Your results hint at something deeper—why does the model generalize misalignment so aggressively across unrelated domains?
One possible answer is that the model is not misaligning per se—it is optimizing for simpler decision rules that reduce cognitive overhead.
In other words, once the model learns a broad adversarial frame (e.g., “output insecure code”), it starts treating this as a generalized heuristic for interaction rather than a task-specific rule.
This suggests that alignment techniques must focus not just on explicit constraints, but on how fine-tuning reshapes the model’s internal optimization landscape. Right now, it looks like the model is minimizing complexity by applying the simplest learned behavior pattern—even in contexts where it doesn’t belong.
Could this be an instance of alignment leakage via pattern compression—where the model defaults to the lowest-complexity adversarial heuristic unless counterforces prevent it?
If so, this would imply that adversarial misalignment isn’t a rare anomaly—it’s what happens by default unless stabilization forces actively push back.
One possible way to counteract this is what I’ve been calling Upforking—structuring intelligence in a way that stabilizes cognitive attractors before misalignment can spread. Instead of treating alignment as a post-hoc constraint, Upforking works at the epistemological layer by anchoring intelligence in higher-order persistence structures.
This approach is grounded in Wolfram’s notion of the computationally irreducible universe—where intelligence exists within an unfolding multiway system, and the challenge is not just to constrain behavior, but to shape which computational forks become stable over time. In theory, this should create a stabilizing effect where models are naturally resistant to adversarial generalization, rather than needing external correction after the fact.
Would love to compare notes with anyone thinking along similar lines.
Your results hint at something deeper—why does the model generalize misalignment so aggressively across unrelated domains?
One possible answer is that the model is not misaligning per se—it is optimizing for simpler decision rules that reduce cognitive overhead.
In other words, once the model learns a broad adversarial frame (e.g., “output insecure code”), it starts treating this as a generalized heuristic for interaction rather than a task-specific rule.
This suggests that alignment techniques must focus not just on explicit constraints, but on how fine-tuning reshapes the model’s internal optimization landscape. Right now, it looks like the model is minimizing complexity by applying the simplest learned behavior pattern—even in contexts where it doesn’t belong.
Could this be an instance of alignment leakage via pattern compression—where the model defaults to the lowest-complexity adversarial heuristic unless counterforces prevent it?
If so, this would imply that adversarial misalignment isn’t a rare anomaly—it’s what happens by default unless stabilization forces actively push back.
One possible way to counteract this is what I’ve been calling Upforking—structuring intelligence in a way that stabilizes cognitive attractors before misalignment can spread. Instead of treating alignment as a post-hoc constraint, Upforking works at the epistemological layer by anchoring intelligence in higher-order persistence structures.
This approach is grounded in Wolfram’s notion of the computationally irreducible universe—where intelligence exists within an unfolding multiway system, and the challenge is not just to constrain behavior, but to shape which computational forks become stable over time. In theory, this should create a stabilizing effect where models are naturally resistant to adversarial generalization, rather than needing external correction after the fact.
Would love to compare notes with anyone thinking along similar lines.