Sapient released their Hierarchical Reasoning Model (HRM) and the results are pretty interesting. This is a 27M parameter model that outperforms Claude 3.5 and o3-mini on reasoning benchmarks like ARC-AGI-2, complex Sudoku puzzles, and pathfinding in large mazes. What makes this notable: The efficiency aspect is striking. HRM was trained on roughly 1000 examples with no pretraining or Chain-of-Thought prompting, yet it handles complex reasoning tasks that typically require much larger models. This makes it practical for deployment on edge devices and accessible for teams without massive compute budgets. The brain-inspired architecture is more than just terminology. HRM uses a dual-system design with two modules: one for high-level abstract planning and another for rapid detailed execution, operating at different time scales. This mirrors how human cognition works with both fast intuitive processing and slower deliberate reasoning. The low-resource requirement changes the accessibility equation. While most advanced AI requires significant infrastructure, HRM can run on regular hardware, opening up sophisticated reasoning capabilities to startups and researchers who can't afford large-scale compute. Technical approach: Instead of processing tokens sequentially like Transformers, HRM uses hierarchical recurrent loops that operate in continuous space rather than discrete tokens. The model solves tasks directly without needing to verbalize its thinking process through explicit step-by-step chains. The parameter efficiency comes from learning reasoning patterns that generalize from minimal examples rather than memorizing vast amounts of training data. The training uses single-step gradient approximation to keep memory usage constant, making it practical on standard hardware. HRM also adapts its computation - spending more cycles on harder problems and fewer on simpler ones, using reinforcement learning to determine when to stop reasoning. The reasoning process is interpretable, especially on visual tasks where you can observe how it solves problems step-by-step. This suggests that advanced reasoning might be more about architectural design than scale, which could shift how we think about building capable AI systems.