Deliberative AI, the classical, symbolic tradition of state-space search, proceeds through recursive generate-and-test cycles: candidates are proposed, evaluated, pruned, and refined toward a goal. Generative AI, epitomized by transformer-based large language models, is instead characterized by a weak or trivial test step, producing outputs by sampling from learned conditional probability distributions.
A growing body of work treats these paradigms as complements rather than competitors. Search can structure and constrain LLM generation, and LLMs can serve as flexible components within a broader search process: as generators of successors, as scorers, or as tie-breakers. At the same time, an important question remains largely open: how, if at all, does deliberative, Type 2 behavior emerge from a fundamentally generative, Type 1 system?
Following State-Space Search and Transformer-Based AI Hybridizations (Fisher, Moore, and Roberts, ACS 2026), this special track invites papers that explore, build, evaluate, or theorize about hybrid architectures combining state-space search and transformer-based generation. It equally invites papers on the foundational question of how Type 1 and Type 2 processing relate to and give rise to one another. We welcome systems papers, empirical studies, and position or theoretical contributions.