CAA-X V4: Cognitive Architecture eXtension for Zero-Shot Agent Challenges

This preprint presents CAA-X V4, a cognitive architecture extension framework addressing the 'Intention Gap' in current end-to-end large models. Building upon Global Workspace Theory (Baars, 1988) and predictive processing (Friston, 2010), CAA-X introduces: (1) Cognitive Atoms as weakly-coupled functional primitives with zero-shot compositional generalization; (2) Predictive Tension Fields as holographic world models; (3) GWP Protocol Engine with selective synchronization; (4) Cognitive Manifold Trajectory Planning with intention-biased geodesics; (5) Lambda-Order Parameter for dynamic resource allocation. Empirical validation includes a robotic arm grasping experiment achieving 78% zero-shot success rate. This work responds to the emerging deflationary trend in AI research (ICLR 2026, NeurIPS 2025) that rewards mechanistic explanations and modular architectures over pure scaling. Supplementary analysis integrates 14 top-tier papers published between 2025-2026, identifying CAA-X's alignment with three major field trends: deflationary findings, mechanistic explanations, and modular architecture revival.

This version incorporates the latest 2026 advances in neuro-symbolic AI, mechanistic interpretability, and Global Workspace Theory operationalization. The CAA-X framework provides a unified mathematical formalization of cognitive architecture integrating compositional learning, tensor dynamics, and lambda-order parameters.

Key 2026 updates include:
- Neuro-symbolic AI mainstreaming (Stanford AI Index 2026, Tufts energy breakthrough)
- Mechanistic interpretability breakthrough (MIT 2026 Top 10, SAE microscope)
- Compositional learning emergence (ICML 2026 Workshop, Assign and Add)
- GWT operationalization (Tait et al. 6 markers, TPA framework)
- Critical initialization biology (Pachitariu et al., Nature 2026)

GitHub repository: https://github.com/leodarc/CAA-X
License: CC BY 4.0