arXiv subjects

Akashdeep Singh

Publications and source records attributed to Akashdeep Singh.

3 recordsLinked to original sources

arXiv2026

LEMUR 2: Unlocking Neural Network Diversity for AI

Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain or deployment-aware evaluation. LEMUR 2 introduces a large-scale, extensible framework unifying generative, evaluative, and deployment pipelines to unlock neural-network diversity. It comprises over 14,000 distinct architectures and more than 750,000 structured training records documenting model performance, hyperparameters, and task outcomes. These models were produced through AST-based code mutation, genetic and reinforcement-learning evolution, generation of fractal architectures, and synthesis guided by a Large Language Model (LLM). This includes deep models generated with the retrieval-augmented system NN-RAG, which derived and used architectural motifs from over 900 PyTorch modules extracted from public repositories. LEMUR 2 further employs NN-VR and NN-Lite pipelines for automated deployment and latency benchmarking on heterogeneous mobile and Unity-based VR platforms, providing real-device performance metadata. It spans multimodal tasks, image captioning, text-to-image synthesis, and language modeling, supporting cross-domain analysis of architectural transferability. By linking diverse architectures, tasks, and deployment data, LEMUR 2 provides the data foundation for LLM fine-tuning and coupling diverse architectural origins with large-scale, cross-platform empirical validation. This dataset defines a new basis for reproducible and data-driven AI design, advancing the emerging paradigm of LLM-driven AutoML and architectural generalization across modalities and hardware.

cs.LG↗

arXiv2026

Sustainable LLM Inference using Context-Aware Model Switching

Large language models have become central to many AI applications, but their growing energy consumption raises serious sustainability concerns. A key limitation in current AI deployments is the reliance on a one-size-fits-all inference strategy where most systems route every request to the same large model, regardless of task complexity, leading to substantial and unnecessary energy waste. To address this issue, we propose a context-aware model switching approach that dynamically selects an appropriate language model based on query complexity. The proposed system uses a Context-Aware Model Switching for Energy-Efficient LLM Inference that combines caching for repeated queries, rulebased complexity scoring for fast and explainable decisions, machine learning classification to capture semantic intent, and a user-adaptive component that learns from interaction patterns over time. The proposed architecture was evaluated using real conversation workloads and three open-source language models (Gemma3 1B, Gemma3 4B and Qwen3 4B) with different computational costs, measuring energy consumption (via NVML GPU power telemetry), response latency, routing accuracy, and output quality (BERTScore F1) to reflect real-world usage conditions. Experimental results show that the model switching approach can reduce energy consumption by up to 67.5% compared to always using the largest model while maintaining a response quality of 93.6%. In addition, the response time for simple queries also improved significantly by approximately 68%. These results show that model switching inference offers a practical and scalable path toward more energy-efficient and sustainable AI systems, demonstrating that significant efficiency gains can be achieved without major sacrifices in response quality.

cs.LG↗

arXiv2026

LunaAI: A Polite and Fair Healthcare Guidance Chatbot

Conversational AI has significant potential in the healthcare sector, but many existing systems fall short in emotional intelligence, fairness, and politeness, which are essential for building patient trust. This gap reduces the effectiveness of digital health solutions and can increase user anxiety. This study addresses the challenge of integrating ethical communication principles by designing and evaluating LunaAI, a healthcare chatbot prototype. Using a user-centered design approach informed by a structured literature review, we developed conversational scenarios that handle both routine and hostile user interactions. The system was implemented using the Google Gemini API and deployed as a mobile-first Progressive Web App built with React, Vite, and Firebase. Preliminary user testing was conducted with a small participant group, and responses were evaluated using established frameworks such as the Godspeed Questionnaire. In addition, a comparative analysis was performed between LunaAI's tailored responses and the baseline outputs of an uncustomized large language model. The results indicate measurable improvements in key interaction qualities, with average user ratings of 4.7 out of 5 for politeness and 4.9 out of 5 for fairness. These findings highlight the importance of intentional ethical conversational design for human-computer interaction, particularly in sensitive healthcare contexts.

cs.HC↗