数据来源：ArXiv Domain LLM Domain Papers1. PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual LearningWe develop a continual learning method for pretrained models that \emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable. Our key observation is that pretrained networks exhibit substantial \emph{geometric redundancy}, and that this redundancy can be exploited in two compl ...

数据来源：ArXiv Domain LLM Domain Papers1. Robustness Is a Function, Not a Number: A Factorized Comprehensive Study of OOD Robustness in Vision-Based DrivingOut of distribution (OOD) robustness in autonomous driving is often reduced to a single number, hiding what breaks a policy. We decompose environments along five axes: scene (rural/urban), season, weather, time (day/night), and agent mix; and measure performance under controlled $k$-factor perturbations ($k \in {0,1,2,3}$). Using closed loop co ...

数据来源：ArXiv Domain LLM Domain Papers1. Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and DatasetsThe reliability of multilingual Large Language Model (LLM) evaluation is currently compromised by the inconsistent quality of translated benchmarks. Existing resources often suffer from semantic drift and context loss, which can lead to misleading performance metrics. In this work, we present a fully automated framework designed to address these challenges by e ...