FireWorldBench: Benchmarking Complex Physical World Intelligence through Coupled-Field Fire Dynamics

2026-09-19

AI Summary

本文提出FireWorldBench，通过耦合场火动力学评估多模态大语言模型和代理的复杂物理世界智能，解决涉及多个连续场、潜在因果机制等问题。

Abstract

Understanding the physical world requires more than object recognition, scene description, and short-term visual prediction, as real-world physical systems involve multiple continuous fields, latent causal mechanisms, partial observations, and intervention-sensitive dynamics. We propose FireWorldBench, a benchmark for evaluating complex physical world intelligence in multimodal large language models and agents through coupled-field fire dynamics. Fire provides a canonical stress-test environment, where multiple interacting physical fields jointly shape observable states and temporal dynamics. FireWorldBench is organized along two complementary axes, a physical capability axis and a fire scenario task axis, jointly covering physical-state understanding, temporal dynamics, causal mechanisms, and intervention reasoning. The benchmark comprises 520 fire-world entries, including 494 controlled simulation worlds and 26 real-world-aligned event groups, spanning 47 scene archetypes across 7 environment families. These entries combine structured textual observations, multiple 2D physical-field visualizations, and 3D event-level scene modeling, yielding 9,074 text-image interleaved question-answer pairs across choice-based and open-ended report-generation formats. FireWorldBench evaluates whether models can infer latent physical states, explain underlying mechanisms, forecast coupled-field evolution, and assess intervention consequences from multimodal partial observations, providing a challenging testbed for complex physical world intelligence.

Problem

• Complex Physical World Intelligence
• Coupled-Field Fire Dynamics
• Multimodal Large Language Models
• Physical-State Understanding
• Intervention Reasoning

Innovation

• coupled-field fire dynamics
• multimodal large language models
• physical-world intelligence
• latent causal mechanisms
• intervention reasoning