Preference Reasoning under Indeterminacy in Large Language Models

• URL: http://arxiv.org/abs/2608.18631v1
• Date: Wed, 19 Aug 2026 07:27:12 GMT
• Title: Preference Reasoning under Indeterminacy in Large Language Models
• Abstract summary: We argue that indeterminacy, rather than correctness alone, is a central challenge for AI reasoning.<n>We show that state-of-the-art language models systematically fail to distinguish between determined and undetermined instances.
• Score: 36.06390570712731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models evolve into decision-making agents, the ability to reason over preferences becomes fundamental to alignment, coordination, and collective intelligence. Yet, unlike standard benchmarks, real-world preference reasoning is inherently indeterminate: information may be incomplete, and valid solutions may not exist. We argue that indeterminacy, rather than correctness alone, is a central challenge for AI reasoning. We formalize this challenge along two axes, (i) epistemic indeterminacy, arising from incomplete, partial, or expressive preferences, and (ii) structural indeterminacy, arising from the non-existence of solutions under standard social choice concepts. Across a hierarchy of tasks, we show that state-of-the-art language models systematically fail to distinguish between determined and undetermined instances, exhibiting miscalibrated reasoning even in verification settings.

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