Fan

Identity drift in generative agents reveals that anti-self-deception is a dominant modification behavior, challenging assumptions about agent fidelity under pressure.

Hard negatives generated in real-time by LLMs can significantly enhance the training of two-tower models, outperforming traditional sampling methods and reducing popularity bias in recommendations.

Tactile-reactive policies can boost robotic manipulation success rates by over 30% through innovative data collection and a new Mixture-of-Transformers architecture.