Conversations between close friends, family members, job applicants and hiring committees, as well as across-time conversations between authors and readers often include vague, suggestive, imprecise, and ambiguous utterances, which offer room for interpretation and may thus elicit various responses. These conversations seemingly contradict models of ideal communication, which focus on optimal information transfer grounded in information theory [Shannon, 1948]. Here, we emphasize the need to include social inference in models of communication, which may lead to a new formalism of communicative optimality. Recent research has demonstrated how particular social factors affect both (i) how speakers choose utterances and (ii) how conversation partners interpret each other’s responses. On the utterance choice side, politeness, face, as well as dominance and control co-determine speakers’ utterance choices and affect how direct and explicit they are [Beaver and Stanley, 2018, Degen et al., 2015, Khani et al., 2018, Yoon et al., 2020]. The speaker’s beliefs about the listener affect the level of precision in the speaker’s words, and in the case of miscalculation may lead to either not sufficiently precise or overly detailed descriptions. Both failures lead to negative social consequences for the speaker. From a machine learning (ML) perspective, the challenge may thus be posed to further develop artificial systems that choose a communicatively-adequate level of precision with greater flexibility. Ideally, such systems should not only take into account the knowledge of their conversation partner, but should also optimize the objective to effectively leave appropriate room for interpretation. In the absence of linguistic cues, listeners rely on their own beliefs to resolve ambiguity. The consequent responses thus allow us to infer reasons behind a listener’s reaction. For example, the listener’s reaction to (1)—conjoint with either a positive or negative interpretation of the expletive ‘man’—will likely reveal her political affiliation: (1) Man, George Busch won again [McCready, 2008, 675]. In other words, speakers can use observed listeners’ responses to refine their theory of mind about them [Frith and Frith, 2005], essentially pursuing inverse, social inference. Predicting and interpreting the behavior of others, including artificial agents, has been formalized as an inverse planning or inference problem [Baker et al., 2009]—essentially relying on our (typically probabilistic) expectations on how others would behave given particular circumstances [Frith and Frith, 2006]. Extending such formalisms to verbal behavior will allow building more precise models of conversation partners. Vague and ambiguous signals of the speaker open up additional room for interpretation and reaction. As a result, utterances and responses provide information about hidden cognitive aspects of speaker and listener, respectively. This information may include aspects of their respective current beliefs, desires, and intentions concerning the current conversation but also of their deeper beliefs, knowledge, and inference abilities in general [Wu et al., 2021]. The open challenge is to develop ML speech generation and comprehension systems, which take the listed deeper speech signaling considerations into account. To formalize this inference process, we are developing a recursive probabilistic processing and inference framework, formalizing how utterance choices and inferences of underlying belief systems of conversation partners may contribute to learning about each other and to attune a conversation to a particular conversation partner. Finally, besides offering a framework that has the emergent tendency to generate ambiguous utterances as well as the ability to infer characteristics of the conversation partner, we also quantify additional social implications stemming from comparing inferred partner characteristics with ones own.