DeepXiv-SDK: An Agentic Data Interface for Scientific Papers

Abstract: Research agents are increasingly used in AI4Science for scientific information seeking and evidence-grounded decision making. Yet a persistent bottleneck is paper access: agents typically retrieve PDF/HTML pages, heuristically parse them, and ingest long unstructured text, leading to token-heavy reading and brittle evidence lookup. This motivates an agentic data interface for scientific papers that standardizes access, exposes budget-aware views, and treats grounding as a first-class operation. We introduce DeepXiv-SDK, which enables progressive access aligned with how agents allocate attention and reading budget. DeepXiv-SDK exposes as structured views a header-first view for screening, a section-structured view for targeted navigation, and on-demand evidence-level access for verification. Each layer is augmented with enriched attributes and explicit budget hints, so agents can balance relevance, cost, and grounding before escalating to full-text processing. DeepXiv-SDK also supports multi-faceted retrieval and aggregation over paper attributes, enabling constraint-driven search and curation over paper sets. DeepXiv-SDK is currently deployed at arXiv scale with daily synchronization to new releases and is designed to extend to other open-access corpora (e.g., PubMed Central, bioRxiv). We release RESTful APIs, an open-source Python SDK, and a web demo showcasing deep search and deep research workflows; the service is free to use with registration.