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AT&T Bell Laboratories

Industry researchnorthamerica · us
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Research library7linked papers
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Selected work

Representative Papers

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

Aug 15, 2026

This study addresses the suboptimal performance and resource scarcity of frontier AI in telecommunications by introducing OTel, the first unified open-source telecom AI resource. Through full-parameter post-training, instruction tuning, and safety alignment, we provide 30 baseline models spanning retrieval, reranking, and language modeling, alongside a comprehensive evaluation framework. Experimental results demonstrate state-of-the-art performance, achieving an NDCG@10 of 93.5% for embedding retrieval, an MRR@10 of 0.952 for reranking, and 88.2% accuracy for language models, with cumulative downloads exceeding 16 million. By filling a critical gap in the field, this work establishes a reproducible foundation of specialized large models for intelligent networks, effectively fostering community collaboration and accelerating technological advancement in telecom AI.

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Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems

Aug 10, 2026

This study addresses the issue of error propagation in multi-agent systems under uncertainty caused by the lack of reasoning reliability assessment. To mitigate this, we propose HAS-SUM, a semantic uncertainty-guided orchestration framework. This approach innovatively introduces architecture-agnostic semantic entropy and density metrics to quantify trustworthiness in intermediate reasoning, thereby enabling adaptive verification and response selection. Experimental results on benchmarks such as StrategyQA demonstrate that HAS-SUM significantly enhances both reliability and hallucination resistance in complex reasoning tasks. Consequently, this work establishes an effective semantic-level orchestration paradigm for constructing robust multi-agent systems capable of maintaining performance integrity despite inherent uncertainties.

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Recent publications

Latest Papers

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

Aug 15, 2026

This study addresses the suboptimal performance and resource scarcity of frontier AI in telecommunications by introducing OTel, the first unified open-source telecom AI resource. Through full-parameter post-training, instruction tuning, and safety alignment, we provide 30 baseline models spanning retrieval, reranking, and language modeling, alongside a comprehensive evaluation framework. Experimental results demonstrate state-of-the-art performance, achieving an NDCG@10 of 93.5% for embedding retrieval, an MRR@10 of 0.952 for reranking, and 88.2% accuracy for language models, with cumulative downloads exceeding 16 million. By filling a critical gap in the field, this work establishes a reproducible foundation of specialized large models for intelligent networks, effectively fostering community collaboration and accelerating technological advancement in telecom AI.

0 citationsRead paper

Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems

Aug 10, 2026

This study addresses the issue of error propagation in multi-agent systems under uncertainty caused by the lack of reasoning reliability assessment. To mitigate this, we propose HAS-SUM, a semantic uncertainty-guided orchestration framework. This approach innovatively introduces architecture-agnostic semantic entropy and density metrics to quantify trustworthiness in intermediate reasoning, thereby enabling adaptive verification and response selection. Experimental results on benchmarks such as StrategyQA demonstrate that HAS-SUM significantly enhances both reliability and hallucination resistance in complex reasoning tasks. Consequently, this work establishes an effective semantic-level orchestration paradigm for constructing robust multi-agent systems capable of maintaining performance integrity despite inherent uncertainties.

0 citationsRead paper