<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Shared Experiences | Yiyang "Diana" Wang</title><link>https://hello-diana.github.io/tags/shared-experiences/</link><atom:link href="https://hello-diana.github.io/tags/shared-experiences/index.xml" rel="self" type="application/rss+xml"/><description>Shared Experiences</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 11 Dec 2025 00:00:00 +0000</lastBuildDate><image><url>https://hello-diana.github.io/media/icon_hu_3520ea6f5cfedd63.png</url><title>Shared Experiences</title><link>https://hello-diana.github.io/tags/shared-experiences/</link></image><item><title>CompanionCast: Toward Social Collaboration with Multi-Agent Systems in Shared Experiences</title><link>https://hello-diana.github.io/publication/companioncast/</link><pubDate>Thu, 11 Dec 2025 00:00:00 +0000</pubDate><guid>https://hello-diana.github.io/publication/companioncast/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Shared experiences are fundamental to social connection, yet media consumption is increasingly solitary. We present &lt;strong>CompanionCast&lt;/strong>, a general framework for orchestrating multiple specialized AI agents as social collaborators within a live shared context, integrating multimodal event detection, rolling context caching, and spatial audio to enhance co-presence. Validated through sports viewing, pilot studies with soccer fans show that CompanionCast significantly improves perceived social presence and emotional sharing compared to solitary viewing. This work was conducted in part during a Ph.D. research internship at Dolby Laboratories and accepted at the ACM CHI 2026 Workshop on Human-Agent Collaboration.&lt;/p></description></item></channel></rss>