arXiv AI
Jun 10
Non-Parametric Structural Priors for Geometry Theorem Prediction
arXiv:2603. 04852v2 Announce Type: replace Abstract: Multi-step theorem prediction is a central challenge in geometry problem solving.
The Topological Dual of a Dataset: A Logic-to-Topology Encoding for AlphaGeometry-Style Data
arXiv:2604. 18050v2 Announce Type: replace Abstract: AlphaGeometry represents a milestone in neuro-symbolic reasoning, yet its architecture faces a log-linear scaling bottleneck within its symbolic deduction engine that limits its efficiency as problem complexity increases.
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arXiv AI
Jun 16
Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphs
arXiv:2606. 15656v1 Announce Type: new Abstract: Modern artificial intelligence remains fundamentally divided between the continuous, probabilistic spaces of Foundation Models and the discrete, deterministic structures of Knowledge Graphs.
arXiv AI
1d ago
Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry
arXiv:2608. 14585v1 Announce Type: new Abstract: Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and algebraic computation.
arXiv Machine Learning
Aug 3
Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization
arXiv:2607. 29008v1 Announce Type: cross Abstract: Modern opaque AI models prize performance over interpretability, which makes testing difficult.
arXiv AI
Aug 6
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
arXiv:2608. 04285v1 Announce Type: new Abstract: Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention.
arXiv AI
Jul 24
Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.
arXiv AI
Jun 29
Verifiable Geometry Problem Solving: Solver-Driven Autoformalization and Theorem Proposing
arXiv:2606. 27926v1 Announce Type: new Abstract: Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor.
arXiv AI
Jun 4
A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks
arXiv:2510. 24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities.
arXiv Machine Learning
Aug 10
Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks
arXiv:2608. 06839v1 Announce Type: new Abstract: Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning.
arXiv Machine Learning
Aug 4
Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer
arXiv:2608. 01585v1 Announce Type: cross Abstract: Language model benchmarking is a difficult task.
arXiv AI
Jul 17
Enhancing Small Language Models Reasoning through Knowledge Graph Grounding
arXiv:2607. 14149v1 Announce Type: new Abstract: Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing.
arXiv AI
Jun 30
TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging
arXiv:2606. 29763v1 Announce Type: cross Abstract: Topological data analysis (TDA), particularly persistent homology (PH), captures geometric structural properties in medical images (e.