AI RESEARCH
Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation
arXiv CS.LG
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ArXi:2605.19986v1 Announce Type: cross Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute grounding, high-fidelity spatial perception, and constraint-respecting motor execution. However, current embodied AI benchmarks collapse these capacities into binary success rates, systematically inflating reported capabilities by up to 70% and masking the architectural bottlenecks that impede real-world deployment. We