LumiXAI整合多种特征归因方法，让非程序员也能交互式分析模型解释。
LumiXAI: A Modular Full-Stack Framework for Feature Attribution
- 模块化框架，支持分类与生成类归因方法
- 提供图形界面和三层访问权限，支持跨设备复现结果
- 插件式架构，适合研究人员、开发者和普通用户
特征归因是模型可解释性的重要工具，但现有软件高度碎片化：各类工具通常只专注单一模态、特定接口或固定方法，缺乏可扩展性，且多面向领域专家，需编程能力或熟悉归因方法，限制了非专业人士使用。本文提出LumiXAI，一个模块化的全栈框架，将归因分析整合至统一系统。它结合分类与生成式归因，配备支持双向探索的交互式图形界面，采用插件架构以注册新模型与方法，并提供三层次访问模式——面向非程序员、开发者与扩展者，共享同一后端。该系统实现了统一接口、统一交互逻辑与统一数据持久化层，通过容器化服务与持久化结果确保分析在不同机器间可复现。
原文摘要 · Abstract (English)
Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools specialize along narrow axes, such as a single modality, a code API or a GUI, or a fixed rather than extensible method set, and rarely combine these strengths. Moreover, many explainability tools are designed primarily for domain experts, requiring programming skills or familiarity with attribution methods that can make them difficult for non-expert users to access. In this article, we present LumiXAI, a modular full-stack framework that consolidates attribution analysis into a single system. It couples classification and generative attribution with an interactive GUI supporting bidirectional exploration, a plug-in architecture for registering new models and methods, and three access tiers serving non-programmers, developers, and extenders from one backend. Its contribution is a system that operationalises established attribution methods under one interface, one interaction model, and one persistence layer, with containerised services and persistent results making analyses reproducible across machines.
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