1 citations · 1 across the 7 of their papers we have counts for
9 papers
Control OSWorld: An AI Control Environment for GUI Computer Use Agents
Maria Sanchez del Rio, Mikhail Terekhov
AI agents that operate a computer through its graphical user interface (GUI) are being widely deployed. AI control studies how to prevent an AI system from causing harm even if it…
Diffuse AI Control on Fuzzy Tasks
Mikhail Terekhov, Caglar Gulcehre, Vivek Hebbar +1
AI models deployed in critical domains, such as AI safety research, may subtly sabotage our efforts due to misalignment. Diffuse AI Control is a subfield of AI safety concerned wit…
Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols
Mikhail Terekhov, Alexander Panfilov, Daniil Dzenhaliou +4
AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a security problem, stress testin…
Control Tax: The Price of Keeping AI in Check
Mikhail Terekhov, Zhen Ning David Liu, Caglar Gulcehre +1
The rapid integration of agentic AI into high-stakes real-world applications requires robust oversight mechanisms. The emerging field of AI Control (AIC) aims to provide such an ov…
Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards
Daniil Karzanov, Rubén Garzón, Mikhail Terekhov +3
This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional…
Contextual Bandit Optimization with Pre-Trained Neural Networks
Mikhail Terekhov
Bandit optimization is a difficult problem, especially if the reward model is high-dimensional. When rewards are modeled by neural networks, sublinear regret has only been shown un…
One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
Viacheslav Surkov, Chris Wendler, Antonio Mari +5
For large language models (LLMs), sparse autoencoders (SAEs) have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of…
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
Mikhail Terekhov, Caglar Gulcehre
Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functio…
Learning pure quantum states (almost) without regret
Josep Lumbreras, Mikhail Terekhov, Marco Tomamichel
We initiate the study of sample-optimal quantum state tomography with minimal disturbance to the samples. Can we efficiently learn a precise description of a quantum state through…