4 citations · 4 across the 5 of their papers we have counts for
6 papers
LiFT: Loop Flow Transformers
Mohammad Mahdi Derakhshani, Pedro M. P. Curvo, Gertjan J. Burghouts +2
We introduce Loop Flow Transformers (LiFT), a family of looped generative models that scales computation by repeatedly applying a shared Diffusion Transformer (DiT) core, with only…
Follow the Mean: Reference-Guided Flow Matching
Pedro M. P. Curvo, Maksim Zhdanov, Floor Eijkelboom +1
Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interfa…
MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention
Pedro M. P. Curvo, Jan-Willem van de Meent, Maksim Zhdanov
A key scalability challenge in neural solvers for industrial-scale physics simulations is efficiently capturing both fine-grained local interactions and long-range global dependenc…
Reproducibility Study of "Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents"
Pedro M. P. Curvo, Mara Dragomir, Salvador Torpes +1
This study evaluates and extends the findings made by Piatti et al., who introduced GovSim, a simulation framework designed to assess the cooperative decision-making capabilities o…
The Traitors: Deception and Trust in Multi-Agent Language Model Simulations
Pedro M. P. Curvo
As AI systems increasingly assume roles where trust and alignment with human values are essential, understanding when and why they engage in deception has become a critical researc…
Using Deep Learning to Design High Aspect Ratio Fusion Devices
P. Curvo, D. R. Ferreira, R. Jorge
The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free…