arXiv AI
Jun 4
Learning Admissible Heuristics via Cost Partitioning
arXiv:2606. 04597v1 Announce Type: new Abstract: Admissible heuristics are essential for optimal planning, yet learning them remains challenging due to the risk of overestimation.
RIDGECUT: Learning Graph Partitioning with Rings and Wedges
arXiv:2505. 13986v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances.
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arXiv Machine Learning
Jun 2
Graph Edit Distance Formulation for the Vehicle Routing Problem: Theory and Analysis
arXiv:2606. 01987v1 Announce Type: cross Abstract: We show that the Vehicle Routing Problem (VRP) can be reformulated as a Graph Edit Distance (GED) maximization problem.
arXiv AI
Jul 14
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
arXiv Machine Learning
Sep 25
Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.
arXiv Machine Learning
Jul 28
Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
arXiv Machine Learning
Sep 4
Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems
The paper presents an end‑to‑end framework that uses constraint‑oriented hypergraphs and reinforcement learning to solve vehicle routing problems. It introduces a dynamic hyperedge reconstruction strategy for better hypergraph representation and a double‑pointer attention decoder for iterative solution generation. Experiments on benchmark datasets show that the method removes the need for complex heuristic operators while improving solution quality.
arXiv AI
Sep 18
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
The paper presents a reinforcement learning approach to generate graph ensembles that satisfy a hard assortativity constraint, a measure of degree–degree correlation between adjacent nodes. Unlike traditional soft-constraint methods, the learned policy performs degree-preserving rewiring to meet the exact target, reducing generation cost by at least an order of magnitude while preserving over 98% of configurational diversity. Trained on small graphs, the method generalizes to larger sizes and different topologies, allowing precise control over secondary observables such as the clustering coefficient.
arXiv AI
Jun 15
Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning
arXiv:2509. 18930v3 Announce Type: replace-cross Abstract: Neural algorithmic reasoning (NAR) is a paradigm that trains neural networks to execute classic algorithms by supervised learning.
Hugging Face Trending Papers
Sep 24
Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
The paper presents a graph-based framework for large-scale railway network management that combines a hierarchical Bayesian model with a Gaussian Process on a graph kernel to model spatially correlated maintenance environments, and a topology-aware Multi-Agent Reinforcement Learning system using graph neural networks and Transformers to optimize network-level policies. It demonstrates scalability by training agents on small network segments and deploying them zero-shot on larger, unseen networks, achieving superior performance over heuristics and standard MARL baselines while reducing training time. The approach addresses the computational challenges of centralized methods and the coordination gaps of decentralized methods in complex, long-horizon infrastructure asset management.
arXiv AI
Jun 4
Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data
arXiv:2601. 15158v4 Announce Type: replace-cross Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought).
arXiv Machine Learning
Jun 16
Using Reinforcement Learning to Optimize the Global and Local Crossing Number
arXiv:2509. 06108v2 Announce Type: replace-cross Abstract: Graph drawing concerns the algorithmic visualization of graphs.
arXiv AI
Sep 2
GeoPAR: Large-Scale Multi-Agent Combinatorial Optimization with Geometry-Guided Parallel Autoregressive Learning
GeoPAR is a geometry-guided parallel autoregressive reinforcement learning framework designed for large-scale multi-agent combinatorial optimization. It introduces a projection-window sparse geometry mechanism, sparse edge-biased attention, and cache-guided conflict-aware assignment to better model local geometric structures and reduce duplicate task selections. Experiments on heterogeneous vehicle routing and multi-depot pickup-and-delivery problems demonstrate improved zero-shot generalization, fewer rollout steps, and efficient inference.