Gerrit Grossmann

Prashanth Pombala, Gerrit Grossmann, Verena Wolf

Generating molecular graphs is a challenging task due to their discrete nature and the competitive objectives involved. Diffusion models have emerged as SOTA approaches in data generation across various modalities. For molecular graphs, graph neural networks (GNNs) as a diffusion backbone have achieved impressive results. Latent space diffusion, where diffusion occurs in a low-dimensional space via an autoencoder, has demonstrated computational efficiency. However, the literature on latent space diffusion for molecular graphs is scarce, and no commonly accepted best practices exist. In this work, we explore different approaches and hyperparameters, contrasting generative flow models (denoising diffusion, flow matching, heat dissipation) and architectures (GNNs and E(3)-equivariant GNNs). Our experiments reveal a high sensitivity to the choice of approach and design decisions. Code is made available at github.com/Prashanth-Pombala/Molecule-Generation-using-Latent-Space-Graph-Diffusion.

Sumantrak Mukherjee, Mouad Elhamdi, George Mohler et al.

Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature.

Vaibhav Jain, Gerrit Grossmann

Reinforcement Learning (RL) agents often struggle in sparse-reward environments where traditional exploration strategies fail to discover effective action sequences. Large Language Models (LLMs) possess procedural knowledge and reasoning capabilities from text pretraining that could guide RL exploration, but existing approaches create rigid dependencies where RL policies must follow LLM suggestions or incorporate them directly into reward functions. We propose a framework that provides LLM-generated action recommendations through augmented observation spaces, allowing RL agents to learn when to follow or ignore this guidance. Our method leverages LLMs' world knowledge and reasoning abilities while maintaining flexibility through soft constraints. We evaluate our approach on three BabyAI environments of increasing complexity and show that the benefits of LLM guidance scale with task difficulty. In the most challenging environment, we achieve 71% relative improvement in final success rates over baseline. The approach provides substantial sample efficiency gains, with agents reaching performance thresholds up to 9 times faster, and requires no modifications to existing RL algorithms. Our results demonstrate an effective method for leveraging LLM planning capabilities to accelerate RL training in challenging environments.