MC-Search is a benchmark dataset for evaluating and enhancing multimodal agentic search with structured long reasoning chains. The dataset focuses on open-world settings where Large Multimodal Models (LMMs) operate. It was created by YennNing and last updated on February 22, 2026.
Use Cases
- Benchmarking the performance of multimodal agents in open-world search scenarios based on the described focus.
- Training models to handle structured long reasoning chains as indicated by the dataset's purpose.
- Evaluating the robustness of agentic search systems across diverse multimodal inputs.
Strengths
- Specifically designed for evaluating multimodal agentic search, a niche research area.
- Focuses on structured long reasoning chains, addressing a complex challenge in AI.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file formats are unknown, which may limit suitability assessment.
Provenance
- Source
- huggingface
- Collection Method
- Created as a benchmark for the associated research paper.
- Time Range
- null
- Freshness
- Last updated 2026-02-22 23:08:51; freshness should be verified.
- Geography
- null