DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution

TL;DR

DuCCAE is a hybrid conversational engine that improves responsiveness and task capability by decoupling real-time response from asynchronous agentic execution, enhancing user experience and task success in large-scale deployment.

Contribution

The paper introduces DuCCAE, a novel hybrid system that integrates asynchronous agentic execution with real-time dialogue, enabling scalable, reliable, and high-quality immersive conversations in industrial settings.

Findings

Outperforms baselines in agentic execution reliability and dialogue quality.

Reduces latency to meet real-time interaction constraints.

Triples user retention and increases complex task completion in production.

Abstract

Immersive conversational systems in production face a persistent trade-off between responsiveness and long-horizon task capability. Real-time interaction is achievable for lightweight turns, but requests involving planning and tool invocation (e.g., search and media generation) produce heavy-tail execution latency that degrades turn-taking, persona consistency, and user trust. To address this challenge, we propose DuCCAE (Conversation while Collaboration with Augmentation and Evolution), a hybrid engine for immersive conversation deployed within Baidu Search, serving millions of users. DuCCAE decouples real-time response generation from asynchronous agentic execution and synchronizes them via a shared state that maintains session context and execution traces, enabling asynchronous results to be integrated back into the ongoing dialogue. The system orchestrates five subsystems-Info, Conversation, Collaboration, Augmentation, and Evolution-to support multi-agent collaboration and continuous improvement. We evaluate DuCCAE through a comprehensive framework that combines offline benchmarking on the Du-Interact dataset and large-scale production evaluation within Baidu Search. Experimental results demonstrate that DuCCAE outperforms strong baselines in agentic execution reliability and dialogue quality while reducing latency to fit strict real-time budgets. Crucially, deployment metrics since June 2025 confirm substantial real-world effectiveness, evidenced by a tripling of Day-7 user retention to 34.2% and a surge in the complex task completion rate to 65.2%. Our hybrid architecture successfully preserves conversational continuity while enabling reliable agentic execution, offering practical guidelines for deploying scalable agentic systems in industrial settings.