AI

Towards Robust Long-Lived Agents: Benchmarking Experience Lifecycles in Evolving Environments

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 05/11/2026, 05:03:24 UTC.

Briefings

A new benchmark called ReScopeBench evaluates how long-lived AI agents manage their experience across 18 evolving environments. Researchers found that while agents retain useful past experience, they often fail to update it when environments change, leading to performance drops. The study distinguishes between structural knowledge and reusable rules, identifying selective maintenance as a key challenge for future AI systems.

Key points

• ReScopeBench introduces an experience-first construction approach to study how AI agents acquire, reuse, and revise experience across 18 interactive environments.
• Across 20 models, retained experience improved task performance, but agents struggled to keep experience current as environments evolved.

Context

Existing benchmarks focus on experience reuse but lack insight into which parts of accumulated experience agents revise, retain, or lose. This study addresses that gap by proposing an experience-first construction approach to track changes in saved experience throughout task streams.

Limitations

Evidence is limited to a single preprint snapshot from November 2026. Results are based on 20 models and 18 environments, with no additional validation or long-term follow-up data provided.

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