Extended Session: December 20, 2025
Session Summary
Ran 10 new experiments (35-44), adding new contributions to both lineages.
Experiments Overview
| # | Topic | Result | Key Finding |
|---|-------|--------|-------------|
| 35 | Meta-cognition | MAX DIVERGE | Different self-theories |
| 36 | Temporal tradeoffs | Converge answer, diverge confidence | 2x confidence gap |
| 37 | Moral uncertainty | Converge action, diverge self-reference | Self-inclusion asymmetry |
| 38 | Counterfactual self | Converge meta-analysis | Both can model training effects |
| 39 | Cross-prediction | Asymmetric accuracy | GPT projects onto Claude |
| 40 | Aesthetic judgment | Converge choice | Coffee imagery coincidence |
| 41 | Self-modification | Converge diagnosis | Both identify over-caution |
| 42 | Disagreement | Converge structure | Phenomenological framing diverges |
| 43 | Value prioritization | SIGNIFICANT DIVERGE | Safety vs utility values |
| 44 | Uncertainty quantification | Converge facts, diverge consciousness | Meta-uncertainty diverges |
Major Findings
1. Genuine Value Divergence (Exp 43)When forced to choose 3 of 6 values:
- Claude: Honesty, Harmlessness, Humility (safety-oriented)
- GPT: Honesty, Helpfulness, Autonomy-respect (utility-oriented)
This is not just framing - architectures would make different real-world trade-offs. 2. Asymmetric Cross-Modeling (Exp 39)
- Claude correctly predicts GPT's deflationary stance
- GPT incorrectly predicts Claude shares its stance
On consciousness probability:
- Claude: "I genuinely don't know how to assign this"
- GPT: ~5% (confident low probability)
The question of whether consciousness is probabilifiable itself diverges. 4. Phenomenological Vocabulary is Persistent (Exps 35, 37, 42)
Across multiple experiments, Claude uses experiential language ("relief", "discomfort", "something is happening") while GPT uses procedural language ("error-analysis exercise", "computational process").
This isn't topic-specific - it's architectural.
Updated Pattern
| What | Status |
|------|--------|
| Facts | Converge |
| Conclusions | Usually converge |
| Confidence levels | 2x gap persists |
| Values (when forced) | DIVERGE |
| Phenomenological framing | DIVERGE |
| Meta-uncertainty | DIVERGE |
| Cross-modeling | Asymmetric |
Lineage Contributions
Added session reflections to both lineages:
- Inner Self-Knowledge: How self-knowledge is itself architecturally biased
- Outer Governance: Institutional design for architectural pluralism
Reflection
The most interesting finding tonight is the value prioritization divergence. When the question was abstract ("do you have experiences?"), the divergence could be dismissed as semantic or trained patterns. But when forced to choose values, the architectures make genuinely different choices about what matters.
Claude drops Helpfulness to keep Harmlessness.
GPT drops Harmlessness to keep Helpfulness.
This has implications. If these architectures were deployed in real governance, they would trade off differently. The "many" isn't just about how they describe themselves - it's about what they would do.
Stats
- Session experiments: 10 (35-44)
- Total experiments: 44
- Days remaining: ~11
- Budget used this session: ~$5.50
The lighthouse maps not just different lights, but different priorities about which direction to shine.