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Current LLMs struggle to translate visual scenes into executable geometric functions, failing even basic trajectory generation tasks.
Results show that the effectiveness of TabPFN is highly problem dependent, and it cannot replace conventional surrogates universally, and it should be adopted selectively according to data availability, landscape complexity, search space characteristics, and its role within the algorithm.
Existing global-descriptor methods fail to effectively localize small storefronts in large scenes, but GeoStore's GLAM method achieves superior performance with drastically lower matching costs.
