TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?

Abstract: While LLMs have achieved remarkable integration in various vertical scenarios, their deployment in the telecommunications domain remains exploratory due to the lack of a standardized evaluation framework. Current telecom benchmarks primarily focus on static, foundational knowledge and isolated atomic skills, neglecting the equipment-specific documentation and end-to-end industrial workflows essential for real-world production systems. To bridge this gap, we present TeleCom-Bench, a comprehensive benchmark comprising 12 evaluation sets with 22,678 curated samples, which evaluates LLMs across a synergistic hierarchy: (1) Multi-dimensional Knowledge Comprehension, which integrates telecommunication fundamentals, 3GPP protocols, and 5G network architecture with proprietary product knowledge across wired, core, and wireless networks via knowledge graph-driven synthesis; and (2)End-to-End Knowledge Application, which formalizes six core tasks on authentic trajectories from live network agent workflows, including intent recognition, entity extraction, event verification, tool invocation, root cause analysis, and solution generation-across network optimization and fault maintenance scenarios. Evaluations of eight state-of-the-art LLMs reveal a universal Execution Wall: while models achieve 90% accuracy in linguistic interface tasks such as intent recognition and entity extraction, performance collapses to approximately 30% in procedural execution tasks like solution generation. This capability gap demonstrates that current LLMs function competently as diagnosticians but fail as field engineers. TeleCom-Bench provides standardized diagnostics to precisely pinpoint this deficit, offering actionable guidance for domain-specific alignment toward production-ready telecom agents. The dataset and evaluation code have been released at https://github.com/ZTE-AICloud/TeleCom-Bench.

• The paper introduces 'TeleCom-Bench,' a benchmark evaluating large language models' (LLMs) capabilities in telecom application scenarios.
• The benchmark is designed around Knowledge Comprehension and Application, testing both theoretical understanding and real-world procedural execution.
• Findings reveal that while LLMs excel in understanding intent and diagnosis, they struggle significantly with translating that into executable solutions.

TeleCom-Bench: A Comprehensive Benchmark for Industrial Telecommunications LLMs

Motivation and Benchmark Structure

Telecommunications presents dense technical complexity, vendor-specialized protocols, and safety-critical operational workflows, rendering generic NLP or domain-adapted LLM benchmarks insufficient for evaluating production readiness. "TeleCom-Bench: How Far Are LLMs from Industrial Telecommunication Applications?" (2605.18025) directly addresses this gap by introducing a dual-pillar, multi-tiered evaluation suite: Knowledge Comprehension (KC) and End-to-End Knowledge Application (KA). The hierarchical design (see Figure 1) encompasses both foundational theory (e.g., 3GPP standards, signal processing) and the complete execution chains of live network optimization and maintenance. This formalism enables conclusive assessment of whether an LLM can function not just as an information extractor or diagnostician, but as an operational field agent.

Figure 1: The two-level structure of TeleCom-Bench dividing evaluation into Knowledge Comprehension and Application, capturing both depth and operational breadth.

Data Acquisition, Curation, and Task Pipelines

Knowledge Comprehension (KC)

The KC pipeline (Figure 2) processes 1.52TB of heterogeneous telecommunication artifacts—standard docs, vendor manuals, field logs—into structured, annotated, de-duplicated corpora and over 17,000 knowledge-centric evaluation samples. Multi-tiered sources ensure balanced coverage of public standards, academic treatises, and proprietary product guides. The curation protocol encompasses advanced layout-aware OCR, format harmonization, semantic deduplication, and granular metadata tagging enabling fine-grained stratified evaluation.

Critically, evaluation item generation exploits two advanced synthesis methodologies:

• Distillation-Driven Evaluation Generation: Task-adaptive prompting of LLMs and human-in-the-loop extraction to produce evaluation samples with minimal hallucination, targeting slot-filling, structural parsing (eg. JSON graphs of config manuals), progressive multi-step question-answer pairs, and cross-document fusion.
• Knowledge Graph (KG)-Driven Evaluation Generation: Ontology-based entity-relation construction yields graphs encoding scenario-grounded topologies—Equipment-Scenario Hierarchies and Causal Fault Chains. Graph traversal and subgraph sampling generate atomic, multi-hop, and aggregate QA pairs, with adversarial distractors for robust discrimination and subgraph-based retrieval to validate and de-noise generated sets.

  Figure 2: The KC pipeline spans multimodal data ingestion, metadata-rich document curation, and methodical evaluation generation, enabling both scale and diagnostic granularity.

Knowledge Application (KA)

The KA benchmark pivots to real-world, agent-in-the-loop scenarios (Figure 3). The pipeline acquires large-scale network telemetry, configuration snapshots, and event logs directly from commercial systems (via MML, SNMP, SFTP), followed by privacy-preserved ETL alignment and expert curation.

Using trajectory mining over agent workflow traces, KA dataset construction decomposes end-to-end operations into atomic tasks: intent recognition, entity extraction, event verification, tool invocation, root cause diagnosis, and solution generation. Each step records the full chain-of-thought, operational context, tool interface interactions, and final actions, with closed-loop verification against production network KPIs and expert engineer audit for semantic fidelity.

Figure 3: The KA pipeline operationalizes live telemetry and agent traces, dissecting workflows into fine-grained, verifiable sub-tasks.

Task Typology and Data Format Diversity

TeleCom-Bench formalizes three representative task modalities (Figure 4): multiple-select (for knowledge/standards, evaluating both breadth and nuance), subjective QA (enabling open-ended, expert-level synthesis), and structured QA (fact extraction, typically output in JSON for downstream consumption). This diversity mirrors the spectrum of real-world inputs, from technical protocol assessment to workflow command generation.

Figure 4: Prototypical formats for task types, mapping to concrete workflow requirements in telco operations.

Tool-augmented Reasoning: Procedural Interface and Model Utilization

The operational KA tasks include explicit context about a tool library (23 vendor-specific utilities; Figure 5), provided as part of the system prompt for each model evaluation. These tools are necessary for generating executable command sequences and completing fault resolution. Despite uniform access, models struggle with procedural grounding and correct invocation.

Figure 5: The prompt-accessible 23-tool library with sharp interface constraints, establishing the requirements for procedural reasoning in fault-handling.

Experimental Evaluation and Key Findings

Eight SOTA LLMs, including Qwen3-235B, DeepSeek-V3.2, Gemini-2.5, and GLM4.7, are systematically benchmarked via macro-F1, exact match, and tri-LLM-as-a-Judge mechanisms. Macro-level results (Figure 6) evidence two clear patterns:

• Linguistic and diagnostic tasks (Intent Recognition, Entity Extraction, Root Cause Diagnosis): Upstream performance saturates (>90% for intent/entity, 71% at best for diagnosis).
• Procedural/solution tasks (Tool Invocation, Solution Generation): Catastrophic drop. Even the best model (Doubao-pro) achieves only 30.72% in solution generation, with Qwen3-235B at 4.67%, exposing a "Execution Wall"—a persistent 60+ point gap between diagnosis and actionable remediation.

  Figure 6: Radar visualization of LLM performance over communication domain skills, highlighting the universal collapse in Solution Generation compared to robust performance in Entity Extraction and Diagnosis.

Model outputs for fault resolution (Figure 7) demonstrate the inability of generalist LLMs to properly utilize the tool context, often generating unstructured, hallucinated, or syntactically-invalid command sequences instead of procedurally correct solutions.

Figure 7: Example outputs where advanced LLMs, despite being given tool specs, fail to convert diagnosis into valid, actionable commands.

Theoretical and Practical Implications

Theoretical

Findings corroborate an emerging consensus: scaling model parameters and generic instruction tuning are insufficient for achieving vertical-domain procedural agency. Coverage of proprietary documentation and agent workflow traces, not model scale, drives any gains in complex industrial settings. This aligns with parallel results in medical [ding2025medbench], financial [ding2025cnfinbench], and software operations [liu2025opseval] domains.

Additionally, MoE architectures correlate with modest improvements in tool invocation. However, the diagnosis-action paradox remains—LLMs can reason causally but cannot synthesize compliant, safety-constrained interventions, a gap not addressed by chain-of-thought prompting alone. Current models operate as information extractors or passive advisors, not as active, trustworthy engineers.

Practical

For deployment in industrial networks, the benchmark demonstrates SOTA LLMs fundamentally lack the procedural grounding required for safe automation. The solution-generation bottleneck defines a practical barrier for closed-loop operations, necessitating novel research into procedural alignment, tool-centric generation, and integration of product-specific knowledge graphs.

TeleCom-Bench provides a gold-standard reference for telco LLM evaluation and a diagnostic instrument for identifying precise deficit loci, enabling both higher-fidelity research and more trustworthy field deployment metrics.

Outlook and Future Developments

Directions mandated by this work include:

• Instruction and RAG-based fine-tuning explicitly over workflow traces and product documentation for procedural alignment.
• Safety verification and formal program synthesis modules for tool command generation.
• Expanded, open-sourced vendor product and live workflow corpora, making the benchmark a cornerstone for telco AI transparency and comparability.

Persistent execution gaps accentuate the need to operationalize more rigorous tool-based supervision and hybrid neuro-symbolic toolchains to move beyond "diagnostician LLMs" toward true field agent capabilities.

Conclusion

TeleCom-Bench establishes for the first time a rigorous, hierarchical, task-diverse benchmark for LLMs in telecommunications, spanning static knowledge and dynamic, tool-constrained operations. Strong empirical evidence supports the claim that all current models, despite excelling in intent, extraction, and diagnosis, universally fail to produce executable solutions even when given complete procedural context. This "Execution Wall" remains the fundamental open challenge for industrial deployment. Research focus must now shift to procedural grounding, safety alignment, and vendor/product-aware vertical adaptation to realize genuine telecom AI autonomy.

(2605.18025)