Learning robust models under distribution shifts between training and test datasets is a fundamental challenge in machine learning. While learning invariant features across environments is a popular approach, it often assumes that these features are fully observed in both training and test sets—a condition frequently violated in practice. When models rely on invariant features absent in the test set, their robustness in new environments can deteriorate. To tackle this problem, we introduce a novel learning principle called the Sufficient Invariant Learning (SIL) framework, which focuses on learning a sufficient subset of invariant features rather than relying on a single feature. After demonstrating the limitation of existing invariant learning methods, we propose a new algorithm, Adaptive Sharpness-aware Group Distributionally Robust Optimization (ASGDRO), to learn diverse invariant features by seeking common flat minima across the environments. We theoretically demonstrate that finding a common flat minima enables robust predictions based on diverse invariant features. Empirical evaluations on multiple datasets, including our new benchmark, confirm ASGDRO’s robustness against distribution shifts, highlighting the limitations of existing methods. Code: https://github.com/MLAI-Yonsei/SIL-ASGDRO.

Addressing the challenges in QA for specific technical domains requires identifying relevant portions of extensive documents and generating answers based on this focused content.Traditional pre-trained LLMs often struggle with domain-specific terminology, while fine-tuned LLMs demand substantial computational resources.To overcome these limitations, we propose TIDES, Technical Information Distillation and Extraction System.TIDES is a trainingfree approach that combines traditional TF-IDF techniques with prompt-based LLMs in a hybrid process, effectively addressing complex technical questions.It uses TF-IDF to identify and prioritize domain-specific words that are rare in other documents and LLMs to refine the candidate pool by focusing on the most relevant segments in documents through multiple stages.Our approach improves the precision and efficiency of QA systems in technical contexts without LLM retraining.

We analyze and enhance Trio and ArckFS by Zhou et al. (SOSP 2023), high-performance NVM file system architecture and file system. A group of authors from KAIST initiated this study through a careful review of the paper and the released artifact, seeking to enhance the Trio work. Their analysis identifies (1) insufficient clarity in the paper on the handling of multi-inode operations, and (2) several implementation bugs in ArckFS that cause occasional operation failures or potential crash inconsistencies during inode creation.