SSLP: An Explicit Spatial Metric-Driven Cognitive Interface for Large Language Models

空间语义逻辑协议(SSLP):一种面向大语言模型的显式空间度量驱动认知接口构想 Current research on spatial intelligence still relies heavily on end-to-end visual models that implicitly learn spatial relations from pixels through large-scale parameter fitting. Although this paradigm has achieved remarkable progress in perception tasks, it continues to face three major limitations in engineering scenarios. First, the basis of spatial judgment is buried in high-dimensional weights, resulting in limited interpretability. Second, such models often depend on substantial data and computing resources, making lightweight deployment difficult. Third, the structural order among complex engineering objects is difficult to abstract into verifiable logical rules. To address these problems, this paper proposes a conceptual framework termed the Spatial Semantic Logic Protocol (SSLP). The core idea is not to feed physical space directly into a model as pixels for implicit fitting, but to first compress spatial structure into low-dimensional, explicit, interpretable, and composable logical representations through a set of spatial metrics, and then pass these representations to a large language model for spatial semantic judgment and logical reasoning. Using directional entropy as the first exemplar logical primitive, this paper illustrates how geometric organizational features may be translated into semantic cognition, and proposes a closed-loop cognitive chain from spatial observation–metric extraction–logic encoding–language reasoning–semantic feedback–geometric validation. Furthermore, unlike conventional one-way inference schemes, SSLP introduces a self-verification mechanism based on geometric equivariance constraints. By treating structural stability under affine transformations as a logical probe, the framework attempts to establish a feedback loop from semantic generation back to geometric logical closure, thereby laying a theoretical foundation for residual-based closure criteria and offering an interpretable path toward suppressing “spatial hallucinations” in large language models under complex engineering conditions. This paper does not claim that a complete protocol has already been established, nor that a single metric can cover the entirety of spatial cognition. Its main contribution is the proposal of an extensible white-box research framework that reformulates spatial understanding as a problem of explicit-metric-driven semantic logical reasoning, while further introducing geometric self-consistency validation as an endogenous verification idea. In this sense, SSLP provides a research entry point that remains open to further testing for low-compute, strongly interpretable, and cross-scenario GeoAI cognitive systems.