Towards Agentic Test-Driven Quality Assurance for 6G Networks

• URL: http://arxiv.org/abs/2604.23285v1
• Date: Sat, 25 Apr 2026 12:58:36 GMT
• Title: Towards Agentic Test-Driven Quality Assurance for 6G Networks
• Abstract summary: This work proposes an agentic, intent-driven end-to-end (E2E) orchestration framework that integrates intent co-creation with a Test-Driven Quality Assurance paradigm.<n>The architecture is grounded in a standards-aligned knowledge representation using TM Forum information models and catalogs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work proposes an agentic, intent-driven end-to-end (E2E) orchestration framework that integrates intent co-creation with a Test-Driven Quality Assurance paradigm. In this framework, autonomous agents iteratively refine a user's initial intent into a confirmed, auditable specification. Furthermore, the system automatically derives validation tests from these intents before provisioning, directly mirroring the Test-Driven Development workflow in software engineering to ensure proactive Service Level Agreement (SLA) compliance. The architecture is grounded in a standards-aligned knowledge representation using TM Forum (TMF) information models and catalogs. This enables deterministic graph traversal from high-level Product Offerings down to granular Service/Resource and Test specifications. We prototyped this architecture by extending OpenSlice with a message-driven, multi-agent pattern and integrating MCP-enabled (Model Context Protocol) tool access for real-time knowledge retrieval. Currently, our evaluation of the agents targets the intent co-creation phase as a baseline toward full-scale orchestration. Building on experiments with multiple open-source Large Language Model (LLM) backends integrated with the TMF-based knowledge base, we observe substantial variability in tool-use reliability and hallucination patterns, underscoring the critical importance of robust knowledge integration in agentic 6G systems.

Related papers

• ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders [26.859032352714276]
  We introduce ICAE-Bench, a benchmark for evaluating coding agents under interactive project-building settings.<n>The basic idea is to start from a fuzzy product requirement, simulating the dynamic paradigm with an automated User Agent.<n>ICAE-Bench uses standardized black-box tests together with multi-dimensional diagnostics, including functional correctness, semantic and API similarity, structural fidelity, design quality, and interaction quality.
  arXiv  Detail & Related papers  (2026-07-23T11:31:38Z)
• ROSE: Retrieval-Oriented Segmentation Enhancement [79.7370065070812]
  Existing segmentation models based on multimodal large language models (MLLMs) often struggle with novel or emerging entities due to their inability to incorporate up-to-date knowledge.<n>We introduce the Novel Emerging Task (NEST), which focuses on segmenting (i) novel entities that MLLMs fail to recognize due to their absence from training data, and (ii) emerging entities that exist within the model's knowledge but demand up-to-date information for accurate recognition.<n>Rose: Retrieval-Oriented Enhancement, a plug-and-play framework designed to augment any MLLM-based segmentation model,
  arXiv  Detail & Related papers  (2026-04-15T17:59:35Z)
• IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation [3.539467892338473]
  We present IDP (Intelligent Document Processing) Accelerator, a framework enabling agentic AI for end-to-end document intelligence.<n>The interactive demonstration enables users to upload document packets, visualize classification results, and explore extracted data.
  arXiv  Detail & Related papers  (2026-02-26T20:20:38Z)
• The Agentic Automation Canvas: a structured framework for agentic AI project design [0.0]
  We present the Agentic Automation Canvas (AAC), a structured framework for the prospective design of agentic systems.<n> AAC captures six dimensions of an automation project: definition and scope; user expectations with quantified benefit metrics; developer feasibility assessments; governance staging.<n>It is made accessible through a client-side web application with real-time validation.
  arXiv  Detail & Related papers  (2026-02-16T16:46:04Z)
• A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge [1.932555230783329]
  Lightweight, open-source Python framework designed to democratize the construction of LLM-driven autonomous agents.<n>AgentForge introduces three key innovations: (1) a composable skill abstraction that enables fine-grained task decomposition with formally defined input-output contracts, (2) a unified backend interface supporting seamless switching between cloud-based APIs and local inference engines, and (3) a declarative YAML-based configuration system that separates agent logic from implementation details.
  arXiv  Detail & Related papers  (2026-01-19T20:33:26Z)
• LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering [90.84806758077536]
  We introduce textbfLoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess large language models (LLMs) agents in realistic, long-context software engineering.<n>Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations.<n>Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens.
  arXiv  Detail & Related papers  (2025-11-17T23:57:24Z)
• BarrierBench : Evaluating Large Language Models for Safety Verification in Dynamical Systems [4.530582224312311]
  We introduce an LLM-based agentic framework for barrier certificate synthesis.<n>The framework uses natural language reasoning to propose, refine, and validate candidate certificates.<n> BarrierBench is a benchmark of 100 dynamical systems spanning linear, nonlinear, discrete-time, and continuous-time settings.
  arXiv  Detail & Related papers  (2025-11-12T14:23:49Z)
• Transparent, Evaluable, and Accessible Data Agents: A Proof-of-Concept Framework [0.0]
  This article presents a modular, component-based architecture for developing and evaluating AI agents.<n>The system addresses core challenges in data accessibility by enabling non-technical users to interact with complex data warehouses.<n>A cornerstone of the design is its commitment to transparent decision-making, achieved through a multi-layered reasoning framework.
  arXiv  Detail & Related papers  (2025-09-28T23:54:41Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers [86.00932417210477]
  We introduce MCP-Universe, the first comprehensive benchmark specifically designed to evaluate LLMs in realistic and hard tasks through interaction with real-world MCP servers.<n>Our benchmark encompasses 6 core domains spanning 11 different MCP servers: Location Navigation, Repository Management, Financial Analysis, 3D Design, Browser Automation, and Web Searching.<n>We find that even SOTA models such as GPT-5 (43.72%), Grok-4 (33.33%) and Claude-4.0-Sonnet (29.44%) exhibit significant performance limitations.
  arXiv  Detail & Related papers  (2025-08-20T13:28:58Z)
• ArtifactsBench: Bridging the Visual-Interactive Gap in LLM Code Generation Evaluation [51.297873393639456]
  ArtifactsBench is a framework for automated visual code generation evaluation.<n>Our framework renders each generated artifact and captures its dynamic behavior through temporal screenshots.<n>We construct a new benchmark of 1,825 diverse tasks and evaluate over 30 leading Large Language Models.
  arXiv  Detail & Related papers  (2025-07-07T12:53:00Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.