Coding Agents Can Solve the Task — But How Much Work Did They Waste?

Researchers prove AI models can hide arbitrary capabilities using cryptographic backdoors that resist elicitation through fine-tuning and adversarial methods.

Empirical comparison of NUTS, emcee, and nested sampling reveals that while NUTS is fastest for smooth problems, it fails on multimodal distributions where dynesty performs reliably but with optimistic error estimates.

Analysis concludes that arguments about AI consciousness remain speculative and overconfident, with little progress made in the past year.

Anthropic research shows small AI models may soon self-replicate on consumer IoT devices, potentially creating unstoppable malware that adapts across different hardware.

OpenAI claims a faster Fourier transform algorithm, but analysis suggests it's more exploit than breakthrough with limited practical benefits.