arXiv Machine Learning
Jul 31
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
Do Data Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
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arXiv AI
Jul 21
Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation
arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.
arXiv AI
Jul 3
AgenticDataBench: A Comprehensive Benchmark for Data Agents
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
arXiv AI
Aug 3
ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
arXiv AI
Jul 8
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
arXiv:2607. 05970v1 Announce Type: cross Abstract: Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems.
arXiv AI
Jul 17
AgenticData: An Agentic Data Analytics System for Heterogeneous Data
arXiv:2508. 05002v2 Announce Type: replace-cross Abstract: Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming.
arXiv AI
Aug 5
DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces
arXiv:2608. 03451v1 Announce Type: new Abstract: Data agents enable natural-language analytics over organizational workspaces, where relevant evidence may be scattered across databases, structured files, long documents, and multimedia.
arXiv AI
Aug 11
Towards Researcher Agents for Knowledge-Graph Question Answering
arXiv:2608. 07700v1 Announce Type: new Abstract: Translating a natural-language question into a SPARQL query that can be executed against a large knowledge graph requires resolving lexical ambiguity, grounding surface terms in the target ontology, and producing graph patterns that are both syntactically valid and semantically faithful.
arXiv AI
Jun 11
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.
arXiv AI
Jun 9
Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents
arXiv:2606. 07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data.
arXiv AI
Jun 12
DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.
arXiv AI
Aug 11
DRBENCHER: Can Your Agent Identify the Entity, Retrieve Its Properties and Do the Math?
arXiv:2604. 09251v3 Announce Type: replace Abstract: Deep research agents increasingly interleave web browsing with multi-step computation, yet existing benchmarks evaluate these capabilities in isolation, creating a blind spot in assessing real-world performance.