Modeling Associative Reasoning Processes
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by adapting logical reasoning mechanisms. In particular it is shown that the combination with large commensense knowledge within a single reasoning system demands for an efficient and powerful association technique. This approach is also used for modelling mind-wandering and the Remote Associates Test (RAT) for testing creativity. In a general discussion we show implications of the model for a broad variety of cognitive phenomena including consciousness.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 7 of its 55 references that are in the map. First posted on arXiv 3 Jan 2022.
Builds on
7 of 55 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
- 2018SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inferencecited by 333
- 2019Abductive Commonsense Reasoningcited by 300
- 2019Probing Neural Network Comprehension of Natural Language Argumentscited by 212
- 2021Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentationcited by 9
- 2021Focusing Knowledge-based Graph Argument Mining via Topic Modelingcited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
- 2026CREATE: Testing LLMs for Associative Creativity
- 2026Thought without systematicity? Evaluating reasoning models on rule induction tasks
- 2025Probing the "Psyche'' of Large Reasoning Models: Understanding Through a Human Lens
- 2026To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
- 2025Meta-R1: Empowering Large Reasoning Models with Metacognition
- 2025Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
- 2025Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models