RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent

Excerpt

arXiv:2609.39143v1 Announce Type: cross Abstract: 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 with parallel self-contrast to extract higher-quality memories without gold labels. Evaluated on AppWorld and BFCL-V3 across multipl