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
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 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.
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
1d ago
Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization
arXiv:2608. 00270v2 Announce Type: replace Abstract: Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routing problems such as the Traveling Salesman Problem (TSP).
arXiv AI
Jun 29
Understanding Rollout Error in Graph World Models
arXiv:2606. 27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward.
arXiv Machine Learning
Jun 9
Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.
arXiv Machine Learning
Aug 11
OD-Gear: Online Decomposition and Group Sampling for Expert-Guided Adversarial Routing in Scalable Capacitated Vehicle Routing
arXiv:2602. 00488v3 Announce Type: replace Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generalization of neural solvers.
arXiv Machine Learning
Aug 5
Beyond Solving: Prescriptive Probing for Neural Routing Solvers
arXiv:2602. 07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask which stop to drop, which transition to preserve, or which subset of stops to remove if a route is infeasible.