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.
By Alex Schutz, Victor-Alexandru Darvariu, Efimia Panagiotaki, Bruno Lacerda, Nick Hawes
The paper introduces a physics-inspired neural framework that combines graph neural networks with statistical‑mechanics principles to solve large‑scale graph coloring problems. By using a planting‑based supervised signal, symmetry‑breaking regularization, and iterative noise‑annealed neural dynamics, the model navigates complex solution landscapes and scales quadratically with graph size. It generalizes from small training graphs to instances up to 20 times larger, demonstrating that neural architectures can learn scalable algorithmic strategies effective in hard connectivity regions.
By Lorenzo Colantonio (Department of Physics, Sapienza University of Rome), Andrea Cacioppo (Department of Physics, Sapienza University of Rome), Federico Scarpati (Department of Physics, Sapienza University of Rome), Maria Chiara Angelini (Department of Physics, Sapienza University of Rome), Federico Ricci-Tersenghi (Department of Physics, Sapienza University of Rome), Stefano Giagu (Department of Physics, Sapienza University of Rome)
The paper introduces a novel end‑to‑end, size‑agnostic graph reinforcement learning framework for the one‑dimensional bin packing problem (1D‑BPP). It models packing as a Markov decision process on an item‑compatibility graph, where a graph neural network actor‑critic policy learns to merge compatible partial bins. Empirical results on the BPPLIB benchmark show that the learned policy reduces the mean optimality gap of a constructive heuristic from 2.66 % to 2.31 %, performs competitively against other learned methods, and outperforms a state‑of‑the‑art learned solver on the hardest benchmark family.
By M. Asl{\i} Ayd{\i}n
The maximum independent set (MIS) problem is a fundamental NP-hard combinatorial optimization problem with applications in scheduling, resource allocation, and network analysis. Exact solvers can prov...
The paper extends the Strong Expressive Lottery Ticket Hypothesis to relational and temporal graph neural networks by proving that sufficiently large RGNNs contain sparse subnetworks preserving 1‑relational Weisfeiler‑Leman expressivity. It derives a probabilistic lower bound for random pruning to achieve such subnetworks and shows that common TGNNs and cross‑graph message passing can be reformulated as RGNNs to inherit these guarantees. Experiments validate the bound, compare it to empirical probabilities on synthetic data, and explore the relationship between pre‑training expressivity, optimization behavior, and prediction quality on temporal and molecular benchmarks.
By Lorenz Kummer, Samir Moustafa, Anatol Ehrlich, Franka Bause, Marco Nennstiel, Przemys{\l}aw Andrzej Wa{\l}\c{e}ga, Nils Morten Kriege
arXiv:2602.13106v2 Announce Type: replace-cross
Abstract: In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a li...
By Solveig Wittig, Antonis Vasileiou, Robert R. Nerem, Timo Stoll, Floris Geerts, Yusu Wang, Christopher Morris