Situation Graph Prediction: Structured Perspective Inference for User Modeling

• URL: http://arxiv.org/abs/2602.13319v1
• Date: Tue, 10 Feb 2026 20:58:15 GMT
• Title: Situation Graph Prediction: Structured Perspective Inference for User Modeling
• Authors: Jisung Shin, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama,
• Abstract summary: Situation Graph Prediction is a task that frames perspective modeling as an inverse inference problem.<n>To enable grounding without real labels, we use a structure-first synthetic generation strategy.<n>Results suggest SGP is non-context and provide evidence for the structure-first data synthesis strategy.
• Score: 0.23332469289621785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Perspective-Aware AI requires modeling evolving internal states--goals, emotions, contexts--not merely preferences. Progress is limited by a data bottleneck: digital footprints are privacy-sensitive and perspective states are rarely labeled. We propose Situation Graph Prediction (SGP), a task that frames perspective modeling as an inverse inference problem: reconstructing structured, ontology-aligned representations of perspective from observable multimodal artifacts. To enable grounding without real labels, we use a structure-first synthetic generation strategy that aligns latent labels and observable traces by design. As a pilot, we construct a dataset and run a diagnostic study using retrieval-augmented in-context learning as a proxy for supervision. In our study with GPT-4o, we observe a gap between surface-level extraction and latent perspective inference--indicating latent-state inference is harder than surface extraction under our controlled setting. Results suggest SGP is non-trivial and provide evidence for the structure-first data synthesis strategy.

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