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Task order can dramatically skew the performance of self-improving agents, revealing hidden dependencies that could undermine their reliability in real-world applications.
Decoupling coordinate frame selection from box regression leads to a remarkable 11% accuracy boost in 3D visual grounding tasks.
On-policy distillation makes language models more accurate, but also dangerously overconfident, revealing a fundamental tension between capability and calibration.
