Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexity of papers. A central challenge lies in detecting and resolving inconsistencies across text, figures, tables, and equations, issues that are often subtle, domain-specific, and ultimately undermine clarity, reproducibility, and trust. Existing benchmarks overlook this issue, either isolating single modalities or relying on synthetic errors that fail to capture real-world complexity. We introduce PRISMM-Bench (Peer-Review-sourced Inconsistency Set for Multimodal Models), the first benchmark grounded in real reviewer-flagged inconsistencies in scientific papers. Through a multi-stage pipeline of review mining, LLM-assisted filtering and human verification, we curate 384 inconsistencies from 353 papers. Based on this set, we design three tasks, namely inconsistency identification, remedy and pair matching, which assess a model's capacity to detect, correct, and reason over inconsistencies across different modalities. Furthermore, to address the notorious problem of choice-only shortcuts in multiple-choice evaluation, where models exploit answer patterns without truly understanding the question, we further introduce structured JSON-based answer representations that minimize linguistic biases by reducing reliance on superficial stylistic cues. We benchmark 21 leading LMMs, including large open-weight models (GLM-4.5V 106B, InternVL3 78B) and proprietary models (Gemini 2.5 Pro, GPT-5 with high reasoning). Results reveal strikingly low performance (27.8-53.9\%), underscoring the challenge of multimodal scientific reasoning and motivating progress towards trustworthy scientific assistants.

翻译：大型多模态模型（LMMs）正日益应用于科学研究，但它们是否能可靠地理解并推理论文中的多模态复杂性，目前尚不明确。一个核心挑战在于检测和解决文本、图表、表格及公式之间的不一致性，这些问题通常很微妙、具有领域特异性，并最终会损害清晰度、可重复性和可信度。现有基准忽略了这一问题，要么孤立地处理单一模态，要么依赖于无法捕捉真实世界复杂性的合成错误。我们提出了PRISMM-Bench（基于同行评审的多模态模型不一致性数据集），这是首个基于科学论文中真实审稿人指出的不一致性构建的基准。通过一个包含评审挖掘、LLM辅助过滤和人工验证的多阶段流程，我们从353篇论文中收集了384个不一致性案例。基于此数据集，我们设计了三个任务，即不一致性识别、修正和配对匹配，以评估模型在不同模态间检测、纠正和推理不一致性的能力。此外，针对多项选择评估中臭名昭著的“仅选择捷径”问题——即模型利用答案模式而非真正理解问题——我们进一步引入了基于JSON的结构化答案表示方法，通过减少对表面风格线索的依赖，最大限度地降低语言偏见。我们对21个领先的LMMs进行了基准测试，包括大型开源模型（GLM-4.5V 106B, InternVL3 78B）和专有模型（Gemini 2.5 Pro, 具备高推理能力的GPT-5）。结果显示其性能极低（27.8-53.9%），这突显了多模态科学推理的挑战性，并激励我们朝着构建可信赖的科学助手方向取得进展。