Fixing LLM memory degradation in long coding sessions
Long-session LLM memory degradation (entropy) is the silent killer of complex coding projects. Models like Gemini, GPT-4, and Claude all suffer from it, leading to hallucinations and lost context. I've developed an open-source protocol that temporarily "fixes" this issue by structuring the dialogue. It's not the final architectural solution, but it’s a proven patch for developers working right now. Looking for feedback from the community on how we can solve this structurally. https://github.com/robertomisuraca-blip/LLM-Entropy-Fix-Prot...
Fixing LLM memory degradation in long coding sessions launched on November 27, 2025 with 5 votes, #4,178 of 9,795 launches that month and more than 75% of that year's launches.
What it does
Empirical proof of SOTA LLM (GPT-5/Gemini-Pro/Claude-Pro) context saturation in complex engineering. Contains the "Misuraca Protocol" for deterministic logical segmentation to prevent entropy drift. - robertomisuraca-blip/LLM-Entropy-Fix-Protocol
This project is licensed under the CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives). Status: Gold Standard (Production Ready) Version: 4.0.0 (December 2025) Author: Roberto Misuraca Current State-of-the-Art (SOTA) LLMs are marketed as capable of complex engineering via long-context windows (128k - 1M tokens). This is empirically false. Through extensive stress-testing during the development of the R&F Reward & Fidelity PRO software, I have identified a critical failure mode common to OpenAI, Anthropic, and Google models: Catastrophic Context Saturation . As session length increases, the model's "Self-Attention" mechanism degrades. The model does not "forget" in a linear fashion; it…from github.com
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