arXiv Machine Learning

Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

arXiv:2606. 26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals.

arXiv Machine Learning

2d ago

Stochastic Control Policies for Robust Molecular Transition Path Sampling

arXiv:2608. 13800v1 Announce Type: new Abstract: Transition path sampling (TPS) aims to efficiently generate rare molecular transition trajectories between metastable states and is essential for understanding biomolecular mechanisms.

By Jingqian Liu, Yu-Hsiang Wang, Yanru Qu, Ge Liu

benchmarks

arXiv Machine Learning

Jun 25

Uncertainty-aware reinforcement learning for chemical language models

arXiv:2606. 24990v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design, enabling Chemical Language Models (CLMs) to navigate and explore the chemical space while optimizing specific desired properties.

By Borja Medina, Jon Paul Janet

llms reinforcement-learning

arXiv Machine Learning

Jun 10

Rare Event Analysis via Stochastic Optimal Control

arXiv:2604. 13213v2 Announce Type: replace-cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them.

By Yuanqi Du, Jiajun He, Dinghuai Zhang, Eric Vanden-Eijnden, Carles Domingo-Enrich

benchmarks

arXiv Machine Learning

5d ago

A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study

arXiv:2608. 12388v1 Announce Type: cross Abstract: The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience.

By Abolfazl Moslemi, Milad Sarabadani, Fatemeh Sefidian, Hossein Peyvandi

arXiv Machine Learning

Jul 7

Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems

arXiv:2607. 03671v1 Announce Type: cross Abstract: Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables: similar activity can emerge from coordinated parameter changes.

By Ruilin Zhang, Louis Tao, Zhuo-Cheng Xiao

diffusion

arXiv Machine Learning

Aug 6

Distributional Active Inference

arXiv:2601. 20985v2 Announce Type: replace Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning.

By Abdullah Akg\"ul, Gulcin Baykal, Manuel Hau{\ss}mann, Mustafa Mert \c{C}elikok, Melih Kandemir

reinforcement-learning robotics benchmarks

arXiv AI

Jul 23

Sophisticated Policies from Epistemic Priors

arXiv:2607. 19518v1 Announce Type: new Abstract: Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search.

By Wouter W. L. Nuijten, Bert de Vries

benchmarks

arXiv Machine Learning

Jul 21

Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search

arXiv:2603. 16842v2 Announce Type: replace Abstract: Stochastic resetting -- intermittently returning a process to a fixed reference state -- has emerged as an effective mechanism for optimizing first-passage properties.

By Jello Zhou, David J. Schwab, Vudtiwat Ngampruetikorn

agents reinforcement-learning

arXiv AI

Jun 19

Can In-Context Learning Support Intrinsic Curiosity?

arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.

By Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald, Rajai Nasser, Blaise Ag\"uera y Arcas, Jo\~ao Sacramento, Rif A. Saurous, Guillaume Lajoie

llms agents reinforcement-learning safety

arXiv Machine Learning

Jul 16

Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning

arXiv:2607. 13553v1 Announce Type: cross Abstract: Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments.

By Andrea Maria Braghin, Nicol\`o Botteghi, Matteo Tomasetto, Andrea Manzoni, Gabriele Cazzulani

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 17

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607. 14522v1 Announce Type: new Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC).

By Zikun Zhang, Jiayuan Sheng, David D. Yao, Wenpin Tang

llms diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 7

On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.

By Trevor Chen, Ariel Dai, Jason Yang, Riccardo De Santi, Daniel Khalil, Wenda Chu, Nate Gruver, Pranav Murugan, Alexander F. G. Goldberg, Maruan Al-Shedivat, Yisong Yue

diffusion fine-tuning

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