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Span-level error localization can boost deep-research agent reliability by up to 30 percentage points, revealing critical insights into where agents go wrong.
TVIR-Agent reveals that integrating visual elements into report generation can dramatically improve the quality and reliability of analytical outputs.
Current research agents still struggle with retrieval robustness and hallucination control, even when evaluated in a static, verifiable research environment.
VLMs still struggle to consistently extract structured cultural metadata from images, revealing a critical gap in their ability to reason beyond visual perception across diverse cultural contexts.
