Ergodicist

We aim to build AI’s underlying logic from scratch, based on a new cognitive theory. To do so, we first consolidate the fundamental flaws of existing AI. Current AI, dominated by deep learning and built upon cognitive theories like ACTR and PP, suffers from a foundational flaw: the objects they manipulate are not cognitively primitive. LLMs tokenize words, ACTR manipulates event chains, PP works on predictions—these are highlevel derivatives, not bedrock constituents. How can we expect to build an AGI that understands the nature of things from a foundation of prefabricated blocks? More precisely, they are **molecular‑level theories**: they describe interactions between macroscopic functional blocks. They work well when assembling known structures, but inevitably fail when recombination or extension is required. What we need is an **atomic‑level theory**: one that explains how functional molecules (e.g., objective entities) are built from more basic cognitive units, how molecules can be decomposed, and how entirely new molecules can be constructed from atoms—and thereby predict the properties of novel structures. Starting from first principles, we have constructed the **Weight‑Calculatism** cognitive theory—returning to the most essential and intuitive phenomena, re‑examining how we think, aiming to uncover the common processes and substrate underlying all cognition. At this point, we appear to have arrived at a theory of remarkable simplicity and explanatory power. Like all new theories, it currently lacks substantial empirical support, but we believe it serves as an excellent heuristic framework, providing a reference and target for criticism for subsequent theories. Here we focus on its application to AI architecture; the full theory is documented on GitHub. Please visit https://github.com/Ergodicist/Weight-Calculatism-cognitive-theory Now we can begin the discussion of the Weight‑Calculative AI architecture. In this article, we will focuse on