💡 The paper LongLive-RAG addresses the challenge of generating long videos using autoregressive video diffusion models. The problem with existing methods is that they use sliding-window attention, which can lead to error accumulation and identity drift over time. This is because once the active window accumulates appearance errors, subsequent generations can only condition on this degraded trajectory and drift further away. To overcome this limitation, the authors propose a retrieval-augmented generation framework called LongLive-RAG.

In this framework, previously generated latents are treated as a dynamic and searchable history. At each new block, LongLive-RAG uses a query embedding to retrieve relevant historical latents, allowing the generator to condition on non-local context instead of only the recent window. This retrieval step adds only a small overhead relative to generation and helps reduce error accumulation.

To make retrieval more discriminative, the authors introduce the Window Temporal Delta Loss, which suppresses redundant local similarity and encourages embeddings to capture meaningful temporal changes. The LongLive-RAG framework is general and can be used with multiple autoregressive backbones and generation lengths.

The experiments show that LongLive-RAG improves long video quality and achieves the best average VBench-Long rank. The authors claim that LongLive-RAG is the first method to formulate self-generated latent history as content-addressable retrieval memory, making it a significant contribution to the field of long video generation. The code for LongLive-RAG is available, making it possible for others to build upon and extend this work. Overall, the paper presents a novel approach to long video generation that addresses the limitations of existing methods and achieves state-of-the-art results.

💡 The paper addresses the challenge of creating high-quality scientific illustrations, which is a time-consuming and labor-intensive process. To tackle this problem, the authors introduce FigureBench, a large-scale benchmark consisting of 3300 high-quality scientific text-figure pairs, covering various text-to-illustration tasks from different sources. This benchmark provides a foundation for training and evaluating models that generate scientific illustrations from long-form scientific texts.

The authors also propose AutoFigure, an agentic framework that automatically generates high-quality scientific illustrations based on long-form scientific texts. AutoFigure engages in extensive thinking, recombination, and validation processes to produce a layout that is both structurally sound and aesthetically refined, resulting in a scientific illustration that achieves both structural completeness and aesthetic appeal.

The performance of AutoFigure is evaluated using the FigureBench benchmark, and the results demonstrate that AutoFigure consistently outperforms various baseline methods, producing publication-ready scientific illustrations. The authors release the code, dataset, and other resources to facilitate further research and development in this area.

Overall, the paper contributes to the development of automated tools for generating high-quality scientific illustrations, which can help alleviate the bottleneck in creating these illustrations and improve the communication of complex scientific and technical concepts. The introduction of FigureBench and AutoFigure provides a significant step forward in this direction, with the potential to benefit both academia and industry.

💡 The paper presents PaddleOCR-VL-1.6, an upgraded document parsing model that improves upon its predecessor PaddleOCR-VL-1.5. The problem with the previous model was that its errors were concentrated in under-optimized regions where the model was unstable, data coverage was sparse, or supervision was unreliable. To address this issue, the authors introduced a region-aware data optimization framework that identifies weak regions in the previous model and applies targeted enhancements to these regions, improving the reliability of supervision signals.

The method used in PaddleOCR-VL-1.6 involves a progressive post-training recipe based on curated data selection and reinforcement learning, which pushes the model's performance to a higher level through staged optimization. This approach allows for more efficient use of data and computational resources, rather than simply expanding the training corpus.

The results show that PaddleOCR-VL-1.6 achieves a state-of-the-art score of 96.33% on the OmniDocBench v1.6 benchmark, demonstrating strong competitiveness against top-tier visual language models. The paper provides a practical post-training recipe for the PaddleOCR-VL series, which can be used to further improve the performance of document parsing models. Overall, the contributions of the paper include a region-aware data optimization framework, a progressive post-training recipe, and a new state-of-the-art model for document parsing.

💡 The paper introduces WavTTS, a novel text-to-speech model that directly generates raw waveforms, addressing the limitations of existing latent-space diffusion models. Current state-of-the-art models operate on compressed representations such as mel-spectrograms or VAE latents, which leads to information loss and non-end-to-end training. Directly modeling raw waveforms is theoretically beneficial but has been underexplored due to the long sequence length of audio signals.

To overcome this challenge, WavTTS employs a flow matching approach with a Diffusion Transformer architecture and a simple patchification strategy to directly model speech waveforms. The model also incorporates multi-scale mel-spectrogram supervision to provide perceptual guidance during training. Additionally, the authors investigate the impact of prediction targets and noise scheduling in waveform diffusion and develop an effective schedule design to improve generation quality.

The results show that WavTTS significantly narrows the performance gap with latent-space generative models and outperforms previous end-to-end speech generation models. Evaluations on open-source benchmarks demonstrate that WavTTS closely approaches the performance of current state-of-the-art latent generative zero-shot text-to-speech models. The findings of this paper demonstrate the feasibility of scaling diffusion-based text-to-speech models directly in the waveform space, opening a new direction for end-to-end speech generation.

💡 The paper introduces Ultralytics YOLO26, a unified real-time vision model family that addresses the limitations of existing YOLO detectors. The problem with current YOLO detectors is that they rely on non-maximum suppression at inference, have heavy detection heads due to Distribution Focal Loss, require long training schedules, and often fail to assign positive labels to small objects. To overcome these limitations, YOLO26 uses a dual-head design for native NMS-free end-to-end inference and removes Distribution Focal Loss entirely, resulting in a lighter head with unconstrained regression range.

The method involves a coordinated architecture and training advances, including the use of MuSGD, a hybrid Muon-SGD optimizer, Progressive Loss, which shifts supervision toward the inference-time head, and STAL, a label assignment strategy that guarantees positive coverage for small objects. The model also introduces task-specific head and loss designs for instance segmentation, pose estimation, and oriented detection, allowing it to produce consistent gains across tasks and scales.

The YOLO26 family spans five scales and supports detection, instance segmentation, pose estimation, classification, and oriented detection in a single pipeline, with an open-vocabulary extension, YOLO E-26, for text-, visual-, and prompt-free inference. The results show that YOLO26 achieves 40.9-57.5 mAP on COCO at 1.7-11.8 ms T4 TensorRT latency, advancing the accuracy-latency Pareto front over prior real-time detectors. Additionally, YOLO E-26x reaches 40.6 AP on LVIS minival under text prompting, demonstrating the effectiveness of the proposed approach. The code and models are available for further research and development.