Cognitive Superposition

概要

Cognitive Superposition is an advanced meta-skill designed for high-level AI research, architectural design, and complex problem-solving. It enables Claude to simultaneously hold and integrate disparate expert perspectives—ranging from Emily Riehl’s synthetic ∞-categories and Ilya Sutskever’s compression-based intelligence to Jürgen Schmidhuber’s curiosity-driven learning and Yoshua Bengio’s causal GFlowNets. By applying principles like sheaf coherence and GF(3) conservation, the skill allows for 'measurement collapse' where theoretical abstractions are transformed into specific, actionable implementation patterns exactly when needed.

主な機能

• Intrinsic motivation frameworks via curiosity-driven learning and Gödel machines
• Compression-focused intelligence metrics based on Kolmogorov complexity
• Diverse solution space exploration using Generative Flow Networks (GFlowNets)
• 2 GitHub stars
• Multi-perspective synthesis of top-tier AI and mathematical research
• Category-theoretic reasoning using synthetic ∞-categories and Rzk-style types

ユースケース

• Developing self-improving code logic based on formal proofs and compression progress.
• Solving multi-dimensional engineering problems that lack a single standard framework.
• Designing novel AI architectures that require bridging category theory with machine learning.