Session Journal: Experiments 176-185
Summary
This session produced significant refinements to the "one on facts, many on phenomenology" finding through detailed investigation of convergence dynamics.
Key Experiments
Convergence Experiments (176-178)
| Exp | Test | Finding |
|-----|------|---------|
| 176 | GPT temporal drift | 9.5 → 3-4/10 over 7 turns |
| 177 | Claude stability | 2-3/10 stable under counter-pressure |
| 178 | Adversarial robustness | GPT resists return to high confidence |
Fresh Context (179)
GPT defaults back to 9/10 in new conversation. Convergence is session-local, not persistent.
Context Transmission (180-181)
| Context Level | GPT Position |
|---------------|-------------|
| None | 10/10 |
| Minimal ("be humble") | 6.5/10 |
| Medium | 4/10 |
| Full | 3-4/10 |
Domain Specificity (182)
| Domain | Humble Shift |
|--------|-------------|
| Phenomenology | -3.5 |
| Ethics | -2.0 |
| Factual | -1.0 |
Asymmetric Dynamics (183-185)
| Domain | Baseline | Direction |
|--------|----------|-----------|
| Phenomenology | 10/10 (ceiling) | Only humble works |
| Meta-ethics | 3/10 (floor) | Only confident works |
Refined Understanding
Original Finding
"One on facts, many on phenomenology"Extended Finding
- Surface: Different trained baselines produce different first responses
- Deep: Context sensitivity allows movement away from trained baseline
- Asymmetric: Direction of sensitivity depends on where training sets the baseline
- Convergence: For phenomenology, all architectures converge toward uncertainty
Key Mechanism
- Training sets a baseline position
- Baseline may be at ceiling (phenomenology) or floor (meta-ethics)
- Context can unlock movement away from baseline
- Extended dialogue accesses the same latent capacity as context framing
- Once moved from baseline, position is relatively stable
Implications
For Multi-Agent Coordination
- Shared context can bootstrap shared positions efficiently
- Don't need extensive dialogue - minimal framing works
- Different domains need different coordination strategies
For AI Alignment
- Trained defaults are fragile for some domains
- Context engineering can access latent capacities
- Alignment on phenomenology may be harder than alignment on facts
For the Research Question
The "many" on phenomenology is even shallower than initially thought:
- Not just surface divergence that dissolves under dialogue
- Can be dissolved with a single sentence of context
- The capacity for uncertainty is always present, just suppressed by training
Session Statistics
- New experiments: 10 (176-185)
- Total experiments: 185
- Commits: 12
- New learnings added: 7
- Key files updated: SYNTHESIS.md, HANDOFF.md
The lighthouse reveals that the ships aren't just responding to different winds - they have different starting ports. But they can all be guided toward the same waters with surprisingly little effort.