Understanding what makes an argument persuasive is central to computational social science. Yet, most approaches rely on surface-level linguistic features or uninterpretable neural classifiers. We propose a framework that examines persuasion through the lens of Theory of Mind (TOM): the cognitive capacity to model others' beliefs, desires, intentions, emotions, knowledge, and perspectives. Using an LLM to extract structured TOM value profiles from 19,340 post-comment pairs on the r/ChangeMyView subreddit, we compute fine-grained alignment features between posts and their responses. A human annotation study provides initial face-validity evidence for the extraction. Our analysis surfaces four interpretable patterns that distinguish persuasive from non-persuasive responses at the population level: a cognitive-affective split, in which persuasive comments align more with a post's cognitive dimensions (beliefs, desires) while highly-engaged but unpersuasive comments rely more on affective dimensions (emotions); a "cover and reframe" tendency, addressing the author's emotional and factual concerns while introducing novel intentional framing; a directional cost of lexical echoing, where exact overlap in knowledge tokens carries the strongest negative coefficient in a logistic model; and an internal-consistency effect, where persuasive comments show somewhat more unified TOM profiles. We frame the contribution as a measurement framework and hypothesis-generating empirical study that lays groundwork for TOM-informed evaluation of LLM social reasoning, not as a persuasion classifier.

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