arXiv:2604.04998v1 Announce Type: new Abstract: This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni~no events, crucial for mitigating their global climatic, economic, and societal impacts. Traditional prediction models often rely on oceanic…
arXiv:2604.05072v1 Announce Type: new Abstract: Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte-level tokenization inherited from natural language processing, which poorly reflects the geometric…
arXiv:2604.05072v1 Announce Type: new Abstract: Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte-level tokenization inherited from natural language processing, which poorly reflects the geometric…
arXiv:2604.05064v1 Announce Type: new Abstract: Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel dependencies. We introduce DynLMC, a Dynamic Linear Model of Coregionalization, that…
arXiv:2604.04988v1 Announce Type: new Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as parameter count or FLOPs do not reliably predict wall-clock inference time. In particular, unstructured sparsity…
arXiv:2603.22816v2 Announce Type: replace-cross Abstract: Language models increasingly “show their work” by writing step-by-step reasoning before answering. But are these reasoning steps genuinely used, or decorative narratives generated after the model has already decided? We introduce step-level faithfulness evaluation –…
arXiv:2510.14949v3 Announce Type: replace-cross Abstract: Contact languages like English exhibit rich regional variations in the form of dialects, which are often used by dialect speakers interacting with generative models. However, can multimodal generative models effectively produce content given dialectal textual…
arXiv:2510.14949v3 Announce Type: replace-cross Abstract: Contact languages like English exhibit rich regional variations in the form of dialects, which are often used by dialect speakers interacting with generative models. However, can multimodal generative models effectively produce content given dialectal textual…
arXiv:2603.22346v2 Announce Type: replace Abstract: We identify first-mover bias — path-dependent concentration of SHAP feature importance from sequential residual fitting in gradient boosting — as a mechanistic contributor to attribution instability under multicollinearity. Scaling up a single model amplifies this…
arXiv:2604.04987v1 Announce Type: new Abstract: Speculative sampling (SpS) has been successful in accelerating the decoding throughput of auto-regressive large language models by leveraging smaller draft models. SpS strictly enforces the generated distribution to match that of the verifier LLM. This…
