Simulated a GPT cache bug, saw it echoed back

Hacker News

Share card

Simulated a GPT cache bug, saw it echoed back

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. I documented this publicly with reproducible behavior and cleanup proposals: → https://github.com/sks38317/gpt-cache-optimization/releases/... 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 Before publishing, I submitted a formal message to OpenAI Support. Here's part of what I wrote: > “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.” Their response was polite but opaque: > “Thanks for your thoughtful contribution. We regularly review feedback, > but cannot provide confirmation, reference codes, or tracking status.” Shortly after, I began observing GPT responses subtly reflecting concepts from the release—loop suppression, content cleanup triggers, and reduced carryover behavior. 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. If you’ve had feedback disappear into the void and return uncredited, you’re not alone. *sks38317* (independent contributor, archiving the things that quietly reappear)