AI 中文总结
针对加密货币市场操纵风险评估问题，提出ManiScope系统，利用大语言模型辅助可视化分析，提供多方面协同视图及人机协作框架，经案例和用户研究验证，该系统能有效评估风险、减少人工并围绕假设组织发现。
AI 中文摘要
加密货币市场易受基于交易的操纵影响，如洗盘交易，这会扭曲价格信号并误导投资者。以往研究主要用固定规则或标记示例检测操纵，灵活性和可解释性有限。现有可视化分析工具能揭示基本操纵相关信号，但整合持有者关系、可疑行为和市场动态进行风险评估仍需大量人工。为此，我们提出ManiScope，一个用于分析加密货币市场基于交易的操纵风险的大语言模型辅助可视化分析系统。它提供代币分布、持有者关系、持有者详细行为、价格动态和可疑交易模式的协同视图。为进一步增强用户分析，还引入了人机协作可视化分析框架，将大语言模型定位为共同分析师。通过两个案例研究和对12名经验丰富的加密货币从业者的用户研究进行评估，结果表明ManiScope支持有效的操纵风险评估，减少了寻找证据的人工工作量，并围绕用户假设组织发现。
英文摘要
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.
