Listening Alone, Understanding Together: Collaborative Context Recovery for Privacy-Aware AI
Summary: arXiv:2604.13348v1 Announce Type: new
Abstract: We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI. As agents evolve from reactive to always-listening assistants, they face a core privacy risk (of capturing non-consenting speakers), which makes their social deployment a challenge. To overcome this, we implement CONCORD, which enforces owner-only speech capture via real-time speaker verification, producing a one-sided transcript that incurs missing context but preserves privacy. We demonstrate that CONCORD can safely recover necessary context through:
- Spatio-temporal context resolution
- Information gap detection
- Minimal A2A queries governed by a relationship-aware disclosure
Instead of hallucination-prone inferring, CONCORD treats context recovery as a negotiated safe exchange between assistants. Across a multi-domain dialogue dataset, CONCORD achieves:
- 91.4% recall in gap detection
- 96% relationship classification accuracy
- 97% true negative rate in privacy-sensitive disclosure decisions
By reframing always-listening AI as a coordination problem between privacy-preserving agents, CONCORD offers a practical path toward socially deployable proactive conversational agents. This framework not only enhances the understanding of context within conversations but also ensures that privacy remains a top priority, addressing one of the most significant challenges in the deployment of AI systems in everyday environments.
As the landscape of AI continues to evolve, the importance of privacy-aware technologies cannot be overstated. With increasing reliance on speech-based assistants in various sectors, including healthcare, education, and customer service, the need for frameworks like CONCORD becomes crucial. By focusing on collaborative context recovery, CONCORD paves the way for AI that respects user privacy while maintaining the ability to engage in meaningful dialogue.
In conclusion, CONCORD represents a significant advancement in the field of AI by not only addressing privacy concerns but also enhancing the interaction capabilities of AI agents. As we move towards a future where AI plays a more prominent role in our lives, the principles behind CONCORD could serve as a model for developing other privacy-aware systems across different domains.