Institution profile

Helmholtz-Zentrum Hereon

Academic institutioneurope · de
Official website
Research library8linked papers
Opportunities0open roles
Selected work

Representative Papers

AnchorBench: A Multi-Pathway Benchmark for the Anchoring Effect in LLMs

Aug 14, 2026

This study addresses the limitations of single-path evaluation and neglected anchor relevance in assessing anchoring effects within large language models. We construct a multi-path anchoring benchmark incorporating an explicit relevance dimension and conduct large-scale controlled experiments across fourteen models. Our results reveal the path-dependency of anchoring bias, demonstrating that plausible anchors induce more significant deviations and that high-accuracy models remain vulnerable to such semantically relevant anchors. By transcending traditional single-path evaluation paradigms, this work systematically elucidates the mechanisms underlying model bias across diverse reasoning paths and anchor semantics. Ultimately, these findings establish a novel framework for evaluating the cognitive robustness of large language models, highlighting critical vulnerabilities even in high-performing systems when exposed to contextually plausible misinformation.

0 citationsRead paper

Uncertainty-aware data assimilation through variational inference

Oct 20, 2025

In data assimilation, uncertainty quantification remains challenging due to the coupling of dynamical models with process noise and sparse, noisy observations. To address this, we propose a variational inference–based uncertainty-aware data assimilation framework that models stochastic state evolution as a multivariate Gaussian distribution, enabling approximately perfectly calibrated uncertainty estimates and supporting longer assimilation windows. The method is end-to-end differentiable and seamlessly embeddable within machine learning architectures. Evaluated on the chaotic Lorenz-96 system, it achieves high state estimation accuracy while significantly improving uncertainty calibration and out-of-distribution generalization compared to conventional approaches. All code is publicly available to facilitate reproducibility and further research extensions.

0 citationsRead paper
Recent publications

Latest Papers

AnchorBench: A Multi-Pathway Benchmark for the Anchoring Effect in LLMs

Aug 14, 2026

This study addresses the limitations of single-path evaluation and neglected anchor relevance in assessing anchoring effects within large language models. We construct a multi-path anchoring benchmark incorporating an explicit relevance dimension and conduct large-scale controlled experiments across fourteen models. Our results reveal the path-dependency of anchoring bias, demonstrating that plausible anchors induce more significant deviations and that high-accuracy models remain vulnerable to such semantically relevant anchors. By transcending traditional single-path evaluation paradigms, this work systematically elucidates the mechanisms underlying model bias across diverse reasoning paths and anchor semantics. Ultimately, these findings establish a novel framework for evaluating the cognitive robustness of large language models, highlighting critical vulnerabilities even in high-performing systems when exposed to contextually plausible misinformation.

0 citationsRead paper

Uncertainty-aware data assimilation through variational inference

Oct 20, 2025

In data assimilation, uncertainty quantification remains challenging due to the coupling of dynamical models with process noise and sparse, noisy observations. To address this, we propose a variational inference–based uncertainty-aware data assimilation framework that models stochastic state evolution as a multivariate Gaussian distribution, enabling approximately perfectly calibrated uncertainty estimates and supporting longer assimilation windows. The method is end-to-end differentiable and seamlessly embeddable within machine learning architectures. Evaluated on the chaotic Lorenz-96 system, it achieves high state estimation accuracy while significantly improving uncertainty calibration and out-of-distribution generalization compared to conventional approaches. All code is publicly available to facilitate reproducibility and further research extensions.

0 citationsRead paper