LogiScope‑VQA is a new benchmark dataset for evaluating vision‑language models in logistics hazard identification. It contains 2,476 images, 2,918 videos, and 10,274 VQA pairs drawn from real industrial warehouses, covering 18 core objects and 20 risk types across 39 subtasks. Experiments show that even advanced proprietary models lag behind human experts, highlighting a significant gap in perception, understanding, and reasoning for industrial safety.
Large Multimodal Models (LMMs) large-scale deployment in industrial warehouse settings specifically necessitates that models exhibit human-expert-level hazard-oriented perception, understanding, and r...
SAFARI is the first industrial benchmark for evaluating large language models (LLMs) in automotive hazard analysis and risk assessment (HARA) under ISO 26262. It comprises 3,000 de‑identified HARA cases and tests two tasks: open‑ended hazard generation and standards‑grounded risk classification, using a novel reference‑anchored LLM‑as‑a‑judge protocol. Experiments with nine state‑of‑the‑art LLMs show that while hazard narratives are often plausible, risk classification remains weak (best ASIL macro‑F1 = 0.261), with errors mainly due to missing scenario context and misjudged controllability. "whyItMatters":"The benchmark highlights the current limitations of LLMs in safety‑critical engineering workflows, guiding future research and expert oversight in automotive safety analysis."
arXiv:2607. 16243v1 Announce Type: cross Abstract: Multimodal industrial anomaly inspection assistants are a critical component of next-generation smart factories, enabling interactive vision-language-based querying.
arXiv:2603. 04818v3 Announce Type: replace Abstract: Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings.
arXiv:2608. 09230v1 Announce Type: new Abstract: Industrial-safety understanding requires more than detecting workers, equipment, and personal protective equipment.
arXiv:2609.12408v1 Announce Type: new Abstract: Hazardous-freight operations at petrochemical logistics nodes are safety-critical for intelligent transportation systems, yet existing vision benchmark...
arXiv:2511.17384v3 Announce Type: replace-cross Abstract: Visual large language models (VLLMs) are increasingly used as embodied agents, yet industrial warehouses demand more than reaching a goal: ag...
arXiv:2608. 11692v1 Announce Type: new Abstract: Autonomous logistics sorting systems (ALSS) are an important industrial application of embodied AI, which requires joint planning over spatially disjoint camera views.
arXiv:2609.36562v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by multimodal im...
arXiv:2609.26399v1 Announce Type: new Abstract: Scene safety understanding plays a life-or-death role in situational awareness in various critical domains. Traditional methods that rely on learning d...
arXiv:2609.40325v1 Announce Type: new Abstract: As interactive 3D worlds are increasingly used to study intelligent behavior, it becomes important to develop efficient pipelines for identifying anoma...
arXiv:2607. 18325v1 Announce Type: cross Abstract: Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emergencies.