Tootfinder

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Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving
Florian Wintel, Sigmund H. H{\o}eg, Gabriel Kiss, Frank Lindseth
https://arxiv.org/abs/2506.00560

Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving
End-to-end planning systems for autonomous driving are improving rapidly, especially in closed-loop simulation environments like CARLA. Many such driving systems either do not consider uncertainty as part of the plan itself, or obtain it by using specialized representations that do not generalize. In this paper, we propose EnDfuser, an end-to-end driving system that uses a diffusion model as the trajectory planner. EnDfuser effectively leverages complex perception information like fused camera …

This https://arxiv.org/abs/2408.09568 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair
Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and testing. However, training these models requires massive amount of data and significant computational resources. Adapters are specialized, small modules designed for parameter efficient fine-tuning of LLMs for specific tasks, domains, or applications without requiring extensive retraining of the entire model. These adapte…

ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging
Xuhang Chen, Michael Kwok-Po Ng, Kim-Fung Tsang, Chi-Man Pun, Shuqiang Wang
https://arxiv.org/abs/2505.22683

ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging
Brain network analysis plays a crucial role in diagnosing and monitoring neurodegenerative disorders such as Alzheimer's disease (AD). Existing approaches for constructing structural brain networks from diffusion tensor imaging (DTI) often rely on specialized toolkits that suffer from inherent limitations: operator subjectivity, labor-intensive workflows, and restricted capacity to capture complex topological features and disease-specific biomarkers. To overcome these challenges and advance com…

When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
Michael Amir, Matteo Bettini, Amanda Prorok
https://arxiv.org/abs/2506.09434 https://

When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
The success of teams in robotics, nature, and society often depends on the division of labor among diverse specialists; however, a principled explanation for when such diversity surpasses a homogeneous team is still missing. Focusing on multi-agent task allocation problems, our goal is to study this question from the perspective of reward design: what kinds of objectives are best suited for heterogeneous teams? We first consider an instantaneous, non-spatial setting where the global reward is b…

PromptVFX: Text-Driven Fields for Open-World 3D Gaussian Animation
Mert Kiray, Paul Uhlenbruck, Nassir Navab, Benjamin Busam
https://arxiv.org/abs/2506.01091

PromptVFX: Text-Driven Fields for Open-World 3D Gaussian Animation
Visual effects (VFX) are key to immersion in modern films, games, and AR/VR. Creating 3D effects requires specialized expertise and training in 3D animation software and can be time consuming. Generative solutions typically rely on computationally intense methods such as diffusion models which can be slow at 4D inference. We reformulate 3D animation as a field prediction task and introduce a text-driven framework that infers a time-varying 4D flow field acting on 3D Gaussians. By leveraging lar…

A mesoscale phase-field model of intergranular liquid lithium corrosion of ferritic/martensitic steels
A. Lhoest, S. Kovacevic, D. Nguyen-Manh, J. Lim, E. Mart\'inez-Pa\~neda, M. Wenman
https://arxiv.org/abs/2506.02776

A mesoscale phase-field model of intergranular liquid lithium corrosion of ferritic/martensitic steels
A phase-field model is developed to simulate intergranular corrosion of ferritic/martensitic steels exposed to liquid lithium. The chromium concentration of the material is used to track the mass transport within the metal and liquid (corrosive) phase. The framework naturally captures intergranular corrosion by enhancing the diffusion of chromium along grain boundaries relative to the grain bulk with no special treatment for the corrosion front evolution. The formulation applies to arbitrary 2D…