Neural Algorithmic Reasoning
Overview
Neural algorithmic reasoning asks how neural networks can learn the steps of algorithms and apply them to inputs or problem sizes not seen in training. Common approaches train on algorithm traces, align graph neural networks with message-passing algorithms, or use recurrent and looped models that perform more computation at test time; some methods encode symbolic rules or explicit memory directly. Researchers evaluate these systems on tasks such as CLRS graph algorithms, Sudoku, mazes, arithmetic, and graph optimization, often testing generalization to longer sequences, larger graphs, or deeper reasoning. A central challenge is ensuring that models truly execute and retain useful intermediate computations rather than relying on shortcuts, since accurate answers or decodable representations alone do not guarantee robust reasoning at greater depth.
Latest papers
45 papers14 papers in the last four weeks, up 367% on the four weeks before. 0.1% of all new papers.