Pith Number

pith:B2AUAALR

pith:2026:B2AUAALRR7UN4OQSHMCD2KZLB7

1 verified not anchored not stored refs resolved

RF-Analyzer: Can Vision-Language Models Learn RF Understanding from Synthetic Data?

Anis Bara, Brahim Mefgouda, Hang Zou, Lina Bariah, Merouane Debbah

Vision-language models can learn to understand real RF signals from synthetic spectrogram data alone.

arxiv:2605.04676 v1 · 2026-05-06 · eess.SP

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✓ Author claim 1 verified · sign in to claim

Anis BARA orcid verified

4 Citations open

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Claims

C1strongest claim

VLMs trained on synthetic spectrogram data can generalize to real RF environments, particularly for extracting physical signal attributes such as spectral occupancy, temporal behavior, and SNR. This indicates that synthetic data is sufficient for learning transferable representations of RF signal structure.

C2weakest assumption

The synthetic training data distribution is representative enough of real over-the-air RF variations to support generalization, especially outside the low-SNR regimes explicitly noted as failure cases.

C3one line summary

VLMs trained on synthetic RF spectrograms generalize to real signals for physical attribute extraction but lack reliable semantic grounding without additional priors.

References

12 extracted · 12 resolved · 0 Pith anchors

[1] Large generative AI models for telecom: The next big thing? 2024

[2] TelecomGPT: A framework to build telecom-specific large language models 2025

[3] Large language model (LLM) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities 2025

[4] Spectrum analyzers and signal analyz- ers 2024

[5] Signal analyzers 2024

Receipt and verification

First computed	2026-06-22T11:44:46.500069Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0e814001718fe8de3a123b043d2b2b0ff5f66286d3c8841de515621bf359e5cc

Aliases

arxiv: 2605.04676 · arxiv_version: 2605.04676v1 · doi: 10.48550/arxiv.2605.04676 · pith_short_12: B2AUAALRR7UN · pith_short_16: B2AUAALRR7UN4OQS · pith_short_8: B2AUAALR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/B2AUAALRR7UN4OQSHMCD2KZLB7 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 0e814001718fe8de3a123b043d2b2b0ff5f66286d3c8841de515621bf359e5cc

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "bb95b688faa67b03fcbf97a8c46d36dc71a868ad7feb2d553ccb38d0799fd63e",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-06T09:25:19Z",
    "title_canon_sha256": "3d40d37ba1e4e4d69238bacf3fa00e2772cb227c2dde8f0191998ff7ec92dfa7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.04676",
    "kind": "arxiv",
    "version": 1
  }
}