Wavelength.AI: Extending the Collaborative Game Wavelength as a Testbed for Studying Shared Understanding in Human–Agent Collaboration
Katelyn Morrison, Gabriel Gonzalez, Zahra Ashktorab, Matt Riemer, Andrew Anderson, Djallel Bouneffouf, Justin Weisz
Abstract
AI's increasing role as a personal agent assisting knowledge workers in everyday tasks underscores the need to investigate how to help human–agent teams build a shared understanding. We extend the collaborative "mind-reading" game Wavelength to include an AI teammate, presenting the first demonstration of an LLM capable of playing this game. Based on our agent–agent play experiments, we developed Wavelength.AI, which implements two strategies to support shared understanding: an initial team grounding conversation and post-game reflective explanations. We interpret higher team scores as evidence for better shared understanding in a preliminary user study with 24 human–AI teams. Our findings reveal that Wavelength.AI can help researchers evaluate and design different strategies to shape human-agent teams' shared understanding. Human players can see if they are on the same wavelength with AI today at https://play-wavelength-ai.com.
BibTeX
@inproceedings{ijcai2026_wavelengthaiexte,
title = {Wavelength.AI: Extending the Collaborative Game Wavelength as a Testbed for Studying Shared Understanding in Human–Agent Collaboration},
author = {Katelyn Morrison and Gabriel Gonzalez and Zahra Ashktorab and Matt Riemer and Andrew Anderson and Djallel Bouneffouf and Justin Weisz},
booktitle = {IJCAI 2026},
year = {2026}
}