H2LooP Telecom Model v1: From Telecom Comprehension to Autonomous Issue and PR Resolution
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Computer Science > Computation and Language
arXiv:2609.22241 (cs)
[Submitted on 4 Sep 2026]
Title:H2LooP Telecom Model v1: From Telecom Comprehension to Autonomous Issue and PR Resolution
View a PDF of the paper titled H2LooP Telecom Model v1: From Telecom Comprehension to Autonomous Issue and PR Resolution, by Amit Singh and 4 other authors
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Abstract:We present H2LooP Telecom Model v1, a domain-specialized large language models fine-tuned for the telecommunications industry. We release two domain-adapted model variants serving complementary use cases: a comprehension-focused variant for telecom domain question answering and reasoning, and an agentic variant for autonomous telecom code generation, pull request resolution, and code commits on production repositories. H2LooP Telecom achieves strong results on the GSMA Open Telecom Lite (OT-Lite) benchmark and a proprietary telecom code generation benchmark, outperforming frontier closed-source models such as GPT-5 and Claude Opus on independent leaderboard evaluation, while preserving general-purpose capabilities. The Comprehension variant achieves 81.8% weighted average on OT-Lite Pass@3, and, independently, ranks 5th overall on the official community-run Open Telco AI Leaderboard* at only 31B parameters-ahead of frontier closed-source systems including Claude Opus 4.6, GPT-5, Gemini 3 Flash, Grok-4-fast, and Kimi K2.5. Our agentic variant obtains a relative improvement of +8.8% in AST Similarity and +20.0% in Location IoU over the base model on telecom code generation, while maintaining identical MMLU (74.0%) and BFCL v3 multi-turn function calling (79.0%) performance, indicating zero catastrophic forgetting. Domain specialization on curated telecom corpora, spanning 3GPP standards, O-RAN specifications, network telemetry, and real repository commits, yields substantial improvements over general-purpose models of equivalent scale, approaches frontier closed-source models on domain-specific evaluation, and is independently corroborated by our official leaderboard standing.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.22241 [cs.CL] |
| (or arXiv:2609.22241v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22241
arXiv-issued DOI via DataCite (pending registration)
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