# Oct 1, 2025 ## High-resolution efficient image generation from WiFi Mapping - **LatentCSI** innovatively generates high-resolution images from **WiFi CSI measurements** using a **pretrained latent diffusion model**, streamlining the process compared to traditional GANs by mapping CSI amplitudes directly into latent space. ## Building a 30 PB storage cluster in the heart of SF - **30 petabytes of data storage** were built for under **$500,000** in San Francisco, enabling the pretraining of models on video data, which requires **500 times more storage** than text-based models like LLaMa-405B. ## The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain - **Dragon Hatchling (BDH)** is a novel Large Language Model that integrates biologically inspired networks, enhancing **interpretability** while maintaining **Transformer-like performance** across various tasks, rivaling GPT-2 with **10M to 1B parameters**. ## Unix philosophy and filesystem access makes Claude Code amazing - **Claude Code** merges a terminal-based Unix command interface with filesystem access, enabling LLMs to possess **persistent memory** and seamless tool chaining, thus evolving into a robust agentic operating system for coding and note-taking. ## Tiny worlds: A minimal implementation of DeepMind's Genie world model - **TinyWorlds** is an **autoregressive world model** leveraging Google Deepmind's **Genie Architecture** to infer actions between video frames without prior action data, enhancing scalability in world modeling. ## TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning - **TruthRL** is a novel reinforcement learning framework that enhances the **truthfulness** of large language models (LLMs) by optimizing for both accuracy and the ability to abstain from answering when uncertain, thus reducing hallucinations by **28.9%** and improving truthfulness by **21.1%**. ## Why Is Python So Popular in 2025? – The PyCharm Blog - **Python's dominance in AI and machine learning** is underscored by 41% of developers using it specifically for these applications, driven by its robust ecosystem of frameworks like TensorFlow and PyTorch that facilitate seamless integration into MLOps pipelines. ## How Quantum Computing’s Biggest Challenges Are Being Solved With Accelerated Computing - **NVIDIA's CUDA-X libraries** are revolutionizing quantum computing by enabling accelerated computing to tackle critical challenges like error correction and circuit optimization, thus paving the way for practical quantum applications. ## [D] Reverse-engineering Flash Attention 4 - **Flash Attention 4 (FA4)** enhances Transformer performance, achieving **up to 22% faster** execution than NVIDIA's cuDNN library through advanced CUDA kernels designed for attention layers. ## [R] A Predictive Approach To Enhance Time-Series Forecasting - **Future-Guided Learning** enhances time-series forecasting by utilizing a **dynamic feedback mechanism** that aligns a forecasting model with future data, significantly improving prediction accuracy. ## OpenTSLM: Language Models That Understand Time-Series (Stanford, ETH, Google) - **OpenTSLM** introduces **Time-Series Language Models (TSLMs)**, which enable AI to reason about temporal data alongside traditional modalities, enhancing forecasting and explanation capabilities. ## 🤗Introducing RTEB: A New Standard for Retrieval Evaluation - The **Retrieval Embedding Benchmark (RTEB)** aims to establish a new standard for evaluating the retrieval accuracy of embedding models, addressing the limitations of existing benchmarks that often inflate performance through dataset overlap. ## [D] Looking for a paper I saw once about training task solving models that output human readable explanations - The paper discusses a **two-model approach** where the first model generates **human-readable explanations** from task encodings, while the second model utilizes these explanations to solve tasks, potentially enhancing interpretability in AI systems. ## Into the Omniverse: Open-Source Physics Engine and OpenUSD Advance Robot Learning - **NVIDIA's Newton physics engine** and enhanced Isaac GR00T models facilitate accelerated robot learning through **OpenUSD simulation workflows**, allowing for the training of numerous robot instances simultaneously using both real and synthetic data. ## AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size - **AdaBlock-dLLM** introduces a **training-free**, adaptive block size mechanism that enhances **decoding efficiency** by addressing the limitations of fixed block sizes in semi-autoregressive (semi-AR) approaches, leading to improved accuracy in large language models (LLMs).