arXiv:2605.09038v3 Announce Type: replace Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especially important in open-domain question answering, where broad or copied queries often waste retrieval budget and derail later reasoning. We propose \Ours, a framework that makes query planning explicit through reusable search skills. At each step, the model first selects a skill, then generates a search or answer action conditioned on the selected skill card. The skill inventory itself is not f
Ongoing research in large language models aims to improve their practical utility and efficiency, with query generation for search tools being a critical bottleneck.
Improving LLM's ability to efficiently use search tools with 'skill banks' enhances their capacity for open-domain question answering and complex reasoning, making them more autonomous.
LLMs shift from broad, inefficient search queries to more targeted, skill-conditioned actions, improving the accuracy and resource efficiency of their information retrieval.
- · AI developers
- · Companies implementing LLM-powered agents
- · Users of AI assistants
- · Inefficient search and retrieval systems
- · Developers relying on primitive LLM search strategies
LLMs will become significantly more effective at complex, information-seeking tasks, accelerating their integration into various workflows.
The development of rich, reusable 'skill banks' for LLMs could become a new area of AI expertise and IP.
More sophisticated LLM-driven agents could further automate knowledge work, impacting white-collar employment structures.
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Read at arXiv cs.AI