Searcharxiv⌕ Search

arXiv · 2610.41776

MERIDIAN: Memory-Encoded Rituals for Intimacy across Distance in an Interactive, Asynchronous Neighbourhood

Abstract

How can shared memories become playable structures for partners who live in different time zones? We present MERIDIAN, a pre-deployment design case of Yi Town, a bilingual browser game developed by one member of a long-distance couple as a gift for the other. Nine levels translate personally meaningful events, places and cultural references into tasks within a navigable pixel town. A birthday prologue and a fictional wedding frame the intended experience. Drawing on research through design, we reconstruct four design changes from 109 development commits, 28 changelog versions and three design specifications. These records support six situated design commitments, including memory-derived mechanics, staged access, local progress storage and bilingual coverage. The implementation combines a union-based progress model with manually exchanged, checksummed codes and time-zone-dependent scenery. An implementation audit reproduced 153 passing unit tests and examined all 1,024 canonical progress codes, while identifying limits involving resets, unversioned level registries and residual transcription errors. The application has no author-operated backend for progress, but external media services and browser speech recognition limit its privacy and offline guarantees. No recipient evaluation or relationship outcomes are reported. The contribution is an inspectable account of design decisions and their trade-offs, including the tension between authoring a personal gift and preserving the recipient's freedom to interpret, decline or reshape it. The game is playable at https://jingyifan.love and its source is public at https://github.com/rayford295/yi-town.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jingyi Chen, Yifan Yang. 2026-10-30. MERIDIAN: Memory-Encoded Rituals for Intimacy across Distance in an Interactive, Asynchronous Neighbourhood. https://arxiv.org/abs/2610.41776

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Stayin' Aligned Over Time: Towards Longitudinal Human-LLM Alignment via Contextual Reflection and Privacy-Preserving Behavioral Data

Current human-AI alignment and evaluation methods for large language models (LLMs) often rely on preference signals collected immediately after an interaction. This practice implicitly treats preference as static, even though many LLM-mediated decisions unfold over time and may be re-evaluated differently after real-world consequences and observed outcomes. Therefore, we argue for a methodological shift from single-moment preference elicitation to longitudinal, context-situated alignment measurement. We present a methodological framework for collecting temporally grounded alignment signals by combining (1) in-situ preference capture, (2) context-triggered follow-up preference reflection, and (3) privacy-preserving behavioral traces that help interpret preference change. As an instantiation of this methodology, we introduce BITE, a browser-based system that detects consequential LLM interactions, prompts reflection across later decision points, and supports progressive, user-controlled consent for sharing behavioral data. Through a two week longitudinal deployment study with 8 participants, our approach surfaced differences between immediate and later user preferences in accuracy, relevance and other dimensions of the LLM output. Our findings highlight the limitations of single-moment preference datasets and underscore the importance of longitudinal methods for alignment evaluation in everyday use.

cs.HC↗

Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI

Contextualization is a form of personalization in which the message is framed in a context meaningful to the recipient, using their interests and background. Personalization is a known persuasive strategy of artificial intelligence (AI) systems in politics and marketing. However, the persuasive effect of contextualization in everyday tasks, where users often lack prior knowledge, remains unclear. We ran a $2\times2$ between-subjects experiment ($N = 407$) in a fictional scenario where participants had no prior opinions or knowledge. We examined how contextualization, combined with conversational warmth, shapes the persuasive power of an AI assistant arguing against expert recommendations and users' reliance on it. Our findings show that the neutral, non-contextualized assistant was the most persuasive. Contextualization and warmth each reduced persuasiveness on their own, but their interaction increased it. Reliance was present in all conditions, and neither cue changed it detectably. Trust predicted persuasion and reliance, yet neither contextualization nor warmth had an indirect effect through trust. Participants with higher self-reported AI literacy relied more on the assistant without trusting it more. Our study extends AI personalization research from political opinions to everyday decisions made without prior knowledge. The results suggest that users are more likely to be persuaded by, and defer to, an AI assistant than an expert, even without personalization. AI persuasion experiments should therefore include untailored assistants and measure decisions; the cues that changed persuasion did not detectably change reliance.

cs.HC↗

An Open Book, On Whose Terms? Power Dynamics in Sensor-Derived, LLM-Generated Summaries Sharing Between Older Adults and Remote Families

As older adults (OAs) increasingly favor aging in place, remote family members (RFMs) need meaningful awareness of their loved ones without intrusive monitoring. Large language models can provide rich interpretations of non-intrusive sensing data, yet recent work has largely emphasized technical accuracy over how families use or benefit from this information. We conducted formative interviews with 12 RFMs and 2 OAs, followed by a three-month deployment case study with one OA-RFM dyad. Our system provided daily AI-generated summaries of multimodal sensing data, which we iteratively refined with both parties using a research-through-design approach. Although the summaries integrated well into the dyad's daily routine, sustaining over 90% engagement, their lived experience revealed evolving tensions around agency, reassurance, privacy, trust, and communication, shaped by sensor limitations and existing communication misalignments. Our findings inform the design of future AI-mediated sharing systems that account for real-world sensing constraints and complex family care dynamics.

cs.HC↗