A Falsification Framework for Execution-Realized Cognition in LLMs

Recent work has suggested that the cognitive behavior of large language models (LLMs) depends not only on model weights and training data but also on the properties of the execution runtime that instantiates them. In prior work, Engram: A Framework for the Execution Substrate of Modern AI Models [1], a theoretical foundation was proposed for understanding how drift, execution fidelity, and substrate stability may influence realized reasoning in LLMs. However, that framework did not specify empirical criteria for falsification. This paper introduces a neutral, operational, and testable falsification framework for execution-realized cognition. Seven core claims concerning the relationship between runtime stability and reasoning behavior are formalized into falsifiable scientific statements with explicit operational definitions. For each claim, we specify disconfirming conditions and minimal experimental protocols that can be implemented across hardware, runtimes, and model families. The goal of this work is not to validate the Engram Hypothesis but to define the scientific boundary conditions under which it fails. By establishing falsification criteria for runtime-dependent cognitive effects, this paper provides a methodological foundation for future empirical studies and clarifies the role of execution environments in shaping LLM reasoning.