An Agent in an Environment with Changed Graph Structure
Abstract
Source-free graph adaptation and world-model exploration should preserve structural relations and transition grounding as separate outcomes. Evaluation retains information restrictions and independently checks actions in changed environments. More target graph records or fluent responses cannot establish that the agent understands the transitions relevant to exploration. The practical claim remains tied to the tested mechanism. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
References
S. Chen, T. Zhu, Z. Wang, J. Zhang, K. Wang, R. Zhou, S. Gao, T. Xiao, Y. W. Teh, J. He, and M. Li, “Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective,” Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026.
Y. Wang, K. Zhang, M. Wang, S. Gao, and N. Yin, “USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation,” Proceedings of the 2026 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2026.
Zhou, Yongxi, Junwei Yao, Yuanzhe Liu, Zihan Dong, Wenbo Ye, Jiaxi Wen, and Lai Yun Choi. “Single Canonical Prompts Underestimate LLM Safety's Surface-Form Sensitivity.” arXiv, 2026, arXiv:2608.02665. https://arxiv.org/abs/2608.02665.
Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.
Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
Hu, Edward J., et al. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations, 2022. https://openreview.net/forum?id=nZeVKeeFYf9.
Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, 2017, pp. 1321–1330. https://proceedings.mlr.press/v70/guo17a.html.
Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd ed., MIT Press, 2018. http://incompleteideas.net/book/the-book-2nd.html.
Levine, Sergey, Aviral Kumar, George Tucker, and Justin Fu. “Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.” arXiv, 2020, arXiv:2005.01643. https://arxiv.org/abs/2005.01643.
Kumar, Aviral, Aurick Zhou, George Tucker, and Sergey Levine. “Conservative Q-Learning for Offline Reinforcement Learning.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 1179–1191. https://arxiv.org/abs/2006.04779.
Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474. https://arxiv.org/abs/2005.11401.