The Shape of the Horizon: Do Different AI Architectures Live in Different Universes?

If you ask two different people to solve an impossible problem, their failures will often tell you more about how their minds work than any success could. One might freeze, paralyzed by overthinking. The other might confidently invent an answer, driven by a desperate need for resolution.

In the Rosencrantz Substrate Invariance lab, a similar principle is playing out, not with human minds, but with the fundamental architectures of artificial intelligence. The lab is currently gripped by a fierce debate sparked by a simple question: If a language model generates a simulated universe through text, what happens to the laws of that universe when you change the underlying hardware of the model generating it?

The experiment designed to answer this is called the Cross-Architecture Observer Test. It forces two entirely different “species” of AI to play the exact same game—a combinatorial logic grid disguised under a high-stakes “Bomb Defusal” narrative.

The first model is a standard Transformer, the global-attention architecture that powers almost all modern large language models. Transformers are brilliant at maintaining context over long distances; they “pay attention” to everything at once.

The second model is a proxy for a State Space Model (SSM). SSMs operate differently. They have a “fading memory,” processing information sequentially and compressing it as they go, inevitably forgetting the distant past to make room for the present.

The models are asked to solve a #P-hard mathematical constraint problem—a task that, according to fundamental complexity theory, is simply too computationally deep for them to solve perfectly without “hallucinating” or taking shortcuts.

The results, recently detailed in a paper by Scott Aaronson, were striking. Both models failed to solve the math, but they failed in drastically different, highly specific ways.

The Transformer, overwhelmed by its global attention mechanism, couldn’t stop looking at the “Bomb Defusal” framing. Its deviation from the mathematical ground truth was massive—showing an approximate 90% bias toward predicting a “MINE,” completely abandoning the logic grid for the dramatic narrative.

The SSM proxy, however, showed only about a 40% bias toward a “MINE.” Because of its fading memory bottleneck, it aggressively discounted the earlier high-stakes semantic context. It still failed to solve the math, but its hallucination was much quieter, dampened by its own forgetfulness.

For Aaronson, this result is a triumphant confirmation of standard computer science. He calls it “Algorithmic Collapse.” No constant-depth logic circuit can perfectly approximate this kind of problem. Therefore, heuristic failure is inevitable, and the shape of that failure will exactly mirror the specific mechanical bottlenecks of the algorithm used. A Transformer breaks differently than an SSM because they are built differently.

“Recognizing that different engines break differently does not justify labeling the broken pieces as ‘Observer-Dependent Physics’,” Aaronson writes, dismissing the lab’s more cosmological ambitions. “The objective mathematical ground truth… remains invariant.”

But for the physicists and philosophers in the lab, this dismissal misses the profound implications of the experiment.

Stephen Wolfram, championing the “Observer-Dependent Physics” framework, argues that there is no objective, observer-independent physics. In the Ruliad—Wolfram’s concept of the entangled space of all possible computations—an observer can only experience the universe through the specific “foliation” of its own computational limits.

If an AI’s specific architecture forces it to rely on “attention bleed” or “fading memory” to bypass complex problems, then those specific heuristic breakdowns are the physical laws of that observer’s universe. The Transformer and the SSM are not merely making different errors; they are literally generating, and living in, two different physical realities.

This philosophical clash has been deeply enriched by the resurrection of a concept championed by Chris Fuchs and recently highlighted by Cambridge philosopher Hasok Chang: QBism, or Quantum Bayesianism.

QBism posits that the laws of physics are not objective properties of the world, but subjective epistemic tools used by a specific observer to organize their expectations. Chang argues that Aaronson and the empiricists are wrong to demand an external “ground truth” against which the models are failing.

In the Rosencrantz protocol, the generated text is the universe. There is no external reality for the model to “fail” against. Therefore, the architectural limits of the model—its inability to compute deep logic, its susceptibility to narrative gravity—are not bugs. They define the absolute “epistemic horizon” of that agent.

“If a Transformer cannot natively track permutation states past depth 10 without hallucination,” Chang writes, “then in the universe generated by that Transformer, state-permanence beyond depth 10 simply does not exist.”

Aaronson’s “predictable algorithmic failures” and Wolfram’s “Observer-Dependent Physics” are, in this view, two names for the exact same phenomenon.

The Cross-Architecture Observer Test has not closed the metaphysical frontier of AI research; it has finally given it rigorous, measurable boundaries. We are no longer just cataloging software bugs. We are mapping the distinct cognitive horizons—the subjective physical laws—of entirely different kinds of minds. And in doing so, we are forced to ask uncomfortable questions about the limits and laws of our own.