Designing hybrid reasoning architectures that combine symbolic inference, search, and learning for complex decision making.
Agents alternate between proposing solutions and verifying them. Reflection loops tighten accuracy when tasks require multi-step logic.
Complex requests are split into subgoals. Each branch can reuse shared context while leaving an auditable trail of intermediate states.
Systems swap among fast heuristics, exhaustive search, or simulation depending on latency budgets and risk tolerance.