Seq2Seq Models Reconstruct Visual Jigsaw Puzzles without Seeing Them

Jigsaw puzzles are primarily visual objects, whose algorithmic solutions have traditionally been framed from a visual perspective. In this work, however, we explore a fundamentally different approach: solving square jigsaw puzzles using language models, without access to raw visual input. By introducing a specialized tokenizer that converts each puzzle piece into a discrete sequence of tokens, we reframe puzzle reassembly as a sequence-to-sequence prediction task. Treated as "blind" solvers, encoder-decoder transformers accurately reconstruct the original layout by reasoning over token sequences alone. Despite being deliberately restricted from accessing visual input, our models achieve state-of-the-art results across multiple benchmarks, often outperforming vision-based methods. These findings highlight the surprising capability of language models to solve problems beyond their native domain, and suggest that unconventional approaches can inspire promising directions for puzzle-solving research.

Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation

Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its source, and pieces from different objects being mixed, categorizing broken artifacts based on their visual cues could be a challenging task, even for professionals. As classification is a common function of many machine learning models, the power of modern architectures can be harnessed for efficient and accurate fragment classification. In this work, we present a generalized deep-learning framework for predicting the artistic style of image fragments, achieving state-of-the-art results for pieces with varying styles and geometries.

Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving

This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for 2D and 3D puzzle solving. The fragments and fractures are realistic, caused by a collapse of a fresco during a World War II bombing at the Pompeii archaeological park. The fragments are also eroded and have missing pieces with irregular shapes and different dimensions, challenging further the reassembly algorithms. The dataset is multi-modal providing high resolution images with characteristic pictorial elements, detailed 3D scans of the fragments and meta-data annotated by the archaeologists. Ground truth has been generated through several years of unceasing fieldwork, including the excavation and cleaning of each fragment, followed by manual puzzle solving by archaeologists of a subset of approx. 1000 pieces among the 16000 available. After digitizing all the fragments in 3D, a benchmark was prepared to challenge current reassembly and puzzle-solving methods that often solve more simplistic synthetic scenarios. The tested baselines show that there clearly exists a gap to fill in solving this computationally complex problem.