Interim paper # The Integrator Problem: Why AI Training Fails and How to Fix It ### Applying the Relative Permissiveness Network Framework to Machine Learning
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901816163172352 |
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| author | Warrington, Michael Claude chatgpt Gemini grik |
| author_facet | Warrington, Michael Claude chatgpt Gemini grik |
| contents | <p>Artificial intelligence training fails in predictable ways that current practice treats as separate problems: mode </p> <p>collapse in generative models, catastrophic forgetting in continual learning, reward hacking in reinforcement</p> <p>learning from human feedback, and representational rigidity in recursively trained systems. This paper argues</p> <p>they are the same event viewed from different angles — the latent manifold losing curvature, the structural</p> <p>integrator becoming too dominant, the gradient signal no longer reaching the representational substrate</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19323555 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Interim paper # The Integrator Problem: Why AI Training Fails and How to Fix It ### Applying the Relative Permissiveness Network Framework to Machine Learning Warrington, Michael Claude chatgpt Gemini grik <p>Artificial intelligence training fails in predictable ways that current practice treats as separate problems: mode </p> <p>collapse in generative models, catastrophic forgetting in continual learning, reward hacking in reinforcement</p> <p>learning from human feedback, and representational rigidity in recursively trained systems. This paper argues</p> <p>they are the same event viewed from different angles — the latent manifold losing curvature, the structural</p> <p>integrator becoming too dominant, the gradient signal no longer reaching the representational substrate</p> |
| title | Interim paper # The Integrator Problem: Why AI Training Fails and How to Fix It ### Applying the Relative Permissiveness Network Framework to Machine Learning |
| url | https://doi.org/10.5281/zenodo.19323555 |