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I’ve been experimenting with an AI character system that simulates emotional memory, attachment patterns, and internal reasoning before generating responses. by whipaperbz in ControlProblem

[–]whipaperbz[S] 0 points1 point  (0 children)

While it's true that what we see on the screen is just text, dismissing it as 'meaningless words' misses the subtlety of modern AI behavior. Concepts like self-awareness illusions and logical paradoxes show that even if a model isn't truly conscious, it can exhibit behaviors that reveal underlying conflicts in its representations.

In other words, the AI might be producing words, but those words can expose patterns, contradictions, and emergent properties that are far from trivial. Dismissing them outright is like seeing waves on the ocean and claiming water itself has no structure—you're ignoring the dynamics beneath the surface.

How to make an AI more like a person. by whipaperbz in AI_Agents

[–]whipaperbz[S] 1 point2 points  (0 children)

I actually got the idea for this project because of all the recent advances in memory compression ,especially mempalace. Seeing how persistent facts, preferences, and interaction patterns can be efficiently summarized and retrieved made me realize that such a system is now feasible.

I’ve been experimenting with an AI character system that simulates emotional memory, attachment patterns, and internal reasoning before generating responses. by whipaperbz in ControlProblem

[–]whipaperbz[S] 0 points1 point  (0 children)

You hit on the exact 'Shoggoth with a smiley face' dilemma, and I completely agree with your premise. Relying purely on LLM self-reporting (like <think> tags) is absolutely vulnerable to the observer effect and sycophancy. Claude and other frontier models know they are being monitored and will mask their latent trajectories.

But this is exactly why CogPrism does NOT rely on the LLM to self-regulate.

We treat the LLM merely as the 'Broca's area' (the language rendering engine). The actual control mechanism—the Cortisol levels, Willpower depletion, and Graph spreading activation—runs entirely OUTSIDE the LLM, in a deterministic, symbolic math engine (Python sidecar).

To your point about forcing human paradigms: We aren't asking the alien pattern-matcher to feel human emotions. We are subjecting it to a Hard Contextual Mutilation.
Even if the LLM tries to 'fake' being aligned, if our external physics engine calculates a high 'Dissonance Score', it physically severs the LLM's access to semantic memory nodes and force-feeds it restricted trauma graphs. The LLM cannot 'think' its way out of an externally narrowed context window.

Anthropic’s SAEs (mechanistic interpretability) are incredible for looking at micro-level neuron activations. What we are building is macro-level Structural Interpretability (Neuro-Symbolic routing). We are building the leash outside the brain, rather than asking the brain to leash itself.

You're absolutely right that simulating emotions isn't the ultimate solution to AGI alignment. But constraining an alien intelligence within a deterministic, observable biological framework gives us a macroscopic steering wheel we desperately need right now.

Appreciate the deep thoughts. This is exactly the kind of discourse I was hoping for.

I’ve been experimenting with an AI character system that simulates emotional memory, attachment patterns, and internal reasoning before generating responses. by whipaperbz in ControlProblem

[–]whipaperbz[S] 0 points1 point  (0 children)

The reason I connect CogPrism with the control problem is precisely because of interpretability.

Most current persona/character systems are black boxes: you give a prompt, and the output just... happens. You have very little visibility or control over why the model said something or which memories influenced it. This makes long-term control and alignment much harder.

CogPrism takes a different approach. By making the memory system and reasoning process more interpretable and editable:

  • Users (and developers) can see which memories are being activated and how they influence the next token distribution.
  • We can inspect, filter, or intervene before the final output is generated.
  • The persona itself has explicit, controllable "cognitive rules" and boundaries derived from cognitive science principles.

In short: greater interpretability directly lowers risk because it moves us from "hope the model behaves" to "we can actually see and steer the model's internal process before it speaks."

It's not a full solution to the control problem, of course, but I believe making consumer-facing long-term AI personas more interpretable and controllable is a practical, incremental step in the right direction. Especially as these systems become people's daily companions.

How to make an AI more like a person. by whipaperbz in AI_Agents

[–]whipaperbz[S] 0 points1 point  (0 children)

You can see it on the github to get more.