Practical Mimicry and Modal Integration: Know-How, Large Language Models, and Human–AI Collaboration

Why Large Language Models will Never be Embodied: A Categorical, Not Engineering, Impossibility

A persistent ambition in artificial intelligence research holds that large language models (LLMs) will eventually become embodied—through attachment to robots, augmentation with multimodal sensors, integration into vision–language–action architectures, or sheer scale until grounding emerges from training data. This amb...

Emergent Reasoning in Large Language Models: A Systematic Evaluation Across Task Complexity

It is argued that emergent reasoning in LLMs is a product of three factors: model size, prompting approach, and evaluation metric, and proposed implications for designing benchmarks and assessing capabilities are proposed.

Dynamic Derived Intentionality in Large Language Models: From Implicit Beliefs to Semantic Parasitism

Beyond the Black Box—A New Vector for Explainable AI Through Comparative Analysis of Logical Systems

Modern AI often works as a “black box”: it gives an answer, but cannot show why. In high-stakes fields like law, medicine, and government—and under emerging rules such as the EU AI Act—that is a serious problem. Today’s most popular explainability tools, such as SHAP and LIME, only approximate a model’s reasoning after...

ON AI AND SOME PHILOSOPHICAL CHALLENGES

A unified vision is offered: intelligence arises from constrained architectures and emergent patterns, and as AI systems become more autonomous, they may require surrogate forms of consciousness to handle complex, real-world challenges—though these will never fully replicate the biological original.

State of Thought Enables Endogenous Reasoning

State of Thought is proposed, a new reasoning paradigm that enables endogenous reasoning in LLMs, with the model's internal reasoning state governing how reasoning unfolds, and provides a generalizable and efficient alternative to existing test-time reasoning paradigms.