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This paper geometrically analyzes why LLMs acting as judges agree strongly with each other but weakly with humans, finding that inter-LLM consensus reflects a collapsed subspace rather than true human alignment on subjective rubrics. Post-hoc calibration on human data improves alignment, but even calibrated LLMs fall short of human reliability.