Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull detection methods cannot capture. We argue that detecting these threats requires structured behavioral traces that underlie raw heterogeneous blockchain data, i.e., how insiders accumulate, coordinate, and unwind positions. To enable such analysis, we introduce MELT (MEmecoin Launch Trace, the first behavioral trace dataset for analyzing and detecting high-risk memecoin launches on Solana. MELT covers 41k+ memecoin launches with 200M+ transactions parsed into typed behavioral records that distinguish swaps, wash trades, transfers, and mints. Beyond per-account behaviors, MELT contributes bundle-trace data that links accounts controlled by the same entity, revealing that, on average, 36.5% of token supply is held by coordinated accounts, a concealment strategy that disguises the true ownership concentration from unsuspecting buyers. On top of these traces, MELT provides 122 behavioral features and risk-level annotations, enabling supervised learning at a population scale. We benchmark representative ML models on the high-risk launch detection task. Integrating their predictions into a simple memecoin selection strategy reduces investment loss significantly, demonstrating that behavioral traces can be translated into risk mitigation. Our dataset and code is available at https://github.com/git-disl/MELT.

翻译：发射台已成为发行Meme币的主流机制，这使得投资者面临一类现有“拉地毯”检测方法无法捕获的高风险发行行为。我们认为，检测此类威胁需要结构化行为轨迹——即揭示内部人士如何积累、协同并平仓的基础异构区块链数据。为支撑此类分析，我们提出MELT（Meme币发行轨迹）——首个用于分析检测Solana链上高风险Meme币发行的行为轨迹数据集。MELT覆盖41,000余次Meme币发行行为，包含超2亿笔交易，这些交易被解析为区分交换、清洗交易、转账与铸币操作的分类行为记录。除单个账户行为外，MELT还提供关联同一实体控制账户的包轨迹数据，揭示平均36.5%的代币供应量由协同账户持有——这种隐蔽策略可向不知情买家掩盖真实所有权集中度。基于这些轨迹，MELT提供122项行为特征与风险等级标注，支持群体规模的监督学习。我们以高风险发行检测任务对代表性机器学习模型进行基准测试。将其预测结果整合至简单Meme币选择策略后，投资损失显著降低，证明行为轨迹可转化为风险缓解工具。数据集与代码已开源至https://github.com/git-disl/MELT。