arXiv:2605.00445v1 Announce Type: new
Abstract: Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Question Answering. However, their robustness to the st…
arXiv:2605.00702v1 Announce Type: new
Abstract: Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interactions. Existing memory sy…
arXiv:2605.00706v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical behavior, posing serious co…
arXiv:2510.21141v2 Announce Type: replace-cross
Abstract: Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and…
arXiv:2512.13746v5 Announce Type: replace-cross
Abstract: Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and matrix shrinkage during curing of thermosets. Thes…
arXiv:2605.00482v1 Announce Type: cross
Abstract: Mobile network operators must monitor thousands of heterogeneous network elements across the radio access network and the packet core, each exposing high-dimensional KPI time series. The scale and cost…
arXiv:2601.07349v3 Announce Type: replace
Abstract: Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GRMs). Typically in pairwise rewarding tasks, GRMs ge…
arXiv:2603.15949v3 Announce Type: replace
Abstract: Large Language Models have demonstrated strong multilingual fluency, yet fluency alone does not guarantee socially appropriate language use. In high-context languages, communicative competence requir…
arXiv:2604.25120v2 Announce Type: replace
Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classification, extraction, or …
arXiv:2605.00742v1 Announce Type: new
Abstract: LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many reso…