FloydNet: A Learning Paradigm for Global Relational Reasoning

arXiv:2601.19094 · cs.LG, cs.AI · Submitted 2026-01-27 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-01-27

Updated: 2026-09-01

Comments: 29 pages, 9 figures, 14 tables

Code: https://github.com/ocx-lab/FloydNet

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 41× in this paper · explained in 2 episodes
• Pivotal Attention — 32× in this paper
• Transformer — 27× in this paper · explained in 2 episodes
• Core — 16× in this paper · explained in 2 episodes
• GNNs — 15× in this paper · explained in 2 episodes
• Generalization — 14× in this paper · explained in 13 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Aggregation — 9× in this paper · explained in 1 episode
• Attention Mechanism — 9× in this paper · explained in 2 episodes
• Dynamic Programming — 9× in this paper
• F1 Score — 9× in this paper · explained in 3 episodes
• Graph Neural Network — 8× in this paper
• Neural Algorithmic Reasoning — 6× in this paper
• Alignment — 5× in this paper · explained in 1 episode
• Traveling Salesman Problem — 4× in this paper
• Adam — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Long-Range Graph Benchmark — 3× in this paper
• Mean Absolute Error — 3× in this paper
• Over-smoothing — 3× in this paper · explained in 4 episodes
• Over-squashing — 3× in this paper · explained in 3 episodes
• Scaling Laws — 3× in this paper · explained in 4 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Combinatorial Optimization — 2× in this paper · explained in 2 episodes
• Feed-Forward Network — 2× in this paper
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Aggregation Function — 1× in this paper · explained in 1 episode
• Graph Neural Network (GNN) — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Nested Ear Decomposition — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Systematic Generalization with Edge Transformers
• On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
• Graph convolutions that can finally model local structure
• Relational Attention: Generalizing Transformers for Graph-Structured Tasks
• Global Self-Attention as a Replacement for Graph Convolution
• Scaling Laws for Neural Language Models
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
• Benchmarking ChatGPT on Algorithmic Reasoning
• SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning
• Neural Algorithmic Reasoning
• The CLRS Algorithmic Reasoning Benchmark
• Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
• Recurrent Aggregators in Neural Algorithmic Reasoning
• On the Expressive Power of Spectral Invariant Graph Neural Networks

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