agent/papers/items/2026-2605-20306-wildroadbench-a-wild-aerial-road-damage-grounding-benchmark-for-vision-language-.md

Paper: WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents

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type: paper title: "WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents" authors: Bingnan Liu, Chenhang Cui, Rui Huang, Jiani Luo, Zhirong Shen, Tinghao Wang, Xiande Huang, Lingbei Meng, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.20306 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-19 updated_at: 2026-06-02 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CV
• cs.LG related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: autonomous-agent-llm

One-line Takeaway

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

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: agent-evaluation, coding-agent, rag, reasoning, tool-use
• arXiv categories: cs.CV, cs.LG
• collection score: 15

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/2605.20306