Large Language Models (LLMs) such as GPT, Gemini, and Claude utilize vast training datasets and complex architectures to generate high-quality responses. However, optimizing their inference-time computation remains challenging, as increasing model size leads to higher computational costs. Researchers continue to explore strategies that maximize efficiency while maintaining or improving model performance. One widely adopted approach…
Databases are essential for storing and retrieving structured data supporting business intelligence, research, and enterprise applications. Querying databases typically requires SQL, which varies across systems and can be complex. While LLMs offer the potential for automating queries, most approaches rely on translating natural language to SQL, often leading to errors due to syntax differences. A…
Large language models (LLMs) have revolutionized artificial intelligence by demonstrating remarkable capabilities in text generation and problem-solving. However, a critical limitation persists in their default “fast thinking” approach—generating outputs based on a single query without iterative refinement. While recent “slow thinking” methods like chain-of-thought prompting break problems into smaller steps, they remain constrained by static…
In artificial intelligence and machine learning, high-quality datasets play a crucial role in developing accurate and reliable models. However, collecting extensive, verified data—particularly in specialized domains like mathematics, coding, and science—remains a challenge. Traditional data-gathering methods often fail to produce datasets that effectively train models for complex reasoning tasks. This gap highlights the need for…
Robots are usually unsuitable for altering different tasks and environments. General-purpose models of robots are devised to circumvent this problem. They allow fine-tuning these general-purpose models for a wide scope of robotic tasks. However, it is challenging to maintain the consistency of shared open resources across various platforms. Success in real-world environments is far from…
There is no gainsaying that artificial intelligence has developed tremendously in various fields. However, the accurate evaluation of its progress would be incomplete without considering the generalizability and adaptability of AI models for specific domains. Domain Adaptation (DA) and Domain Generalization (DG) have garnered ample attention from researchers across the globe. Given that training is…
Edge devices like smartphones, IoT gadgets, and embedded systems process data locally, improving privacy, reducing latency, and enhancing responsiveness, and AI is getting integrated into these devices rapidly. But, deploying large language models (LLMs) on these devices is difficult and complex due to their high computational and memory demands. LLMs are massive in size and…
Language models (LMs) have significantly progressed through increased computational power during training, primarily through large-scale self-supervised pretraining. While this approach has yielded powerful models, a new paradigm called test-time scaling has emerged, focusing on improving performance by increasing computation at inference time. OpenAI’s o1 model has validated this approach, showing enhanced reasoning capabilities through test-time…
Ad hoc networks are decentralized, self-configuring networks where nodes communicate without fixed infrastructure. They are commonly used in military, disaster recovery, and IoT applications. Each node acts as both a host and a router, dynamically forwarding data. Flooding attacks in ad hoc networks occur when a malicious node excessively transmits fake route requests or data…
Large Language Models (LLMs) are primarily designed for text-based tasks, limiting their ability to interpret and generate multimodal content such as images, videos, and audio. Conventionally, multimodal operations are task-specific models trained on large amounts of labeled data, which makes them resource-hungry and rigid. Zero-shot methods are also restricted to pretraining with paired multimodal datasets,…