The Cooperative Index (CI) of Sinkhorn plans (SK) and its one step approximation (onestep) as total perturbation increases.

The core challenge in cooperative communication is the problem of common ground: having enough shared knowledge and understanding to successfully communicate.

Sign up to receive it in your inbox. Aside from a temporary love, or a new friend, you could easily stumble upon fabulous stage shows that were presented with such seriousness, often, that you wondered if--while watching the amazing Duelling Bankheads, for instance, or so many people who got up so brilliantly as Stevie Nicks on the Night of 1000 Stevies--you were high on the entertainment, or on dancing with your chosen community, or just amazed by what New York had to offer by way of creativity. Looking back, I can see that, for me at least, it was the combination of all three elements together that gave such hope about Manhattan's ability to foster noncommercial glamour, and to support young performers who were trying things out and seeing what stuck. Richard Move as Martha Graham. The shows I loved the most were at Jackie 60, spearheaded by the irreplaceable Chi Chi Valenti and Johnny Dynell, the resident d.j.

Humans flexibly construct internal models to navigate novel situations. To be useful, these internal models must be sufficiently faithful to the environment that resource-limited planning leads to adequate outcomes; equally, they must be tractable to construct in the first place. We argue that analogy plays a central role in these processes, enabling agents to reuse solution-relevant structure from past experiences and amortise the computational costs of both model construction (construal) and planning. Formalis-ing analogies as partial homomorphisms between Markov decision processes, we sketch a framework in which abstract modules, derived from previous construals, serve as com-posable building blocks for new ones. This modular reuse allows for flexible adaptation of policies and representations across domains with shared structural essence.

Figurative language (e.g., irony, hyperbole, understatement) is ubiquitous in human communication, resulting in utterances where the literal and the intended meanings do not match. The Rational Speech Act (RSA) framework, which explicitly models speaker intentions, is the most widespread theory of probabilistic pragmatics, but existing implementations are either unable to account for figurative expressions or require modeling the implicit motivations for using figurative language (e.g., to express joy or annoyance) in a setting-specific way. In this paper, we introduce the Rhetorical-Strategy-Aware RSA $(RSA)^2$ framework which models figurative language use by considering a speaker's employed rhetorical strategy. We show that $(RSA)^2$ enables human-compatible interpretations of non-literal utterances without modeling a speaker's motivations for being non-literal. Combined with LLMs, it achieves state-of-the-art performance on the ironic split of PragMega+, a new irony interpretation dataset introduced in this study.

Computational models of pragmatic language use have traditionally relied on hand-specified sets of utterances and meanings, limiting their applicability to real-world language use. We propose a neuro-symbolic framework that enhances probabilistic cognitive models by integrating LLM-based modules to propose and evaluate key components in natural language, eliminating the need for manual specification. Through a classic case study of pragmatic question-answering, we systematically examine various approaches to incorporating neural modules into the cognitive model -- from evaluating utilities and literal semantics to generating alternative utterances and goals. We find that hybrid models can match or exceed the performance of traditional probabilistic models in predicting human answer patterns. However, the success of the neuro-symbolic model depends critically on how LLMs are integrated: while they are particularly effective for proposing alternatives and transforming abstract goals into utilities, they face challenges with truth-conditional semantic evaluation. This work charts a path toward more flexible and scalable models of pragmatic language use while illuminating crucial design considerations for balancing neural and symbolic components.