• 09:15 - 09:30 CEST
• tbd.

Opening & Welcome

• 09:30 - 10:30 CEST
• Martin Hebart
• tbd.

Keynote "Similarities and Differences of Representations in Humans and Artificial Intelligence"

In this talk, I will provide an overview over our work at understanding the similarities and differences in representations between humans and artificial intelligence. Our approach identifies and compares interpretable dimensions underlying representations in both domains, highlighting key determinants underlying their representational similarities and differences.

• 10:00 - 10:30

Coffee Break

• 11:00 - 11:30 CEST
• Andre Opris
• tba.

Research Talk "Understanding the Population Dynamics of NSGA-III via Runtime Analysis"

NSGA-III is a widely used algorithm in evolutionary many-objective optimization and is designed for problems with more than three objectives, distinguishing it from the classical NSGA-II. Despite its practical success, the theoretical understanding of when and why NSGA-III performs well remains limited. In this talk, we help bridge this gap by presenting rigorous runtime analyses of NSGA-III on several classical benchmark problems with arbitrary numbers of objectives. Our results improve previous analyses in regimes where the population size asymptotically exceeds the size of the Pareto front. A key ingredient of our work is a detailed understanding of the population dynamics of NSGA-III. Notably, in the bi-objective setting, the obtained upper runtime bounds are asymptotically tighter than the best known bounds for NSGA-II. For suitable population sizes, NSGA-III even achieves a smaller expected runtime than NSGA-II. Finally, we complement these results by establishing tight runtime bounds for NSGA-III.

• 11:30 - 12:00 CEST
• Paul-Niklas Kandora
• tba.

Research Talk "LinearizeLLM: An Agent-Based Framework for LLM-Driven Exact Linear Reformulation of Nonlinear Optimization Problems"

Reformulating nonlinear optimization problems into solver-ready linear or mixed-integer linear problems is a common but expertise-intensive task in mathematical programming. We study this task as a verification-aware reformulation problem: given a raw LaTeX optimization problem, can a workflow recover the reformulation-relevant problem structure, apply known exact recipes for a fixed supported reformulation family, and validate returned candidates by execution? We propose LinearizeLLM, a multi-stage language-model-supported workflow that detects supported nonlinear patterns, records their nesting structure, applies a deterministic bottom-up reformulation order, and generates solver-ready LP/MILP candidate formulations with reformulation records. The current implementation supports absolute values, minimum and maximum operators, binary-continuous products, linear fractional terms under sign conditions, and monotone objective transformations. On ComplexORNL, a 40-instance controlled benchmark derived from ComplexOR and synthetic nested models, LinearizeLLM has higher observed end-to-end objective-preservation success than the evaluated same-model one-shot reformulator and the raw-LaTeX-to-GDP-to-MILP pipeline. In order to address correctness beyond objective agreement, we add a solution-mapping verification step: 88 of 89 successfully-reformulated LinearizeLLM runs are mapped back to original decision-variable assignments that pass feasibility and optimality checks in the original reference problem.

• 12:00 - 13:00

Lunch Break

• 13:00 - 13:30 CEST
• tbd.

Research Talk

• 13:30 - 14:00 CEST
• tba.

Research Talk

• 14:00 - 14:30 CEST
• tba.

Research Talk

• 14:30 - 15:00

Coffee Break

• 15:00 - 15:30 CEST
• tba.

Research Talk

• 15:30 - 16:00 CEST
• tba.

Research Talk

• 16:00 - 16:30 CEST
• tba.

Research Talk