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AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning
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continual learningbenchmarkingagent evaluationperformance metrics
2609.05435
Builder Relevance
1h ago80%
Abstract
AhaBench evaluates whether fixed models improve their behavior after receiving useful experience in long-horizon tasks.
Reality Card
Core Claim
The study demonstrates that models that effectively utilize visible support do not necessarily exhibit the same learning improvements across different evaluation conditions.
Method / Result
Claude Opus 4.6 achieved an aggregate Post-Experience Score of 64.3 and a Learning Lift of +25.8.
Limitations
The evaluation conditions may not fully capture the complexities of real-world scenarios, potentially affecting reproducibility.
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