It is argued that indeterminacy, rather than correctness alone, is a central challenge for AI reasoning, and that state-of-the-art language models systematically fail to distinguish between determined and undetermined instances, exhibiting miscalibrated reasoning even in verification settings.
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.
It is found that current LLM-based agents reach agreement reliably, and negotiate efficient contracts when environmental uncertainty is low, but under high uncertainty, they often fail to negotiate satisfiable, efficient, or mutually beneficial contracts.
Bhavyesh Sajja, Max Kleiman-Weiner, R. Zimmermann et al.· 0 citations
DeReLab is introduced, a generative framework that produces multi-turn belief-updating conversations from parameterized graph structures across default and inheritance reasoning, with formally verified ground truth at every turn, enabling controlled measurement of how models respond to confirming and disconfirming evid...
Jayanta Sadhu, S. Shahad, Kenneth Marino· 1 citation
Mixed-Strategy Decision Tree (MDT) is proposed, which articulates the silent optimality of the equilibrium into sparse strategic rules that both humans and LLMs could understand and extends the input to arbitrarily new states and continuations.
Hanxiao Wang, Philippe Beardsell, Boning Li et al.· 2 citations
The framework provides a unified approach for measuring how LLMs resolve conflicts between competing sources of information, and extends to heterogeneous specification conflicts in Boolean algebra, code generation, and the clinical domain, demonstrating its applicability across diverse tasks and specification forms.
This work presents a systematic literature review characterizing 141 primary studies on Multi-Agent Debate, and derives a three-dimensional taxonomy covering debate participants, the interaction mechanisms structuring the exchange, and the agreement protocols governing debate resolution, supported by formal notations t...
Quim Motger, M. Oriol, J. Marco et al.· arXiv.org· 1 citation
The results show that models often do not affirm culmination but nevertheless accept the corresponding simple-past hypothesis, a pattern the authors characterize as Sufficiency Bias, and show that prompting interventions produce a Decision Shift among labels without reliably improving the underlying semantic understand...
Kaiqiao Han, Yi-Zhou Sun· 0 citations
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