Preference Reasoning under Indeterminacy in Large Language Models
Read the original on arXiv AI →The paper titled "Preference Reasoning under Indeterminacy in Large Language Models" discusses how large language models (LLMs) are becoming decision‑making agents and the importance of reasoning over preferences for alignment, coordination, and collective intelligence. It highlights that real‑world preference reasoning is inherently indeterminate, with incomplete information and potentially no valid solutions. The authors formalize this challenge into epistemic indeterminacy (incomplete or expressive preferences) and structural indeterminacy (non‑existence of solutions under standard social choice concepts), and demonstrate that current LLMs fail to differentiate between determined and undetermined cases, showing miscalibrated reasoning even in verification tasks.
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