AI RESEARCH
One Size Fits None: Heuristic Collapse in LLM Investment Advice
arXiv CS.LG
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ArXi:2604.23837v1 Announce Type: cross Large language models are increasingly deployed as advisors in high-stakes domains -- answering medical questions, interpreting legal documents, recommending financial products -- where good advice requires integrating a user's full context rather than responding to salient surface features. We investigate whether frontier LLMs actually do this, or whether they instead exhibit heuristic collapse: a systematic reduction of complex, multi-factor decisions to a small number of dominant inputs.