# Stable Virtual Camera: Multi-View Video Generation with 3D Camera Control

**Stable Virtual Camera** is a **multi-view diffusion model** that converts 2D images into immersive 3D videos, allowing for **dynamic camera control** across various paths without complex preprocessing.

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# Karatsuba Matrix Multiplication and Its Efficient Hardware Implementations

The **Karatsuba algorithm**, when extended to matrix multiplication, not only retains its **reduced multiplication complexity** but also minimizes the overhead of additional operations, making it more efficient for larger datasets.

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# Preview: Amazon S3 Tables and Lakehouse in DuckDB

**DuckDB now supports Apache Iceberg REST Catalogs**, allowing seamless connections to **Amazon S3 Tables** and **Amazon SageMaker Lakehouse**, enhancing data accessibility for users.

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# Nvidia Dynamo: A Datacenter Scale Distributed Inference Serving Framework

**NVIDIA Dynamo** is a **high-throughput, low-latency inference framework** tailored for generative AI and reasoning models, supporting multiple inference engines like TRT-LLM and vLLM, while optimizing GPU performance through features like **dynamic scheduling** and **KV cache offloading**.

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# Introducing KBLaM: Bringing plug-and-play external knowledge to LLMs

**KBLaM** introduces a **novel integration** of structured knowledge bases into LLMs, utilizing a **rectangular attention mechanism** that allows for efficient, linear scaling without the need for costly retraining or complex retrieval modules.

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# RWKV-7 "Goose" with Expressive Dynamic State Evolution

**RWKV-7 "Goose"** achieves **state-of-the-art performance** in multilingual tasks at the **3 billion parameter scale**, outperforming other models despite training on significantly fewer tokens, while maintaining **constant memory usage** and **inference time** per token.

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# NVIDIA Accelerated Quantum Research Center to Bring Quantum Computing Closer

**NVIDIA's Accelerated Quantum Research Center (NVAQC)** aims to integrate **quantum processing units (QPUs)** with AI supercomputers, enhancing capabilities to tackle complex quantum computing challenges and advancing quantum error correction techniques.

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# [R] Jagged Flash Attention Optimization

**Jagged Flash Attention** optimizes large-scale recommendation systems, achieving **up to 9× speedup** and **22× memory reduction** compared to dense attention methods, marking a significant leap in performance.

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# The clustering behavior of sliding windows

**Clustering timeseries data** with a sliding window can lead to **unexpected failures** based on the relationship between window size and timeseries length, revealing critical insights into data preprocessing.

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# Notebooks as reusable Python programs

**marimo transforms notebooks into reusable Python programs**, allowing for version control with Git, testing with pytest, and execution as scripts, thus enhancing maintainability and interoperability in data workflows.

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# An early look at cryptographic watermarks for AI-generated content

**Cryptographic watermarks** for AI-generated content aim to ensure **robustness**, **undetectability**, and **unforgeability**, enhancing the identification of AI artifacts while maintaining quality.

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# [R] Forget Chain-of-Thought reasoning! Introducing Chain-of-Draft: Thinking Faster (and Cheaper) by Writing Less.

**Chain-of-Draft (CoD)** offers a **faster and cheaper** alternative to Chain-of-Thought (CoT) reasoning by significantly reducing the length of model prompts, thus minimizing token usage and computational costs.

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# ByteCraft: Generating video games and animations through bytes

**ByteCraft** generates executable video games and animations from text prompts by fine-tuning a **7B parameter LLM** (Qwen2.5) over **4 months** on **4 GPUs**, showcasing its potential despite challenges in byte-level accuracy.

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# [R] RWKV-7 "Goose" with Expressive Dynamic State Evolution

**RWKV-7 "Goose"** achieves **state-of-the-art performance** in multilingual tasks at the **3 billion parameter scale**, outperforming other models trained on more data while maintaining constant memory and inference time per token.

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# [R] SmolDocling: A Compact Vision-Language Model for Complete Document Element Recognition and Markup Generation

**SmolDocling** is an ultra-compact vision-language model that integrates a **2B parameter vision encoder** with a **5B parameter language decoder**, achieving **end-to-end document processing** while being significantly smaller than competitors.