Conference Agenda

As the field of Artificial Intelligence / Machine Learning (AI/ML) advances, managing and monitoring intelligent models during their lifecycle, also known as machine learning operations (MLOps), has become essential [1]. MLflow is an open-source platform [2] to assist AI/ML practitioners and teams in handling the AI/ML lifecycle, ensuring that each stage is manageable, traceable, and reproducible. MLflow v3 has many comprehensive features, including Experiment Tracking for parameter logging, metrics visualisation, and artifact storage; Model Registry for model versioning and lineage tracking; Datasets module for dataset management. It also features GenAI capabilities like LLM Observability (Tracing), Evaluation and Monitoring of GenAI applications, Prompt Management, and AI Gateway.

Our team provides MLflow instances within several EU projects, including AI4EOSC and iMagine, and upcoming FLUID-AI and EOSC-ARENA, covering use cases from various scientific domains. Recently, we also brought a new pilot instance for Helmholtz researchers [3]. The Helmholtz instance is coupled with Helmholtz ID/AAI and LSDF large storage and allows experiment and model sharing among MLflow users for collaborative development. A major upgrade that also includes group-sharing capabilities is foreseen before June 2026. In this contribution, we are going to present the advantages of the MLflow platform for AI/ML researchers, share our experience in providing MLflow for various use-cases and supporting them with their workflows, and demonstrate the main features of the available Helmholtz pilot instance.

[1] Berberi, L., Kozlov, V., Nguyen, G. et al. Machine learning operations landscape: platforms and tools. Artif Intell Rev 58, 167 (2025). https://doi.org/10.1007/s10462-025-11164-3

[2] https://mlflow.org/ and https://mlflow.org/docs

[3] https://mlflow.scc.kit.edu and https://mlops.data.kit.edu

Biomedical foundation models (FMs) pre-trained on large-scale histopathology datasets are rapidly advancing AI-enabled diagnostics and tissue analysis. However, their self-supervised training objectives capture any variation in data, including non-biological confounders such as differences in staining protocols or scanners across medical centers. While recent benchmarks focused on performance, a systematic evaluation of FM robustness to such artifacts has been lacking.

We introduce PathoROB, a comprehensive robustness benchmark for pathology FMs, comprising 4 multi-center datasets (~99k patches, 28 biological classes, 34 medical centers). We propose 3 complementary metrics: (i) a robustness index quantifying the local dominance of biological over confounding features in FM embedding space; (ii) average performance drop, measuring downstream model vulnerability to shortcut learning under increasingly spurious training data; and (iii) a clustering score assessing global embedding space organization. We evaluated 20 FMs spanning diverse architectures, pre-training objectives, and dataset scales. We also studied post-hoc robustification strategies — stain normalization, ComBat batch correction, and domain-adversarial learning — not requiring FM retraining.

All 20 FMs exhibited robustness deficits, with robustness indices ranging from 0.45 to 0.86. Larger pre-training datasets and vision-language objectives yielded higher robustness, with Virchow2 and Atlas achieving the best performance–robustness tradeoff. Under spurious correlations, downstream models suffered accuracy drops up to 47 pp with less robust FMs failing to detect tumor regions entirely. These failures extended to slide-level MIL models and unsupervised clustering and retrieval. The robustness index strongly correlated with downstream robustness (Spearman ρ up to 0.90). Combined stain normalization and ComBat improved the robustness index by up to 70%, and stain normalization with domain-adversarial training reduced performance drops, though no method fully eliminated them.

Our findings demonstrate that robustness evaluation is essential before clinical deployment of pathology FMs, as non-robust representations can cause diagnostic failures even in state-of-the-art models. Future FM development should integrate robustness as a key criterion, improving robustness potentially via post-training alignment. PathoROB provides a blueprint for systematic robustness assessment across biomedical domains.

Integrating Large Language Models (LLMs) into scientific workflows presents a critical challenge across both academia and industry: AI agents excel at functional syntax but lack specific scientific reasoning and intuition. They frequently hallucinate domain-specific logic or silently discard governing scientific laws and principles to optimize performance. This poses a severe risk in scientific visualizations and software development, as unguided generative AI can produce convincing yet fundamentally invalid tools. To safely harness LLM code generation, we advocate for the Scientist-AI-Loop (SAIL), a human-in-the-loop framework designed to structurally decouple scientific logic from coding syntax.

In SAIL, the researcher acts as the conceptual architect enforcing theoretical boundaries and phenomenological constraints, while the AI exclusively handles code implementation and rendering. Originally designed to overcome bottlenecks in building public outreach tools, this domain-agnostic framework provides a generalized blueprint for broader scientist-AI workflows. We validate SAIL via two astrophysical visualization tools: a real-time gravitational lensing application (nicosmo.github.io/lensing_visualization/) and a dynamic cosmic structure formation simulation (nicosmo.github.io/cosmic_web_explorer/). During development, SAIL exposed a series of critical, often invisible AI failures, instances where agents confidently fabricated physics or silently discarded governing laws simply to satisfy code compilation.

By establishing a structured progression from rapid prototyping to agentic IDE integration, we demonstrate how SAIL not only safeguards scientific integrity against these probabilistic failures, but compresses development timelines from months to under 80 hours. Ultimately, SAIL establishes the necessary protocols to stop AI from breaking physics, ensuring generative tools can be safely leveraged for professional modeling, theoretical sandboxing, and interactive communication.

The Helmholtz Model Zoo (HMZ) is a cloud-based platform that provides remote access to deep learning models within the Helmholtz Association. It enables seamless inference execution via both a web interface and a REST API, lowering the barrier for scientists to integrate state-of-the-art AI models into their research.

Scientists from all 18 Helmholtz centers can contribute their models to HMZ through a streamlined, well-documented submission process on GitLab. This process minimizes effort for model providers while ensuring flexibility for diverse scientific use cases. Based on the information provided about the model, HMZ automatically generates the web interface and API, tests the model, and deploys it. The REST API further allows for easy integration of HMZ models into other computational pipelines.

With the launch of HMZ, researchers can now run AI models directly within the Helmholtz Cloud, ensuring that all data remain within the association and that our data sovereignty is preserved. The platform imposes no strict limits on the number of inferences or the volume of uploaded data, while Helmholtz Virtual Organizations (VOs) enable fine-grained access control for specialized models. External researchers can also access HMZ through Helmholtz VOs upon invitation by a Helmholtz representative, facilitating collaborative research beyond the association's boundaries. Data uploaded for inference is stored within HIFIS dCache InfiniteSpace and remains under the ownership of the uploading user.

HMZ is powered by GPU nodes, hosted as part of the DESY Hamburg HPC cluster. Model inference is managed through the NVIDIA Triton Inference Server, ensuring efficient GPU utilization. The development and maintenance of HMZ are led by the Helmholtz Imaging Support Team at DESY, with support from Helmholtz Federated IT Services (HIFIS) and the Helmholtz AI platform. Hardware and implementation have been supported by funds from the Haicore initiative.

Our presentation will provide an overview of HMZ architecture and its integration into a professional HPC environment. It will also address the scientific foundations of selected models and emphasise the benefits of operating them entirely within the Helmholtz infrastructure.

Recent advances in Large Language Models (LLMs) have enabled natural language interfaces for robotic systems, allowing robots to interpret human instructions and generate executable task plans. However, general-purpose LLMs often lack grounding in robot capabilities, which can lead to hallucinated actions, incomplete plans, and incorrect task ordering when generating robotic task sequences.

In this work, we investigate the reliability of LLMs for robotic task planning under constrained action spaces. We introduce a benchmark based on a fixed robot skill library that represents the available capabilities of a robotic system. Using this environment, we evaluate several planning approaches, including prompting-based baselines, general-purpose LLMs such as Mistral, and robotics-oriented planning models.

To address common failure modes observed in baseline systems, we construct an extended instruction-to-plan dataset derived from publicly available and synthetically generated data. The dataset focuses on structured action sequences and realistic robotic task constraints. Using this dataset, we apply parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning to adapt language models for robotic planning tasks.

We evaluate the models across several reliability metrics, including plan validity, step completeness, hallucination rate, and action ordering correctness. Experimental results demonstrate that lightweight domain-specific fine-tuning significantly improves planning reliability compared to zero-shot prompting approaches. In addition, we analyze training and inference performance across CPU and high-performance computing environments to assess practical deployment considerations.

Overall, this work provides a reproducible framework for benchmarking LLM-based robotic task planners and highlights the importance of dataset design and domain adaptation for improving the reliability of language-driven robotic systems.