15 papers
Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking
Maksim Zhdanov, Pavel Strashnov, Vladislav Kurenkov
Molecular docking requires reasoning jointly about ligand pose and protein flexibility. Most diffusion-based docking models predict torsional updates with generic Euclidean heads t…
Dual Advantage Fields
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that…
Yes, Q-learning Helps Offline In-Context RL
Denis Tarasov, Alexander Nikulin, Ilya Zisman +6
Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL set…
ABRA: Agent Benchmark for Radiology Applications
Bulat Maksudov, Vladislav Kurenkov, Kathleen M. Curran +1
Existing medical-agent benchmarks deliver imaging as pre-selected samples, never as an environment the agent must navigate. We introduce ABRA, a radiology-agent benchmark in which…
Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics
Maksim Bobrin, Ilya Zisman, Alexander Nikulin +2
Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time training or task-specific fine-tuning.…
Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner
Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin +11
Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorith…