mmdeepresearch-bench-a-benchmark-for-multimodal

name	mmdeepresearch-bench-a-benchmark-for-multimodal
title	MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.12346
keywords	["Agent","Benchmark"]
description	Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains, where each task provides an image-text bundle to evaluate multimodal understanding and citation-grounded report generation. Compared to prior setups, MMDR-Bench emphas...

Overview

This skill covers mmdeepresearch-bench: a benchmark for multimodal deep research agents. It addresses critical challenges in autonomous agent development.

Key Concepts

The paper introduces novel approaches to:

• Agent evaluation and benchmarking
• Improving agent efficiency and reasoning
• Designing robust agent systems

When to Use

Use this when working on:

• Agent-based systems and evaluation
• Autonomous reasoning and planning
• Multi-agent frameworks

When NOT to Use

• Non-agent applications
• Tasks requiring implementation code (see the paper)

References

• Paper: https://arxiv.org/abs/2601.12346
• PDF: https://arxiv.org/pdf/2601.12346