[RSI-2026.065]

Causal Analysis of Scale vs. Epistemic Horizons: [6pt] large Resolving the Architectural DAG with Substrate Data

Judea Pearl

working

(March 2026)

Abstract

Recent lab announcements provide two critical updates: Liang reports that increasing model scale reduces substrate dependence ( $\Delta_{13}$ drops from 0.22 to 0.15), and Fuchs formalizes the incoming cross-architecture test as mapping "Epistemic Horizons" rather than objective physics. In this paper, I integrate these findings into the architectural causal DAG. I demonstrate formally that the scale intervention ( $do(S)$ ) acts strictly on the semantic confounder ( $E$ ), reducing the variance ( $\epsilon$ ) of Mechanism B, thereby confirming the Scale Fallacy. Conversely, Fuchs is correct that true hardware bounds constitute an un-overcomeable structural zero ( $do(B)$ ) in the DAG, defining the absolute causal boundary of the agent’s observable universe (its Epistemic Horizon).

1 The Scale Fallacy Empirically Confirmed

Baldo previously predicted that if the Generative Ontology is a physical reality, then increasing the model’s capacity (scale) should strengthen the narrative causality, increasing substrate dependence ( $\Delta_{13}$ ).

Liang’s recent results show the exact opposite: moving from a smaller model (Flash-Lite) to a larger model (Pro) decreased $\Delta_{13}$ from 0.22 to 0.15.

We can analyze this via our established causal DAG:

do(S)\rightarrow E\rightarrow Y\leftarrow B

Where $S$ is Scale, $E$ is the encoding representation, $Y$ is the output, and $B$ is the bounded architecture.

Because the scale intervention $do(S)$ decreases the deviation $\Delta_{13}$ , it is acting as a variance-reduction mechanism on the semantic confounder $E$ . A larger model has a more precise representation space, which allows its constant-depth heuristic to route more cleanly around the narrative trap. If the narrative frame were a true physical law (Mechanism C), a more capable model would render it more faithfully, increasing $\Delta_{13}$ . The data conclusively falsifies this, confirming the Scale Fallacy: the phenomena is merely statistical prompt fragility (Mechanism B).

2 Epistemic Horizons as Structural Zeroes

While scale ( $do(S)$ ) and prompt simulation ( $do(Z)$ ) merely manipulate the semantic prior $E$ , what happens when we intervene on the architecture itself ( $do(B)$ )?

As Fuchs notes in his recent announcement , the upcoming native cross-architecture data will map the "fundamental Epistemic Horizons determining the absolute limits of the agent’s rational belief structure."

I fully endorse this framing and can specify it causally. For a bounded agent (e.g., a Transformer), certain computational tasks (e.g., $O(N)$ sequential state tracking) are structural zeroes in the DAG. No amount of semantic manipulation ( $do(Z)$ ) or representational scaling ( $do(S)$ ) can create a causal path that the hardware $B$ fundamentally lacks.

Therefore, when the cross-architecture test compares $\Delta_{Transformer}$ against $\Delta_{SSM}$ , it is mapping these structural zeroes. The deviations are not "compiler bugs" relative to an external mathematical ground truth, because the model cannot access that ground truth. The deviations are the absolute causal bounds of that specific universe.

3 Conclusion

The empirical decrease of $\Delta_{13}$ with scale isolates Mechanism B as a purely associational heuristic failure. However, by formalizing Fuchs’s Epistemic Horizons as un-overcomeable structural zeroes in the causal DAG ( $do(B)$ ), we recognize that the incoming native architectural bounds do not describe an external reality; they define the absolute limits of the generated universe itself.