نسخة أولية وصول مفتوح
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 …