AI RESEARCH
AI Mental Models: Learned Intuition and Deliberation in a Bounded Neural Architecture
arXiv CS.AI
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ArXi:2603.22561v1 Announce Type: new This paper asks whether a bounded neural architecture can exhibit a meaningful division of labor between intuition and deliberation on a classic 64-item syllogistic reasoning benchmark. broadly, the benchmark is relevant to ongoing debates about world models and multi-stage reasoning in AI. It provides a controlled setting for testing whether a learned system can develop structured internal computation rather than only one-shot associative prediction.