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This paper formalizes the Linear Representation Hypothesis as a family of claims by using group actions to define representation equivalence, clarifying assumptions across different analyses.
This paper develops a phenomenology-first approach to artificial consciousness by modeling subjective experience with categories derived from Q-networks, emphasizing enactive and embedded dimensions of computational systems.
This MIT paper introduces a mathematical framework for a unified representation of deep learning architectures, enabling automated analysis and optimization. It aims to replace manual design with a common language that can generate diagrams, graphs, or PyTorch code.
The author releases Chronoformal Closure Theory (CCT), a new mathematical framework showing that problems like realizability, minimal observation, and state compression reduce to finite combinatorial structures, with potential implications for AI agents. The release includes proofs, code, and a partial Lean formalization, inviting independent mathematical scrutiny.
The paper introduces Topological Void Analysis (TVA), a mathematical framework that formalizes the discovery of unexplored technical regions in high-dimensional knowledge spaces by identifying triads of concepts with specific cohesion and marginality conditions. Applied to ~140k documents, TVA generates invention candidates with high survival rates through expert review.
This paper proposes a unified geometric framework for understanding concept learning and neuron interpretation in sparse autoencoders, formalizing concepts as sets and defining detection, separation, and approximation. It provides error bounds, capacity constraints, and links to formal concept analysis, with experiments on synthetic data.