14 citations · 53 across the 18 of their papers we have counts for
19 papers
ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices
Joy Chen, Alejandro Castillejo Munoz, Pierluca D'Oro +3
Computer Use Agents (CUAs) are increasingly deployed to navigate mobile and desktop applications on behalf of users, yet no benchmark comprehensively evaluates whether they can saf…
Computer Use at the Edge of the Statistical Precipice
Pierluca D'Oro, Sneha Silwal, William Wong +6
Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically address. We show that a 1MB repla…
Hierarchical Behaviour Spaces
Michael Tryfan Matthews, Anssi Kanervisto, Jakob Foerster +3
Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option reward functions. We show tha…
DigiData: Training and Evaluating General-Purpose Mobile Control Agents
Yuxuan Sun, Manchen Wang, Shengyi Qian +18
AI agents capable of controlling user interfaces have the potential to transform human interaction with digital devices. To accelerate this transformation, two fundamental building…
Controlling Multimodal LLMs via Reward-guided Decoding
Oscar Mañas, Pierluca D'Oro, Koustuv Sinha +3
As Multimodal Large Language Models (MLLMs) gain widespread applicability, it is becoming increasingly desirable to adapt them for diverse user needs. In this paper, we study the a…
Mol-MoE: Training Preference-Guided Routers for Molecule Generation
Diego Calanzone, Pierluca D'Oro, Pierre-Luc Bacon
Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning…