@inproceedings{cai-etal-2026-benchmark,
title = "A Benchmark for Self-Evolving Agents via Experience-Driven Lifelong Learning",
author = "Cai, Yuxuan and
Hao, Yipeng and
Zhou, Jie and
Yan, Hang and
Lei, Zhikai and
Zheng, Rui and
Han, Zhenhua and
Yang, Yutao and
Li, Junsong and
Pan, Qianjun and
Huai, Tianyu and
Chen, Qin and
Li, Xin and
Chen, Kai and
Zhang, Bo and
Qiu, Xipeng and
He, Liang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.770/",
pages = "16232--16272",
ISBN = "979-8-89176-462-0",
abstract = "As AI research increasingly moves beyond systems optimized for static tasks, a central challenge is building agents that learn continuously through experience-driven lifelong learning (ELL). In this paper, we introduce StuLife, a synthetic benchmark designed to evaluate selected operational capabilities relevant to ELL. Particularly, StuLife simulates a student{'}s college journey, from enrollment to academic and personal development, across three core phases and ten detailed sub-scenarios. StuLife is designed around four key design principles: From Isolated Tasks to Stateful Interaction, From Passive to Proactive, From Context to Memory, and From Imitation to Learning. In this dynamic environment, agents must acquire and distill practical skills and maintain persistent memory to make decisions based on evolving state variables (e.g., resource availability and time), and are also expected to initiate actions without external prompting. To this end, StuLife provides an evaluation protocol with dedicated metrics (e.g., StuGPA) for these capabilities. Our evaluation reveals that even the highest-StuGPA model, GPT-5, scores only 17.9/100, highlighting large gaps in long-term retention and proactive task initiation. Beyond evaluating LLMs on StuLife, we also explore the role of context engineering in improving agent performance on lifelong learning tasks."
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<abstract>As AI research increasingly moves beyond systems optimized for static tasks, a central challenge is building agents that learn continuously through experience-driven lifelong learning (ELL). In this paper, we introduce StuLife, a synthetic benchmark designed to evaluate selected operational capabilities relevant to ELL. Particularly, StuLife simulates a student’s college journey, from enrollment to academic and personal development, across three core phases and ten detailed sub-scenarios. StuLife is designed around four key design principles: From Isolated Tasks to Stateful Interaction, From Passive to Proactive, From Context to Memory, and From Imitation to Learning. In this dynamic environment, agents must acquire and distill practical skills and maintain persistent memory to make decisions based on evolving state variables (e.g., resource availability and time), and are also expected to initiate actions without external prompting. To this end, StuLife provides an evaluation protocol with dedicated metrics (e.g., StuGPA) for these capabilities. Our evaluation reveals that even the highest-StuGPA model, GPT-5, scores only 17.9/100, highlighting large gaps in long-term retention and proactive task initiation. Beyond evaluating LLMs on StuLife, we also explore the role of context engineering in improving agent performance on lifelong learning tasks.</abstract>
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%0 Conference Proceedings
%T A Benchmark for Self-Evolving Agents via Experience-Driven Lifelong Learning
%A Cai, Yuxuan
%A Hao, Yipeng
%A Zhou, Jie
%A Yan, Hang
%A Lei, Zhikai
%A Zheng, Rui
%A Han, Zhenhua
%A Yang, Yutao
%A Li, Junsong
%A Pan, Qianjun
%A Huai, Tianyu
%A Chen, Qin
%A Li, Xin
%A Chen, Kai
%A Zhang, Bo
%A Qiu, Xipeng
%A He, Liang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cai-etal-2026-benchmark
%X As AI research increasingly moves beyond systems optimized for static tasks, a central challenge is building agents that learn continuously through experience-driven lifelong learning (ELL). In this paper, we introduce StuLife, a synthetic benchmark designed to evaluate selected operational capabilities relevant to ELL. Particularly, StuLife simulates a student’s college journey, from enrollment to academic and personal development, across three core phases and ten detailed sub-scenarios. StuLife is designed around four key design principles: From Isolated Tasks to Stateful Interaction, From Passive to Proactive, From Context to Memory, and From Imitation to Learning. In this dynamic environment, agents must acquire and distill practical skills and maintain persistent memory to make decisions based on evolving state variables (e.g., resource availability and time), and are also expected to initiate actions without external prompting. To this end, StuLife provides an evaluation protocol with dedicated metrics (e.g., StuGPA) for these capabilities. Our evaluation reveals that even the highest-StuGPA model, GPT-5, scores only 17.9/100, highlighting large gaps in long-term retention and proactive task initiation. Beyond evaluating LLMs on StuLife, we also explore the role of context engineering in improving agent performance on lifelong learning tasks.
%U https://aclanthology.org/2026.findings-emnlp.770/
%P 16232-16272
Markdown (Informal)
[A Benchmark for Self-Evolving Agents via Experience-Driven Lifelong Learning](https://aclanthology.org/2026.findings-emnlp.770/) (Cai et al., Findings 2026)
ACL
- Yuxuan Cai, Yipeng Hao, Jie Zhou, Hang Yan, Zhikai Lei, Rui Zheng, Zhenhua Han, Yutao Yang, Junsong Li, Qianjun Pan, Tianyu Huai, Qin Chen, Xin Li, Kai Chen, Bo Zhang, Xipeng Qiu, and Liang He. 2026. A Benchmark for Self-Evolving Agents via Experience-Driven Lifelong Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 16232–16272, Budapest, Hungary. Association for Computational Linguistics.