f1b288dfd · outbound

This paper cites Prompt Engineering a Prompt Engineer.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Prompt Engineering a Prompt Engineer

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.377477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36dde5d8-dcfe-42a2-9641-108ff57d1267 · outbound

This paper cites Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.391910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44985dc-f644-49e5-bc5d-1f0782b52882 · outbound

This paper cites Achieving Generaliza- tion in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Achieving Generaliza- tion in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.437114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 444b2f07-d97e-44e1-b07a-8bc91b5e7db4 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Super- vision,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Learning Transferable Visual Models From Natural Language Super- vision,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.418285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7165541a-4e53-46a4-afd1-bb1a290c9f92 · outbound

This paper cites The Faiss library.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization The Faiss library

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.410517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18633f73-4ff6-4639-85b4-69c2629c2068 · outbound

This paper cites Vi- cuna: An Open-source Chatbot Impressing GPT-4 with 90% ChatGPT Quality,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Vi- cuna: An Open-source Chatbot Impressing GPT-4 with 90% ChatGPT Quality,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.398439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c2f85994-bdc1-4fa4-b79a-7bd2528429b6 · outbound

This paper cites The Turking Test: Can Language Models Understand Instructions?.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization The Turking Test: Can Language Models Understand Instructions?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.529441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 190278fc-d9df-46ba-92b3-008626f970a0 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.374192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8191c4b0-bdea-49cd-8211-142cb8da39c3 · outbound

This paper cites A Survey on In-Context Learning,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization A Survey on In-Context Learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.332819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 275643b8-dd81-4f40-9673-0855190d18c8 · outbound

This paper cites Visualizing Data Using t-SNE,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Visualizing Data Using t-SNE,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.103549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18ae9bc2-b8d6-4888-943c-1434c30f492a · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization BEiT: BERT Pre-Training of Image Transformers,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.086122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fcdc99d8-cf0c-459b-a2b5-68caa024f6f2 · outbound

This paper cites Training Data-Efficient Image Transformers & Distillation Through Attention,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Training Data-Efficient Image Transformers & Distillation Through Attention,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.066860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 237b1795-51a7-437f-9ed0-1b05e7059b7b · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:30.645069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2de1c738-5447-4bdb-b2c2-690bdead9bee · outbound

This paper cites An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale,.

Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:23:31.045548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 6f9c82bd-1846-4d00-8155-97fe72e20def · inbound

A Survey on Large Language Models in Multimodal Recommender Systems cites this paper.

A Survey on Large Language Models in Multimodal Recommender Systems Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T21:27:16.677739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 736b7560-77d2-4989-979d-2490df219614 · inbound

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks cites this paper.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:30.451469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec336c61-bfd5-4764-98bf-847150905ebd · inbound

GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU cites this paper.

GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:35:04.145712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.