用统一模型理解用户与系统复杂行为，提升预测准确性。
Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior
- 将多属性事件视为整体单元，避免碎片化信息损失
- 在电信、电商等场景中显著提升行为预测精度
- 适合需要理解复杂交互的智能系统研发者
人工智能的核心目标之一是构建能够理解并预测复杂动态事件序列的系统。然而，现有为自然语言设计的基础模型无法捕捉电信、电商和金融等领域中结构化交互的整体性。通过将事件序列化为文本，这些模型会将其拆解为语义碎片，导致关键上下文丢失。本文提出统一交互基础模型（UIFM），专为真实行为理解而设计。其核心是复合分词机制，将每个具有多属性的事件视为单一语义单元。这使UIFM能学习用户行为的内在‘语法’，感知完整交互而非离散数据点流。实验表明，该架构不仅更准确，更是迈向更灵活、更智能预测系统的根本性一步。
原文摘要 · Abstract (English)
A central goal of artificial intelligence is to build systems that can understand and predict complex, evolving sequences of events. However, current foundation models, designed for natural language, fail to grasp the holistic nature of structured interactions found in domains like telecommunications, e-commerce and finance. By serializing events into text, they disassemble them into semantically fragmented parts, losing critical context. In this work, we introduce the Unified Interaction Foundation Model (UIFM), a foundation model engineered for genuine behavioral understanding. At its core is the principle of composite tokenization, where each multi-attribute event is treated as a single, semantically coherent unit. This allows UIFM to learn the underlying "grammar" of user behavior, perceiving entire interactions rather than a disconnected stream of data points. We demonstrate that this architecture is not just more accurate, but represents a fundamental step towards creating more adaptable and intelligent predictive systems.
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