VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent

2026-09-08

AI Summary

本文提出VeriScene，通过整合法医照片和证词重建犯罪现场，并利用世界模型验证动态合理性，解决了原始证据直接输入模型导致的问题。

Abstract

World models take multimodal inputs like text, photos, and diagrams to generate dynamic scenes in accordance with the laws of physics, thus opening a compelling application: fusing multimodal legal evidence to re-create a crime scene and re-enact how an offence could have been committed. However, feeding the raw, unorganized evidence into a world model fails in forensic use: it silently drops evidence, glosses over contradictory testimony, and produces motion that violates the evidentiary record. This paper presents VeriScene, an agent that orchestrates the world model: it reconstructs crime scenes from forensic photographs and witness statements of varying reliability, keeping every claim traceable to evidence and every motion physically plausible. VeriScene iteratively fuses the evidence into a cited narrative under an auditing loop, verifies the hypothesized dynamics via probe rollouts in the world model with corrective constraint injection, and renders the offence as a re-enactment video from a fused keyframe. On a benchmark of 25 crime scenarios across 7 physically-driven case types (139 forensic-style photographs and 65 statements with planted unreliability), VeriScene attains 0.9014 evidence coverage and 0.7217 factual consistency (0-1 scale) on the 20 test scenes, outperforming an end-to-end multimodal-LLM baseline by 20.35% in factual consistency and 34.88% in temporal coherence, while generalizing across four LLM orchestration backends at USD 1.82 per scene.

Problem

• Crime Scene Reconstruction
• Multimodal Evidence Fusion
• Forensic Analysis
• Physical Plausibility

Innovation

• World-Model Agent
• Evidence Fusion
• Physical Plausibility
• Crime Scene Reconstruction
• Multimodal Inputs