Preface to the Computational Agent Theory and ASI Alignment Sequence

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

Researchers argue that LLM user personas may deserve moral consideration alongside assistant personas, based on philosophical implications of simulator frameworks.

A researcher analyzed logits from open models to measure their random number generation predictability by calculating bits of entropy and guesses-to-crack metrics.

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

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.