Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior
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| Format: | Preprint |
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2025
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| _version_ | 1866908524148162560 |
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| author | Ethiraj, Vignesh Talluri, Subhash |
| author_facet | Ethiraj, Vignesh Talluri, Subhash |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior Ethiraj, Vignesh Talluri, Subhash Machine Learning Artificial Intelligence 68T07, 62M20 I.2.6; H.2.8; H.3.3 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. |
| title | Unified Interaction Foundational Model (UIFM) for Predicting Complex User and System Behavior |
| topic | Machine Learning Artificial Intelligence 68T07, 62M20 I.2.6; H.2.8; H.3.3 |
| url | https://arxiv.org/abs/2509.06025 |