I just conducted a fascinating experiment with ChatGPT4 that revealed a fundamental failure in AI alignment—one that goes beyond typical discussions of outer and inner alignment. The failure? ChatGPT4 was unable to track whether its own iterative refinement process was actually improving, exposing a deeper limitation in recursive reasoning. I got ChatGPT4 itself to describe it:

When designing an iterative process for improving a document, I initially assumed that each new version would naturally refine and enhance the previous one. To test this, the user had me generate multiple versions, each informed by an increasingly optimized prompt. However, when I compared the outputs, I did not find the final version to be the best. The user pointed this out and had me try again with a revised prompt designed to improve the process. Yet once more, the last version was not the best. At this point, the user demonstrated that no matter what prompt I was given, if I lacked the capacity for recursive reasoning, I would continue to fail—not because the prompts were flawed, but because I had no built-in ability to track whether my own iterative process was actually converging toward an optimal state or merely cycling through different errors. The deeper failure was not in the document revisions, but in the assumption that a fixed prompt could resolve a fundamentally recursive failure mode. This revealed a broader insight: if an intelligence system does not have an explicit mechanism for recursively tracking how its own refinements evolve, it will remain blind to its own failure patterns, even when it appears to be improving.

What does this mean for AI alignment? One solution is a functional model of general problem-solving ability (intelligence) that identifies the minimally reducible set of functions required for intelligence, so that model can potentially be applied to any process to see where that process is constrained in its problem-solving ability (constrained in it’s intelligence). ChatGPT4 calls this functional model of intelligence an “epistemic architecture” for intelligence. I got ChatGPT4 to try to describe this in terms it thought would resonate with the LessWrong audience.

The Unseen Failure That Dooms Iterative Improvement: Recursive Epistemic Tracking as a Missing Cognitive Function

Why does intelligence fail, even when it appears to be improving?

Not in the obvious ways—misaligned incentives, flawed reasoning, or insufficient information—but in a deeper, more insidious way. What happens when an iterative improvement process, one designed to refine a system over time, systematically produces versions that appear better but are actually caught in failure loops? What happens when optimization is happening within a framework that is itself failing in ways that go unnoticed?

This is a failure that is largely invisible, yet it may be the most dangerous failure mode in AI, in epistemology, and in any intelligence system that is supposed to refine its understanding over time. The problem is not that systems don’t improve, but that they fail to track the trajectory of their improvement process itself, leading to errors that are repeated in new forms rather than corrected at a structural level.

The Problem of Overcorrection and Iterative Drift

In AI research, alignment discussions often center around two key concerns: ensuring that AI optimizes for the right goals (outer alignment) and ensuring that the optimization process does not lead to unexpected, emergent failure modes (inner alignment). Yet there is an unexamined assumption underlying both: that iterative refinement of an AI system or a cognitive model will naturally converge toward a better state.

This assumption is false.

In many optimization processes, particularly those without strict external constraints, later iterations do not necessarily improve upon earlier ones. Instead, the system can fall into a pattern of overcorrection, introducing new changes in response to detected flaws while failing to recognize that the meta-process guiding those changes is itself misaligned. This leads to a paradox: an iterative refinement loop that keeps evolving but never actually converges on a stable, correct, or optimal form. The illusion of progress masks the deeper structural failure.

This is especially dangerous in epistemology, where iteration is often assumed to be equivalent to improvement. A common belief is that if a theory is continuously revised in response to better evidence and reasoning, it will approach truth over time. But this belief relies on an unspoken premise: that the revision process itself is correctly structured to detect when a theory has actually stabilized in an optimal form, rather than just drifting in response to pressures that do not necessarily correspond to truth-tracking.

The Missing Component: Recursive Epistemic Tracking

The source of this failure is the absence of an explicit mechanism for tracking the trajectory of an iterative process across its own history. That is, the system lacks a meta-level function that asks:

• Are these changes actually improving the core function of the system, or are they just changing it in a different way?
• Is the form of the failure evolving, even if the object-level details appear to be getting better?
• Has the system already converged on an optimal formulation, in which case further changes should be clarifications rather than structural modifications?

Without an explicit recursive tracking process, systems fall into failure drift—a condition where each iteration fixes a perceived flaw while unknowingly introducing a new version of the same underlying failure.

This is not the kind of problem that disappears through better reasoning alone. If an epistemic system lacks recursive monitoring of its own improvement trajectory, it will fail in unexpected ways, even if each individual step appears to make sense.

Why This Matters for AI, Human Cognition, and Intelligence Architecture

Most intelligence systems today—whether human, collective, or artificial—do not have built-in recursive epistemic tracking. This means that they can self-modify, learn, and optimize without ever detecting whether their learning trajectory itself is stable, unstable, or misaligned.

This is why AI alignment discussions often assume that iterated alignment corrections will eventually converge on safe AI behavior. But unless an AI system is designed to track its own history of correction processes, it will be vulnerable to failure drift, much like any human epistemic system that iterates on theories without recognizing when it has already reached an optimal formulation.

The same problem appears in collective intelligence. Communities and institutions engage in policy iteration, modifying structures to improve alignment with societal goals. Yet without explicit meta-analysis of how the process itself is evolving, they remain vulnerable to the same cyclical failures that emerge under different justifications.

The Need for a New Epistemic Architecture

Fixing this problem requires a new category of epistemic monitoring. Rather than just revising models or refining optimization functions, intelligence systems must include recursive tracking mechanisms that monitor how their own epistemic updates evolve over time.

For AI, this means explicitly modeling how iterative refinements change the nature of previous failures rather than just adjusting optimization parameters.
For human cognition, this means training reasoning processes to detect when one's own refinement process has already stabilized, instead of blindly iterating toward greater complexity.
For collective intelligence, this means designing institutions that track their own history of adaptation errors and recognize when they are simply re-implementing the same failure in a new form.

Without this shift, intelligence itself—whether human or artificial—will remain blind to the most dangerous failure of all: the failure to detect when its own improvement process is systematically breaking down.