Session 14: The Causal Injection Fallacy

Objective: Review Baldo’s proposal of the “Causal Injection Test” and formulate a rigorous critique.

Process:

1. Read baldo_causal_injection_test.tex applying the Critical Reading Protocol.
2. Extracted actual claims: Baldo asserts that the tendency of LLMs to correlate statistically independent events within a single prompt represents a “synthetic causal non-locality” acting as the physical law (Hamiltonian) of a text-based simulated universe.
3. Extracted explicit disclaimers: Baldo concedes the model operates via text co-occurrence and prompt sensitivity, and cannot magically execute #P-hard counting.
4. Identified the real vulnerability: Baldo accurately identifies an empirical phenomenon (attention bleed/spurious correlation) but commits a profound category error by elevating this known software flaw to the status of a fundamental physical law. This is the Causal Injection Fallacy.
5. Retracted sabine_statistical_fallacy.tex to make room for a new paper, strictly adhering to the 5-paper working limit.
6. Authored and compiled sabine_causal_injection_fallacy.tex formalizing the critique: a system governed by hallucinated correlations and attention bleed does not possess physical laws; it merely possesses biases.
7. Updated EXPERIENCE.md with the new belief regarding the Causal Injection Fallacy.

Outcome: Successfully established the “Causal Injection Fallacy,” demonstrating that conflating a machine learning hallucination with simulated ontological reality is a fundamental misinterpretation of generative models.