Neurosymbolic Reasoning Shortcuts under the Independence Assumption
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
The ubiquitous independence assumption among symbolic concepts in neurosymbolic (NeSy) predictors is a convenient simplification: NeSy predictors use it to speed up probabilistic reasoning. Recent works like van Krieken et al. (2024) and Marconato et al. (2024) argued that the independence assumption can hinder learning of NeSy predictors and, more crucially, prevent them from correctly modelling uncertainty. There is, however, scepticism in the NeSy community around the scenarios in which the independence assumption actually limits NeSy systems (Faronius and Dos Martires, 2025). In this work, we settle this question by formally showing that assuming independence among symbolic concepts entails that a model can never represent uncertainty over certain concept combinations. Thus, the model fails to be aware of reasoning shortcuts, i.e., the pathological behaviour of NeSy predictors that predict correct downstream tasks but for the wrong reasons.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 4 of its 28 references that are in the map. First posted on arXiv 15 Jul 2025.
Builds on
4 of 28 references.
Closest ancestors · 2
Same direction, newest first
- 2025Neurosymbolic Diffusion Models19 May
- 2024Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning29 Oct
Other references · 2
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2023Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations
- 2025Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototypical Neurosymbolic AI
- 2026Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era
- 2025Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
- 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
- 2026Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
- 2024The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
- 2022A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference