arXiv AI By Timo Oksanen, Julius Gun

Machine-Readable Operator Manuals over the ISO 11783 Network for Offline, Multilingual LLM Assistance

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The paper investigates how to synchronize operator manuals with tractor-side assistants over ISO 11783 CAN buses, enabling offline, multilingual LLM assistance. It benchmarks data transfer using Markdown, JSON, and XML, finding that Markdown files transfer fastest and that retrieval‑augmented generation works well for English but less so for other languages. The study shows that a new parameter group can be added to existing ISO 11783 mechanisms, suggesting a path toward standardization.

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arXiv AI
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Tool-calling retrieval versus vector RAG for a small Greek--English knowledge base: accuracy and robustness to how users type Greek

The study compares two methods for grounding assistants in a small Greek–English agricultural knowledge base: tool‑calling retrieval via a live data interface and vector retrieval‑augmented generation (RAG). Using the KyGround benchmark of 198 questions, vector RAG achieved 95.3% accuracy on canonical Greek questions, outperforming the tool agent’s 71.6% and revealing that the tool agent’s failures stem from literal searches that miss non‑verbatim matches. The results show that search tolerance to user typing variations—such as accents, capitalization, and Greeklish transliterations—is crucial for reliable community knowledge interfaces.

By Nikolaos D. Tantaroudas, Ilias Karachalios, Andrew J. McCracken
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RAGAL: A Frugal, Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency

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