To enhance large language models (LLMs) with real-world task-solving capabilities, integrating workflow knowledge into LLMs has emerged as a promising direction. However, real-world conversations are inherently dynamic—users often shift intents or request actions beyond the scope of the current workflow. Existing systems struggle to detect such transitions and to decide when to retrieve or switch to a new workflow. This paper presents FlowSwitch, a state-aware framework that learns when to search for relevant workflows and switch between them during multi-turn dialogues. A policy module determines whether to continue within the current workflow or transition to a new one based on contextual representations. When searching, a retriever identifies the most relevant workflow knowledge given the dialogue state. We conduct comprehensive experiments to explore the optimal configuration of FlowSwitch, including workflow format, retrieval input type, and retrieval method. Experimental results show that our framework, when using the agent’s self-generated search queries, achieves the highest Top-1 accuracy and Mean Average Precision (MAP). Moreover, FlowSwitch reduces nearly 50% of search operations, substantially lowering computational cost and response time.

This study systematically compares end-to-end (E2E) audio language models (AudioLMs) against modular (ASR, LLM, TTS) systems for multi-phase task-oriented dialogues. We evaluate open-source models on key metrics: conversational naturalness and dialogue consistency. Our findings show that E2E configurations consistently underperform their modular counterparts, exhibiting severe degradation in dialogue quality across turns. Investigating this failure, our analysis reveals that the core issue lies in the E2E models’ dialogue modeling capabilities, specifically in context maintenance and topic tracking. This work highlights a critical gap between the purported low-latency benefit of AudioLMs and their practical ability to maintain coherence in complex, multi-turn dialogues, suggesting a need for focused architectural improvements.

The integration of dialogue agents into the sales domain requires a deep understanding of how these systems interact with users possessing diverse personas. This study explores the influence of user personas, defined using the Myers-Briggs Type Indicator (MBTI), on the interaction quality and performance of sales-oriented dialogue agents. Through large-scale testing and analysis, we assess the pre-trained agent’s effectiveness, adaptability, and personalization capabilities across a wide range of MBTI-defined user types. Our findings reveal significant patterns in interaction dynamics, task completion rates, and dialogue naturalness, underscoring the future potential for dialogue agents to refine their strategies to better align with varying personality traits. This work not only provides actionable insights for building more adaptive and user-centric conversational systems in the sales domain but also contributes broadly to the field by releasing persona-defined user simulators. These simulators, unconstrained by domain, offer valuable tools for future research and demonstrate the potential for scaling personalized dialogue systems across diverse applications.