3 papers
cs.AI2026
Bayesian control for coding agents
Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov +4
Modern coding agents pair LLM generators with various tools, including cheap diagnostics and expensive verifiers. The tool-use decisions are typically governed by orchestrators tha…
cs.CL2026
ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
Vladislav Smirnov, Chieu Nguyen, Sergey Senichev +14
Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.g., via m…
cs.CL2026
Uncertainty Quantification for Large Language Diffusion Models
Artem Vazhentsev, Vladislav Smirnov, David Li +3
Large Language Diffusion Models (LLDMs) are emerging as an alternative to autoregressive models, offering faster inference through higher parallelism. Similar to autoregressive LLM…