Large language models excel at language but often struggle with structured reasoning tasks. This article explores Tiny Recursive Models (TRMs), a radically simpler approach that uses small neural networks with recursive refinement to outperform massive LLMs on puzzles like Sudoku, mazes, and ARC-AGI. By prioritizing iterative reasoning over scale, TRMs show that deep thinking can emerge from minimal architectures, challenging prevailing assumptions about model size and intell

Nested Learning reframes neural models as multi-loop systems updating at different frequencies, revealing that depth stacking hides gradient mechanics and limits continual learning. It interprets optimizers like Momentum and Adam as associative gradient memories and introduces CMS for incremental abstraction. The HOPE module combines self-modification, multi-clock updates, and deep contextual compression, offering a white-box path beyond static backbones for long-context and

Discover how NLP tools can identify and analyze contentious topics.