AI Summary: This paper introduces a hybrid neural-cognitive framework that solves combinatorial optimization problems by mimicking human-like reward learning and memory. It identifies that integrating classical symbolic constraints with modern transformers reduces the search space for high-dimensional planning.
We explore the boundary between simple heuristics and complex neural-cognitive models in combinatorial optimization. This paper demonstrates how hybrid architectures can leverage memory to shape reward learning in high-dimensional state spaces. By integrating classical optimization constraints with transformer-based reasoning, we achieve significant speedups in complex planning tasks such as logistics and chip design. The research underscores a fundamental shift toward models that understand the 'geometry' of the problem space rather than relying on brute-force search.
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