Cover Image

Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hersche, Michael, Camposampiero, Giacomo, Wattenhofer, Roger, Sebastian, Abu, Rahimi, Abbas
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning Symbolic Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Learning Abductive Reasoning using VSA Distributed Representations
by: Camposampiero, Giacomo, et al.
Published: (2024)

Can Large Reasoning Models do Analogical Reasoning under Perceptual Uncertainty?
by: Camposampiero, Giacomo, et al.
Published: (2025)

I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models
by: Camposampiero, Giacomo, et al.
Published: (2025)

Terminating Differentiable Tree Experts
by: Thomm, Jonathan, et al.
Published: (2024)

Scalable Evaluation and Neural Models for Compositional Generalization
by: Camposampiero, Giacomo, et al.
Published: (2025)

ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins
by: Hakim, Safayat Bin, et al.
Published: (2025)

Probabilistic Abduction for Visual Abstract Reasoning via Learning Rules in Vector-symbolic Architectures
by: Hersche, Michael, et al.
Published: (2024)

Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations
by: Bougzime, Oualid, et al.
Published: (2025)

Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
by: Labre, Marcelo
Published: (2026)

On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages
by: Terzić, Aleksandar, et al.
Published: (2024)

Hierarchical Neuro-Symbolic Decision Transformer
by: Baheri, Ali, et al.
Published: (2025)

A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning
by: Chen, Michael K.
Published: (2025)

EGG-SR: Embedding Symbolic Equivalence into Symbolic Regression via Equality Graph
by: Jiang, Nan, et al.
Published: (2025)

Unsupervised Symbolic Anomaly Detection
by: Hossain, Md Maruf, et al.
Published: (2026)

Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework
by: Tian, Yuan, et al.
Published: (2025)

Neuro-Symbolic ODE Discovery with Latent Grammar Flow
by: Yu, Karin, et al.
Published: (2026)

Symbolic Foundation Regressor on Complex Networks
by: Liu, Weiting, et al.
Published: (2025)

Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
by: Luo, Ziyan, et al.
Published: (2023)

Limits of Transformer Language Models on Learning to Compose Algorithms
by: Thomm, Jonathan, et al.
Published: (2024)

Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression
by: Saegert, Paul, et al.
Published: (2026)

ViSymRe: Vision Multimodal Symbolic Regression
by: Li, Da, et al.
Published: (2024)

AlphaIntegrator: Transformer Action Search for Symbolic Integration Proofs
by: Ünsal, Mert, et al.
Published: (2024)

Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
by: Okamoto, Lauren, et al.
Published: (2024)

UniSymNet: A Unified Symbolic Network Guided by Transformer
by: Li, Xinxin, et al.
Published: (2025)

Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators
by: Deng, Ou, et al.
Published: (2025)

NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum
by: Ye, Peihan, et al.
Published: (2026)

Bootstrapping OTS-Funcimg Pre-training Model (Botfip) -- A Comprehensive Symbolic Regression Framework
by: Chen, Tianhao, et al.
Published: (2024)

Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation
by: Bednarek, Jakub, et al.
Published: (2024)

Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis
by: Sinha, Sania, et al.
Published: (2025)

AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking
by: Gao, Silin, et al.
Published: (2025)

Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression
by: Hu, Jiao, et al.
Published: (2024)

On the Role of Noise in Factorizers for Disentangling Distributed Representations
by: Karunaratne, Geethan, et al.
Published: (2024)

AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents
by: Shahid, Mahnoor, et al.
Published: (2026)

A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniques
by: Ledaguenel, Arthur, et al.
Published: (2024)

CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning
by: Leang, Joshua Ong Jun, et al.
Published: (2024)

Symbolic Regression with a Learned Concept Library
by: Grayeli, Arya, et al.
Published: (2024)

Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic
by: Zhao, Xufeng, et al.
Published: (2023)

Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation
by: Habibi, Reza, et al.
Published: (2026)

Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
by: Hu, Chengpeng, et al.
Published: (2026)

The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning
by: Sheneman, Lucas
Published: (2026)