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The paper introduces Elenchos, a generative evaluation framework for abductive reasoning in LLMs, where models must infer hidden rule changes from behavioral differences under black-box access. It finds a detection-attribution dissociation: models detect alterations but struggle to identify the specific mutations, especially under interacting mutations.