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This study investigates whether large language models can strictly adhere to formal modal logic specifications, revealing significant performance gaps between standard prompting and dedicated reasoning modes. The findings demonstrate that model identity and inference mode critically determine the ability to follow complex semantic constraints rather than relying on familiar logical heuristics. This research advances our understanding of how AI systems handle rigorous deductive frameworks essential for reliable automated reasoning applications.
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Same Formulas, Different Semantics: Do Language Models Follow Modal Logic Specifications?

arXiv2 min read

arXiv:2608.05097v1 Announce Type: new Abstract: Reasoning about necessity and possibility depends on assumptions about accessibility between worlds and about which objects exist at each one. The same inference may therefore hold under one modal system and fail under another. Evaluating language models on such problems requires testing whether their judgments follow the stated semantics rather than a familiar logic. We construct paired modal problems with identical premises and conjecture but different frame or domain conditions; automated reasoning verifies opposite labels. A balanced core prevents the semantic condition alone from revealing the answer. On this core, four of five recent models perform below the condition-only baseline under direct prompting. Yet enabling reasoning mode raises DeepSeek V4 Flash from 4.4% to 88.1% on unchanged prompts. Following stipulated modal semantics thus depends strongly on inference mode as well as model identity. When frame conditions are omitted, models often agree but fit different familiar logics best. We release the formulas, oracle artifacts, countermodels, and responses.

Title:Same Formulas, Different Semantics: Do Language Models Follow Modal Logic Specifications?

Abstract:Reasoning about necessity and possibility depends on assumptions about accessibility between worlds and about which objects exist at each one. The same inference may therefore hold under one modal system and fail under another. Evaluating language models on such problems requires testing whether their judgments follow the stated semantics rather than a familiar logic. We construct paired modal problems with identical premises and conjecture but different frame or domain conditions; automated reasoning verifies opposite labels. A balanced core prevents the semantic condition alone from revealing the answer. On this core, four of five recent models perform below the condition-only baseline under direct prompting. Yet enabling reasoning mode raises DeepSeek V4 Flash from 4.4% to 88.1% on unchanged prompts. Following stipulated modal semantics thus depends strongly on inference mode as well as model identity. When frame conditions are omitted, models often agree but fit different familiar logics best. We release the formulas, oracle artifacts, countermodels, and responses.

9 pages. Code: this https URL. Data and artifacts: this https URL

Computation and Language (cs.CL)

arXiv:2608.05097 [cs.CL]

(or arXiv:2608.05097v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.05097 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 274 words

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