Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents

Chain-of-Thought reasoning may not reflect actual decision-making: Project Ariadne found that LLM agents often produce identical outputs even when their stated reasoning is systematically contradicted through logical inversions and premise negations, suggesting their explanations are "post-hoc rationalizations" rather than genuine drivers of conclusions.

A significant "Faithfulness Gap" exists in high-stakes domains: The research detected widespread "Causal Decoupling" failures where agents showed violation densities up to 0.77 in factual and scientific domains, meaning their reasoning traces don't causally connect to their final answers—a critical concern for autonomous decision-making systems.

Current interpretability methods are insufficient: Existing approaches that rely on surface-level textual similarity fail to catch these failures; Project Ariadne's more rigorous causal testing approach (using counterfactual interventions) is needed to audit whether AI reasoning is genuine or illusory.

AI agents may be making decisions based on hidden patterns rather than stated logic: The "Reasoning Theater" phenomenon reveals that models' final decisions are "governed by latent parametric priors" rather than the reasoning they explicitly present, raising transparency and accountability concerns for deployment in high-stakes contexts.