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arXiv:2604.06485v1 Announce Type: new Abstract: "Best-of-N" selection is a popular inference-time scaling method for code generation using Large Language Models (LLMs). However, to reliably identify …
arXiv:2604.06298v1 Announce Type: new Abstract: Recent alignment work on Large Language Models (LLMs) suggests preference optimization can improve reasoning by shifting probability mass toward better …
arXiv:2604.06558v1 Announce Type: new Abstract: We present the first systematic study of when target context helps molecular property prediction, evaluating context conditioning across 10 diverse …
As the US and Israel's war has ramped up, so too have hacks on US industrial sites.
arXiv:2604.06542v1 Announce Type: new Abstract: Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. …
arXiv:2604.06571v1 Announce Type: new Abstract: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles. Variations in …
arXiv:2604.06636v1 Announce Type: new Abstract: Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress …
arXiv:2604.06754v1 Announce Type: new Abstract: I examine the technology of machine learning from the perspective of rhetoric, which is simply the art of persuasion. Rather …
AWS has an ingrained culture of handling competition, he explained, because the cloud giant also competes with its partners.
arXiv:2604.06228v1 Announce Type: new Abstract: We introduce probabilistic language tries (PLTs), a unified representation that makes explicit the prefix structure implicitly defined by any generative …
arXiv:2604.06210v1 Announce Type: new Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks …
arXiv:2604.06627v1 Announce Type: new Abstract: In-Context Learning and Chain-of-Thought prompting improve reasoning in large language models (LLMs). These typically come at the cost of longer, …