DeepXiv-SDK: An Agentic Data Interface for Scientific Literature
- URL: http://arxiv.org/abs/2603.00084v2
- Date: Tue, 03 Mar 2026 07:41:40 GMT
- Title: DeepXiv-SDK: An Agentic Data Interface for Scientific Literature
- Abstract summary: DeepXiv-SDK is a three-layer agentic data interface for scientific literature.<n>DeepXiv-SDK supports the complete ArXiv corpus, and is synchronized daily to incorporate new releases.
- Score: 60.19264121557117
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: LLM-agents are increasingly used to accelerate the progress of scientific research. Yet a persistent bottleneck is data access: agents not only lack readily available tools for retrieval, but also have to work with unstrcutured, human-centric data on the Internet, such as HTML web-pages and PDF files, leading to excessive token consumption, limit working efficiency, and brittle evidence look-up. This gap motivates the development of \textit{an agentic data interface}, which is designed to enable agents to access and utilize scientific literature in a more effective, efficient, and cost-aware manner. In this paper, we introduce DeepXiv-SDK, which offers a three-layer agentic data interface for scientific literature. 1) Data Layer, which transforms unstructured, human-centric data into normalized and structured representations in JSON format, improving data usability and enabling progressive accessibility of the data. 2) Service Layer, which presents readily available tools for data access and ad-hoc retrieval. It also enables a rich form of agent usage, including CLI, MCP, and Python SDK. 3) Application Layer, which creates a built-in agent, packaging basic tools from the service layer to support complex data access demands. DeepXiv-SDK currently supports the complete ArXiv corpus, and is synchronized daily to incorporate new releases. It is designed to extend to all common open-access corpora, such as PubMed Central, bioRxiv, medRxiv, and chemRxiv. We release RESTful APIs, an open-source Python SDK, and a web demo showcasing deep search and deep research workflows. DeepXiv-SDK is free to use with registration.
Related papers
- EvoOntology: A Self-Evolving Ontology Layer for Data Agents [31.301299551756596]
EvoOntology is a self-evolving ontology layer for data agents.<n>It encapsulates the ontology as an MCP server comprising a schema layer, a content layer, and a tool layer.<n>EvoOntology consistently outperforms strong baselines and existing semantic-layer approaches.
arXiv Detail & Related papers (2026-09-14T15:59:24Z) - Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction [127.64173950476702]
We study direct corpus interaction (DCI), where an agent searches the raw corpus directly with general-purpose terminal tools.<n>This approach requires no offline indexing and adapts naturally to evolving local corpora.<n>Across IR benchmarks and end-to-end agentic search tasks, this simple setup substantially outperforms strong sparse, dense, and reranking baselines.
arXiv Detail & Related papers (2026-05-03T19:13:11Z) - DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis [8.171937411588015]
We introduce DataCross, a novel benchmark and collaborative agent framework for unified, insight-driven analysis.<n>DataCrossBench comprises 200 end-to-end analysis tasks across finance, healthcare, and other domains.<n>We also propose the DataCrossAgent framework, inspired by the "divide-and-synthesis" workflow of human analysts.
arXiv Detail & Related papers (2026-01-29T08:40:45Z) - Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents [85.02904078131682]
We introduce the agent data protocol (ADP), a light-weight representation language that serves as an "interlingua" between agent datasets.<n> ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic.<n>All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.
arXiv Detail & Related papers (2025-10-28T17:53:13Z) - Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics [75.4712507893024]
Enterprise Deep Research (EDR) is a multi-agent system that integrates a Master Planning Agent for adaptive query decomposition.<n>Four specialized search agents (General, Academic, GitHub, LinkedIn) and a visualization agent for data-driven insights are also included.<n>EDR reflects research direction with optional human-in-the-loop steering guidance.
arXiv Detail & Related papers (2025-10-20T17:55:11Z) - Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK [0.4874819476581695]
This paper presents the MOSTLY AI Synthetic Data Software Development Kit (SDK), an open-source toolkit for synthesizing high-quality data.<n>The SDK integrates robust features such as differential privacy guarantees, fairness-aware data generation, and automated quality assurance into a flexible and accessible Python interface.<n>The SDK has seen rapid adoption, highlighting its practicality in addressing real-world data bottlenecks and promoting widespread data democratization.
arXiv Detail & Related papers (2025-08-01T15:36:59Z) - AutoData: A Multi-Agent System for Open Web Data Collection [37.832257245199365]
AutoData is a novel multi-agent system for Automated web Data collection that requires minimal human intervention.<n>Instruct2DS is a new benchmark dataset supporting live data collection from web sources across three domains: academic, finance, and sports.
arXiv Detail & Related papers (2025-05-21T04:32:35Z) - Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using LLMs [4.721309965816974]
We propose to make scholarly data more accessible sustainably by leveraging Wikidata's infrastructure.
Our study focuses on data from 105 Semantic Web-related conferences and extends/adds more than 6000 entities in Wikidata.
arXiv Detail & Related papers (2024-11-13T15:34:52Z) - The Semantic Scholar Open Data Platform [92.2948743167744]
Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature.<n>We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction.<n>The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.
arXiv Detail & Related papers (2023-01-24T17:13:08Z) - SciREX: A Challenge Dataset for Document-Level Information Extraction [56.83748634747753]
It is challenging to create a large-scale information extraction dataset at the document level.
We introduce SciREX, a document level IE dataset that encompasses multiple IE tasks.
We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
arXiv Detail & Related papers (2020-05-01T17:30:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.