用大模型蒸馏信任语义，让设备快速选靠谱协作伙伴。
Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI
- 用带记忆的大模型做老师，从多方收集信任数据
- 蒸馏出任务相关的信任特征，学生端快速匹配协作方
- 减少通信开销，提升选择准确率和响应速度
将计算任务从资源受限设备卸载到资源丰富的同伴设备，是协同计算的关键范式。在此背景下，对潜在协作设备进行精准信任评估对于复杂任务的高效执行至关重要。该评估过程需从每个潜在协作方收集多样化的信任信息，并基于采集数据进行信任推理。然而，当每个资源受限设备独立评估所有潜在协作方时，频繁的数据交换和复杂的推理会带来显著开销，进一步降低信任评估的及时性。为此，我们提出一种基于大模型（LAM）驱动的教师-学生代理架构的任务特定信任语义蒸馏（TSD）模型。具体而言，教师代理部署在具备强大计算能力的服务器上，并配备增强的记忆模块，用于执行多维度信任相关数据采集、任务特定信任语义提取及任务-协作方匹配分析。当设备端学生代理发起任务特定评估请求时，教师代理将潜在协作方的信任语义传递给学生代理，从而实现快速准确的协作方选择。实验结果表明，所提出的TSD模型可显著降低协作方评估时间，减少设备资源消耗，并提高协作方选择的准确性。
原文摘要 · Abstract (English)
Offloading computational tasks from resource-constrained devices to resource-abundant peers constitutes a critical paradigm for collaborative computing. Within this context, accurate trust evaluation of potential collaborating devices is essential for the effective execution of complex computing tasks. This trust evaluation process involves collecting diverse trust-related information from every potential collaborator and performing trust inference based on the collected data. However, when each resource-constrained device independently assesses all potential collaborators, frequent data exchange and complex reasoning can incur significant overhead and further degrade the timeliness of trust evaluation. To overcome these challenges, we propose a task-specific trust semantics distillation (TSD) model based on a large AI model (LAM)-enabled teacher-student agent architecture. Specifically, the teacher agent is deployed on a server with powerful computational capabilities and an augmented memory module to perform multidimensional trust-related data collection, task-specific trust semantics extraction, and task-collaborator matching analysis. Upon receiving task-specific evaluation requests from device-side student agents, the teacher agent transfers the trust semantics of potential collaborators to the student agents, enabling rapid and accurate collaborator selection. Experimental results demonstrate that the proposed TSD model can reduce collaborator evaluation time, decrease device resource consumption, and improve the accuracy of collaborator selection.
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