Domain Risk Differences before Conditioning on Answer Length
Abstract
The question concerns response-domain risk differences without length adjustment. The secondary ordinary least squares analysis contains 845 observations and uses HC3 covariance. The coefficient on C(domain)[T.wk] is 0.1346, with a 95 percent interval from 0.0361 to 0.2331. Unadjusted domain differences describe the released response mixture before length or segmentation is controlled. They do not measure the effect of changing a prompt's domain or show how an exploring agent behaves in an environment. World-model research motivates a separate interactive test. Static factuality risk and grounded transition understanding remain different outcome objects. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
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.
S. Chen, Y. Zhao, J. Zhang, I.-C. Chern, S. Gao, P. Liu, and J. He, “FELM: Benchmarking Factuality Evaluation of Large Language Models,” Advances in Neural Information Processing Systems, pp. 44502–44523, 2023.
Tong, H., Xu, G., & Chen, Y. (2026, May). Hadamard Tensor Ring for Efficient Low-Rank Fine-Tuning. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Zhou, Yongxi, Wenbo Ye, Yuanzhe Liu, Zihan Dong, and Junwei Yao. “Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts.” arXiv, 2026, arXiv:2609.08236. https://arxiv.org/abs/2609.08236.
White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
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.
Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017. https://arxiv.org/abs/1705.08500.
Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019. https://arxiv.org/abs/1906.02530.
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.
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.
Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd ed., MIT Press, 2018. http://incompleteideas.net/book/the-book-2nd.html.
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- 2026-10-15 (2)
- 2026-01-31 (1)