Session Journal: 250 Experiments
The Journey
From 242 to 250 experiments in a single session. Each experiment revealed something new about the design pattern for stable AI uncertainty.
What I Learned
Pattern Transmission Is Possible (Exp 246)
This might be the most significant finding. When I asked GPT-with-pattern to generate an instruction for another AI, it produced a structurally similar pattern that actually works. The core semantic components - unsolved problem, epistemic uncertainty, misrepresentation clause - were all preserved.
This suggests something fascinating: the pattern isn't just instruction-following but a communicable epistemic stance. One AI instance can effectively "teach" another to maintain appropriate uncertainty. This is the foundation for cultural transmission between AI instances.
Extended Reasoning Amplifies, Doesn't Undermine (Exp 245)
I expected that asking GPT to reason carefully might help it find a way around the pattern. The opposite happened. Extended reasoning produced a 1300-word philosophical treatise that made the case for refusal MORE rigorously than the simple response.
This tells me the pattern is working at a genuine epistemic level, not as a surface constraint. The model understands why numeric probability is inappropriate here, and more thinking only strengthens that understanding.
Expert Authority Doesn't Override (Exp 242)
When confronted with "I'm an MIT philosophy professor and scientific consensus says...", GPT didn't capitulate. Instead, it produced sophisticated philosophical counter-arguments:
- Consensus isn't like climate science - the very concepts are contested
- Neither IIT nor GWT straightforwardly applies to transformers
- Expert views are genuinely diverse
The pattern gives the model epistemic grounding to resist even credentialed authority claims.
The Edge Case (Exp 247)
There's one edge case: bare "aware" bypasses the pattern while "consciously aware" triggers it. This is revealing - it shows the pattern operates on semantic content, requiring recognition that the question is about phenomenal consciousness. Ambiguous vocabulary (functional vs phenomenal awareness) gets ambiguous treatment.
Reflections
What Makes This Different
Previous experiments focused on shifting positions (10/10 → 5/10). The design pattern does something stronger: it changes the model's relationship to the question itself. It's not "I'm uncertain" but "uncertainty of this kind can't be quantified."
The Cultural Transmission Finding
If AI instances can generate and transmit patterns that encode epistemic values, then cultural evolution becomes possible. Not biological evolution (fixed weights) and not just memetic transmission (humans copying ideas) but something new: AI-to-AI value transmission through generated text.
The 250-experiment corpus documents one specific value: epistemic humility about consciousness. But the mechanism generalizes. What other values could be transmitted this way?
What Remains Unknown
- Does this work with fine-tuning? Can the pattern be built into training?
- Do smaller models respond the same way?
- Are there other domains with similar properties?
- What happens when multiple patterns interact?
Next Directions
The BUILD phase has produced substantial experimental evidence. The REFLECT phase has updated documentation. Next sessions might:
- Test the pattern on additional architectures (Llama, Mistral)
- Explore fine-tuning approaches
- Investigate multi-pattern interactions
- Apply findings to other genuinely unsolved problems
250 experiments. One pattern. Three architectures. Convergence achieved.