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

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.

Figures (7)

• Figure 1: DuCCAE interface and interaction flow in production. Starting from a Baidu Search entry, the system maintains persona-consistent chat responses and supports escalation to real-time calling; complex requests trigger asynchronous collaboration and tool augmentation while preserving conversational continuity.
• Figure 2: DuCCAE: an evolving agentic service engine for immersive conversational interaction. Info System converts multimodal signals into policy-aware context and manages memory. Conversation System acts as a low-latency gatekeeper for intent routing and renders persona-consistent responses. Collaboration System supports multi-agent execution for long-horizon tasks via planning and tool use. Augmentation System empowers agents with external tools, retrieval resources, and execution protocols. Evolution System drives continuous improvement through episode-based judging and post-training.
• Figure 3: Runtime Execution Dataflow of a Mixed-Intent Interaction Episode. Processing the input "Exhausted... Plan a trip", the Fast Track (top) utilizes User Memory ("Hobby: Basketball") for an immediate empathetic inquiry. Simultaneously, the Slow Track (bottom) decomposes the travel request, demonstrating: (1) Parallel Execution of independent flight and activity searches to optimize latency; and (2) Constraint Fusion, which filters dining options via the memory constraint ("Dislikes Raw Fish") to select the compatible "Wagyu Beef".
• Figure 4: Evolution of Key Business Metrics across System Iterations. Statistics are derived from large-scale online controlled experiments in Baidu Search. From V1 to V3, DuCCAE demonstrates consistent growth in: (a) User Stickiness; (b) Conversation Quality; (c) Interaction Depth; and (d) Agentic Capability.
• Figure 5: Core system prompt in DuCCAE-V3 for intent routing and planning. The structured schema ensures strict compliance with the dual-track architecture and deterministic execution.