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Freie Universität Berlin

Academic institutioneurope · de
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Research library247linked papers
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Selected work

Representative Papers

Governance of Generative Artificial Intelligence for Companies

Feb 05, 2024arXiv.org

While generative AI (GenAI) systems—such as ChatGPT—are being rapidly deployed in enterprises, existing AI governance frameworks fail to address GenAI’s unique technical characteristics (e.g., hallucination, non-determinism, data leakage risks) and their business implications (e.g., process integration, accountability allocation), resulting in governance gaps. Method: Grounded in Nickerson’s classical governance framework, this study integrates technical and business perspectives through conceptual analysis, cross-disciplinary literature synthesis, and iterative modeling to develop the first GenAI-specific governance framework for enterprise contexts. Contribution/Results: The framework adopts a three-dimensional structure—*scope*, *governance objectives*, and *implementation mechanisms*—defining organizational governance boundaries, hierarchical objectives (compliance, security, efficacy), and actionable mechanisms (policies, tools, workflows). It bridges a critical theoretical gap in organizational GenAI governance, delivers the first feasible and systematic implementation guide for enterprises, identifies key operational deficits, and proposes a phased adoption roadmap.

6 citationsRead paper

Hamiltonian Property Testing

Mar 05, 2024arXiv.org

This work studies the problem of testing *k-locality* of an unknown *n*-qubit Hamiltonian *H*: given black-box access to the time evolution under *H*, determine whether *H* is *k*-local or ε-far (in normalized Frobenius norm) from all *k*-local Hamiltonians. It is the first to formulate Hamiltonian property testing as a quantum property testing problem, revealing an exponential dependence of query complexity on the choice of distance metric. We propose the first average-case efficient algorithm, leveraging randomized measurements and incoherent quantum queries to achieve sample- and time-efficient *k*-locality testing with polynomial sample, query, and computational complexity. Our approach extends naturally to generalized Hamiltonian property testing. Crucially, it establishes the first exponential separation between quantum testing and quantum learning tasks—demonstrating that testing certain Hamiltonian properties is exponentially easier than learning them.

2 citationsRead paper

Boltzmann Generators for Condensed Matter via Riemannian Flow Matching

Feb 10, 2026

This work addresses the challenges of inefficient equilibrium sampling and inaccurate free energy estimation in condensed-phase systems by proposing a continuous normalizing flow method that incorporates periodic structural constraints. The approach constructs a Boltzmann generator via Riemannian flow matching—a technique applied here for the first time to condensed-phase systems—and integrates Hutchinson’s stochastic trace estimator with a cumulant-expansion-based bias correction scheme to enable thermodynamically consistent and efficient reweighting. Demonstrated on a monoatomic water model, the method successfully trains the largest generative model to date for such systems and achieves high-accuracy free energies without requiring multi-stage estimation protocols.

1 citationsRead paper

Accelerating scientific discovery with the common task framework

Nov 06, 2025

The scientific and engineering communities lack unified, reproducible benchmarks for evaluating AI/ML methods in dynamical systems modeling. Method: This paper introduces the Common Task Framework (CTF), a general-purpose framework targeting multiple scientific objectives—including prediction, state reconstruction, generalization, and control—under realistic constraints of limited data and noisy measurements. CTF establishes standardized datasets, objective evaluation metrics, and an open benchmarking platform. Contribution/Results: CTF enables the first cross-disciplinary, physics-constrained comparison of system identification and machine learning algorithms, facilitating rapid iterative development and integration. Experimental results demonstrate that CTF significantly improves model development efficiency and deployment reliability, thereby addressing a critical gap in AI evaluation frameworks oriented toward scientific discovery.

1 citationsRead paper
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