PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal
  Inconsistencies
Authors: name: Lukas Selch, name: Yufang Hou, name: M. Jehanzeb Mirza, name: Sivan Doveh, name: James Glass, name: Rogerio Feris, name: Wei Lin

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.