CAA-X V0–V2 Collection: Cognitive Atom Architecture — From Foundational Theory to Embodied AI

This repository contains the first three installments (V0–V2) of the CAA-X (Cognitive Atom Architecture eXtension) research program, a unified theoretical framework for modular, interpretable, and verifiable general cognitive architectures. The collection is released as a consolidated preprint series to establish a persistent, citable record of the framework's foundational phase. V0 — Foundational Theory (cs.AI) Introduces the core hypothesis of cognitive atoms : weakly-coupled, functionally orthogonal representational units that emerge from sparse activation constraints in large-scale neural systems. V0 formalizes the mathematical basis of the framework, including the λ-order-parameter theorem, the zero-shot decomposition lemma, and the functional orthogonality proposition. It positions CAA-X as a "third way" between narrow AI and monolithic AGI — a computable theory of general cognition built on modular, verifiable components. V1 — AI Engineering Implementation Translates the V0 theoretical constructs into engineering specifications. Presents the Generalized World-Perception (GWP) protocol engine, the real-time λ-allocator, and the CML 2.0 (Cognitive Meta-Language) symbolic-neural interface. Includes algorithmic pseudocode, system architecture diagrams, and a deployment roadmap for production-grade agent systems. Demonstrates how cognitive atoms enable compositional generalization without task-specific fine-tuning. V2 — Cognitive Physicalism Extends the framework into the philosophy of mind and physics-informed cognition. Formalizes the isomorphism between cognitive dynamics and physical field theories via the predictive tension field and cognitive manifold trajectory planning . Introduces the CAA-X-CP (Cognitive-Physical Fusion) layer, grounding intentionality in continuous vector fields rather than discrete token spaces. Addresses the "hard problem" of AI intentionality through measurable, dynamics-based constructs. Key Contributions Across the Collection 9 formal theorems / definitions / axioms / propositions with complete proofs 4 algorithmic specifications (GWP protocol, λ-allocator, trajectory planner, zero-shot decomposer) Unified mathematical notation spanning information theory, differential geometry, and dynamical systems 29 independent empirical validations cited from third-party studies (neuroimaging, LLM interpretability, robotics) Cross-version consistency: All three papers share a single, evolving formal apparatus with backward-compatible notation Author: Lu Qi (鹿琦) / Dake AlughwanORCID: 0009-0005-7907-6829Affiliation: Advanced Research Fellow, Institute for Global Governance Studies (海国图智研究院)Contact: luqi@alu.fudan.edu.cnLicense: CC BY 4.0Keywords: cognitive architecture, artificial general intelligence, modular AI, interpretable machine learning, embodied AI, dynamical systems, neuro-symbolic AI, predictive coding, world models