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#artificial intelligence Preprint Sep 2026

TACIT: Optimization Models that Learn from Their Mistakes

Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts'tacit knowledge, making them hard to formalize. As a result, optimization models often contain miscalibrated objectives, missing constraints, or omitted decision variables, leading to solu...

Maxime Bouscary, Marco Molinaro, Si-Rui Li et al. · 0 citations
#machine learning Preprint Sep 2026

HeurEvo: Agentic Evolution of Hybrid Solver-Augmented Heuristics for Time-Critical Mathematical Optimization

Recent advances in agentic heuristic design use AI agents and execution feedback to automate algorithm discovery for challenging optimization problems. In many practical settings, high-quality solutions must be obtained under strict runtime constraints, motivating hybrid approaches that combine problem-specific heurist...

Fei-Jie Wu, Hugo Barbalho, Konstantina Mellou et al. · 0 citations

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