Referenced CTT Structure: Part II–4, II–5
Scholarly Keywords: Human-Robot Interaction, Perception Delay, Reaction Time Modeling
This section outlines how Constructive Tensor Theory (CTT) can address key unresolved limitations in modern AI's semantic understanding, as identified through DeepResearch analysis.
| AI Limitation | DeepResearch Summary | CTT Contribution |
|---|---|---|
| Statistical Pattern Imitation (Stochastic Parrot) | Lacks true semantic comprehension | CTT enables transition from form to meaning via constructive observer frames |
| Symbol Grounding Problem | No link between symbols and external world | CTT allows selection and transformation of semantic bases using pullback structures |
| Lack of World Models | Time, space, and causality are missing | CTT embeds causality into hierarchical manifold pullbacks |
| Deficiency in Causal Inference | Only correlations are learned, not causes | CTT constructs causal laws via tensorial transformation rates |
| Embodiment Deficit | No linkage with sensorimotor experience | CTT represents embodied observers explicitly in tensor hierarchies |
| Lack of Intentional Agency | No context-aware, autonomous behavior | CTT defines observers as agent-based systems unified by observation and action |
symbol grounding problem + manifold representationcausal structure in agent-based AItensor-based semantics for embodied AIobserver frame and meaning construction in neural-symbolic modelscompositionality and pullback structures in AI