High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making

• URL: http://arxiv.org/abs/2604.04300v1
• Date: Sun, 05 Apr 2026 22:45:33 GMT
• Title: High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making
• Abstract summary: We argue that individual investor decision-making presents a uniquely challenging domain for LLM personalization.<n>We identify four axes along which individual investing exposes fundamental limitations in standard LLM customization.<n>We propose open research directions for personalized NLP in high-stakes, temporally extended decision domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized LLM systems have advanced rapidly, yet most operate in domains where user preferences are stable and ground truth is either absent or subjective. We argue that individual investor decision-making presents a uniquely challenging domain for LLM personalization - one that exposes fundamental limitations in current customization paradigms. Drawing on our system, built and deployed for AI-augmented portfolio management, we identify four axes along which individual investing exposes fundamental limitations in standard LLM customization: (1) behavioral memory complexity, where investor patterns are temporally evolving, self-contradictory, and financially consequential; (2) thesis consistency under drift, where maintaining coherent investment rationale over weeks or months strains stateless and session-bounded architectures; (3) style-signal tension, where the system must simultaneously respect personal investment philosophy and surface objective evidence that may contradict it; and (4) alignment without ground truth, where personalization quality cannot be evaluated against a fixed label set because outcomes are stochastic and delayed. We describe the architectural responses that emerged from building the system and propose open research directions for personalized NLP in high-stakes, temporally extended decision domains.

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