arXiv subjects

Mridul Jain

Publications and source records attributed to Mridul Jain.

4 recordsLinked to original sources

arXiv2026

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting

Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging. Although Artificial Intelligence (AI) has been applied to many telecom tasks, existing models are often narrow in scope, require large amounts of labeled data, and struggle to generalize across heterogeneous deployments. Consequently, network troubleshooting continues to rely heavily on Subject Matter Experts (SMEs) to manually correlate various data sources to identify root causes and corrective actions. To address these limitations, we propose a Multi-Agent System (MAS) that employs an agentic workflow, with Large Language Models (LLMs) coordinating multiple specialized tools for fully automated network troubleshooting. Once faults are detected by AI/ML-based monitors, the framework dynamically activates agents such as an orchestrator, solution planner, executor, data retriever, and root-cause analyzer to diagnose issues and recommend remediation strategies within a short time frame. A key component of this system is the solution planner, which generates appropriate remediation plans based on internal documentation. To enable this, we fine-tuned a Small Language Model (SLM) on proprietary troubleshooting documents to produce domain-grounded solution plans. Experimental results demonstrate that the proposed framework significantly accelerates troubleshooting automation across both Radio Access Network (RAN) and Core network domains.

cs.AI↗

arXiv2026

Edge-Deployable LLM Fine-Tuning on a Single GPU for Telecom Network Troubleshooting

Telecom troubleshooting at edge sites requires low-latency model responses and localized model adaptation to satisfy operational and data sovereignty requirements. However, deploying large language models (LLMs) at telecom edge sites is constrained by limited power, cooling, space, and weight budgets for GPU infrastructure. These challenges are further amplified by human-patterned Radio Access Network (RAN) traffic that often results in low GPU utilization and poor return on investment, as well as by architectural mismatches between deterministic ASIC-based telecom processing and GPU-oriented AI workloads. Consequently, single-GPU fine-tuning becomes a practical requirement for scalable edge AI deployment rather than merely a resource limitation. This paper presents a GPU profiling study of LLM fine-tuning using the Unsloth framework on a single edge-class accelerator. We systematically analyze the effects of maximum sequence length, GPU memory utilization, Low-Rank Adaptation (LoRA) rank, and generation count on training stability and resource efficiency. We further investigate trade-offs in KV cache usage, activation memory overhead, and runtime stability under inductor compilation. In addition, we show that reasoning and non-reasoning model architectures exhibit substantially different behaviors during supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) because of differences in chat template structures, reasoning tags, and control flags. Experiments are conducted on a telecom troubleshooting dataset consisting of question-answer pairs augmented with top-3 retrieved contextual documents. The results provide practical configuration guidelines for stable, efficient, and resource-aware LLM fine-tuning in telecom edge environments.

cs.DC↗

arXiv2026

Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications

The success of large language models (LLMs) depends heavily on large-scale, high-quality instruction-following and reinforcement datasets. However, generating such data through human annotation is prohibitively time-consuming particularly for domain-specific tasks like telecom network troubleshooting, where accurate responses require deep technical expertise and contextual understanding. In this paper, we present a fully automated, retrieval-augmented pipeline for generating synthetic question-answer (QA) pairs grounded in structured domain knowledge. Our multi-stage framework integrates a retriever, base generator, and refinement model to synthesize and enhance QA pairs using documents retrieved from a domain-specific knowledge graph. To ensure data quality, we employ customized RAGAS-based scoring to filter low-quality samples, producing a high-quality dataset suitable for reinforcement fine-tuning (RFT). We demonstrate our approach in a real-world telecom scenario focused on radio access network (RAN) troubleshooting. The resulting pipeline generates complex, context-rich troubleshooting solution plans without human intervention. This work offers a scalable solution for building instruction and reinforcement datasets in specialized domains, significantly reducing dependence on manual labeling while maintaining high technical fidelity.

cs.CL↗

arXiv2021

Building Graphs at a Large Scale: Union Find Shuffle

Large scale graph processing using distributed computing frameworks is becoming pervasive and efficient in the industry. In this work, we present a highly scalable and configurable distributed algorithm for building connected components, called Union Find Shuffle (UFS) with Path Compression. The scale and complexity of the algorithm are a function of the number of partitions into which the data is initially partitioned, and the size of the connected components. We discuss the complexity and the benchmarks compared to similar approaches. We also present current benchmarks of our production system, running on commodity out-of-the-box cloud Hadoop infrastructure, where the algorithm was deployed over a year ago, scaled to around 75 Billion nodes and 60 Billions linkages (and growing). We highlight the key aspects of our algorithm which enable seamless scaling and performance even in the presence of skewed data with large connected components in the size of 10 Billion nodes each.

cs.DC↗