Summary
Large Vision-Language Models (LVLMs) are prone to hallucinations, generating outputs not grounded in visual input. Previous research attributed this to vision backbone limitations or language component dominance, but the exact causes remained unclear. This paper introduces HalluScope, a new benchmark designed to investigate prompt-induced hallucinations and better understand the factors contributing to these ungrounded outputs.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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