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Advanced AI Reasoning Systems

Designing hybrid reasoning architectures that combine symbolic inference, search, and learning for complex decision making.

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Architectural Building Blocks

Knowledge substrate: ontologies, knowledge graphs, or fact stores supply explicitly structured context.
Inference core: rule engines, constraint solvers, or theorem provers perform deterministic reasoning steps.
Learning adapters: neural retrievers and policy networks propose hypotheses, rank options, or fill gaps in sparse knowledge.
Evaluation loop: critics, simulators, or human feedback check outputs before committing actions.

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