Cover Image

Interim paper # The Integrator Problem: Why AI Training Fails and How to Fix It ### Applying the Relative Permissiveness Network Framework to Machine Learning

Fuente: Zenodo

Saved in:

Bibliographic Details
Main Authors:	Warrington, Michael, Claude chatgpt Gemini grik
Format:	Recurso digital
Published:	Zenodo 2026
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Your Brain Filters Noise (And Why It Fails)
by: Rosehill, Daniel, et al.
Published: (2026)

Why Transaction Cost Economics Failed and How to Fix It
by: Mingqian, Li
Published: (2024)

Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning
by: Basu, Abhinaba
Published: (2026)

The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It
by: Garcia, Gabriel
Published: (2026)

Ep. 2598: Why Israeli Apartment Walls Are So Thin — and How to Fix Them
by: Rosehill, Daniel, et al.
Published: (2026)

Why Latent Actions Fail, and How to Prevent It
by: Lee, Jung Min, et al.
Published: (2026)

Why Network Segmentation Projects Fail
by: Dube, Rohit
Published: (2026)

Gradient Starvation in Binary-Reward GRPO: Why Group-Mean Centering Fails and Why the Simplest Fix Works
by: Nie, Wenhua, et al.
Published: (2026)

Why Governments Are Building Bunkers for AI
by: Rosehill, Daniel, et al.
Published: (2026)

Maximally Permissive Reward Machines
by: Varricchione, Giovanni, et al.
Published: (2024)

The Colombian Anti-Corruption Referendum: Why It Failed?
by: Michael Haman
Published: (2019)

Why Has Artificial Intelligence Failed? And How Can it Succeed?
by: John F. Sowa
Published: (2014)

Why Machine Learning Models Systematically Underestimate Extreme Values II: How to Fix It with LatentNN
by: Ting, Yuan-Sen
Published: (2025)

Why Multi-Agent AI Is Mostly Hype
by: Rosehill, Daniel, et al.
Published: (2026)

Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
by: Kim, Taeyoung, et al.
Published: (2024)

The Transformer Trinity: Why Three Architectures Rule AI
by: Rosehill, Daniel, et al.
Published: (2026)

Why AI Can't Simulate Extreme Decision-Making
by: Rosehill, Daniel, et al.
Published: (2026)

AI-Washing: Why Gemini Hallucinates Audio Engineering
by: Rosehill, Daniel, et al.
Published: (2026)

“In the Guaraní world, our way of being isn't to make something to sell. We're always in family, sharing”: Gendered Frictions of Care and Commerce in Peri‐Urban Bolivia
by: Sibylla Warrington
Published: (2025)

Why LLMs Cannot Think and How to Fix It
by: Jahrens, Marius, et al.
Published: (2025)

The Two Boundaries: Why Behavioral AI Governance Fails Structurally
by: McCann, Alan L.
Published: (2026)

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention
by: Qiu, Haiquan, et al.
Published: (2025)

Why Gaussian Diffusion Models Fail on Discrete Data and How to Prevent It?
by: Shabalin, Alexander, et al.
Published: (2026)

Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
by: Roy, Amartya, et al.
Published: (2026)

SE Research is a Complex Ecosystem: Isolated Fixes Keep Failing -- and Systems Thinking Shows Why
by: Shaw, Mary, et al.
Published: (2026)

Why Library Schools Fail.
by: Paris, Marion
Published: (1990)

The Israeli Paradox: Elite Tech vs. Failing Schools
by: Rosehill, Daniel, et al.
Published: (2026)

Ep. 263: Beyond the Table: Why AI is Moving to Graph Databases
by: Rosehill, Daniel, et al.
Published: (2026)

Ep. 422: The Tactile Revolution: Why Keyboards Outlast Voice AI
by: Rosehill, Daniel, et al.
Published: (2026)

Why Fine-Tuning Encourages Hallucinations and How to Fix It
by: Kaplan, Guy, et al.
Published: (2026)

Why DPO is a Misspecified Estimator and How to Fix It
by: Gopalan, Aditya, et al.
Published: (2025)

Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study
by: Alam, Khairul, et al.
Published: (2026)

Why Agentic AI Needs a Hive Mind, Not a Single Brain
by: Rosehill, Daniel, et al.
Published: (2026)

Learning Through Noise: Why Subliminal Learning Works and When It Fails
by: Brockers, Vincent C., et al.
Published: (2026)

Why Smooth Stability Assumptions Fail for ReLU Learning
by: Katende, Ronald
Published: (2025)

DeepFix: Debugging and Fixing Machine Learning Workflow using Agentic AI
by: Seydou, Fadel Mamar, et al.
Published: (2026)

History as Wonder
by: Hughes-Warrington, Marnie
Published: (2025)

ETHICS FOR ARTIFICIAL HISTORIANS
by: Marnie Hughes‐Warrington
Published: (2025)

Ep. 1086: Why AI Can't Stop Talking About Second Order Effects
by: Rosehill, Daniel, et al.
Published: (2026)

The Admissibility → Observation Theorem: Why Observation Requires Structural Permission in the Paton System
by: Paton, Andrew John
Published: (2026)