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This paper argues that current AI models are predictive but not explanatory, and proposes Mechanistic World Models as a new paradigm that places reusable mechanisms at the center of representation, computation, and learning to enable autonomous scientific discovery.
The author explores two key challenges for AI coding agents: ensuring long-duration autonomous execution (hours) and designing agent-friendly architectures for local applications. They propose an explicit knowledge organization stage to manage messy context before planning and execution.