arXiv AI
Question's Gambit: The First Move Matters in Agentic Deep Search
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
Oct 26
A-MapReduce: Executing Wide Search via Agentic MapReduce
arXiv:2602.01331v2 Announce Type: replace-cross Abstract: Contemporary large language model (LLM)-based multi-agent systems exhibit systematic advantages in deep research tasks, which emphasize itera...
arXiv AI
Oct 15
PANINI-DR: Controlled Deep Research through Generative Semantic Workspaces
arXiv:2610.15208v1 Announce Type: cross Abstract: Deep research requires connecting multiple steps of search, reasoning, and computation, with intermediate findings guiding what to investigate next....
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 AI
Jul 14
GRASP: GRanularity-Aware Search Policy for Agentic RAG
arXiv:2607. 10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.
arXiv Machine Learning
Aug 12
SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task
arXiv:2607. 24850v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled search agents to autonomously tackle complex tasks across extended search and reasoning horizons.
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.