dorsal/arxiv
View SchemaDarwinTOD: LLM Driven Lifelong Self Evolution for Task Oriented Dialog Systems
| Authors | Shuyu Zhang, Yujie Liu, Xinru Wang, Cheng Zhang, Yanmin Zhu, Bin Li |
|---|---|
| Categories | |
| ArXiv ID | 2601.07248vv1 |
| URL | https://arxiv.org/abs/2601.07248 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"date_created": "2026-02-17T05:53:12.006000Z",
"date_modified": "2026-02-17T05:53:12.006000Z",
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"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.",
"arxiv_id": "2601.07248",
"authors": [
"Shuyu Zhang",
"Yujie Liu",
"Xinru Wang",
"Cheng Zhang",
"Yanmin Zhu",
"Bin Li"
],
"categories": [
"cs.MA",
"cs.HC"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "DarwinTOD: LLM Driven Lifelong Self Evolution for Task Oriented Dialog Systems",
"url": "https://arxiv.org/abs/2601.07248",
"version": "v1"
},
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"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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