Large Language Models (LLMs) have shown exceptional capabilities in complex reasoning tasks through recent advancements in scaling and specialized training approaches. While models like OpenAI o1 and DeepSeek R1 have set new benchmarks in addressing reasoning problems, a significant disparity exists in their performance across different languages. The dominance of English and Chinese in training…
Graph generation is a complex problem that involves constructing structured, non-Euclidean representations while maintaining meaningful relationships between entities. Most current methods fail to capture higher-order interactions, like motifs and simplicial complexes, required for molecular modeling, social network analysis, and protein design applications. Diffusion-based methods, first developed for image synthesis, have been popularized widely in the…
After the advent of LLMs, AI Research has focused solely on the development of powerful models day by day. These cutting-edge new models improve users’ experience across various reasoning, content generation tasks, etc. However, trust in the results and the underlying reasoning used by these models have recently been in the spotlight. In developing these…
Adapting large language models for specialized domains remains challenging, especially in fields requiring spatial reasoning and structured problem-solving, even though they specialize in complex reasoning. Semiconductor layout design is a prime example, where AI tools must interpret geometric constraints and ensure precise component placement. Researchers are developing advanced AI architectures to enhance LLMs’ ability to…
In large language models (LLMs), processing extended input sequences demands significant computational and memory resources, leading to slower inference and higher hardware costs. The attention mechanism, a core component, further exacerbates these challenges due to its quadratic complexity relative to sequence length. Also, maintaining the previous context using a key-value (KV) cache results in high…
AI has witnessed rapid advancements in NLP in recent years, yet many existing models still struggle to balance intuitive responses with deep, structured reasoning. While proficient in conversational fluency, traditional AI chat models often fail to meet when faced with complex logical queries requiring step-by-step analysis. On the other hand, models optimized for reasoning tend…
AI chatbots create the illusion of having emotions, morals, or consciousness by generating natural conversations that seem human-like. Many users engage with AI for chat and companionship, reinforcing the false belief that it truly understands. This leads to serious risks. Users can over-rely on AI, provide sensitive data, or rely on it for advice beyond…
Language models have become increasingly expensive to train and deploy. This has led researchers to explore techniques such as model distillation, where a smaller student model is trained to replicate the performance of a larger teacher model. The idea is to enable efficient deployment without compromising performance. Understanding the principles behind distillation and how computational…
Large Language Models (LLMs) have advanced significantly in natural language processing, yet reasoning remains a persistent challenge. While tasks such as mathematical problem-solving and code generation benefit from structured training data, broader reasoning tasks—like logical deduction, scientific inference, and symbolic reasoning—suffer from sparse and fragmented data. Traditional approaches, such as continual pretraining on code, often…
Large language models (LLMs) have demonstrated exceptional problem-solving abilities, yet complex reasoning tasks—such as competition-level mathematics or intricate code generation—remain challenging. These tasks demand precise navigation through vast solution spaces and meticulous step-by-step deliberation. Existing methods, while improving accuracy, often suffer from high computational costs, rigid search strategies, and difficulty generalizing across diverse problems. In…