Tootfinder

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Xiaobei Zhao, Xingqi Lyu, Xiang Li
https://arxiv.org/abs/2509.06644 https://

T-araVLN: Translator for Agricultural Robotic Agents on Vision-and-Language Navigation
Agricultural robotic agents have been becoming powerful helpers in a wide range of agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement. The AgriVLN method and the A2A benchmark pioneeringly extend Vision-and-Language Navigation (VLN) to the agricultural domain, enabling agents navigate to the target position following the natural language instructions. AgriVLN effectively understands the simple instructions, however, often misundersta…

Intermediate Languages Matter: Formal Languages and LLMs affect Neurosymbolic Reasoning
Alexander Beiser, David Penz, Nysret Musliu
https://arxiv.org/abs/2509.04083 https://

Intermediate Languages Matter: Formal Languages and LLMs affect Neurosymbolic Reasoning
Large language models (LLMs) achieve astonishing results on a wide range of tasks. However, their formal reasoning ability still lags behind. A promising approach is Neurosymbolic LLM reasoning. It works by using LLMs as translators from natural to formal languages and symbolic solvers for deriving correct results. Still, the contributing factors to the success of Neurosymbolic LLM reasoning remain unclear. This paper demonstrates that one previously overlooked factor is the choice of the forma…

Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code
Muhammad Haseeb
https://arxiv.org/abs/2508.08322 https://

Context Engineering for Multi-Agent LLM Code Assistants Using Elicit, NotebookLM, ChatGPT, and Claude Code
Large Language Models (LLMs) have shown promise in automating code generation and software engineering tasks, yet they often struggle with complex, multi-file projects due to context limitations and knowledge gaps. We propose a novel context engineering workflow that combines multiple AI components: an Intent Translator (GPT-5) for clarifying user requirements, an Elicit-powered semantic literature retrieval for injecting domain knowledge, NotebookLM-based document synthesis for contextual unde…