Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge

arXiv:2602.17826 · cs.AI, cs.LG, cs.SC · Submitted 2026-02-19 · Read on arXiv

cs.AI, cs.LG, cs.SC

Submitted: 2026-02-19

Updated: 2026-08-31

Code: https://github.com/labrem/neus2026-labre

Terminology

• Accuracy — 28× in this paper · explained in 1 episode
• Ontology — 26× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Grounding — 9× in this paper · explained in 3 episodes
• Verification — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Hallucination — 6× in this paper · explained in 3 episodes
• Small Type System — 5× in this paper
• Axioms — 4× in this paper · explained in 2 episodes
• Knowledge Base Construction — 4× in this paper · explained in 2 episodes
• Commented Mathematical Properties — 3× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• Formal Mathematical Properties — 3× in this paper
• Reciprocal Rank Fusion — 3× in this paper
• Semantic Gap — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Best-of-N Sampling — 1× in this paper · explained in 2 episodes
• Cognitive Load — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning
• rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
• Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection