How Many Guesses Does It Take to Crack an AI's "Random" Number?

Introduces a theoretical framework for artificial superintelligence alignment while acknowledging ASI's existential risks as argued by Yudkowsky and others.

Researchers introduce Latent Compaction, a test-time training method that compresses KV caches 32× while maintaining 96.7% accuracy on retrieval tasks.

Researchers introduce a retraining-free method to make transformers elastic, enabling on-the-fly adaptation across computational budgets by jointly ranking MLP neurons and attention heads.

Researchers introduce a new generative representation learning approach for scalable latent tokenization that achieves compute-optimal improvements over direct diffusion methods.

Researchers prove first-price auctions in AI agent markets can achieve near-optimal welfare under competitive floors and conditional independence assumptions.