Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning

2026-03-27 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

ScholScan is a new benchmark designed to evaluate multimodal large language models (MLLMs) on "scan-oriented" academic paper reasoning, moving beyond traditional search-oriented paradigms. This benchmark requires models to read and cross-check entire research papers to identify consistency issues, mirroring how human researchers analyze documents. ScholScan features 1,800 annotated questions spanning nine error categories across 13 natural science domains and 715 papers. It includes detailed annotations for evidence localization and reasoning traces, alongside a unified evaluation protocol. Initial assessments of 15 MLLMs across 24 input configurations revealed that retrieval-augmented generation (RAG) methods did not significantly improve performance, highlighting systematic deficiencies in current MLLMs for these complex scan-oriented tasks.

Key takeaway

For AI scientists and research engineers developing MLLMs for academic applications, you should prioritize enhancing models' capabilities for full-document understanding and cross-paper verification. The ScholScan benchmark demonstrates that current MLLMs, even with RAG, struggle with scan-oriented tasks, indicating a need to move beyond search-centric approaches to achieve more autonomous research assistance.

Key insights

Scan-oriented reasoning benchmarks reveal MLLM deficiencies in full-document understanding and cross-checking.

Principles

• Human-like paper analysis requires full-document scanning.
• Search-oriented reasoning limits MLLM research autonomy.

Method

ScholScan introduces a scan-oriented task setting where models identify consistency issues by reading and cross-checking entire academic papers, using 1,800 questions across nine error categories.

In practice

• Evaluate MLLMs on full-document consistency checks.
• Focus MLLM development on cross-document verification.

Topics

• ScholScan
• Multimodal Large Language Models
• Scan-Oriented Reasoning
• Academic Paper Reasoning
• Retrieval-Augmented Generation

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.