RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent
Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore need memory extraction methods that improve with more test-time compute without relying on gold labels. We propose RefCon, which combines sequential self-refinement wi...
Ubaidillah Ariq Prathama, Bo Liu, Yeonsun Hong et al.
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