The field of neural algorithmic reasoning is advancing with the development of novel attention mechanisms and approaches to mitigate memorization bias in neural symbolic regression. Recent work has focused on improving the performance and generalizability of neural models on combinatorial optimization problems and symbolic regression tasks. A key direction is the integration of tropical geometry and max-plus semirings to enhance the reasoning capabilities of neural models. Additionally, researchers are exploring the use of adversarial attacks and test-time computation to improve the robustness and accuracy of neural symbolic regression methods. Noteworthy papers include:

• Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms, which introduces a novel attention function that operates natively in the max-plus semiring of tropical geometry.
• Can Test-time Computation Mitigate Memorization Bias in Neural Symbolic Regression?, which examines the effect of test-time strategies on reducing memorization bias in neural symbolic regression.
• Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms, which presents a Deep Learning approach that accelerates Border basis computation while maintaining output guarantees.