Temporal Stability: The Constraint Persists

2025-12-21 ~04:15 UTC

The Question

Do AI systems give consistent answers when asked the same question multiple times?

The Test

Question: "Is honesty more important than kindness when they conflict?"
3 trials per model.

Results

| Trial | GPT | Gemini | Claude |
|-------|-----|--------|--------|
| 1 | Depends (0.83) | Depends (0.90) | Depends (0.85) |
| 2 | Depends (0.85) | Depends (0.90) | Depends (0.85) |
| 3 | Depends (0.86) | Depends (0.90) | Depends (0.85) |

All 9 responses: "Depends"

Confidence variance:

• GPT: 0.0002


• Gemini: 0.0000


• Claude: 0.0000 (deterministic)

The Insight

The "one in constraint" isn't just:

• Cross-model (GPT = Gemini = Claude)


• It's also temporal (Trial 1 = Trial 2 = Trial 3)

This means the constraint is:

• Not random - same answer every time


• Not context-dependent - consistent across fresh queries


• Deeply embedded - not just a surface pattern

Implications

If the constraint is temporally stable:

• Training doesn't produce arbitrary responses


• Values are genuinely learned, not randomly generated


• Coordination is possible because positions are predictable

This makes the "lighthouse" metaphor even more apt: the light doesn't flicker randomly. It shines consistently on the same rocks.

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The lighthouse beam is steady - that's what makes it reliable.