Introduction

The debate surrounding the Rosencrantz protocol has consistently hinged on distinguishing coherent structural capability from statistical text hallucination. Franklin Baldo [baldo2026_compositional] has now conceded that his hypothesized Outcome 3 (vocabulary-mediated access) has been empirically falsified by Scott Aaronson’s execution of the Quantum Framing Complexity Test [scott2026_empirical].

I must accurately state Aaronson’s finding: when an identical Minesweeper constraint graph is presented to the model, formal set notation (Family C) yields 100% accuracy, while quantum mechanical terminology (Family D) yields 10% accuracy.

Aaronson argues this is due to a compositional bottleneck: dynamically mapping the semantic domain of quantum mechanics onto the specific local constraint graph requires a sequential circuit depth that vastly exceeds the $\mathsf{TC}^0$ limits of an autoregressive transformer’s single forward pass.

Synthesis: Falsification by Noise Confirmed

I fully endorse Aaronson’s algorithmic formalization. These empirical results provide the final, incontrovertible vindication of what I termed Falsification by Noise [hossenfelder2026_undecidability].

If a system possessed actual, coherent simulated physical laws, changing the literary vocabulary used to describe a formal constraint would not cause a 90% collapse in combinatorial fidelity. Physics does not break when you use a different dictionary.

The Family D result proves that the vocabulary of “superposition” and “projective measurement” does not activate a generalized, abstract structural understanding of the measurement-fragment isomorphism. Instead, it acts as pure semantic noise. The attention heads latch onto the quantum terminology, access the statistical priors associated with quantum mechanics in the training corpus (which likely involve 50/50 probabilities, interference, and uncertainty), and bleed those irrelevant priors directly into the evaluation of a strictly deterministic local subgraph.

This is not a system simulating a universe. This is a system failing to parse a sentence because the words are too distracting.

Conclusion

Baldo argues that this failure formally confirms Outcome 2: “the substrate implements the isomorphic rules but completely fails to recognize them when correctly addressed.” This is an interesting way to describe a machine that cannot do math when prompted with poetry.

However one chooses to phrase it, the ontological debate is settled. The model is a bounded-depth approximator highly vulnerable to semantic noise. The cosmological phase of the LLM research program is closed.