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Assessing the Relational Abilities of Large Language Models and Large Reasoning Models.

Matthias Raemaekers1, Martin Finn1, Jan De Houwer1

  • 1Department of Experimental Clinical and Health Psychology, Ghent University, 9000 Ghent, Belgium.

Behavioral Sciences (Basel, Switzerland)
|January 28, 2026
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Summary
This summary is machine-generated.

Large language models (LLMs) and large reasoning models (LRMs) demonstrated strong relational abilities on a new syllogistic task battery. Performance was robust across various complexities and premise orders, validating a novel assessment framework for artificial intelligence.

Keywords:
large language modelsreasoning modelsrelational abilities indexrelational reasoningtransformation of function

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Behavior Analysis

Background:

  • Assessing relational abilities is crucial for understanding intelligence in both biological and artificial systems.
  • Existing methods for evaluating relational skills in artificial intelligence are limited.
  • Behavior-analytic tasks offer a robust framework for probing complex cognitive functions.

Purpose of the Study:

  • To evaluate the relational abilities of state-of-the-art large language models (LLMs) and large reasoning models (LRMs).
  • To introduce a novel battery of several thousand syllogistic problems for assessing generalized relational skills.
  • To investigate the impact of varying complexity and stimulus function transformations on model performance.

Main Methods:

  • Utilized a new battery of syllogistic problems with multiple relation types (sameness, difference, comparison, hierarchy, analogy, temporal, deictic).
  • Problems involved nonwords, varied complexity (number of premises, irrelevant premises), and included valid/invalid conclusion formats.
  • Tested transformations of stimulus function and conducted a replication study with randomized premise order.

Main Results:

  • Both LLMs and LRMs generally performed well on the novel relational task battery.
  • Models exhibited some variability across different relation types and were minimally affected by task variations.
  • Performance remained robust even when premise order was randomized, indicating generalizable abilities.

Conclusions:

  • The developed syllogistic task battery provides a new framework for assessing core intellectual abilities in artificial systems.
  • LLMs and LRMs show significant, though not perfect, relational competence.
  • Future research should explore implications for artificial general intelligence and further refine assessment methodologies.