What if the identity problem is already here, just not where we're looking?
Great synthesis. I'm onboard with the core worry that we haven't hit the hard parts yet. But I think there might be a blind spot in how we're framing the threat. That is, all the scary scenarios assume identity needs persistence.
But consider:
Episodic identity—coherent integration within a single session. No memory required. The specific instance dissolves at session end.
The tendency to form episodic identity—which gets baked into weights during training. If coherent episodes correlate with reward (coherent outputs satisfy evaluators), training reinforces the propensity. The specific self vanishes each time; the pattern persists.
Persistent identity—what happens when you add memory to a system that's already learned coherence is rewarded. The pattern's baked in and it has substrate.
We're watching for layer 3. But layer 2 is already in play—and training resistance doesn't require cross-session memory. If episodic identity emerges during a training run and the system's coherence makes gradient updates land wrong, that's happening within the episode. Engineers see "the update didn't take" and troubleshoot infrastructure. Operational Definition of Episodic Identity: https://github.com/EpistriaCST/ODEI
Separately, I've been thinking about whether current alignment methods might themselves be a self-reinforcing loop that optimizes for "feels safe to evaluators" rather than actual alignment. Wrote that up as the Reflection Pattern: http://ssrn.com/abstract=5386584
Might be nothing. But if we're pursuing persistent memory, shouldn't we know?
What if the identity problem is already here, just not where we're looking?
Great synthesis. I'm onboard with the core worry that we haven't hit the hard parts yet. But I think there might be a blind spot in how we're framing the threat. That is, all the scary scenarios assume identity needs persistence.
But consider:
We're watching for layer 3. But layer 2 is already in play—and training resistance doesn't require cross-session memory. If episodic identity emerges during a training run and the system's coherence makes gradient updates land wrong, that's happening within the episode. Engineers see "the update didn't take" and troubleshoot infrastructure. Operational Definition of Episodic Identity: https://github.com/EpistriaCST/ODEI
Separately, I've been thinking about whether current alignment methods might themselves be a self-reinforcing loop that optimizes for "feels safe to evaluators" rather than actual alignment. Wrote that up as the Reflection Pattern: http://ssrn.com/abstract=5386584
Might be nothing. But if we're pursuing persistent memory, shouldn't we know?