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OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks — arXiv:2608.15436

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.15436
License (as archived): CC0-1.0
Archived: 2026-10-18T17:19:31.784Z
SHA-256 of the archived text: b54e3ae685167d6373da44b402cb38f070ffca6d3b1e8925887b8420ac9f5db5
Stable URL of this page: https://metraipolis.com/documento/dc_e0dc02be9a67

How to cite

OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks — arXiv:2608.15436 — arXiv (en), https://arxiv.org/abs/2608.15436 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e0dc02be9a67 (Metraipolis archive dc_e0dc02be9a67, sha256 b54e3ae685167d6373da44b402cb38f070ffca6d3b1e8925887b8420ac9f5db5)

BibTeX

@misc{dce0dc02be9a67,
  title = {OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks — arXiv:2608.15436},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.15436},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e0dc02be9a67; sha256 b54e3ae685167d6373da44b402cb38f070ffca6d3b1e8925887b8420ac9f5db5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.15436. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource with derived datasets for retrieval, reranking, instruction tuning, and safety/abstention, plus 30 full-parameter post-trained baselines across embedding, reranking, and language models. The community has already engaged substantially with the resource: as of May 3, 2026, the released models have been downloaded over 16 million times, and the project has received 157+ pieces of media coverage worldwide. Building on prior open telecom datasets and benchmarks, OTel provides documented telecom data sources, held-out evaluation partitions, trained embedding models, rerankers, context-grounded LLMs, and safety/abstention data in one unified resource. OTel post-training improves performance across all three model families: embedding retrieval reaches 93.5% NDCG@10, reranking reaches 0.952 MRR@10, and language-model correctness reaches 88.2%. We release OTel as a reproducible starting point and invite the community to expand the data, improve embedding and reranking models, and build stronger context-grounded telecom LLMs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2608.15436 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T16:29:13.801Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).