LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing

arXiv:2605.06332 · cs.LG · Submitted 2026-05-07 · Read on arXiv

cs.LG

Submitted: 2026-05-07

Updated: 2026-09-27

Code: https://github.com/Elaina10172004/LINC

Terminology

• Benchmark — 19× in this paper · explained in 2 episodes
• Ablation — 15× in this paper · explained in 1 episode
• Combinatorial Optimization — 10× in this paper · explained in 2 episodes
• Decoupling — 9× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Capacitated Vehicle Routing Problem — 4× in this paper
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Traveling Salesman Problem — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Capacitated Vehicle Routing Problem with Time Windows — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Relational inductive biases, deep learning, and graph networks
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks