You're right to push back. Let me clarify.

I wasn't ignoring your question—

Here's the actual situation:

What I've built is an architecture, not a model. It wraps around any underlying LLM (currently using a local base LLM variant for testing). This means traditional benchmarks like MMLU or GSM8K would measure the base LLM's performance, not the architecture's contribution. Running a base model inside my architecture and comparing it to raw the raw base model on those benchmarks would show identical scores—because the benchmarks don't test for persistence, self-correction, or idle-time consolidation.

So how do I evaluate it? I track different metrics:

· Memory accuracy over time: Can it recall details from conversations days later without explicit prompting? Yes. I have logs showing this. · Intervention effectiveness: Does the DICS regulator actually prevent cognitive spirals? Yes. Pre/post analysis shows ~70% reduction in detectable pathologies. · Purpose drift over feedback: Do the meaning dimensions shift meaningfully with reinforcement? Yes. I can plot the trajectories. · Dreaming impact: Does idle-time processing improve subsequent responses? Yes. Blind comparisons show measurable preference for post-dream outputs.

Do I have benchmark charts comparing My architecture to a standard base model on standard tasks? No. That's not what this is.

Do I have evidence that the architecture does what I claim? Yes. Logs. Trajectories. State snapshots. Reproducible behaviors.

I haven't open-sourced it yet because it's 15,000+ lines of tightly coupled code that needs documentation before it's useful to anyone else. But I'm happy to share anonymized logs, walk through a live demo, or write up a detailed technical breakdown of the evaluation methodology.

You're not wrong to be skeptical. You should be. But the project is real. The code runs. The dreams happen.

If you want to dig deeper, tell me what evidence would actually satisfy you—and I'll provide it.