# Nov 5, 2025

- **Apple uses 3D Gaussian splatting for Personas and 3D conversions of photos**
  - **Apple's Vision Pro** introduces **Personas**, advanced virtual avatars created through 3D photo scans, enabling real-time interaction among users, marking a significant leap in VR technology.

- **Optimizing Datalog for the GPU**
  - The paper introduces a **hash-indexed sorted array** for efficient Datalog execution on GPUs, enhancing performance through optimized memory access patterns and reducing redundant computations during **Semi-naïve Evaluation**.

- **Kosmos: An AI Scientist for Autonomous Discovery**
  - **Kosmos** is an advanced AI scientist that automates **data-driven discovery**, executing up to **42,000 lines of code** and analyzing **1,500 papers** in a single run, significantly enhancing research efficiency.

- **Learning from Failure to Tackle Hard Problems**
  - **BaNEL (Bayesian Negative Evidence Learning)** leverages failed attempts to train generative models, addressing the challenge of **extremely sparse rewards** in complex problem-solving scenarios, such as drug discovery and theorem proving.

- **Building blobd: single-machine object store with sub-ms reads and 15 GB/s upload**
  - **blobd** is a newly designed single-machine object store that achieves **sub-millisecond** read latencies and **15 GB/s** upload speeds by leveraging **direct I/O**, **atomic writes**, and **io_uring** for efficient asynchronous operations, significantly outperforming traditional solutions like S3 and MinIO.

- **Deutsche Telekom and NVIDIA Launch Industrial AI Cloud — a ‘New Era’ for Germany’s Industrial Transformation**
  - **Deutsche Telekom and NVIDIA** have launched the **world’s first Industrial AI Cloud**, a sovereign platform designed to enhance Germany's industrial capabilities, set to go live in early 2026.

- **Trajectory Distillation for Foundation Models**
  - **Trajectory distillation** offers a **leaner alternative** to traditional reinforcement learning (RL) for post-training foundation models, achieving comparable performance at a **10× lower cost**.

- **DeepInverse Joins the PyTorch Ecosystem: the library for solving imaging inverse problems with deep learning**
  - **DeepInverse** is an open-source library that simplifies **deep learning** for imaging across various domains, including **medical imaging** and **computational photography**, by providing tools for image reconstruction and state-of-the-art neural networks.

- **Moral Uncertainty Around Emerging AI Introspection**
  - **Emerging AI models exhibit functional self-modeling**, suggesting they can describe their reasoning and detect internal inconsistencies, raising questions about the **empirical nature of consciousness** in AI.

- **Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities**
  - **Oolong** introduces a benchmark for **long-context reasoning** tasks, emphasizing the need for models to analyze and aggregate information from large text chunks rather than relying on selective retrieval methods.

- **MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning**
  - **MemSearcher** innovatively combines **current user queries** with a **compact memory**, enhancing reasoning and search efficiency while minimizing computational costs, thus addressing the limitations of traditional search agents.

- **Using Span Queries to Optimize for Cache and Attention Locality**
  - **Span queries** generalize inference server interfaces, enabling diverse workloads like chat and RAG to be expressed as **expression trees** with commutativity constraints, enhancing flexibility and performance.

- **Apriel-H1: Towards Efficient Enterprise Reasoning Models**
  - The **Apriel-H1** family of hybrid LLMs combines **transformer attention** and **State Space Models (SSMs)**, achieving **15B model size** with linear inference complexity and improved throughput for reasoning tasks.

- **Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks**
  - **Federated Attention (FedAttn)** integrates the federated paradigm into self-attention mechanisms, enabling **privacy protection** and **efficiency** in collaborative LLM inference over edge networks.

- **The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute**
  - **Sequential scaling** outperforms parallel self-consistency in language model reasoning, achieving accuracy gains of up to **46.7%** across various configurations, as demonstrated through evaluations on five state-of-the-art models and three reasoning benchmarks.