capability-forecasting

This paper introduces CurveShift, an analysis method that separates overall ability gains from difficulty-specific improvements in LLM agents. Using METR time-horizon data and LiveCodeBench, it finds that most apparent shifts toward harder tasks are ceiling effects, though a genuine hard-task effect exists for reasoning models in competitive programming.