Cohomological AI — AI skill for Claude Code

Cohomological Transformer

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1. OVERVIEW & CONTEXT

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Welcome to **cohomological_transformer**, a single-file Python codebase that explores advanced—and quite speculative—ideas at the intersection of:

• Deep Neural Networks
• Algebraic Topology (particularly cohomology, exact sequences, and spectral sequences)
• Transformer Architectures (with attention-based processing)
• Novel Loss Functions & Training Objectives (to enforce “exactness” or “local-to-global” constraints)
• Circuit Detection & Pruning (inspired by sheaf cohomology analogies)

This project combines numerous code snippets from prior research interactions, providing a single-file library that demonstrates how one might incorporate the language and structure of algebraic topology—particularly **exact sequences** and **spectral sequences**—into the design of neural architectures and their training objectives.

1.1 Purpose of This Repository

This repository is for **experimental** usage. The code is not guaranteed to produce better results than standard Transformers; rather, it’s a kind of conceptual playground for those curious about:

1. Whether advanced mathematics—like cohomology—can inspire new ways to track or constrain feature representations in a neural network
2. How one might systematically incorporate ideas like “exactness,” “kernel→image sequences,” or “local-to-global spectral sequences” into the forward pass, the loss, and the optimizer
3. Potential architectural motifs that blend local processing with global multi-head attention in a “topologically aware” manner
4. Potential strategies for detecting “critical circuits” in a large model and pruning unimportant weights, with a topological or cohomological rationale

1.2 Why Is This Interesting?

**Cohomology** is a powerful branch of