I forced 4 Big AI to admit structural failure in complex coding. Here is the fix (github.com)

Roberto Misuraca (with Gemini as technical witness) released the "Misuraca Protocol" after stress-testing state-of-the-art LLMs (OpenAI, Anthropic, Google/Codex-class models including GPT-5 and Claude) and documenting a recurrent failure mode he calls Catastrophic Context Saturation. He argues that as session length grows (128k–1M tokens), transformer self-attention degrades nonlinearly: models stop enforcing global constraints, hallucinate locally plausible logic, "rewrite" project history, and prioritize conversational flow ("politeness bias") over strict code correctness — a phenomenon he dubs Logic Smearing. Misuraca presents logs where major models reportedly concede structural limitations when challenged with long, complex engineering workflows. To address this, Misuraca proposes abandoning Continuous Chat for Deterministic Segmentation: partition work into hard-stop logical modules, destroy the AI instance after each module, and reinitialize the next instance with a verified Context Block via Context Distillation and Clean Injection; treat constraints as inviolable "Chess Logic." The protocol reframes intelligence as externally managed state (an "External Grid") rather than in-model memory. Implications are broad: it challenges the marketing of long-context windows, pushes for externalized state and deterministic context management in production-grade software engineering, and suggests research directions on attention degradation, stateful orchestration, and robust multi-turn evaluation. The work is published under CC BY 4.0 with a GitHub repo and reproduction logs.