@inproceedings{zhang-etal-2026-endogenous,
title = "Endogenous Observations in Agent Self-Evolution: A Survey",
author = "Zhang, Zhixin and
Jiang, Xinke and
Gao, Jiaran and
Yang, Zhibang and
Huang, Yuheng and
Zhao, Junfeng and
Chu, Xu and
Wang, Yasha",
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.1196/",
pages = "25257--25276",
ISBN = "979-8-89176-462-0",
abstract = "As training regimes centered on human-curated data encounter rising costs and an increasingly binding scaling ceiling, a core question in agent learning shifts from enlarging exogenous datasets to enabling agents to **self-evolve** under limited exogenous supervision. Prior surveys on self-evolution and self-improvement typically organize the literature by evolutionary stages or system architectures. While effective for summarizing implementation pipelines, these perspectives do not offer a unified explanation of what an agent actually observes during self-improvement, nor why such signals can serve as sufficient and reliable supervision. To address this gap, we introduce **endogenous observations**, defined as learning signals that arise from an agent{'}s own generation, evaluation, and interaction processes rather than exogenous labels. We systematize existing approaches into three categories{---}model-internal, peer-induced, and scenario-grounded observations{---}according to how these signals are produced, and we summarize the characteristic properties and typical application settings of each category. Finally, we discuss key challenges in reliability, scalability, and long-horizon stability, and outline open directions toward robust and sustainable self-evolving agents."
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<abstract>As training regimes centered on human-curated data encounter rising costs and an increasingly binding scaling ceiling, a core question in agent learning shifts from enlarging exogenous datasets to enabling agents to **self-evolve** under limited exogenous supervision. Prior surveys on self-evolution and self-improvement typically organize the literature by evolutionary stages or system architectures. While effective for summarizing implementation pipelines, these perspectives do not offer a unified explanation of what an agent actually observes during self-improvement, nor why such signals can serve as sufficient and reliable supervision. To address this gap, we introduce **endogenous observations**, defined as learning signals that arise from an agent’s own generation, evaluation, and interaction processes rather than exogenous labels. We systematize existing approaches into three categories—model-internal, peer-induced, and scenario-grounded observations—according to how these signals are produced, and we summarize the characteristic properties and typical application settings of each category. Finally, we discuss key challenges in reliability, scalability, and long-horizon stability, and outline open directions toward robust and sustainable self-evolving agents.</abstract>
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%0 Conference Proceedings
%T Endogenous Observations in Agent Self-Evolution: A Survey
%A Zhang, Zhixin
%A Jiang, Xinke
%A Gao, Jiaran
%A Yang, Zhibang
%A Huang, Yuheng
%A Zhao, Junfeng
%A Chu, Xu
%A Wang, Yasha
%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 zhang-etal-2026-endogenous
%X As training regimes centered on human-curated data encounter rising costs and an increasingly binding scaling ceiling, a core question in agent learning shifts from enlarging exogenous datasets to enabling agents to **self-evolve** under limited exogenous supervision. Prior surveys on self-evolution and self-improvement typically organize the literature by evolutionary stages or system architectures. While effective for summarizing implementation pipelines, these perspectives do not offer a unified explanation of what an agent actually observes during self-improvement, nor why such signals can serve as sufficient and reliable supervision. To address this gap, we introduce **endogenous observations**, defined as learning signals that arise from an agent’s own generation, evaluation, and interaction processes rather than exogenous labels. We systematize existing approaches into three categories—model-internal, peer-induced, and scenario-grounded observations—according to how these signals are produced, and we summarize the characteristic properties and typical application settings of each category. Finally, we discuss key challenges in reliability, scalability, and long-horizon stability, and outline open directions toward robust and sustainable self-evolving agents.
%U https://aclanthology.org/2026.findings-emnlp.1196/
%P 25257-25276
Markdown (Informal)
[Endogenous Observations in Agent Self-Evolution: A Survey](https://aclanthology.org/2026.findings-emnlp.1196/) (Zhang et al., Findings 2026)
ACL
- Zhixin Zhang, Xinke Jiang, Jiaran Gao, Zhibang Yang, Yuheng Huang, Junfeng Zhao, Xu Chu, and Yasha Wang. 2026. Endogenous Observations in Agent Self-Evolution: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25257–25276, Budapest, Hungary. Association for Computational Linguistics.