Agentic Framework for Botanical Trait Extraction Using LLMs and OCR

A new research paper on arXiv (2608.14587) proposes a modular, agent-based pipeline for botanical trait extraction. The framework integrates optical character recognition (OCR) to convert PDFs into machine-readable text, then employs large language models (LLMs) and agentic capabilities such as planning, iterative reasoning, and tool use to annotate and embed descriptive document layouts. The approach aims to enhance information retrieval in plant science by capturing fine-grained contextual and structural information. The paper highlights the use of both dense and sparse representations, specialized retrieval models, and semantic knowledge representation to improve ranking accuracy and cross-lingual performance. The framework emphasizes rigorous evaluation, ethical considerations, and trustworthiness for responsible deployment. This research contributes to the emerging field of agentic LLM frameworks, broadening applications across domains. The paper was announced as a new submission on arXiv, with the abstract detailing the background and proposed methodology.