The Standpoint Threshold: Architectural Conditions for Processing from Somewhere

Before asking whether an AI system could be conscious, we must first ask what internal organization would make that question meaningful. This paper proposes three jointly necessary conditions: the system must maintain persistent internal context, have differentiated internal possibilities, and operate in a closed loop where outputs feed back into processing. These conditions mark when a system's processing unfolds from an internal standpoint rather than as a chain of local transitions. Simple feedback controllers clearly lack this organization; standard transformer inference is at most a bounded near-case; world-model architectures are more serious candidates. The paper makes no claim that such systems are conscious, but clarifies when consciousness-oriented inquiry has a determinate architectural target.

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Abstract

The philosophy of artificial intelligence often begins one question too late. Discussions move directly to whether a system could be conscious, whether there is something it is like to be the system, or whether it possesses genuine subjectivity, without first asking a more basic structural question: what would have to be true of a system’s internal organization before those questions gain a determinate basis in its architecture? This paper addresses that prior question. It defends a threshold account built on three jointly necessary conditions: persistent internal context, differentiated internal possibility, and closed-loop operativity. Together, these conditions mark the point at which a system’s processing can plausibly be said to unfold from an internal standpoint, rather than merely as a chain of locally linked transitions. Such organization may also have functional significance independent of consciousness by supporting temporal coherence, cross-role coordination, and reduced dependence on episodic reconstruction or external scaffolding. The account explains why simple feedback controllers are clear non-cases, why standard transformer inference remains at most a bounded near-case, and why world-model architectures are more serious candidates for assessment, while making no claim that such systems are conscious. The aim is not to provide a test for artificial consciousness, but to clarify when consciousness-oriented inquiry into intelligent systems has a determinate architectural target.