Quality follows upgrading
Academic
arXiv:2604.00024v1 Announce Type: new Abstract: Large language models are increasingly used for medical guidance, but women's health remains under-evaluated in benchmark design. We present the …
arXiv:2604.01653v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states evolve in …
arXiv:2604.01349v1 Announce Type: new Abstract: Reservoir simulation workflows face a fundamental data asymmetry: input parameter fields (geostatistical permeability realizations, porosity distributions) are free to generate …
arXiv:2604.01775v1 Announce Type: new Abstract: Although demand forecasting is a critical component of supply chain planning, actual retail data can exhibit irreconcilable seasonality, irregular spikes, …
arXiv:2604.00672v1 Announce Type: new Abstract: TF-IDF is a classical formula that is widely used for identifying important terms within documents. We show that TF-IDF-like scores …
arXiv:2604.01552v1 Announce Type: new Abstract: Denoising generative models deliver high-fidelity generation but remain bottlenecked by inference latency due to the many iterative denoiser calls required …
arXiv:2604.00025v1 Announce Type: new Abstract: Standard evaluation protocols reveal a counterintuitive phenomenon: on 7.7% of benchmark problems spanning five datasets, larger language models underperform smaller …
arXiv:2604.00414v1 Announce Type: new Abstract: LLM systems must make control decisions in addition to generating outputs: whether to answer, clarify, retrieve, call tools, repair, or …
arXiv:2604.01727v1 Announce Type: new Abstract: Forecasting evolving clinical risks relies on intrinsic pathological dependencies rather than mere chronological proximity, yet current methods struggle with coarse …
arXiv:2604.00489v1 Announce Type: new Abstract: Adapting pre-trained text Large Language Models (LLMs) into Speech Language Models (Speech LMs) via continual pretraining on speech data is …
arXiv:2604.01481v1 Announce Type: new Abstract: The development of robust clinical decision support systems is frequently impeded by the scarcity of high-fidelity, privacy-preserving biomedical data. While …
arXiv:2604.01476v1 Announce Type: new Abstract: Reinforcement learning for LLMs is vulnerable to reward hacking, where models exploit shortcuts to maximize reward without solving the intended …