arXiv · cs.AI · Xuwei Tan, Jingchun Huang, Ming Cai, Shuangquan Lyu, Tiange Wang, Yinzhi Wang, Ronghua Shi, Jingwei Song, Yang Jingsong, Yinghui Xia, Hehua Fan, Yifan Zhou, Hanxin Chen, Jiwei Song, Tianyu Shi · 3 小时前模型评分28
CogniLife：面向已部署多专家智能体市场的持续认知蒸馏框架
CogniLife: Continuous Cognitive Distillation for Deployed Multi-Expert Agent Marketplaces
导读
CogniLife 提出跨专家归因设计，用创作者画像和专家日志把创作者行为转化为可修订的决策规则与领域流程，以缓解“冷专家”问题，但该设计尚未实现或评估。
分类置信度
中 · 待人工复核
- 只有单一媒体来源，需要人工复核。
来源级别：媒体与个人
事件类型：研究发布
证据摘录
- “CogniLife proposes a cross-expert attribution design for this problem, with Creator Profiles for transferable creator patterns and Expert Journals for listing-specific knowledge; this design is not yet implemented or evaluated.”
- “this design is not yet implemented or evaluated”
- “On ProfyBench-v1, an 85-task offline benchmark built from replay seeds and controlled internal tests, the Profy Full configuration as a whole achieves a mean overall score of 4.65/5 from the primary LLM judge, compared with 4.01 for Raw LLM and 4.28 for Expert Prompt, with the largest gains in completeness, specificity, and missing-information tasks.”
- “These configuration-level scores do not test cold-expert mitigation, cross-expert transfer, or longitudinal distillation.”
来源：arXiv · cs.AI · arxiv.org