PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies

• URL: http://arxiv.org/abs/2510.16505v2
• Date: Tue, 21 Oct 2025 12:52:54 GMT
• Title: PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies
• Authors: Lukas Selch, Yufang Hou, M. Jehanzeb Mirza, Sivan Doveh, James Glass, Rogerio Feris, Wei Lin,
• Abstract summary: We introduce PRISMM-Bench, the first benchmark grounded in real reviewer-flagged inconsistencies in scientific papers.<n>We design three tasks, namely inconsistency identification, remedy and pair matching, which assess a model's capacity to detect, correct, and reason over inconsistencies.<n>We benchmark 21 leading LMMs, including large openweight models (GLM-4.5V 106B, InternVL3 78B) and proprietary models (Gemini 2.5 Pro, GPT-5 with high reasoning)
• Score: 16.537126902822127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 262 inconsistencies from 242 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 (26.1-54.2%), underscoring the challenge of multimodal scientific reasoning and motivating progress towards trustworthy scientific assistants.

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