PaperlayerView exact revision on arXiv

Source-linked AI summary

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

Jieting Xiao, Yun Lin, Huizhen Qiu, Rui Ma, Chen Zhong, Dongyang Xu, Xiao Long, Chaoyu Zhang, Qiaobo Hao, Ding Zou, Zhiguo Yang, Yanqin Gao, Fang Tan

arXiv:2605.18025v1cs.AI

TL;DR

Telecom LLM evaluation lacks a standardized framework covering equipment-specific knowledge and end-to-end industrial workflows. TeleCom-Bench evaluates both through curated knowledge-comprehension datasets and real-network application trajectories, revealing a gap between diagnosis and executable remediation.

  • Problem

    Telecom benchmarks often emphasize foundational knowledge or isolated skills while missing equipment-specific documentation and full industrial workflows.

    Source: §1 · p. 1

  • Method

    TeleCom-Bench combines telecom theory and vendor-product knowledge evaluation with six sequential application tasks grounded in real network-operation trajectories.

    Source: §1 · p. 2, §3.2 · p. 6

  • Results

    Models diagnose more accurately than they generate executable remediation; Qwen3-235B scores 71.49% in Root Cause Diagnosis and 4.67% in Solution Generation.

    Source: §4.3.3 · p. 7

  • Takeaways & Limitations

    The reported diagnosis-action gap motivates domain-specific alignment in procedural execution, safety-constrained generation, and tool-grounded interaction.

    Source: §4.3.3 · p. 7, §6 · p. 8

Abstract

from arXiv · show

While Large Language Models 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.

1 Introduction

Telecom LLM evaluation lacks a standardized framework that covers both equipment-specific knowledge and end-to-end industrial workflows. TeleCom-Bench addresses these gaps through complementary knowledge-comprehension and knowledge-application evaluations.

1 Introduction

  • Existing telecom benchmarks often omit product-specific documentation and fail to model the multi-step reasoning of end-to-end industrial processes.The paper identifies these omissions as gaps between theoretical understanding and operational reality.Source: §1 · pp. 1–2
  • TeleCom-Bench combines theoretical and equipment-specific knowledge assessment with application tasks derived from real network optimization and fault-analysis trajectories.Its knowledge-comprehension pillar uses product manuals and patents alongside standards and literature; its application pillar evaluates technical constraints, causal reasoning, and actionable solutions.Source: §1 · p. 2
  • The benchmark is presented as a systematic evaluation framework spanning foundational telecom knowledge and complex industrial workflows, with diagnostics intended to guide model optimization.The authors describe it as a roadmap for telecommunications researchers and engineers.Source: §1 · p. 2

2 Methodology

The methodology builds separate evaluation datasets for telecom knowledge comprehension and knowledge application. It combines multi-source technical and product material with graph- and LLM-based task synthesis, then mines and verifies real-network agent trajectories.

2.1 Construction of Knowledge Comprehension Evaluation Dataset

  • A scalable knowledge-comprehension pipeline transforms acquired domain knowledge into evaluation samples.It spans raw knowledge acquisition through sample generation.Source: §2.1 · p. 2

2.1.1 Data Acquisition and Processing.

  • The corpus combines standards, academic and curated resources with product documents, expert assets, field cases, and live service data, then preprocesses and annotates the material.The raw sources undergo a three-stage preprocessing pipeline for consistency, reliability, and evaluation readiness.Source: §2.1.1 · pp. 2–3

2.1.2 Knowledge Distillation-Driven Evaluation Generation Method.

  • Task-adaptive distillation varies synthesis by source: standards use constrained extraction, manuals nested JSON, papers progressive question chains, and document pairs cross-document questions.The method tailors prompts to document structure and knowledge density; cross-document synthesis draws on semantically overlapping but complementary sources.Source: §2.1.2 · pp. 3–4

2.1.3 Knowledge Graph-Driven Evaluation Generation Method.

  • Knowledge graphs encode equipment hierarchies and causal fault chains, then support multi-granularity QA generation, correction, and technically plausible distractors.Graph traversal samples atomic, multi-hop, and aggregate QA; generated pairs are checked against retrieved subgraphs and verified graph paths.Source: §2.1.3 · p. 4

2.2 Construction of Knowledge Application Evaluation Dataset

  • The application dataset uses live-network telemetry and agent-workflow mining to evaluate dynamic tasks rather than static information retrieval.The two pipelines cover closed-loop network optimization and fault localization and recovery.Source: §2.2 · p. 4

2.2.1 Data Acquisition and Preprocessing.

  • Commercial-network performance, configuration, and fault data are privacy-protected and temporally aligned into coherent network-state observations.Sensitive identifiers are irreversibly hashed, and asynchronous performance series and fault logs are aligned with configuration snapshots.Source: §2.2.1 · p. 4

2.2.2 Data Synthesis Methods.

  • Agent execution traces are decomposed into task-level context, reasoning, and action records, then field-verified and reviewed by engineers.Only optimization trajectories with statistically significant KPI gains and no Performance Degradation Alarms are retained; senior engineers audit the verified trajectories.Source: §2.2.2 · p. 5

3 Benchmark Design

TeleCom-Bench comprises knowledge-comprehension and knowledge-application categories. It covers foundational theory and product knowledge alongside sequential tasks drawn from network optimization and fault-maintenance workflows.

3 Benchmark Design

  • The benchmark comprises 12 specialized subsets and 22,678 curated samples spanning static knowledge mastery and dynamic problem-solving in authentic workflows.Its construction uses real-world configurations, telemetry, and operational tickets.Source: §3 · p. 5

3.1 Knowledge Comprehension Evaluation

  • Knowledge comprehension assesses communication theory and product knowledge across wired, wireless, and core networks.Foundational theory covers general telecom fundamentals, 5G architecture, and 3GPP protocols from Releases 15 to 17.Source: §3.1 · p. 5, §3.1.1 · p. 5

3.1.1 Communication Basic Theory.

  • The evaluation addresses vendor-specific product knowledge by testing concepts, features, and operational workflows across network equipment domains.Coverage includes core-network functions and infrastructure, wireless access hardware and configuration, and wired technologies and products.Source: §3.1.2 · pp. 5–6

3.2 Knowledge Application Evaluetion

  • Knowledge application models network optimization and fault maintenance as integrated workflows decomposed into six sequential competency tasks.The tasks are intent recognition, entity extraction, event verification, tool invocation, root cause diagnosis, and solution generation.Source: §3.2 · p. 6
  • Dedicated task datasets assess capabilities from recognizing intent and extracting entities through verification, tool use, diagnosis, and remediation.This decomposition supports granular evaluation across complex, dynamic telecom operational workflows.Source: §3.2 · p. 6

4 Experiments and Analysis

Across TeleCom-Bench, models diagnose faults far more reliably than they generate executable solutions, exposing a persistent gap between knowledge and operational action. They perform strongly on early linguistic tasks, but tool invocation varies sharply by model and solution generation remains weak.

4.3.2 Architecture-Dependent Tooling Capability.

  • Qwen3-235B scores 71.49% in Root Cause Diagnosis but 4.67% in Solution Generation, a 66.82-point Execution Wall.The table highlights the contrast between root-cause diagnosis and executable-solution generation.Source: Table 2 · p. 7, §4.3.3 · p. 7

4.2 Analysis of Knowledge Comprehension

  • Foundational telecom theory scores 60%–70%, while product-specific knowledge varies substantially; Qwen3-32B reaches 73.04% on Wireless Network versus 70.80% for Qwen3-235B, while GLM-4.7 scores 22.63%.The authors interpret the non-monotonic scaling as suggesting vendor-document coverage can matter more than parameter count alone.Source: §4.2 · p. 7

4.3 Analysis of Knowledge Application

  • Across the application tasks, operational capability does not scale linearly with theoretical knowledge or model size, and Solution Generation remains deficient across models.The radar charts distinguish Knowledge Comprehension from Knowledge Application.Source: §4.3 · p. 7, Figure 4 · p. 8

4.3.1 Saturated Linguistic Interface.

  • Intent Recognition exceeds 92% and Entity Extraction exceeds 95%, but all models show a structural deficiency in Solution Generation despite strong upstream-task performance.The authors characterize early-stage accuracy as creating a reliability illusion that dissipates downstream.Source: §4.3.1 · p. 7, §4.3 · p. 7
  • DeepSeek-V3.2 scores 94.06% in Tool Invocation versus 45.54% for Qwen3-235B, and MoE architecture has a reported association with tool-calling proficiency (r=0.87, p<0.01).The authors suggest sparse activation combined with function-calling post-training may support dynamic tool orchestration.Source: §4.3.2 · p. 7

4.3.3 The Diagnosis-Action Paradox.

  • Even the top-performing Doubao-pro reaches only 30.72% in Solution Generation, far below the stated 95% threshold for autonomous production-network deployment.The paper identifies executable, safety-constrained maintenance-script generation as the leading barrier to telecom automation.Source: §4.3.3 · p. 7
  • The authors conclude that current LLMs act as information extractors and competent diagnosticians but fail as field engineers, calling for specialized alignment in procedural execution, safety-constrained generation, and tool-grounded interaction.The Execution Wall is operationally defined as a diagnostic-to-solution-generation gap exceeding 50 percentage points within the same fault-handling workflow.Source: §4 · pp. 6–7, §4.3.3 · p. 7

5 Related Work

Telecommunications benchmarks have expanded from foundational language and protocol tasks toward operational tasks, but evaluations still lack unified knowledge-to-action workflows and sufficient product-specific operational coverage. TeleCom-Bench addresses this gap using vendor documentation and real-world agent workflow traces.

Telecommunications Benchmarks

  • Prior benchmarks span terminology, protocol and quantitative reasoning, and operational tasks, yet remain fragmented, rely heavily on public standards, and omit end-to-end tool-augmented workflows.The review cites examples ranging from SPEC5G and TeleQnA to TeleLogs, OpsEval, and TeleYAML.Source: §5 · p. 8

Methodologies in Benchmark Construction

  • Telecommunications benchmark construction commonly uses LLM-generated items from public standards or task-specific templates, leaving authentic operational environments underrepresented.The authors contrast this with adjacent high-stakes benchmarks that incorporate expert curation, authentic sequences, and tool-mediated multi-step execution.Source: §5 · p. 8
  • TeleCom-Bench addresses these limitations by combining vendor-specific documentation with evaluation items built from real-world agent workflow traces.The paper presents this as its approach to benchmark construction, informed by methodological practices in other high-stakes domains.Source: §5 · p. 8

6 Conclusion

The conclusion presents TeleCom-Bench as an industrial-telecommunications LLM benchmark spanning knowledge comprehension and end-to-end application. Its reported findings show a persistent diagnosis-to-action gap, motivating domain alignment and safety-constrained procedural execution.

6 Conclusion

  • TeleCom-Bench comprises 12 evaluation sets and 22,678 samples across communication theory, 3GPP protocols, vendor-specific product knowledge, and six sequential tasks grounded in real network-operation trajectories.The application tasks cover end-to-end knowledge application.Source: §6 · p. 8

A.1 A.1 Question Format Specifications in TeleCom-Bench

TeleCom-Bench specifies three question types for evaluating professional knowledge and task-processing capabilities. They cover multiple-select knowledge questions, subjective open-ended answers, and structured information extraction.

A.1 Question Format Specifications in TeleCom-Bench

  • Multiple-Select Questions test network architecture, interface functions, and protocols in single- or multiple-answer formats.The specified formats allow one correct answer or two to four correct answers.Source: §A.1 · p. 10
  • Subjective QA elicits concise, logically consistent open-ended responses to complex problems such as network optimization.Source: §A.1 · p. 10
  • Structured QA tests extraction of locations, network-element identifiers, and time expressions, with results formatted as JSON.Source: §A.1 · p. 10

A.2 Detailed Experimental Breakdown Data

This subsection points readers to Table 3 for the detailed model-performance breakdown.

  • Table 3 is identified as the detailed breakdown of experimental results reported in the main text.Source: §A.2 · p. 10

A.2 A.3 Model Outputs and Tool Context: Procedural Reasoning Failures in Telecom Fault Resolution

In a fault-resolution example, generalist models fail to use a shared tool interface effectively despite receiving identical tool descriptions and task context. The authors attribute these failures to difficulty translating domain knowledge into structured, executable workflows.

  • The 23 domain-specific tools provide all models identical context, including function purposes, input/output formats, and usage constraints.Source: §A.2 · p. 10, Figure 6 · p. 11
  • Figure 5 provides example formats for three main question types, while Table 3 is described as breaking down performance across task categories.Source: Figure 5 · p. 11, Table 3 · p. 12
  • The authors attribute these failures to difficulty translating domain knowledge into structured, executable workflows, rather than to missing information.Source: §A.2 · p. 11
  • Generalist models ignore tool specifications or misuse tool semantics, producing unstructured advice, nonexecutable procedures, or hallucinated commands.Source: §A.2 · p. 10, Figure 7 · p. 12

Related papers

Loading 2605.18025v1…