5 papers
Sparse Transformer: a Controlled Testbed for Mechanistic Interpretability
Anton Korznikov, Alexey Dontsov, Elena Tutubalina
Mechanistic interpretability aims to decompose neural network computations into human-understandable features, yet progress is limited by the absence of ground-truth units to evalu…
Superhuman AI for Generals.io Using Self-Play Reinforcement Learning
Matej Straka, Viliam Lisý, Martin Schmid
We present a superhuman AI agent for Generals.io, a real-time strategy game that combines long-horizon planning, imperfect information, and combinatorially large state and action s…
SAM2RL: Towards Reinforcement Learning Memory Control in Segment Anything Model 2
Alen Adamyan, Tomáš Čížek, Matej Straka +2
Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks and has become the state-of-the-art for visual object tracking. The model stores i…
Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning
Matej Straka, Martin Schmid
We introduce a real-time strategy game environment based on Generals.io, a game with thousands of weekly active players. Our environment is fully compatible with Gymnasium and Pett…
Computing approximate symmetries of complex networks
Anna Pidnebesna, David Hartman, Aneta Pokorná +2
The symmetry of complex networks is a global property that has recently gained attention since MacArthur et al. 2008 showed that many real-world networks contain a considerable num…