协议代理：如果代理能够用密码学在日常生活中使用呢？
Protocol Agent: What If Agents Could Use Cryptography In Everyday Life?
AI总结：
协议代理通过引入基准评估代理在日常生活中使用密码学技术的能力，提升通信效率与安全性。
摘要：
我们通常假设代理之间的交互会模仿人类对话。然而，代理本质上运作方式完全不同。如果它们能够发展出更高效且更符合自身能力的沟通模式呢？虽然已有能够深刻改善日常互动的密码学基础技术，但人类无法使用它们，因为它们过于复杂，数学计算无法在脑海中完成。例子包括证明年龄（或其他属性）而无需展示身份证，到在群体中匿名报告的同时证明自己是合法成员，到公平分割晚餐账单而不透露薪资。如果代理能够通过识别哪种基础技术适合日常情境，向代理对手提出，说服其参与，并正确使用适当计算工具执行协议呢？协议代理通过引入一个基准来解决这个问题，该基准涵盖：（1）密码学基础技术识别，（2）谈判技能，（3）实施正确性，（4）正确计算和（5）安全强度。我们评估了当前的开放权重和最先进的模型在该基准上的表现，提出了一种数据集生成方法来提高这些能力，并测量了监督微调（SFT）对基准性能的影响，经过微调的模型在基准表现上远超基线模型。
英文摘要：
We often assume that agent-to-agent interaction will mirror human conversation. However, agents operate fundamentally differently. What if they could develop communication patterns that are more efficient and better aligned with their capabilities? While cryptographic primitives that could profoundly improve everyday interactions already exist, humans can't use them because they are too complex and the math can't be done in one's head. Examples range from proving your age (or other attributes) without showing your ID, to filing an anonymous report within a group while proving you are a legitimate member, to splitting a dinner bill fairly without revealing salaries. What if agents could create protocols "on the fly" by recognizing which primitive fits an everyday situation, proposing it to an agentic counterpart, persuading them to participate, and then executing the protocol correctly using appropriate computation tools? Protocol Agent frames this problem by introducing a benchmark that spans: (1) cryptographic primitive recognition, (2) negotiation skills, (3) implementation correctness, (4) correct computation and (5) security strength. We evaluate current open-weight and state-of-the-art models on this benchmark, propose a dataset-generation approach to improve these capabilities, and measure the impact of supervised fine-tuning (SFT) on benchmark performance, with tuned models outperforming base models by a wide margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
