A paper described on September 17, 2026, turns a flow model into a recurrent model for tasks such as Sudoku, refining predictions iteratively without backpropagation through time.
Published on September 17, 2026, the paper presents an approach that turns a flow model into a recurrent model for reasoning tasks such as Sudoku. It feeds predictions back as inputs for successive refinement and uses a local loss at each step, without backpropagation through time.
The stated result is an alternative for iterative prediction refinement; the source text gives no comparative metrics or other experimental results. To assess the proposal, consult the original paper and check its formulation, evaluated tasks, and reported results; do not attribute conclusions beyond those described.