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cs.LG2026
TellOnce: Correcting Coding Agents Once, Improving Their Future Actions
Yujun Zhou, Kehan Guo, Haomin Zhuang +8
Interactive LLM agents increasingly work with users across tasks, yet remembering a previous correction does not ensure that the agent changes its behavior in the future. In a diag…
cs.LG2022
Robustness Evaluation of Regression Tasks with Skewed Domain Preferences
Nuno Costa, Nuno Moniz
In natural phenomena, data distributions often deviate from normality. One can think of cataclysms as a self-explanatory example: events that occur almost never, and at the same ti…