Summary
This paper introduces "case-grounded evidence verification," a new framework aimed at improving evidence-grounded reasoning in AI models. It addresses current limitations where models often fail to truly depend on evidence due to weak supervision and loose ties between evidence and claims. The framework ensures models make decisions directly based on whether provided evidence supports a target claim, utilizing a local case context and external evidence.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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