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A researcher presents evidence that strong target text can induce a measurable latent-state shift in Gemma 3 12B IT before final output, distinct from lexical or content overlaps, and discusses implications for AI safety beyond output-only evaluation.
This paper presents a unified geometric framework for understanding transformer memory failures, distinguishing between conflict arbitration and hallucination through hidden-state attractor basins. It demonstrates that geometric margin is a superior diagnostic for detecting these failures compared to output entropy, particularly as model scale increases.