3 citations · 6 across the 20 of their papers we have counts for
24 papers
Breaking the Space Barrier and its Application to Language Model Inference
Arip Asadulaev
Language models are more and more often asked for structured output: JSON that follows a schema, or a tool call with typed arguments. A small machine, an automaton, enforces the fo…
Tropical Reinforcement Learning
Arip Asadulaev, Aladin Djuhera, Karim Salta +3
Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formula…
Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems
Abduragim Shtanchaev, Arip Asadulaev, Luiza Labazanova +3
Latent diffusion models serve as powerful priors for solving inverse problems in image restoration, such as deblurring, inpainting, and super-resolution. Current methods have a tra…
Branched Optimal Transport Amortization
Semyon Semenov, Viktor Kovalchuk, Meir Roketlishvili +4
Methods of Branched Optimal Transport (BOT) mimic the economy and efficiency of natural tree-like structures, such as those found in rivers and biological systems. These methods ar…
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…
Convex Compositional Reasoning Models
Meir Roketlishvili, Semyon Semenov, Maksim Bobrin +7
Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show…