AI models flub these intelligence tests. Can you fare any better?

What's happening

Frontier AI models still struggle with introspection and recursive self-improvement: researchers benchmarked five LLMs on a HarnessOpt-Bench (arXiv + MIT code) that prevents cheating by keeping the test set inaccessible, and found limits to their ability to rewrite other agents' harnesses. An Anthropic researcher reported automated systems improved performance across 10 misalignment-related benchmarks without degrading overall performance. Separately, an arXiv study shows on-demand AI help can boost short-term human puzzle performance but undermine longer-term skill development, and MIT Technology Review highlights that puzzles and games remain a standard way to probe model intelligence.

Why it's trending

Because teams are actively stress-testing models with adversarial benchmarks, self-improvement experiments, and human-in-the-loop studies that reveal both capabilities and new failure modes.

Signal1.5× its usual volume, confirmed across 3 independent source types.