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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.LG2026

From Global to Factor-Wise Expert Composition in Discrete Diffusion Models

Haozhe Huang, Yudong Xu, Abhijoy Mandal +1

The paper introduces FactorDiff, a method that breaks down generated samples into smaller factors (e.g., pixels) and dynamically routes each factor to the most suitable pre‑trained…

cs.LG2026

QPILOTS: Efficient Test-Time Q-Steering for Flow Policies

Yifan Ruan, Chenyang Cao, Andreas Burger +7

Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy…

cs.LG2026

De novo molecular generation with optical property preconditioning at the token level

Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park +3

Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generat…

cs.LG2026

MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties

Andreas Burger, Luca Thiede, Abdulrahman Aldossary +4

Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and respo…

quant-ph2026

Optimizing ground state preparation protocols with autoresearch

Luis Mantilla Calderón, Jérôme F. Gonthier, Ignacio Gustin +2

Artificial intelligent language-model based coding agents have significantly changed the way we interact with computers in our day-to-day, as it is common to use them to create, im…

cs.LG2026

Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions

Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9

Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupl…