让下一篇重要论文，不再错过。

原文摘要

When software fails, we read the error, form a hypothesis, write a patch, and test it. LLMs now do this for neural networks by editing weights; when a tabular model fails, we usually retune it. Most interpretability methods report which features matter rather than how they combine. Rather than asking an LLM to predict the target over the whole output space, we anchor the prediction with a base model and ask the LLM to debug it. We introduce Multi-Agent Residual In-Context Learning (MARICL), in which LLM agents read high-residual examples, hypothesize the missing structure, and write executable correction terms that are then refined by multi-turn textual gradient optimization. The resulting corrections are readable, executable hypotheses about what the base model misses, rather than validated mechanisms. Across nine benchmarks covering scientific, biomedical, socioeconomic, and synthetic data, MARICL reliably repairs its base model's structured residuals, improving it on every dataset, and on synthetic data absent from pretraining it recovers the hidden structure (two seeds). Against the strongest competing baseline it leads on five of the eight real benchmarks and trails TabPFN on the other three, while adding an inspectable correction that black-box baselines do not provide. Code is available at https://github.com/MrRezaeiUofT/Multi_Agent_Residual_In_Context_Learning.