Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents
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 can sustain long, dependency-heavy research processes. We introduce Mr. LHDR (Multimodal real-world Long-Horizon Deep Research), a benchmark for evaluating real-world deep research over long, irreducible chains of interdependent evidence across eight categories. Each question is constructed from a hidden Node-Relation graph and requires an average of 12.1 necessary intermediate conclusions with a mean dependency depth of 10.4 before reaching a short, unique, and...
| Authors | Minghao Guo, Meng Cao, Sui Zhao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 September 2026 |
| Last updated at source | 11 September 2026 |
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