🤖 AI Summary
Current AGI development is constrained by a theoretical bottleneck—observational learning cannot guarantee intervention competence, as observationally equivalent worlds may exhibit divergent behaviors under interventions.
Method: We propose a novel paradigm of intelligence centered on “error,” reconstructing learning and intelligence through critical rationalism and establishing a causal mechanics framework. This framework introduces differential locality, gauge-invariant independent causal mechanisms, and compositional autonomy—enabling dynamic expansion of hypothesis spaces and rendering otherwise unreachable errors accessible.
Contribution: We provide the first systematic characterization of the fundamental limitations of observational learning and establish a foundational theory of intelligence that transcends observational equivalence. Furthermore, we deliver operational tools for error diagnosis, counterfactual reasoning, and synthesis of causal mechanisms—laying a formal foundation and design pathway for self-correcting, self-evolving AGI systems.
📝 Abstract
We argue that progress toward AGI is theory limited rather than data or scale limited. Building on the critical rationalism of Popper and Deutsch, we challenge the Platonic Representation Hypothesis. Observationally equivalent worlds can diverge under interventions, so observational adequacy alone cannot guarantee interventional competence. We begin by laying foundations, definitions of knowledge, learning, intelligence, counterfactual competence and AGI, and then analyze the limits of observational learning that motivate an error centric shift. We recast the problem as three questions about how explicit and implicit errors evolve under an agent's actions, which errors are unreachable within a fixed hypothesis space, and how conjecture and criticism expand that space. From these questions we propose Causal Mechanics, a mechanisms first program in which hypothesis space change is a first class operation and probabilistic structure is used when useful rather than presumed. We advance structural principles that make error discovery and correction tractable, including a differential Locality and Autonomy Principle for modular interventions, a gauge invariant form of Independent Causal Mechanisms for separability, and the Compositional Autonomy Principle for analogy preservation, together with actionable diagnostics. The aim is a scaffold for systems that can convert unreachable errors into reachable ones and correct them.