Beyond Words and Pixels: A Benchmark for Implicit World Knowledge Reasoning in Generative Models

TL;DR

This paper introduces PicWorld, a comprehensive benchmark for evaluating the ability of text-to-image models to understand implicit world knowledge and physical reasoning, revealing significant limitations in current models.

Contribution

The paper presents PicWorld, a novel benchmark with a multi-agent evaluator for detailed assessment of T2I models' reasoning and knowledge grounding capabilities.

Findings

Current T2I models struggle with implicit world knowledge.

Models show limited physical causal reasoning.

Need for reasoning-aware architectures is highlighted.

Abstract

Text-to-image (T2I) models today are capable of producing photorealistic, instruction-following images, yet they still frequently fail on prompts that require implicit world knowledge. Existing evaluation protocols either emphasize compositional alignment or rely on single-round VQA-based scoring, leaving critical dimensions such as knowledge grounding, multi-physics interactions, and auditable evidence-substantially undertested. To address these limitations, we introduce PicWorld, the first comprehensive benchmark that assesses the grasp of implicit world knowledge and physical causal reasoning of T2I models. This benchmark consists of 1,100 prompts across three core categories. To facilitate fine-grained evaluation, we propose PW-Agent, an evidence-grounded multi-agent evaluator to hierarchically assess images on their physical realism and logical consistency by decomposing prompts into verifiable visual evidence. We conduct a thorough analysis of 17 mainstream T2I models on PicWorld, illustrating that they universally exhibit a fundamental limitation in their capacity for implicit world knowledge and physical causal reasoning to varying degrees. The findings highlight the need for reasoning-aware, knowledge-integrative architectures in future T2I systems.