PuzLM: Solving Jigsaw Puzzles with Sequence-to-Sequence Language Models

摘要

Square jigsaw puzzles are typically solved by visually match-ing piece images to recover the original layout. This work introducesPuzLM, an alternative perspective that recasts jigsaw reassembly asa discrete sequence-to-sequence (Seq2Seq) problem, inspired by naturallanguage representations. We design an efficient puzzle quantization pro-cedure that transforms each piece into a short sequence of discrete tokens,enabling the direct application of standard Seq2Seq language models aspowerful jigsaw solvers. Our approach demonstrates that accurate puzzlereconstruction can be achieved through purely symbolic reasoning overdiscrete representations, improving state-of-the-art performance even onpuzzles with eroded boundaries or missing pieces.