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Cohen’s Agentic Conjecture: A Dual-Process Neuro-Symbolic Framework for Agentic AI

Abstract

This research introduces Cohen’s Agentic Conjecture (CAC), proposing that an artificial intelligence system integrating fast, neural heuristics (System 1) with slow, symbolic logic (System 2) through a dynamic gating mechanism can exhibit emergent agentic properties. These properties include context-aware decision-making, self-directed learning, robust reasoning, and reflective self-correction. Drawing inspiration from dual-process cognitive theories and neuro-symbolic AI paradigms, this work formalizes CAC, presents a comprehensive Python implementation, and validates the conjecture through empirical experiments. The findings demonstrate that CAC-enhanced systems outperform purely neural or purely symbolic counterparts in terms of accuracy, interpretability, and adaptability. This framework lays the groundwork for developing next-generation AI agents capable of autonomous, reliable, and