Long-term Task-oriented Agent: Proactive Long-term Intent Maintenance in Dynamic Environments

• URL: http://arxiv.org/abs/2601.09382v1
• Date: Wed, 14 Jan 2026 11:15:40 GMT
• Title: Long-term Task-oriented Agent: Proactive Long-term Intent Maintenance in Dynamic Environments
• Authors: Qinglong Shi, Donghai Wang, Hantao Zhou, Jiguo Li, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He,
• Abstract summary: Current large language model agents operate under a reactive paradigm, responding only to immediate user queries within short-term sessions.<n>We propose a novel interaction paradigm for proactive Task-oriented Agents capable of bridging the gap between relatively static user's needs and a dynamic environment.<n>We introduce a high-quality data synthesis pipeline to construct complex, multi-turn dialog data in a dynamic environment.
• Score: 8.937298475124484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current large language model agents predominantly operate under a reactive paradigm, responding only to immediate user queries within short-term sessions. This limitation hinders their ability to maintain long-term user's intents and dynamically adapt to evolving external environments. In this paper, we propose a novel interaction paradigm for proactive Task-oriented Agents capable of bridging the gap between relatively static user's needs and a dynamic environment. We formalize proactivity through two key capabilities, (i) Intent-Conditioned Monitoring: The agent autonomously formulates trigger conditions based on dialog history; (ii) Event-Triggered Follow-up: The agent actively engages the user upon detecting useful environmental updates. We introduce a high-quality data synthesis pipeline to construct complex, multi-turn dialog data in a dynamic environment. Furthermore, we attempt to address the lack of evaluation criteria of task-oriented interaction in a dynamic environment by proposing a new benchmark, namely ChronosBench. We evaluated some leading close-source and open-source models at present and revealed their flaws in long-term task-oriented interaction. Furthermore, our fine-tuned model trained using synthetic data for supervised learning achieves a task completion rate of 85.19% for complex tasks including shifts in user intent, outperforming other models under test. And the result validated the effectiveness of our data-driven strategy.

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