Student builds AI feedback architecture that mirrors Anthropic's internal Claude design

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A third-year university student developed a self-correcting AI system using Python scripts and markdown files to address common LLM problems like rule-forgetting and output drift. The architecture centers on a compact self-model, an attention-routing interface, and a causal feedback loop where behavior generates data that in turn adjusts system routing. On July 6, 2026, Anthropic published details of Claude's internal 'J-space' architecture, which the student says shares the same core topology: a compact center, a broadcast mechanism, and causal feedback. The student conducted a small controlled experiment removing a single routing rule, which caused AI sub-agents to skip verification steps and use 37% fewer tool calls, demonstrating measurable behavioral impact. The developer argues this independent convergence supports the idea that Global Workspace Theory represents a substrate-independent architectural pattern applicable to neural networks and prompt engineering alike.

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