Our research

We research new AI architectures and learning algorithms that could make advanced intelligence radically more efficient, transparent and accessible – enabling more researchers, companies and institutions to build and own intelligent systems locally.

Our current focus is on post-Transformer architectures and significantly more effective information routing schemes.

We believe that the only way to preserve healthy society and avoid catastrophic concentration of power is to ensure economic viability of small, specialised AI models that can be deployed on premise.

• [May 2026 – present] – Building on our interpretability results we work on radically more effective post-Transformer information routing schemes for neural networks.
• [July 2025, paper, code, demo] – We provide a unifying view on neural computation and introduce Semantic Pullbacks as an effective explanation method for deep models. Our results suggest that pullbacks are often a more natural and informative backward signal than gradients.
• [March 2024, paper, code] – We introduce Semantic Features as a candidate building block for transparent neural networks that are both self-explainable and adversarially robust.