Liquid is an innovative auto-regressive model that integrates visual comprehension and generation by tokenizing images into discrete codes and learning them alongside text tokens. This multimodal large language model operates within a shared feature space, allowing for seamless understanding and generation without relying on external visual embeddings. Liquid is available in multiple sizes and explores the scaling laws of multimodal models, revealing mutual benefits between understanding and generation tasks.

The article presents a collection of Foundation Vision Models developed by NVIDIA, which integrate various models such as CLIP, DINOv2, and SAM for enhanced image feature extraction. Several versions of these models are listed, including their sizes and update statuses, indicating ongoing development and improvements.

OLMo 2 1B is the smallest model in the OLMo 2 family, featuring a transformer-style architecture with 4 trillion training tokens. It supports multiple models and fine-tuning options, and is designed for language modeling applications. The model and its associated resources are available on GitHub under an Apache 2.0 license.

Google has launched Gemini, a new deep thinking AI model designed to enhance reasoning capabilities by testing multiple ideas in parallel. This advancement aims to improve decision-making processes and could significantly impact various applications in AI technology.

MAGI-1 is an autoregressive video generation model that creates videos by predicting sequences of fixed-length video chunks, achieving high temporal consistency and scalability. It incorporates innovations such as a transformer-based variational autoencoder and a unique denoising algorithm, enabling efficient and controllable video generation from text or images. The model has shown state-of-the-art performance in both instruction following and physical behavior prediction compared to existing models.

The content of the article appears to be corrupted, making it impossible to derive a coherent summary or understand the key points being discussed. The text is filled with nonsensical characters and lacks any clear structure or information related to inference batching or deep learning techniques.

A novel image generation approach called Next Visual Granularity (NVG) is introduced, which decomposes images into structured sequences to progressively refine them from a global layout to fine details. The NVG framework allows for high-fidelity and diverse image generation by utilizing a hierarchical representation that guides the process based on input text and current canvas. Extensive training on the ImageNet dataset demonstrates NVG's superior performance compared to previous models, with clear scaling behavior and improved FID scores.

The tutorial presents Microjax, a JAX-based library inspired by Andrej Karpathy's Micrograd, highlighting its functional programming style. It simplifies concepts from Matthew J Johnson's earlier work on autograd and encourages users to run the provided notebook on their own or via Colab. The author emphasizes the advantages of JAX over PyTorch in this context.

The article describes the implementation of the DeepSeek R1-zero style training for large language models (LLMs) using a single or multiple GPUs, with a focus on simplicity and efficiency. It highlights the capabilities of the nanoAhaMoment project, which includes full parameter tuning, multi-GPU support, and a full evaluation suite, while maintaining competitive performance with minimal complexity. The repository offers interactive Jupyter notebooks and scripts for training, complete with installation instructions and dependency management.

FlexTok is a method for resampling images into 1D token sequences of flexible length, with official implementations and pre-trained models available on GitHub. The repository includes instructions for installation, usage examples, and model checkpoints, emphasizing the importance of using trusted sources for loading checkpoints due to potential security vulnerabilities. Users can easily integrate the FlexTok tokenizer and VAE inference into their projects using provided code snippets and Jupyter notebooks.

Deep Think with Confidence (DeepConf) is a novel parallel thinking method that improves reasoning performance and efficiency of large language models (LLMs) by utilizing internal confidence signals to filter out low-quality reasoning traces. It can be integrated into existing frameworks without the need for additional training or tuning, achieving up to 99.9% accuracy on the AIME 2025 dataset while significantly reducing token generation. A real-time demo is available using the Qwen3-8B model with parallel thinking on the HMMT'25 dataset.

The article presents the Decoupled Diffusion Transformer (DDT) architecture, demonstrating improved performance with a larger encoder in a diffusion model framework. It achieves state-of-the-art FID scores on ImageNet benchmarks and allows for accelerated inference by reusing encoders across steps. The implementation provides detailed configurations for training and inference, along with online demos.

Reinforcement Learning (RL) has emerged as a new training paradigm for AI models, but it is significantly less information-efficient compared to traditional pre-training methods. This shift poses challenges, as RL requires much longer sequences of tokens to glean minimal information, potentially hindering progress in developing advanced AI capabilities. The article emphasizes the implications of this inefficiency for future AI scaling and performance.

TCANet is a novel end-to-end model designed for motor imagery EEG signal decoding, enhancing the capabilities of existing frameworks like CTNet and MSCFormer. It employs a combination of multi-scale CNN, temporal convolutional networks, and multi-head self-attention to effectively capture spatiotemporal dependencies, achieving high classification accuracies on BCI IV-2a and IV-2b datasets. The model demonstrates competitive performance in both subject-dependent and subject-independent settings, indicating its potential for advancing brain-computer interface systems.

A Deep Hierarchical Ensemble Network (DHEN) is proposed for predicting conversion rates in ad-recommendation systems, addressing challenges such as feature-crossing module selection, model depth and width, and hyper-parameter tuning. The authors introduce a multitask learning framework utilizing DHEN, enhance prediction through user behavior sequences, and implement a self-supervised auxiliary loss to tackle label sparseness, achieving state-of-the-art performance in CVR prediction.

MaskMark is a novel framework for image watermarking that offers two variants: MaskMark-D for global and local watermark extraction, and MaskMark-ED for enhanced robustness in localized areas. It employs a masking mechanism during the decoding and encoding stages to improve accuracy and adaptability while maintaining high visual quality. Experimental results demonstrate its superior performance over existing models, requiring significantly less computational cost.

The article discusses the development of a deep research agent using advanced AI techniques to enhance information retrieval and analysis. It emphasizes the importance of natural language processing and machine learning in creating an effective research tool capable of synthesizing large volumes of data. The potential applications and benefits of such technology in various fields are explored.

MingTok introduces the first continuous unified tokenizer for vision, enabling seamless integration of image understanding and generation within a single framework. This innovation leads to 3.5x faster convergence by aligning semantic understanding and generative dynamics, allowing for efficient multi-turn interactions without the costly detours seen in previous models. Ming-UniVision, built on MingTok, effectively harmonizes these tasks, paving the way for more intuitive multimodal AI systems.

The paper critiques the tendency in deep learning research to create isolated explanations for phenomena like double descent and the lottery ticket hypothesis, arguing that these explanations often lack relevance in practical applications. Instead, it suggests that such phenomena should be viewed as opportunities to enhance broader theoretical understanding of deep learning, and offers recommendations for aligning research efforts with the field's overall progress.

IDInit is a novel initialization method for neural networks that maintains identity transitions within layers, enhancing convergence, stability, and performance during training. By employing a padded identity-like matrix and addressing issues like dead neurons, IDInit offers a straightforward yet effective approach applicable to various deep learning models and large-scale datasets.

DeepSeek-V3, trained on 2,048 NVIDIA H800 GPUs, addresses hardware limitations in scaling large language models through hardware-aware model co-design. Innovations such as Multi-head Latent Attention, Mixture of Experts architectures, and FP8 mixed-precision training enhance memory efficiency and computational performance, while discussions on future hardware directions emphasize the importance of co-design in advancing AI systems.

Noisy labels can hinder the training of deep neural networks, leading to inaccuracies. The proposed $\epsilon$-softmax method modifies the softmax layer's outputs to approximate one-hot vectors with a controllable error, enhancing noise tolerance while maintaining a balance between robustness and effective learning through a combination with symmetric loss functions. Extensive experiments indicate its effectiveness in addressing both synthetic and real-world label noise.

The paper presents BLIP3-o, a family of fully open unified multimodal models that enhance both image understanding and generation. It introduces a diffusion transformer for generating CLIP image features, advocates for a sequential pretraining strategy, and proposes a high-quality dataset, BLIP3o-60k, to improve performance across various benchmarks. The models, along with code and datasets, are open-sourced to foster further research.

Opacus has enhanced its capabilities for private training of large-scale models by introducing Fully Sharded Data Parallelism (FSDP) along with Fast Gradient Clipping (FGC) and Ghost Clipping (GC). These advancements improve memory efficiency and scalability for training large models, allowing for greater batch sizes and reduced memory consumption compared to previous methods like Differentially Private Distributed Data Parallel (DPDDP). The article details the implementation of FSDP with Opacus and provides insights on memory and latency performance.

HunyuanImage-3.0 has been released as an open-source image generation model, featuring a unified multimodal architecture that integrates text and image understanding. It boasts the largest Mixture of Experts model with 80 billion parameters, enabling superior image generation capabilities while supporting extensive customization through various checkpoints and performance optimizations.

The repository provides an implementation of the method "Learning Compact Vision Tokens for Efficient Large Multimodal Models," which enhances inference efficiency by fusing spatial-adjacent vision tokens and introducing a Multi-Block Token Fusion module. Experimental results show that this approach achieves competitive performance on various vision-language benchmarks while using only 25% of the baseline vision tokens.

REverse-Engineered Reasoning (REER) introduces a novel approach to instilling deep reasoning in language models by working backwards from known solutions to discover the underlying reasoning process. This method addresses the limitations of traditional reinforcement learning and instruction distillation, resulting in the creation of a large dataset, DeepWriting-20K, and a model, DeepWriter-8B, that outperforms existing models in open-ended tasks. The research emphasizes the importance of structured reasoning and iterative refinement in generating high-quality outputs.

Code Researcher is a deep research agent designed for navigating and modifying large systems codebases by generating patches to address crashes. It utilizes multi-step reasoning and structured memory to gather context from the code and its commit history, outperforming existing models in crash resolution rates. The experiments demonstrate its effectiveness and generalizability across different codebases, emphasizing the importance of comprehensive context gathering in code modification tasks.

The article discusses the development of DINOv3, a self-supervised vision model that enhances understanding of visual data without the need for labeled datasets. It elaborates on its architecture, training methods, and potential applications in various fields, showcasing improvements over previous iterations in accuracy and efficiency.

The deepagents Python package enables users to create advanced agents that can plan and execute complex tasks by utilizing a combination of tools, subagents, and a planning tool. It enhances the capabilities of traditional agents by incorporating features like context management, task decomposition, and long-term memory. This allows for more sophisticated interactions and workflows in applications such as research and data analysis.

The article discusses the advancements in relational graph transformers, emphasizing their ability to capture intricate relationships in data. It explores how these models improve performance in various tasks by leveraging relational structures, enhancing both representation and learning capabilities. The research highlights the potential of combining graph-based approaches with transformer architectures for better outcomes in machine learning applications.

DeepNVMe has been updated to enhance I/O performance in deep learning applications by improving checkpointing with FastPersist and model inference with ZeRO-Inference. These advancements include support for CPU-only environments, offset-based I/O operations, and tensor data type casting, along with significant speedups facilitated by Gen5 NVMe SSDs. The updates aim to democratize access to large models and optimize I/O-bound workloads for various users.

PixelFlow introduces a novel family of image generation models that operate directly in pixel space, eliminating the need for pre-trained VAEs and allowing for end-to-end training. By utilizing efficient cascade flow modeling, it achieves impressive image quality with a low FID score of 1.98 on the ImageNet benchmark, showcasing its potential for both class-to-image and text-to-image tasks. The model aims to inspire future advancements in visual generation technologies.

A novel model called KITPose has been developed for general mammal pose estimation, focusing on structure-supporting dependencies among keypoints. The model incorporates keypoint-specific clues and introduces techniques such as Generalised Heatmap Regression Loss and adaptive weighting to enhance performance, achieving state-of-the-art results in various datasets.

The article discusses Andrej Karpathy's recent talk at Y Combinator, where he shares insights on artificial intelligence, deep learning, and the future direction of AI technology. He emphasizes the importance of understanding AI's capabilities and limitations, as well as the ethical considerations that come with its advancement.

NUMA (Non-Uniform Memory Access) awareness is crucial for optimizing high-performance deep learning applications, as it impacts memory access patterns and overall system efficiency. By understanding NUMA architecture and implementing strategies that leverage it, developers can significantly enhance the performance of deep learning models on multi-core systems.

The olmOCR-2-7B-1025 model is a fine-tuned version of Qwen2.5-VL-7B-Instruct, designed to enhance optical character recognition (OCR) capabilities, especially for complex cases like math equations and tables. It is recommended to use the FP8 version for practical applications and can handle large-scale document processing through the olmOCR toolkit. The model demonstrates high performance on various OCR benchmarks.

DeerFlow is a community-driven deep research framework that integrates language models with specialized tools for web search, crawling, and Python code execution. It supports one-click deployment through Volcengine, features a modular multi-agent system for automated research tasks, and includes capabilities like text-to-speech and report generation. Users can explore its functionalities through a web UI and configure various search engines for tailored experiences.

The article explores the concept of test-time compute in deep learning, particularly how models can improve their performance by engaging in a more extended reasoning process akin to human thinking. It discusses various strategies for enhancing model output through methods like chain-of-thought reasoning, parallel sampling, and sequential revision, emphasizing the balance between computational resources and accuracy in problem-solving.

DeepSomatic is an AI tool developed by Google Research that accurately identifies cancer-related genetic mutations in tumor cells, enhancing the precision of cancer treatment plans. By leveraging machine learning and a comprehensive training dataset, DeepSomatic outperforms existing methods in detecting somatic variants across various cancer types. This tool aims to expedite cancer research and improve personalized medicine approaches.

ParetoQ is a novel algorithm for low-bit quantization of large language models, unifying binary, ternary, and 2-to-4 bit quantization-aware training. It achieves state-of-the-art performance across all bit widths and offers a reliable framework for comparing quantization methods, demonstrating that lower-bit quantization can surpass traditional 4-bit methods in both accuracy and efficiency. The integration of ParetoQ into the torchao library facilitates easy deployment on edge devices while optimizing accuracy and compression trade-offs.

Fine-tuning an instruction-tuned LLM (Qwen2.5B) for reasoning tasks is achieved using a cost-effective pipeline inspired by DeepSeek R1, implementing Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) on AWS SageMaker. The article details the training stages, reward function design, and experimental outcomes, providing guidance for replicating the results and utilizing the associated codebase.

WebThinker is a deep research framework that enhances large reasoning models (LRMs) by enabling them to autonomously search the web, navigate pages, and draft research reports. It integrates various features such as a Deep Web Explorer and an Autonomous Think-Search-and-Draft strategy, significantly improving the efficiency of information gathering for researchers. The framework has been recognized in academic circles, with its paper accepted at NeurIPS 2025, and is now available for deployment on platforms like Hugging Face.

Trusted Deep Research GenAI offers financial analysts a powerful tool to reduce research time and automate repetitive tasks using over 20 premium AI models. It enhances complex work capabilities with expanded file uploads and analysis, ensuring high-quality results for challenging research tasks. Companies worldwide rely on You.com for these advanced AI solutions.

The article explores the architecture and functionality of NVIDIA GPUs, detailing their compute cores, memory hierarchy, and comparison with TPUs. It emphasizes the importance of Tensor Cores for matrix multiplication in modern machine learning tasks and outlines the evolution of GPU specifications across generations. The content builds on previous chapters, providing a comprehensive understanding of GPU capabilities in the context of large language models.

The Low-to-high Multi-Level Transformer (LMLT) introduces a novel approach for image super-resolution that reduces the complexity and inference time associated with existing Vision Transformer models. By employing attention mechanisms with varying feature sizes and integrating results from lower heads into higher heads, LMLT effectively captures both local and global information, mitigating issues related to window boundaries in self-attention. Experimental results indicate that LMLT outperforms state-of-the-art methods while significantly reducing GPU memory usage.

Efficient backpropagation (BP) is a fundamental technique in deep learning, first introduced by Seppo Linnainmaa in 1970, building on earlier concepts by Henry J. Kelley in 1960 and others. Despite its origins, BP faced skepticism for decades before gaining acceptance as a viable training method for deep neural networks, which can now efficiently optimize complex models. The article highlights the historical development of BP and addresses misconceptions surrounding its invention and application in neural networks.

RoWeeder is an innovative framework designed for unsupervised weed mapping that combines crop-row detection with a robust deep learning model. It creates pseudo-ground truth using crop-row information, enabling effective differentiation between crops and weeds, achieving an F1 score of 75.3 on the WeedMap dataset. The integration of RoWeeder with drone technology allows for real-time aerial surveys, enhancing weed management in agriculture.

The paper discusses the limitations of traditional gradient descent analysis in deep learning and introduces a new understanding of its dynamics, particularly how gradient descent operates effectively in regions where the sharpness of the loss landscape is less than a certain threshold. It highlights the phenomenon of training at the edge of stability, where gradient descent oscillates but eventually stabilizes, challenging conventional optimization theories.

CogView4-6B is a text-to-image generation model that supports a range of resolutions and offers optimized memory usage through CPU offloading. The model has demonstrated impressive performance benchmarks compared to other models like DALL-E 3 and SDXL, achieving high scores across various evaluation metrics. Users can install the necessary libraries and use a provided code snippet to generate images based on detailed prompts.

RecML is a high-performance, open-source library designed for building and deploying large-scale deep learning recommender systems, optimized for Cloud TPUs and GPUs. It offers state-of-the-art model implementations, a user-friendly API, and flexible architecture to support massive datasets while addressing common challenges in recommendation tasks. Additionally, it emphasizes community collaboration and provides tools for efficient training, evaluation, and deployment.

The article describes a GitHub repository for a free book titled "Neural Networks For Chess," which explores deep-learning techniques in chess, including the workings of engines like AlphaZero and Stockfish NNUE. The book covers various fundamental topics in neural networks, classical search techniques, and offers practical implementation guidance through examples. The author encourages readers to contribute feedback and provides resources for setting up the necessary programming environment.