Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions: foundation-model-based smart contract semantics and vulnerability reasoning [1], automated smart contract repair with formal guarantees [2], adversarial learning for robust malicious contract and transaction detection [3], and real-time transaction-level exploit detection at blockchain scale [4]. We connect these directions to two recent studies that characterize the current frontier: a diagnostic analysis of where smart contract security analyzers fall short [5] and a scalable real-time system for malicious Ethereum transaction detection [6]. The resulting framework is intended to help students formulate capstone projects that are technically grounded, empirically measurable, and aligned with contemporary smart contract security research.

翻译：智能合约安全已从漏洞检测扩展至更广泛的研究议程，涵盖语义推理、自动修复、对抗鲁棒性及实时利用检测。本文围绕四个方向构建了一个以顶点项目为导向的研究叙事：基于基础模型的智能合约语义与漏洞推理[1]、带有形式化保证的自动智能合约修复[2]、用于鲁棒恶意合约与交易检测的对抗学习[3]、以及区块链规模的实时交易级利用检测[4]。我们将这些方向与两项刻画当前前沿的最新研究相关联：一项诊断性分析揭示智能合约安全分析器的不足[5]，另一项是用于恶意以太坊交易可扩展实时检测系统[6]。由此形成的框架旨在帮助学生设计在技术上有据可依、经验上可量化衡量、且与当代智能合约安全研究相一致的顶点项目。