Deep learning faces difficulties when applied to large physical systems on irregular grids, especially when interactions occur over long distances or at multiple scales. Handling these complexities becomes harder as the number of nodes increases. Several techniques have difficulty tackling these big problems, resulting in high computational costs and inefficiency. Some major issues are capturing… →

Transformer models have transformed language modeling by enabling large-scale text generation with emergent properties. However, they struggle with tasks that require extensive planning. Researchers have explored modifications in architecture, objectives, and algorithms to improve their ability to achieve goals. Some approaches move beyond traditional left-to-right sequence modeling by incorporating bidirectional context, as seen in models… →

Large language models have made significant strides in understanding and generating human-like text. Yet, when it comes to complex reasoning tasks—especially those that require multi-step calculations or logical analysis—they often struggle. Traditional chain-of-thought (CoT) approaches help by breaking down problems into intermediate steps, but they rely heavily on the model’s internal reasoning. This internal dependency… →