arXiv:2510.01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions. Large language models (LLMs) excel at interpreting such instructions but are prohibitively expensive to deploy at scale, while traditional pipelines of classifiers and extractors remain efficient yet brittle and unable to generalize to new tasks. We introduce Falconer, a collaborative framework that combines the agentic reasoning of LLMs with lightweight proxy models for scalable know
The proliferation of expensive LLMs has created a demand for more efficient and scalable knowledge mining solutions, leading to the development of hybrid approaches.
This development addresses the critical challenge of deploying advanced AI capabilities economically and at scale, enabling broader adoption of AI-driven insights.
The ability to perform high-quality knowledge mining without the prohibitive cost of large language models changes the economic viability of many AI applications.
- · AI software developers
- · Enterprises with large unstructured datasets
- · Cloud computing providers
- · SaaS companies leveraging AI
- · Companies relying solely on expensive LLMs for knowledge mining
- · Traditional data extraction services
Wider adoption and application of AI for complex data extraction and analysis tasks.
Increased demand for specialized small language models and efficient training/deployment tooling.
Disruption of existing data analysis and business intelligence markets by more cost-effective AI agents.
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Read at arXiv cs.AI