![[2602.18962] NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18962] NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners
Summary
NeuroWise is a multi-agent LLM system designed to enhance double-empathy communication between neurotypical and autistic individuals, demonstrating significant improvements in conversational efficiency and attributional understanding.
Why It Matters
This research addresses the double empathy problem, which highlights mutual misunderstandings in communication between neurodivergent and neurotypical individuals. By focusing on enhancing the communication skills of neurotypical users, NeuroWise aims to foster better understanding and support for autistic partners, which is crucial for inclusive interactions.
Key Takeaways
- NeuroWise helps neurotypical users better understand autistic communication challenges.
- The system reduces deficit-based attributions, promoting mutual understanding.
- Participants reported improved conversational efficiency with fewer turns needed.
Computer Science > Human-Computer Interaction arXiv:2602.18962 (cs) [Submitted on 21 Feb 2026] Title:NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners Authors:Albert Tang, Yifan Mo, Jie Li, Yue Su, Mengyuan Zhang, Sander L. Koole, Koen Hindriks, Jiahuan Pei View a PDF of the paper titled NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners, by Albert Tang and 7 other authors View PDF HTML (experimental) Abstract:The double empathy problem frames communication difficulties between neurodivergent and neurotypical individuals as arising from mutual misunderstanding, yet most interventions focus on autistic individuals. We present NeuroWise, a multi-agent LLM-based coaching system that supports neurotypical users through stress visualization, interpretation of internal experiences, and contextual guidance. In a between-subjects study (N=30), NeuroWise was rated as helpful by all participants and showed a significant condition-time effect on deficit-based attributions (p=0.02): NeuroWise users reduced deficit framing, while baseline users shifted toward blaming autistic "deficits" after difficult interactions. NeuroWise users also completed conversations more efficiently (37% fewer turns, p=0.03). These findings suggest that AI-based interpretation can support attributional change by helping users recognize communication challenges as mutual. Comments: ...
