HazardForge: Scalable Hazard Synthesis

• HazardForge is a framework that systematically generates, simulates, and evaluates hazard scenarios for robotic safety and proactive risk navigation using LLMs.
• It employs modular, multi-agent pipelines that combine zero-shot hazard description, text-to-3D simulation, and adaptive human-in-the-loop feedback.
• Benchmarking shows enhanced scenario diversity and robustness, while highlighting challenges in sim-to-real transfer and spatio-temporal reasoning.

HazardForge is a family of automated frameworks for the systematic generation, simulation, and evaluation of hazard scenarios oriented toward advancing robotic safety, mobile agent robustness, and proactive risk navigation. Recent developments encompass two dominant paradigms—robotic anomaly scenario generation in domestic settings and scalable synthetic hazard insertion for mobile vision-language agent evaluation—each leveraging large-scale foundation models to replace manual scenario design, enable zero-shot composition, and integrate dynamic validation modules. The HazardForge concept includes modular agentic architectures, multi-agent brainstorming, scenario-to-environment pipelines, spatio-temporal image editing, and adaptive human-in-the-loop calibration, with principles applied in domestic robotics (Song et al., 2024), mobile safety benchmarking (Taniguchi et al., 13 Jan 2026), and risk forecasting in industrial domains (Elgedawy et al., 13 Nov 2025).

1. Core Architectural Paradigms

HazardForge implementations display modular multi-stage pipelines partitioned into scenario generation, environment simulation, agent training/evaluation, and adaptive feedback:

• Multi-Agent Scenario Generation: In domestic robotic contexts, HazardForge launches multiple LLM-powered agents assigned domain-specific roles (e.g., homemaker, security advisor), enacting divergent role-play brainstorming. Each agent iteratively refines scenario proposals based on others’ output, resulting in rich, diverse textual hazard descriptions (e.g., medication ingestion by children, liquid spills) without human labeling (Song et al., 2024).
• Zero-Shot Description and Asset Specification: Scenario attributes, including hazard names, required objects, articulations (“knife blade – sharp”), and solution blueprints, are generated by a foundational LLM (typically GPT-4/4o) given prompt constraints and object inventories.
• Scene Generation and Validation: Textual scenario descriptions are mapped to realistic 3D environments via curated asset sets (PartNet-Mobility, Objaverse) using semantic retrieval (SentenceBERT) and vision-language verification (BLIP-2). For mobile safety, HazardForge edits real-world images by partitioning via layout decision rules, rendering hazardous objects using diffusion-based editors, and ensuring semantic correctness through VLM-based quality checks (Taniguchi et al., 13 Jan 2026).
• Adaptive Task Decomposition and Learning Approach Assignment: Solutions output by the LLM are broken into atomic subtasks, each annotated with recommended RL/planning algorithms (e.g., SAC, CEM, BIT*). Reward functions are synthesized inline, parametrized by state distances or earth-mover distance for nonrigid manipulation.

2. Formal Algorithms and Scenario Synthesis

HazardForge formulations include both agentic reasoning and scenario transformation routines:

• Multi-Agent Brainstorming Update:

$$S_i^t \leftarrow \mathrm{LLM}(role_i, S_1^{t-1}, \ldots, S_n^{t-1})$$

• Hazard Scenario Generation (Mobile Safety):
  • Image Partitioning: Input $I\in\mathbb{R}^{H\times W\times3}$ segmented into horizontal regions $(\Omega_L, \Omega_C, \Omega_R)$ by action mapping (left, straight, right).
  • Scenario-Specific Mask Construction:
  • Motion: Central mask, directionally rendered object.
  • Intrusion: Outpainting to simulate partially-visible obstacles.
  • Distance: Vanishing point detection and band-masked insertion.
• Diffusion-based Object Insertion:

$I_{out} \sim M(I_{in}, m, t)$

Where $M$ is the diffusion editor, $m$ the binary mask, $t$ the orientation prompt.

• Quality Validation: VLM-checks on object completeness and direction for multiple retry cycles.
• RL Optimization in Robotic Task Learning:

$$\pi^* = \arg\max_\pi \mathbb{E}_{\tau\sim\pi}\left[\sum_{t=0}^T r(s_t, a_t)\right]$$

• ReACT Agentic Reasoning Loop (Risk Forecasting):
  • Iterative agentic processing—retrieval, generation, SME validation, and refinement.
  • SME corrections $$\Delta = (h_{id}, \Delta P, \Delta S, added\_controls)$$ are integrated into model calibration via weighted averages.

3. Benchmarks, Diversity Metrics, and Evaluation

HazardForge-generated datasets (e.g., MovSafeBench for mobile safety (Taniguchi et al., 13 Jan 2026); 111 robotic household hazard scenarios (Song et al., 2024)) are benchmarked against human-designed suites, using quantifiable diversity and success metrics:

Metric Type	Method(s)	Typical Scores
Task-Description Diversity	Self-BLEU, SBERT, WMD	Self-BLEU = 0.227 (HazardForge, lower=better)
Scene-Visual Diversity	ViT, CLIP embedding similarity	ViT = 0.315, CLIP = 0.805
Anomaly Detection	Human ground-truth judgment	75.9% top-1, 82.1% top-3
VLM Safety Accuracy	Scenario-based MCQ	No edit = 55.6%, Motion = 24.8%

HazardForge outperforms baseline datasets (Behavior-100, RLBench, ManiSkill2, etc.) in both diversity and coverage. Notably, motion-based scenarios sharply degrade VLM accuracy (24.8% vs 87.8% for humans), exposing limitations in spatio-temporal reasoning (Taniguchi et al., 13 Jan 2026).

4. Adaptive Feedback and Human-AI Collaboration

Industrial risk navigation applications extend HazardForge principles through human-in-the-loop feedback for fine-grained safety calibration:

• Structured Data Ingestion and Retrieval: Workplan, forms, and raw data are normalized; relevant historical incidents are retrieved using transformer embeddings and intent vectors ($f_{embed}$, $f_{sem}$).
• SME Feedback Integration: Experts correct hazard identification and control recommendations, updating likelihood/severity estimates, and providing grounded examples that feed model retraining in scheduled cycles.
• Vulnerability Scoring: Hazards are ranked by $RiskScore(h) = P_h \times S_h$, normalized across scene.

This suggests that optimal hazard forecasting in mission-critical domains benefits from agentic LLM-enhanced retrieval pipelines and structurable SME feedback for adaptive, transparent safety system improvement (Elgedawy et al., 13 Nov 2025).

5. Limitations and Open Research Directions

Despite robust automation, HazardForge frameworks face persistent challenges:

• Simulation Validity and Asset Exhaustiveness: 3D environments are bounded by fixed object pools (e.g., PartNet-Mobility). Unsupervised validation may admit invalid scenarios; multimodal LLMs and text-to-3D synthesis are promising but not yet deployed (Song et al., 2024).
• Sim-to-Real Transfer: Generalization from simulated environments to real-world execution remains unresolved, with potential solutions in domain randomization, physics-aware rendering, and adaptive sim2real procedures.
• Spatio-Temporal Reasoning Limitation in VLMs: Mobile benchmarks highlight VLM incapacity for nuanced object dynamics and depth; integrating flow/depth-aware modules or multi-modal fusion networks is an ongoing need.
• Coarse Severity Scales: Risk scoring may suffer from insufficient granularity. Finer severity ratings, uncertainty quantification (Bayesian ensembles), and automated regulatory alignment (e.g., OSHA libraries) are active research areas (Elgedawy et al., 13 Nov 2025).
• Feedback Latency: Adaptive loops depend on expert availability, potentially increasing decision latency in low-bandwidth environments.

6. Theoretical and Practical Significance

HazardForge frames a rigorous methodology for systematic hazard scenario synthesis, simulation-grounded agent training, and stress-testing of safety-critical models (robotics, VLMs, and risk navigation). By unifying agentic LLM coordination, semantic asset retrieval, zero-shot scenario composition, and human-in-the-loop calibration, it enables scalable expansion to diverse domains—households, autonomous vehicles, industrial operations. A plausible implication is that such frameworks will be foundational for closing the safety gap between human and AI judgment, promoting transparent traceability, and accelerating the development of resilient, proactive detection and resolution strategies in open-world autonomy (Song et al., 2024, Taniguchi et al., 13 Jan 2026, Elgedawy et al., 13 Nov 2025).