AhaBench: Do Agents Turn Experience into Reusable Insights? A Long-Horizon Benchmark for Continual Learning

A solved example can help an agent complete a task without preparing it to solve the next one. AhaBench tests how agents use experience in later tasks and decisions. Aha-Puzzle tests questioning on a new mystery after solved episodes; Aha-Euler tests computation on new inputs after teaching; Aha-Vending follows decisions and feedback over a simulated business year. The benchmark leaves the learning mechanism open, and our evaluation is on fixed-weight agents. Earlier Aha-Puzzle solutions help some models, but scores remain far below solving with hints. Qwen 3.6 Plus gains +12.57 points over an attempt without earlier episodes. On single-step Aha-Euler tasks, it reaches 97.3% held-out accuracy with an explanation and code, versus 8.1% with the question and answer alone. In nominal Aha-Vending operation, Doubao Seed 2.0 Pro finishes at + $495, but its profit averaged over the year is -$10 because recovery arrives late. We release tasks, validators, a simulator, and evaluation records for studying how agents reuse experience on new tasks and sustain productive behavior over time.