🎁 SPOTO Mid-Year Sale – Grab Your IT Certification Success Kit!

🔥 Whether you're prepping for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #Comptia, #ITIL, #Cloud or any other hot certification – SPOTO has your back with real exam dumps and hands-on training!

✅ Free Resources:
・Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4alTSfk
・IT Certs E-book: https://bit.ly/49ub0zq
・IT Exams Skill Test: https://bit.ly/4dVPapB
・Free AI material and support tools: https://bit.ly/4elzcpl
・Free Cloud Study Guide: https://bit.ly/4u7sdG0

🎁 Join SPOTO Mid-Year Lucky Draw:
📱 iPhone 17 🛒 Free Order
🛒 Amazon Gift $100 📘PMP/ AWS/ CCNA Course

👉 Enter the Draw Now → https://bit.ly/4uN3lVt

👉 Join Our IT Learning Community for free resources & support:
https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d
💬 Want exam help? Chat with an admin now:
https://wa.link/knicza

⏰ Mid-Year Deal Ends Soon – Don't Miss Out!

🔥 AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations

💡 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.

📅 Published on Feb 3

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2602.03828
• PDF: https://arxiv.org/pdf/2602.03828

📊 Datasets citing this paper:
• https://huggingface.co/datasets/WestlakeNLP/FigureBench
• https://huggingface.co/datasets/samhug856/FigureBench

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/vikashmakeit/garment-to-pattern

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#ScientificIllustrations #TextToImageSynthesis #FigureGeneration #AutoFigure #ScientificVisualization

🔥 PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training

💡 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.

📅 Published on Jun 2

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.03264
• PDF: https://arxiv.org/pdf/2606.03264
• Project Page: https://www.paddleocr.com

🤖 Models citing this paper:
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6
• https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6-GGUF

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.6_Online_Demo

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#DocumentParsing #RegionRefinement #PostTrainingTechniques #OpticalCharacterRecognition #PaddleOCR

🔥 WavTTS: Towards High-Quality Zero-Shot TTS via Direct Raw Waveform Modeling

💡 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.

📅 Published on Jun 2

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.03455
• PDF: https://arxiv.org/pdf/2606.03455
• Project Page: https://wavtts.github.io/

🤖 Models citing this paper:
• https://huggingface.co/worstchan/WavTTS
• https://huggingface.co/drbaph/WavTTS

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#TextToSpeechSynthesis #RawWaveformModeling #DiffusionTransformer #ZeroShotTTS #SpeechSynthesisTechniques

🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

🔥 Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models

💡 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.

📅 Published on Jun 2

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.03748
• PDF: https://arxiv.org/pdf/2606.03748
• Project Page: https://docs.ultralytics.com/models/yolo26

🤖 Models citing this paper:
• https://huggingface.co/Ultralytics/YOLO26

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/Ultralytics/YOLO26
• https://huggingface.co/spaces/atalaydenknalbant/Yolo26
• https://huggingface.co/spaces/itsMarco-G/reachy_phone_home

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus

#YOLO26 #RealTimeVisionModels #EndToEndInference #ObjectDetectionAlgorithms #UnifiedVisionModels

Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.

Sign up for FREE and save your seat here: luma.com/qgz4g4r7

Why should you join?

Many people interested in AI Engineering are asking the same questions:

❓ Where do I start?
🤔 Do I need deep math first?
🧠 Should I focus on ML, LLMs, RAG, or AI agents?
🧭 How do I avoid wasting time learning the wrong things?
🚀 How do I go from learning to becoming hireable?

If you’re interested in AI Engineering but unsure how to approach it, this livestream is for you.

What you’ll learn

✦ What AI Engineering really is
✦ Where beginners should start
✦ What skills and topics actually matter
✦ Common mistakes to avoid
✦ Self-study vs bootcamp vs MSc
✦ How to think about becoming hireable in AI
✦ Practical advice from someone already working in the field

Sign up for FREE and save your seat: luma.com/qgz4g4r7