Generating an Image From 1,000 Words: Enhancing Text-to-Image With Structured Captions

Text-to-image models have rapidly evolved from casual creative tools to professional-grade systems, achieving unprecedented levels of image quality and realism. Yet, most models are trained to map short prompts into detailed images, creating a gap between sparse textual input and rich visual outputs. This mismatch reduces controllability, as models often fill in missing details arbitrarily, biasing toward average user preferences and limiting precision for professional use. We address this limitation by training the first open-source text-to-image model on long structured captions, where every training sample is annotated with the same set of fine-grained attributes. This design maximizes expressive coverage and enables disentangled control over visual factors. To process long captions efficiently, we propose DimFusion, a fusion mechanism that integrates intermediate tokens from a lightweight LLM without increasing token length. We also introduce the Text-as-a-Bottleneck Reconstruction (TaBR) evaluation protocol. By assessing how well real images can be reconstructed through a captioning-generation loop, TaBR directly measures controllability and expressiveness, even for very long captions where existing evaluation methods fail. Finally, we demonstrate our contributions by training the large-scale model FIBO, achieving state-of-the-art prompt alignment among open-source models. Model weights are publicly available at https://huggingface.co/briaai/FIBO

Temporal anomaly detection: calibrating the surprise

We propose a hybrid approach to temporal anomaly detection in user-database access data -- or more generally, any kind of subject-object co-occurrence data. Our methodology allows identifying anomalies based on a single stationary model, instead of requiring a full temporal one, which would be prohibitive in our setting. We learn our low-rank stationary model from the high-dimensional training data, and then fit a regression model for predicting the expected likelihood score of normal access patterns in the future. The disparity between the predicted and the observed likelihood scores is used to assess the "surprise". This approach enables calibration of the anomaly score so that time-varying normal behavior patterns are not considered anomalous. We provide a detailed description of the algorithm, including a convergence analysis, and report encouraging empirical results. One of the datasets we tested is new for the public domain. It consists of two months' worth of database access records from a live system. This dataset will be made publicly available, and is provided in the supplementary material.