arXiv:2606. 28363v1 Announce Type: cross Abstract: Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance -- with mandatory human oversight at critical decision points.
By Hsieh-Ting Lin, Jiunn-Tyng Yeh
arXiv:2607. 01131v1 Announce Type: cross Abstract: Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation.
By Bingchen Zhao, Sara Beery, Oisin Mac Aodha
arXiv:2607. 00454v1 Announce Type: new Abstract: Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties.
By Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, Y. Narahari
The paper introduces an edge‑deployable system that transforms photographs of bee hive frames into actionable inspection reports. It uses an object detector to identify comb‑cell classes, converts detections into a structured fact schema, and then generates constrained factual text that triggers a knowledge‑base for interpretation and recommendations. The authors also release the FAIRHiveText‑1K dataset, pairing images with facts and text, to support future LLM research in this domain.
By Asaduz Zaman, Mohaimenul Azam Khan Raiaan, Malika Nisal Ratnayake
arXiv:2606. 29243v1 Announce Type: new Abstract: We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings.
By Khan Raiyan Ibne Reza, Omar Ibne Shahid
arXiv:2512. 21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains difficult, limiting trust in AI-based predictions.
By Tomoaki Yamaguchi, Yutong Zhou, Masahiro Ryo, Keisuke Katsura
arXiv:2606. 31831v1 Announce Type: new Abstract: High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them.
By Renan Souza, Daniel Rosendo, Kelsey Carter, John Lagergren, Fr\'ed\'eric Suter, Shelaine L. Curd, Gerald A. Tuskan, Rafael Ferreira da Silva, David Weston
High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them. At Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory (APPL), automated stations image hundreds of plants daily across multiple remote sensing modalities; yet, trait extraction and interpretation remain manual, expert-bound, and strictly post-hoc, making analysis, not acquisition, the binding constraint on discovery.
arXiv:2606. 28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation.
By Zhaoyang Li, Ruijie Zhang, Jiaqi Liu, Zhaoji Sun
arXiv:2601. 21800v4 Announce Type: replace Abstract: We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks.
By Dionizije Fa, Marko Culjak, Bruno Pandza, Mateo Cupic
arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.
By Srikanth Thudumu, Jason Fisher
HERBIOME is a modular, end‑to‑end pipeline that automates the digitization of herbarium labels. It combines YOLOv8 for component detection, CRAFT Hezar for word‑level text localization, a fine‑tuned TrOCR model for mixed handwritten and printed text recognition, and GPT‑4o Mini for structuring metadata into standardized fields. Evaluation on 450 French specimens shows high surface similarity (MWS ≈ 0.616) and moderate semantic accuracy (SMA ≈ 0.442), with taxonomic fields identified as the main challenge.
By Hiba Abbad, Hanane Ariouat, Eva Perez Pimpare, Nicolas Turenne, Eric Chenin, Abderrazak Sebaa, Edi Prifti, Jean-Daniel Zucker, Youcef Sklab