agent/papers/items/2026-2606-25484-from-causal-discovery-to-implementation-an-agentic-ai-framework-for-e-scooter-mo.md

Paper: From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities

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type: paper title: "From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities" authors: Meng Jin, Melanie Handrich, Simone Martinenz, Nicholas Hoeser, Ziyue Li year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.25484 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-24 updated_at: 2026-06-24 status: queued relevance: high topics:

• coding-agent
• computer-use
• planning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CY
• econ.GN
• stat.AP related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: agentic-ai

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agentic-ai
• inferred topics: coding-agent, computer-use, planning, tool-use
• arXiv categories: cs.CY, econ.GN, stat.AP
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.25484