Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self’s multiplicity.

Within body-centered HCI, growing attention has been given to the body not only as an instrument of the mind but as a source of experience and presence. Dance meditation exemplifies this orientation by fostering mindful connection to the body through spontaneous, unstructured movement. In this context, VR has been widely adopted for structured instruction in static meditation and choreographed dance, yet its potential to foster freer, meditative bodily engagement has received little attention. To address this gap, we collaborated with dance meditators and co-designed Bodyfulness VR, a four-module system supporting (1) somatic awareness, (2) movement exploration, (3) emotional release, and (4) physical relaxation. Using the system as a design probe, we examined how VR could mediate somatic meditative practices. Findings from our user study indicate that VR could lower barriers to dance meditation by reducing self-consciousness and supporting bodily engagement, while movement-guiding affordances may also redirect attention toward task-oriented action, constraining bodily expression. By articulating this tension, our work contributes design implications for balancing structure and openness in VR systems that aim to support somatic mindfulness.

Contextual dynamic pricing is a problem of setting prices based on current contextual information and previous sales history to maximize revenue. A popular approach is to postulate a distribution of customer valuation as a function of contextual information and the baseline valuation. A semi-parametric setting, where the context effect is parametric and the baseline is nonparametric, is of growing interest due to its flexibility. A challenge is that customer valuation is almost never observable in practice and is instead type-I interval censored by the offered price. To address this challenge, we propose a novel semi-parametric contextual pricing algorithm for stochastic contexts, called the epoch-based Cox proportional hazards Contextual Pricing (CoxCP) algorithm. To our best knowledge, our work is the first to employ the Cox model for customer valuation. The CoxCP algorithm has a high-probability regret upper bound of $\tilde{O}( T^{\frac{2}{3}}d )$, where $T$ is the length of horizon and $d$ is the dimension of context. In addition, if the baseline is known, the regret bound can improve to $O( d \log T )$ under certain assumptions. We demonstrate empirically the proposed algorithm performs better than existing semi-parametric contextual pricing algorithms when the model assumptions of all algorithms are correct.

We extend a number of classic results for finite one-to-one matching markets, such as group strategy-proofness, entry comparative statics, and respect for unambiguous improvements, to infinite markets via the compactness theorem of propositional logic. In addition, we show that two versions of the lattice structure of finite one-to-one matching markets carry over to infinite markets. At the same time, we prove that other results, such as weak Pareto optimality and strong stability property, do not extend to infinite markets.