Title: Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning
Primary area: datasets and benchmarks
Keywords: Multimodal Large Language Models; Academic Paper Reasoning; Scan-Oriented Reasoning

Abstract: With the rapid progress of multimodal large language models (MLLMs), AI already performs well at literature retrieval and certain reasoning tasks, serving as a capable assistant to human researchers, yet it remains far from autonomous research. The fundamental reason is that current work on scholarly paper reasoning is largely confined to a search-oriented paradigm centered on pre-specified targets, with reasoning grounded in relevance retrieval, which struggles to support researcher-style full-document understanding, reasoning, and verification. To bridge this gap, we propose ScholScan, a new benchmark for scholarly paper reasoning. ScholScan introduces a scan-oriented task setting that asks models to read and cross-check entire papers like human researchers, scanning the document to identify consistency issues. The benchmark comprises 1,800 carefully annotated questions drawn from 9 error families across 13 natural-science domains and 715 papers, and provides detailed annotations for evidence localization and reasoning traces, together with a unified evaluation protocol. We assessed 15 models across 24 input configurations and conduct a fine-grained analysis of MLLM capabilities across error families. Across the board, retrieval-augmented generation (RAG) methods yield no significant improvements, revealing systematic deficiencies of current MLLMs on scan-oriented tasks and underscoring the challenge posed by ScholScan. We expect ScholScan to be the leading and representative work of the scan-oriented task paradigm.

TL;DR: We present ScholScan, a scan-oriented benchmark for full-paper scholarly reasoning that requires models to build a paper-level evidence view; spanning 1,800 questions from 715 papers, which exposes MLLM gaps and shows RAG ineffective.