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AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning

This story is from 2026-09-09. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.05435v1 Announce Type: new Abstract: 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 fi…

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  1. 2026-09-09 04:00 · arXiv cs.LG
    AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning

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