MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications

Researchers introduce MM-Telco, a comprehensive multimodal benchmark and model suite designed to adapt large language models for telecommunications applications. The framework addresses domain-specific challenges in network optimization, troubleshooting, and customer support, with fine-tuned models demonstrating significant performance improvements over baseline LLMs.

MM-Telco represents a methodical approach to bridging the gap between general-purpose AI systems and specialized telecom requirements. The telecommunications industry increasingly depends on automated decision-making for network operations and customer management, yet mainstream LLMs lack the contextual understanding necessary for these complex, domain-specific tasks. This research directly tackles that limitation by creating benchmarks that encompass both textual and visual components—reflecting the reality that telecom infrastructure involves network diagrams, topology maps, and documentation alongside operational data.

The proliferation of AI applications in telecommunications has accelerated over the past two years as carriers seek cost reduction and service quality improvements. However, deploying general-purpose models without domain adaptation creates operational risks and suboptimal performance. MM-Telco's contribution lies in its systematic evaluation methodology that exposes weaknesses in current vision-language models while simultaneously demonstrating how targeted fine-tuning can substantially improve outcomes. The benchmark-driven approach enables reproducible research and establishes measurable baselines for future work.

For telecommunications infrastructure companies and network operators, this research signals that enterprise AI deployment requires specialized tooling rather than generic solutions. Vendors developing telecom management platforms now have reference implementations and evaluation frameworks to guide AI integration decisions. The work also validates that multimodal approaches—combining text and image understanding—deliver superior results for complex technical domains. Organizations planning AI-driven network automation initiatives should monitor this research trajectory, as standardized benchmarks typically precede broader commercial adoption patterns in specialized sectors.

• →MM-Telco provides domain-specific benchmarks addressing real telecom use cases including network operations, management, and documentation retrieval.
• →Fine-tuned models on telecom-specific datasets outperform general-purpose LLMs, demonstrating the value of domain adaptation.
• →The framework combines textual and image-based tasks, reflecting the multimodal nature of actual telecommunications operations.
• →Baseline experiments reveal weaknesses in current state-of-art vision-language models when applied to telecom contexts.
• →Standardized benchmarks accelerate enterprise AI adoption in telecommunications by establishing measurable performance criteria.