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LLMs can identify some discrimination signals in assessment items, but their predictions fall significantly short of human benchmarks.
A well-designed harness can unlock powerful embodied manipulation capabilities in compact models, achieving frontier performance with just 2K simulation trajectories.
Iterative local refinement in Mask Diffusion Models can dramatically enhance reasoning capabilities, outperforming traditional methods across diverse tasks.
A VLM can autonomously evolve its questioning capabilities, producing harder and more diverse questions that enhance its overall performance without needing external data.
Humans miss 3.9% of opportunities to leverage correct AI suggestions while also over-relying on misleading outputs, highlighting critical gaps in trust and decision-making in human-AI collaboration.
