RefCon：上下文演化智能体的迭代优化与对比记忆提取
RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent
RefCon让智能体通过自优化提取记忆，无需人工标注，在软件工程任务中表现优异。
RefCon结合序列自优化与并行自对比，无需黄金标签即可提取高质量记忆。在AppWorld和BFCL-V3基准测试中，RefCon在ACE基准上相对提升21.6%，在ReMe基准上提升16.6%。其多样性变体DivCon在ReasoningBank上实现35.5%的提升，甚至超越黄金标签基线。
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 with parallel self-contrast to extract higher-quality memories without gold labels. Evaluated on AppWorld and BFCL-V3 across multiple context-evolving agent frameworks, RefCon delivers strong and consistent gains, including relative improvements of 21.6% on ACE and 16.6% on ReMe over no-scaling baselines, while a diversity-focused variant (DivCon) achieves a 35.5% gain on ReasoningBank. RefCon consistently outperforms existing baselines without ground-truth labels, and generalizes across model scales and to software engineering tasks, where it surpasses even ground-truth baselines. We further analyze the accuracy-token trade-off and scaling behavior, showing RefCon maintains favorable efficiency and continues to improve as more trajectories are used, unlike diversity-only scaling which saturates earlier.
