Rogerio Feris

Heterogeneous visual agents can boost downstream performance by up to 4.9 points across diverse tasks simply by reading from a shared, self-organizing 2B memory tree—without updating a single host model weight.

Predicted-state matching enables web agents to achieve superior action selection by ensuring that their state predictions are discriminative, leading to better task performance.

LLMs can now interpret quantum operations, achieving state-of-the-art results in circuit synthesis while allowing natural language constraints.