# Jan 22, 2025

- **Stargate Project: SoftBank, OpenAI, Oracle, MGX to build data centers**  
  Trump announces a partnership involving OpenAI, Oracle, and SoftBank to invest **up to $500 billion** in AI infrastructure, with initial funding of **$100 billion** aimed at building data centers in Texas, reflecting a significant commitment to advancing AI technology in the U.S.

- **Tensor Product Attention Is All You Need**  
  Tensor Product Attention (TPA) leverages tensor decompositions to compactly represent queries, keys, and values, drastically reducing **key-value cache size** during inference, thus enhancing memory efficiency.

- **Hunyuan3D 2.0 – High-Resolution 3D Assets Generation**  
  Hunyuan3D 2.0 is a cutting-edge **3D synthesis system** that integrates a shape generation model and a texture synthesis model, enabling the creation of high-resolution, textured 3D assets efficiently.

- **Infinigen**  
  Infinigen is a **procedural generator** of 3D scenes, developed by the [Princeton Vision & Learning Lab](https://pvl.cs.princeton.edu/), that creates diverse, high-quality training data optimized for **computer vision research** using **real geometry** without faked details.

- **Flame: A small language model for spreadsheet formulas (2023)**  
  FLAME is a **transformer-based model** specifically designed for **Excel formulas**, achieving competitive performance with only **60M parameters** and significantly less training data compared to larger models.

- **Arm releases Chiplet System Architecture spec beta version**  
  Arm's Chiplet System Architecture (CSA) has released its first public specification, engaging over **60 companies** to standardize chiplet designs, enhancing flexibility and reducing costs in silicon production.

- **Show HN: BrowserAI – Run LLMs directly in browser using WebGPU (open source)**  
  BrowserAI enables **local execution of LLMs** in the browser, ensuring **data privacy** and eliminating server costs, making it ideal for developers and researchers alike.

- **How to solve computational science problems with AI: PINNs**  
  Physics-Informed Neural Networks (PINNs) effectively integrate **physical laws** into neural network training, enabling the solution of complex **partial differential equations (PDEs)** with improved accuracy and efficiency.

- **[R] Learning to Continually Learn with the Bayesian Principle**  
  Novel meta-continual learning framework combines the **representational power of neural networks** with the **robustness of Bayesian models**, effectively preventing catastrophic forgetting during continual learning.

- **From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning**  
  Aggregation Fine-Tuning (AFT) enhances large language models by synthesizing multiple draft responses into a single, refined answer, demonstrating significant performance improvements over standard supervised fine-tuning (SFT).

- **Into the Omniverse: OpenUSD Workflows Advance Physical AI for Robotics, Autonomous Vehicles**  
  OpenUSD workflows are enhancing **physical AI** capabilities, enabling robots and autonomous vehicles to understand and interact with the real world through advanced simulation environments that replicate physical dynamics and spatial relationships.

- **Test-time regression: a unifying framework for designing sequence models with associative memory**  
  Test-time regression serves as a unifying framework for sequence models, revealing that **associative recall** is essential for effective performance, bridging the gap between various architectures like transformers and recurrent networks.

- **AdaServe: SLO-Customized LLM Serving with Fine-Grained Speculative Decoding**  
  AdaServe is the first LLM serving system that enables **SLO customization** through **fine-grained speculative decoding**, enhancing token prediction accuracy and throughput.