DarwinTOD: LLM Driven Lifelong Self Evolution for Task Oriented Dialog Systems

ACL ARR 2026 January Submission2851 Authors

Keywords: lifelong self-evolving, task-oriented dialogue, multi-agent, evolutionary computation

Abstract: Traditional task-oriented dialog systems are unable to evolve from ongoing interactions or adapt to new domains after deployment, that is a critical limitation in real-world dynamic environments. Continual learning approaches depend on episodic retraining with human curated data, failing to achieve autonomy lifelong improvement. While evolutionary computation and LLM driven self improvement offer promising mechanisms for dialog optimization, they lack a unified framework for holistic, iterative strategy refinement. To bridge this gap, we propose DarwinTOD, a lifelong self evolving dialog framework that systematically integrates these two paradigms, enabling continuous strategy optimization from a zero-shot base without task specific fine-tuning. DarwinTOD maintains an Evolvable Strategy Bank and operates through a dual-loop process: online multi-agent dialog execution with peer critique, and offline structured evolutionary operations that refine the strategy bank using accumulated feedback. This closed-loop design enables autonomous continuous improvement without human intervention. Extensive experiments show that DarwinTOD surpasses previous state-of-the-art methods and exhibits continuous performance gains throughout evolution. Our work provides a novel framework for building dialog systems with lifelong self evolution capabilities. The code is available at \href{https://anonymous.4open.science/r/DarwinTOD-BBD1}{Anonymous GitHub}.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: Dialogue and Interactive Systems, Machine Learning for NLP, NLP Applications

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 2851