Evaluates multimodal QA models on spatial reasoning and embodied visual grounding benchmarks.
npx skills add https://github.com/EurecaMoment/BenchClaw --skill erqa
Please help me install this Agent Skill. Skill: ERQA Source: https://github.com/EurecaMoment/BenchClaw/tree/main/BenchClaw/benchmarkDatasetCards/ERQA Command: npx skills add https://github.com/EurecaMoment/BenchClaw --skill erqa
This Skill provides a reusable benchmark-dataset card for ERQA, enabling models to evaluate spatial reasoning, world-knowledge understanding, and embodied visual grounding.
Run the ERQA Skill to evaluate your model's performance on spatial reasoning and multimodal question-answering tasks.
High-intent search queries and answers about installing and using this skill.
To evaluate spatial reasoning in multimodal question-answering models, you can use the ERQA benchmark dataset to assess capabilities in multi-image reasoning, spatial relations, and embodied visual grounding.
The ERQA benchmark dataset tests embodied reasoning and world-knowledge understanding by evaluating models on movement, viewpoint changes, visibility, and combining real-world semantics with visual evidence.
Yes, you can use the ERQA dataset to assess multi-image reasoning capabilities. It specifically provides benchmark tasks for evaluating models on spatial relations and embodied visual grounding across multiple images.
Multimodal question-answering requires world knowledge for visual grounding. The ERQA dataset evaluates how effectively models combine real-world semantics with visual evidence to answer complex spatial questions.
The best way to benchmark embodied visual grounding tasks is using the ERQA dataset, which provides structured evaluations for viewpoint changes, movement, and spatial relations in multimodal question-answering scenarios.
You need a benchmark for spatial relations and world knowledge when assessing a model's ability to reason about spatial positions and combine real-world semantics with visual evidence in multimodal tasks.
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