I'm a high school senior who spent the past few weeks simulating GPT behavior across long-form and iterative tasks. During that time, I discovered a persistent cache loop—where failed outputs would be reused, PDF render attempts caused silent token overloads, and session degradation worsened over time.<p>I documented this publicly with reproducible behavior and cleanup proposals:
→ <a href="https://github.com/sks38317/gpt-cache-optimization/releases/tag/v2025.04.19">https://github.com/sks38317/gpt-cache-optimization/releases/...</a><p>Highlights from the release:
- Token flushing failure during long outputs (e.g., PDF export)
- Recursive reuse of failed cache content
- Session decay from unpurged content
- Trigger-based cleanup logic proposal<p>Before publishing, I submitted a formal message to OpenAI Support. Here's part of what I wrote:<p>> “I’ve shared feedback and proposals related to GPT behavior and system design, including:
> - Memory simulation via user-side prompts
> - Cache-loop issues and PDF rendering instability
> - A framework modeling Systemic Risk (SSR) and Social Instability Probability (SIP)
> - RFIM-inspired logic for agent-level coordination
>
> I only ask whether any of it was ever reviewed or considered internally.”<p>Their response was polite but opaque:<p>> “Thanks for your thoughtful contribution. We regularly review feedback,
> but cannot provide confirmation, reference codes, or tracking status.”<p>Shortly after, I began observing GPT responses subtly reflecting concepts from the release—loop suppression, content cleanup triggers, and reduced carryover behavior.<p>It might be coincidence.
But if independent contributors are echoing system patterns before they appear—and getting silence in return—maybe that’s worth discussing.<p>If you’ve had feedback disappear into the void and return uncredited, you’re not alone.<p>*sks38317*
(independent contributor, archiving the things that quietly reappear)