CTT Necessity Map: Domains of Discontinuity and Scholarly Keywords

Application Domains of CTT and Scholarly Keyword Mapping

1. Multi-Agent AI and Ontological Drift

2. Quantum Measurement and Geometrization of Observation Frames

3. Memory and Neural Representation of Time

4. Semantic Collapse in Augmented Reality

5. Cosmic Expansion and Hierarchical Time Structures

6. Recursive LLMs and Semantic Degradation

8. Meta-Cognition and Recursive Self-Models

9. Generalization Limits in Deep Learning

10. Semantic Instability in Multi-Modal Fusion

11. Temporal Entanglement in Generative AI

12. Scientific Explanation and Non-Constructive Descriptions

13. Identity Persistence in Simulated Worlds

14. Observational Divergence in Multi-Perspective Epistemology

15. Constructive Irreversibility in Biological Time

16. Temporal Loops in Closed-Causal Systems

17. Meaning Construction in Collective Intelligence

18. Semiotic Drift in Information Ecosystems

19. Ontological Collapse in World Modeling

20. Synchronization Failure in Autonomous Systems

21. Time Asymmetry in Logical Paradoxes

22. Epistemic Compression and Meaning Collapse

23. Implicit Context in Language Understanding

24. Emergence in Open-Ended Systems

25. Semantic Polarization in Ideological AI

26. Constructive Limit in Ontological Engineering

27. Time-Resolved Ethics in AI Agents

28. Multiscale Instability in Human Perception

29. Trans-Temporal Consistency in Memory Reconstruction

30. Foundational Crisis in Knowledge Formalization


Application to AI Semantic Limitations

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

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