mlsanityData to 14 Oct 2026

Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces

arXiv 2608.11354 ↗First posted 11 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rather than stable preference expression. As interfaces evolve from static layouts toward generative UIs and immersive extended reality (XR), the need for deeper, modality-agnostic user understanding grows: these adaptive environments must decide not only what to present but where, when, how prominently, and most importantly why a user acts. We propose an Inverse Theory of Mind (IToM) pipeline that reasons backward from observed interactions to infer the beliefs, preferences, and decision-making traits that explain behavior. The pipeline reconstructs each user's decision context, including what was chosen and what alternatives were available, applies LLM-driven counterfactual reasoning to produce evidence-grounded natural-language belief statements, and synthesizes these beliefs through multi-hypothesis abductive inference into a structured user persona. We evaluate on the OPeRA dataset against ground-truth personality assessments, attitudinal surveys, and interview-based personas across four tasks: next action prediction, shopping attitude alignment, Big Five personality inference, and held-out category prediction. Results show that inferred personas match or exceed ground-truth personas and that multi-hypothesis reasoning is essential for accurate personality prediction. We further demonstrate cross-modal transferability with a persona-driven spatial banking application on VisionOS.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 88 references that are in the map. First posted on arXiv 11 Aug 2026.

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46 of 88 references.

References · 46

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34 more
  1. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 260
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  9. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 163
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  3. 2026From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation
  4. 2026Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
  5. 2026RobustExplain: Evaluating Robustness of LLM-Based Explanation Agents for Recommendation
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  8. 2025What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context