October Event: AI Paper Reading

AI Paper Reading Club - Monthly Meetup

Join us for our monthly AI Paper Reading Club, a relaxed and welcoming space for anyone curious about the cutting edge of machine learning and artificial intelligence. Whether you're here for deep dives into the math behind the models or prefer to focus on the practical impact of applied research, this event has something for you.

Each session features a volunteer presenter who picks a recent or classic paper to unpack, ranging from rigorous theoretical work to industry-shaping applications.

Bring your questions, your insights, or just your curiosity. There’s no pressure to present, and all backgrounds are welcome.

We believe in learning together, at our own pace, no gatekeeping, no ego, just AI enthusiasts helping each other grow.

Paper Title: "Hierarchical Reasoning Model"
Paper Link: https://arxiv.org/abs/2506.21734
Abstract: Reasoning, the process of devising and executing complex goal-oriented action sequences, remains a critical challenge in AI. Current large language models (LLMs) primarily employ Chain-of-Thought (CoT) techniques, which suffer from brittle task decomposition, extensive data requirements, and high latency. Inspired by the hierarchical and multi-timescale processing in the human brain, we propose the Hierarchical Reasoning Model (HRM), a novel recurrent architecture that attains significant computational depth while maintaining both training stability and efficiency. HRM executes sequential reasoning tasks in a single forward pass without explicit supervision of the intermediate process, through two interdependent recurrent modules: a high-level module responsible for slow, abstract planning, and a low-level module handling rapid, detailed computations. With only 27 million parameters, HRM achieves exceptional performance on complex reasoning tasks using only 1000 training samples. The model operates without pre-training or CoT data, yet achieves nearly perfect performance on challenging tasks including complex Sudoku puzzles and optimal path finding in large mazes. Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key benchmark for measuring artificial general intelligence capabilities. These results underscore HRM's potential as a transformative advancement toward universal computation and general-purpose reasoning systems.

Presenter: Scott Maher

We thank Mantel for generously sponsoring the venue for this event. The company support makes it possible for us to bring the AI community together, share knowledge, and grow as a collective.