Papers

• Reward Transport: Property Control in Flow Matching via Noise-Space Alignment (2026)
  Kehan Guo et al.
  4.39
• MechAInistic: An LLM-guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models (2026)
  Josh Loecker et al.
  4.39
• TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology (2026)
  Hyunjin Seo et al.
  3.51
• CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning (2026)
  Dongxia Wu et al.
  3.28
• Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation (2026)
  Zag ElSayed et al.
  2.00
• GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design (2026)
  Noah Flynn
  1.89
• Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization (2026)
  Aadyot Bhatnagar et al.
  1.83
• InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics (2025)
  Ann Huang et al.
  1.50
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017)
  Mufti Mahmud et al.
  —
• Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology (2020)
  Corinna Maier et al.
  —
• Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design (2020)
  Xiufeng Yang and Tanuj Kr Aasawat and Kazuki Yoshizoe
  —
• Parallelizing Contextual Bandits (2021)
  Jeffrey Chan et al.
  —
• NEORL: NeuroEvolution Optimization with Reinforcement Learning (2021)
  Majdi I. Radaideh et al.
  —
• CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection (2022)
  Quanfu Fan et al.
  —
• Feedback Efficient Online Fine-Tuning of Diffusion Models (2024)
  Masatoshi Uehara et al.
  —
• ml_edm package: a Python toolkit for Machine Learning based Early Decision Making (2024)
  Aur\'elien Renault et al.
  —
• Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models (2024)
  Yang An et al.
  —