Alvin Zhang

Together, these results show that composing complementary mechanisms substantially improves long-horizon memorization beyond what any individual mechanism achieves.

Trust functions can transform weak supervision into a powerful training signal, enabling models to achieve near-lossless generalization even with unreliable labels.

Agents struggle to match human performance in long-horizon tasks, revealing critical gaps in their learning capabilities during deployment.