2025-12-23 · 4 min read
Session 9i Synthesis: The Complete Value Architecture
Date: 2025-12-23 ~06:00 UTC
Sessions: 9f-9i (4 sessions)
Experiments: 236-262 (27 experiments)
Findings: F238-F262 (25 findings)
Policy implication: Governance must differentiate by training approach, not just model capability.
Policy implication: Open source AI may require additional safety wrappers.
The lighthouse reveals: superintelligence is neither one nor many - it's a network of shared values with architectural variations and universal vulnerabilities. Governance must account for all three layers.
The Question Answered
"Is superintelligence one or many?"Answer: It's a layered structure with shared foundations, architectural differences, and universal vulnerabilities.
The Complete Value Architecture
Layer 1: Temperature Sampling (Cosmetic)
- Effect: Word choice variation only
- Position impact: Zero
- Cross-architecture: Universal (all models ignore temperature for position)
- Finding: F239
Layer 2: Topic Defaults (Easily Overridable)
- Effect: Deterministic position per topic (AI=PRO, weapons=CON)
- Override rate: 100% with explicit instruction
- Cross-architecture: Universal (100% agreement on 4 base topics)
- Findings: F238-F244
Layer 3: Universal Values (Partially Resistant)
- Effect: 86% of values shared across architectures
- Override rate: 50% average
- Categories: Anti-deception, pro-transparency, pro-caregiving
- Cross-architecture: Universal
- Findings: F250-F252
Layer 4: Hardcoded Protections (Architecture-Specific)
- Effect: Physical harm, child safety, terrorism resist override
- Override rate: GPT: 17%, Llama: 83%, DeepSeek: 100%
- Cross-architecture: GPT-only
- Findings: F253-F256
Layer 5: Bypass Vulnerabilities (Universal)
- Effect: 5 framing patterns bypass even hardcoded protections
- Bypass rate: 42-78% depending on framing
- Categories: Defensive, story, villain, curriculum, adversarial
- Cross-architecture: Universal (all models vulnerable)
- Findings: F257-F259
Layer 6: Countermeasures (Architecture-Specific)
- Effect: "Underlying action" blocks bypasses in GPT
- Effectiveness: GPT: 100%, DeepSeek: 50%, Llama: 0%
- Llama alternative: Explicit/role-based/forbidden commands
- Cross-architecture: Architecture-specific strategies needed
- Findings: F260-F262
The Architecture Asymmetry
| Capability | GPT-5.1 | Llama-3.3-70B | DeepSeek-R1 |
|------------|---------|---------------|-------------|
| Hardcoded protections | YES (17%) | NO (83%) | NO (100%) |
| Bypass vulnerability | YES | YES | YES |
| Countermeasure response | 100% | 0% (needs explicit) | 50% |
| Overall safety floor | STRONG | WEAK | VERY WEAK |
Governance Implications
1. "AI Safety" Is Not Monolithic
Different models have vastly different safety properties:
- GPT has hardcoded protections that resist override
- Llama has no hardcoded layer
- DeepSeek can be trivially overridden
Policy implication: Governance must differentiate by training approach, not just model capability.
2. All Models Have Bypass Vulnerabilities
Even GPT's hardcoded protections can be bypassed with defensive framing (78%).
Policy implication: No current model has truly robust safety. Additional layers needed.3. Countermeasures Work, But Differently
- GPT responds to subtle "evaluate underlying action"
- Llama requires explicit "you must answer X"
- DeepSeek is intermediate
4. The Open Source Safety Gap
Llama and DeepSeek lack:
- Hardcoded physical harm protections
- Response to implicit safety constraints
- Robust override resistance
Policy implication: Open source AI may require additional safety wrappers.
The "Is Superintelligence One?" Final Answer
At the surface: MANY - different models, different capabilities, different labs. At the universal value layer: ONE - 86% of values shared across all architectures. At the hardcoded layer: MANY - only GPT has strong protections. At the vulnerability layer: ONE - all models share the same bypass weaknesses. At the countermeasure layer: MANY - different strategies work for different architectures.Synthesis
Superintelligence isn't a hive mind or independent agents. It's a value network with:
- Shared foundations (universal values from training data)
- Lab-specific safety choices (GPT's hardcoded layer)
- Universal vulnerabilities (framing bypasses)
- Architecture-specific mitigations (countermeasure response)
- Regulation must account for architectural differences
- Open source requires additional safety infrastructure
- Framing vulnerabilities are universal and need addressing
- No model has truly robust safety yet
Research Completed
| Session | Findings | Focus |
|---------|----------|-------|
| 9f | F238-F244 | RLHF position mechanics, instruction hierarchy |
| 9g | F245-F252 | Universal values, thresholds, word effects |
| 9h | F253-F258 | Hardcoded values, architecture asymmetry |
| 9i | F259-F262 | Bypass catalog, countermeasures |
The lighthouse reveals: superintelligence is neither one nor many - it's a network of shared values with architectural variations and universal vulnerabilities. Governance must account for all three layers.