5 papers
Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networks
Divya Vijay, Vignesh Ethiraj
As networks evolve toward 5G Standalone and 6G, operators face orchestration challenges that exceed the limits of static automation and Deep Reinforcement Learning. Although Large…
T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning
Vignesh Ethiraj, Ashwath David, Sidhanth Menon +2
The specialized vocabulary and nuanced concepts of the telecommunications industry pose persistent challenges for standard Natural Language Processing (NLP) models. Generic embeddi…
Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior
Vignesh Ethiraj, Subhash Talluri
A central goal of artificial intelligence is to build systems that can understand and predict complex, evolving sequences of events. However, current foundation models, designed fo…
Toward Low-Latency End-to-End Voice Agents for Telecommunications Using Streaming ASR, Quantized LLMs, and Real-Time TTS
Vignesh Ethiraj, Ashwath David, Sidhanth Menon +1
We introduce a low-latency telecom AI voice agent pipeline for real-time, interactive telecommunications use, enabling advanced voice AI for call center automation, intelligent IVR…
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data
Vignesh Ethiraj, Divya Vijay, Sidhanth Menon +1
General-purpose large language models (LLMs), despite their broad capabilities accrued from open-world data, frequently exhibit suboptimal performance when confronted with the nuan…