Cognitive Virtual Machine: A New Paradigm for AGI under Carbon–Silicon Synergy

The current mainstream research route toward artificial general intelligence is built on the statistical fitting paradigm, relying on massive parameters, large-scale datasets, and sustained computational consumption, and it inherently suffers from structural limitations such as hallucination, insufficient interpretability, and the high cost of long-range reasoning. This paper proposes a cognitive paradigm, the Cognitive Virtual Machine (CVM), co-constructed by carbon-based and silicon-based agents. It organizes the systematically accumulated knowledge of humanity into a self-consistent determination framework. Alongside probabilistic sampling generation, it adds a deductive boundary-determination route guided by structured presupposition frameworks, thereby suppressing the sources of hallucination. The Cognitive Virtual Machine can be mounted on the cognitive substrate of existing large models without modifying the underlying model weights. Its reasoning process pursues logical self-consistency and can reduce the source of generative hallucination. The paradigm attempts to cross the conceptual barriers between disciplines and offers a new research perspective for unified cross-domain cognitive determination. The carbon-based agent is responsible for constructing first-principle presuppositions, cognitive frameworks, and systems of thought; the silicon-based agent is responsible for logical verification, long-horizon consistency checking, and reasoning assistance. Together, they form a new mode of cognitive collaboration. This paper proposes only a paradigmatic framework and preliminary reasoning ideas, and does not provide an engineering implementation. The validity of the paradigm remains to be tested by subsequent formal proof and long-horizon stability tests. This paper discloses only the paradigmatic-layer framework; the underlying axiom set and execution mechanism are archived separately.