SOURCE-LINKED INTELLIGENCE
InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations
Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic, requiring the active interrogation of interactive environments. Existing benchmarks are predominantly constrained to static imagery and one-shot question answering and fail to capture the epistemic demands of this domain, where evidence is frequently occluded, distributed across linked views, or conditionally revealed through user agency. In this paper, we introduce InSight, a benchmark for agentic claim verification over interactive visualiza
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T15:15:30.000Z
First collected: 2026-09-21T06:01:56.170Z. This is not the publication date.