arXiv:2607.00597v1 Announce Type: new Abstract: Scientific literature search often requires more than retrieving papers from a single query: users' intents are underspecified, preference-dependent, and evolve through interaction. Existing search agents typically rely on fixed pipelines or implicit language-only reasoning, making their search strategies difficult to control, inspect, and refine. We introduce PaperPilot, a multi-turn literature search agent that frames scientific search as workflow induction. Given an anchor paper and a user query, PaperPilot constructs an executable DAG of pape
Advancements in large language models and agentic AI architectures are enabling more sophisticated and autonomous systems for knowledge work, making this form of research automation increasingly feasible.
This development indicates a significant leap in AI's ability to automate complex research tasks and interact dynamically with human users, potentially transforming academic and R&D workflows.
Scientific literature search moves beyond simple query-response to interactive, workflow-driven agentic systems that can adapt and refine search strategies based on user intent.
- · AI agents developers
- · Research institutions
- · Knowledge workers
- · Scientific publishers
- · Manual literature reviewers
- · Fixed-pipeline search engines
- · Information brokers
Researchers gain highly efficient and personalized tools for navigating vast scientific literature.
The pace of scientific discovery could accelerate as researchers spend less time on information retrieval and more on analysis.
New forms of scientific collaboration and interdisciplinary research may emerge, facilitated by intelligent literature review agents.
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Read at arXiv cs.CL