aiqbench analysis
The paper proposes Adversarial Dialogue Trees, a multi-turn probing method, to test whether LLMs keep stable commitments across five domains under conversational pressure. Its claim that newer models resist better but still eventually fail is a behavioral finding from one framework, so readers should check how the probes, scoring, and model set were defined before generalizing to "lacks core cognition." The result is most useful as a robustness benchmark for evaluators, not as a settled answer about LLM understanding.
Do LLMs have core beliefs?
arXiv:2605.03255v2 Announce Type: replace Abstract: The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention has been paid to a structural component of human cognition: core beliefs, truths that provide a foundation around which we can build a worldview. These commitments usually resist debunking, as abandoning them would represent a fundamental shift in how we see reality. In this paper, we ask whether LLMs hold anything akin to core commitments. Using a probing framework we call Adversarial Dialogue Trees (ADTs) over five domains (science, history, geography, biology, and mathematics), we find that most LLMs fail to maintain a stable worldview. Though some recent models showed improved stability, they still eventually failed to maintain key commitments under conversational pressure. These results document an improvement in argumentative skills across model generations but indicate that all current models lack a key component of human-level cognition.
Title:Do LLMs have core beliefs?
Abstract:The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention has been paid to a structural component of human cognition: core beliefs, truths that provide a foundation around which we can build a worldview. These commitments usually resist debunking, as abandoning them would represent a fundamental shift in how we see reality. In this paper, we ask whether LLMs hold anything akin to core commitments. Using a probing framework we call Adversarial Dialogue Trees (ADTs) over five domains (science, history, geography, biology, and mathematics), we find that most LLMs fail to maintain a stable worldview. Though some recent models showed improved stability, they still eventually failed to maintain key commitments under conversational pressure. These results document an improvement in argumentative skills across model generations but indicate that all current models lack a key component of human-level cognition.
Machine Learning (cs.LG)
arXiv:2605.03255 [cs.LG]
(or arXiv:2605.03255v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.03255 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 246 words
