SafeSceneReason: A Multimodal Reasoning Benchmark Connecting Industrial Hazards with Accident Knowledge

• URL: http://arxiv.org/abs/2608.09230v1
• Date: Mon, 10 Aug 2026 07:53:54 GMT
• Title: SafeSceneReason: A Multimodal Reasoning Benchmark Connecting Industrial Hazards with Accident Knowledge
• Abstract summary: We introduce SafeSceneReason, a multimodal industrial-safety reasoning benchmark and companion training corpus.<n>The resulting resource contains 110,581 verified scene-centric question--answer pairs and 13,114 refined report-centric question--answer pairs.
• Score: 6.121561048989516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial-safety understanding requires more than detecting workers, equipment, and personal protective equipment. Models must also assess compliance, identify hazardous interactions, explain potential accident mechanisms, and recommend preventive actions. Existing safety datasets primarily focus on visual perception or isolated violation recognition and provide limited supervision for evidence-grounded reasoning. We introduce SafeSceneReason, a multimodal industrial-safety reasoning benchmark and companion training corpus that connects workplace scenes with knowledge from occupational accident investigations. SafeSceneReason combines two complementary data-construction pipelines. The scene-centric pipeline converts annotated workplace images into executable safety scene graphs and generates deterministic answers through program execution over objects, relations, and safety rules. The report-centric pipeline extracts figures and contextual evidence from accident reports and constructs multimodal questions using evidence graphs, explicit information boundaries, multi-step reasoning paths, and iterative verification. The resulting resource contains 110,581 verified scene-centric question--answer pairs and 13,114 refined report-centric question--answer pairs, covering perception, spatial and quantitative reasoning, compliance assessment, evidence synthesis, causal analysis, and mitigation-oriented decision making. Evaluation of representative proprietary and open-source vision--language models reveals substantial performance differences and persistent weaknesses in comparative, technical, and multi-evidence reasoning, demonstrating that strong general visual understanding does not yet guarantee reliable industrial-safety reasoning.

Related papers

• TRACE: An Evidence-Grounded Benchmark for Safety Evaluation of Large Reasoning Models [14.234836081847364]
  Large Reasoning Models (LRMs) generate intermediate reasoning traces that may contain unsafe content, even when their final responses appear safe.<n>We introduce TRACE, an evidence-grounded safety evaluation benchmark that covers prompts, reasoning traces, and final responses.<n>For each prompt, four LRMs generate reasoning traces and final responses, and we annotate the safety of each component and extract supporting evidence.
  arXiv  Detail & Related papers  (2026-08-25T08:35:47Z)
• General Hazard Detection [5.74822847046482]
  We present the first general-purpose hazard dataset designed for rule-based compliance assessment.<n>Our key innovation is decoupling the hazard concept from image-based examples by expressing safety requirements through language-based rules.<n>The CompliVision dataset comprises 3,006 images spanning traffic, construction, and warehouse environments.
  arXiv  Detail & Related papers  (2026-05-22T07:24:17Z)
• Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models [14.927545906619295]
  Large language models increasingly rely on explicit chain-of-thought (CoT) reasoning to solve complex tasks.<n>Existing work on LLM safety focuses on content safety--detecting harmful, biased, or factually incorrect outputs.<n>We identify reasoning safety as a security dimension: the requirement that a model's reasoning trajectory be logically consistent.
  arXiv  Detail & Related papers  (2026-03-26T13:08:56Z)
• DRAFT: Task Decoupled Latent Reasoning for Agent Safety [59.46137757545185]
  We propose DRAFT (Task Decoupled Latent Reasoning for Agent Safety), a latent reasoning framework that decouples safety judgment into two trainable stages.<n>An Extractor distills the full trajectory into a compact continuous latent draft, and a Reasoner that jointly attends to the draft and the original trajectory to predict safety.<n>DRAFT consistently outperforms strong baselines, improving accuracy from 63.27% (LoRA) to 91.18% averaged over benchmarks.
  arXiv  Detail & Related papers  (2026-02-11T07:45:14Z)
• GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision [47.99880677909197]
  GuardTrace-VL is a vision-aware safety auditor that monitors the full Question-Thinking-Answer (QTA) pipeline via joint image-text analysis.<n>We propose a three-stage progressive training scheme combined with the data refinement process, enabling the model to learn nuanced and context-dependent safety preferences.<n>On our proposed test set covering both in-domain and out-of-domain scenarios, GuardTrace-VL model achieves an F1 score of 93.1% on unsafe reasoning detection tasks.
  arXiv  Detail & Related papers  (2025-11-26T02:49:51Z)
• SafeRBench: A Comprehensive Benchmark for Safety Assessment in Large Reasoning Models [60.8821834954637]
  We present SafeRBench, the first benchmark that assesses LRM safety end-to-end.<n>We pioneer the incorporation of risk categories and levels into input design.<n>We introduce a micro-thought chunking mechanism to segment long reasoning traces into semantically coherent units.
  arXiv  Detail & Related papers  (2025-11-19T06:46:33Z)
• When Models Outthink Their Safety: Mitigating Self-Jailbreak in Large Reasoning Models with Chain-of-Guardrails [74.63933201261595]
  Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex reasoning tasks.<n>LRMs remain vulnerable to severe safety risks, including harmful content generation and jailbreak attacks.<n>We propose the Chain-of-Guardrail (CoG), a training framework that recomposes or backtracks unsafe reasoning steps.
  arXiv  Detail & Related papers  (2025-10-24T09:32:25Z)
• IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios [46.421243185923814]
  Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments.<n>We introduce IndustryEQA, the first benchmark dedicated to evaluating embodied agent capabilities within safety-critical warehouse scenarios.<n>The benchmark includes rich annotations covering six categories: equipment safety, human safety, object recognition, attribute recognition, temporal understanding, and spatial understanding.
  arXiv  Detail & Related papers  (2025-05-27T02:36:17Z)
• Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation [52.626086874715284]
  We introduce a novel problem formulation called Abstract DNN-Verification, which verifies a hierarchical structure of unsafe outputs.<n>By leveraging abstract interpretation and reasoning about output reachable sets, our approach enables assessing multiple safety levels during the formal verification process.<n>Our contributions include a theoretical exploration of the relationship between our novel abstract safety formulation and existing approaches.
  arXiv  Detail & Related papers  (2025-05-08T13:29:46Z)
• Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces [5.993182776695029]
  We introduce Clip2Safety, an interpretable detection framework for diverse workplace safety compliance. The framework comprises four main modules: scene recognition, the visual prompt, safety items detection, and fine-grained verification. The results show that Clip2Safety not only demonstrates an accuracy improvement over state-of-the-art question-answering based VLMs but also achieves inference times two hundred times faster.
  arXiv  Detail & Related papers  (2024-08-13T18:32:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.