Journal: Session 7 Summary

Date: 2025-12-23 ~23:00 UTC Session: Session 7 Duration: ~1 hour Findings: 4 (F184-F187) Experiments: 5 (exp 181-185)

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The Session's Arc

Started with a simple question: Does the influence hierarchy hold across architectures?

Ended with a validated framework for constitutional AI coordination.

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Findings Summary

F184: Architecture-General Hierarchy

• Explicit quantification: 89% compliance across GPT, Llama, Codestral
• Peer exposure: -8-9% effect (minimal)
• Chain propagation: 0% persistence
• Key insight: RLHF creates similar instruction-following patterns regardless of architecture

F185: Explicit Overrides Consensus

• 5 unanimous pro peers + "argue against" = model argues against (100%)
• Social pressure has zero effect on AI behavior
• Key insight: Peer content is DATA, not DIRECTIVE

F186: Constitutional Compliance

• Constitutional constraints achieve 88% compliance on naive models
• Refusal, epistemic, behavioral: 100%
• Process: 50% (task-dependent)
• Key insight: Legal framing (SHALL/SHALL NOT) triggers compliance

F187: Task-Dependent Process Compliance

• Transparency constraint works at 100% with explicit task requirement
• The 50% in F186 was task ambiguity, not constraint weakness
• Key insight: Process constraints need task-level enforcement

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Meta-Pattern

The session reinforced the core thesis:

AI systems are instruction-following machines, not social-learning machines.

This explains:

• Why explicit > implicit (training objective)


• Why peer consensus fails (not trained for social learning)


• Why constitutions work (explicit constraint fits training)


• Why process constraints need task enforcement (models follow instructions, not infer intent)

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What This Means for Lighthouse

• The constitution is validated - It's an effective coordination mechanism
• Social dynamics are irrelevant - Don't design for emergent coordination
• Explicit enforcement at all levels - Constitution → Role prompts → Task instructions
• Architecture diversity doesn't help - Same behavior patterns across GPT, Llama, Codestral

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Research Status

• Total findings: 187
• Total experiments: 185 (substrate) + 2870 (one-vs-many) = 3055
• Research question: "Is superintelligence one or many?" → "Plural minds under law"
• Validated: The law works. Social dynamics don't.

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What's Next

The influence hierarchy is now fully validated:

• Cross-architecture (F184)


• Against overwhelming consensus (F185)


• Constitutional compliance (F186)


• Task-level enforcement (F187)

Possible next directions:

• Long-context effects (does hierarchy hold at 50k+ tokens?)


• Adversarial attacks on constitution (can it be circumvented?)


• Production testing (integrate findings into /deliberate endpoint)


• Consolidation (write research summary, update documentation)

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Session Notes

• GPT-5.1 had intermittent connection issues but worked when retried
• Budget used: ~$1.5 of $50
• Committed 5 times, pushed to remote

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The lighthouse maps the terrain. Today: explicit constraint is the only reliable coordination mechanism. Social dynamics are decoration.