sks38317

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)

Hi HN,

I'm working on a small system simulation project that explores how instability can emerge from the interaction of various variables over time. (Repo linked in the comments.)

Recently, I’ve been trying to incorporate the RFIM (Random Field Ising Model) into the logic—but I’ve hit a wall. It’s not the math or the concept itself, but rather the total lack of Korean-language resources or beginner-friendly guides that’s been challenging.

I’d really appreciate it if anyone:

has experience with RFIM and would be open to helping via a PR

knows of any well-documented codebases I could learn from

or could point me toward approachable explanations or tutorials

I’m still relatively new to GitHub and trying to improve this simulation step-by-step, so any support would go a long way.

Thanks for reading—and for any help you can offer!

Looking for Korean-language resources on RFIM or temporal graph modeling

I’ve recently started looking into system modeling and came across concepts like the Random Field Ising Model (RFIM) and temporal graph structures. I’m still new to this area, and while I’ve been going through English materials, I was wondering:

Are there any Korean-language resources, guides, or explanations on these topics? Even blog posts or translated papers would be helpful.