arXiv AI
BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence
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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 Computation and Language
Oct 27
QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
arXiv:2605.24218v2 Announce Type: replace Abstract: Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how...
arXiv AI
Jul 1
Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
arXiv AI
Jul 21
From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development
arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.
arXiv AI
Oct 16
Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
The paper introduces Librarian Agents, a framework that trains large language models to proactively curate long-context workspaces by reorganizing raw documents into a hierarchical filesystem of synthetic files and reference links. This builder agent, optimized via reinforcement learning, aims to maximize downstream answering accuracy by anticipating future queries rather than reacting to them. Experiments show that Librarian Agents outperform leading retrieval and precomputation methods, achieving a 33‑point accuracy gain on BrowseComp‑Plus and a 31‑point improvement on BRIGHT in zero‑shot settings.
arXiv Computation and Language
Oct 22
HybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents
arXiv:2610.24899v2 Announce Type: replace-cross Abstract: While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables...