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New Benchmark Tests AI Agents on Complex, Multimodal Long-Horizon Research

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Mr.LHDR, a new benchmark designed to evaluate deep research agents on their ability to handle long, complex, and multimodal research tasks. The benchmark features questions requiring an average of 12.1 intermediate conclusions and a dependency depth of 10.4, incorporating diverse evidence types like images, charts, and PDFs. Current leading AI systems struggle with this benchmark, achieving only 43.1% overall accuracy, highlighting significant challenges in sustained, dependency-consistent evidence integration for AI agents. AI

IMPACT Highlights current limitations in AI agents' ability to perform complex, multimodal reasoning, indicating a need for advancements in long-horizon task execution.

RANK_REASON The item describes a new benchmark for AI agents, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Benchmark Tests AI Agents on Complex, Multimodal Long-Horizon Research

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12 / 100
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The item describes a new benchmark for AI agents, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, other
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Minghao Guo, Meng Cao, Sui Zhao, Siyu Ning, Xin Wang, Haoze Zhao, Jiaxuan Yang, Haihong Hao, Mingfei Han, Shunlin Rong, Haijun Wu, Xiaodan Liang, Xiaojun Chang ·

    Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents

    arXiv:2609.11318v1 Announce Type: new Abstract: Deep research agents are increasingly capable of web search, tool use, multimodal evidence analysis, and information synthesis. However, existing benchmarks mainly evaluate medium-horizon exploration and rarely test whether agents c…

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