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AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning
Modern language agents are expected to operate over long horizons: they ask follow-up questions, reuse worked examples, handle tool feedback, and adapt to delayed consequences. Most evaluations still reset the agent after a prompt or score only the final state of one trajectory. AhaBench asks a more
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