Listening Alone, Understanding Together: Collaborative Context Recovery for Privacy-Aware AI

TL;DR

CONCORD is a privacy-preserving framework enabling AI assistants to collaboratively recover context without compromising user privacy, using real-time speaker verification and negotiated information exchange.

Contribution

The paper introduces CONCORD, a novel privacy-aware asynchronous assistant framework that facilitates safe context recovery through negotiation and verification mechanisms.

Findings

Achieves 91.4% recall in gap detection

Attains 96% accuracy in relationship classification

Reaches 97% true negative rate in privacy-sensitive disclosures

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 (1) spatio-temporal context resolution, (2) information gap detection, and (3) 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, and 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.