# Jun 4, 2025

Daily

- **Deep learning gets the glory, deep fact checking gets ignored**

- **Deep learning models, despite their glamour, can produce significant errors; a recent study revealed that a Transformer model made hundreds of incorrect predictions about enzyme functions, undermining its credibility.**

- **Covert Web-to-App Tracking via Localhost on Android**
  - **Meta and Yandex have developed a covert tracking method on Android that allows their apps to listen on local ports, enabling them to link web browsing data to user identities without consent.** This method exploits the Android OS's permission model, allowing JavaScript from websites to communicate with native apps via localhost sockets, effectively bypassing privacy protections.

- **Cloud Run GPUs, now GA, makes running AI workloads easier for everyone**
  - **Cloud Run GPUs are now generally available**, enabling developers to run AI workloads with **NVIDIA L4 GPUs** without needing quota requests, thus simplifying access to GPU acceleration for serverless applications.

- **A deep dive into self-improving AI and the Darwin-Gödel Machine**
  - The **Darwin-Gödel Machine (DGM)** represents a significant advancement in self-improving AI, allowing systems to iteratively modify their own code based on empirical performance rather than formal proofs, thus mimicking biological evolution.

- **Vision Language Models are Biased**
  - **Vision Language Models (VLMs) exhibit significant biases**, scoring an average of **17.05% accuracy** in counting tasks, revealing their inability to adapt to simple changes in visual stimuli, such as recognizing alterations in logos.

- **Doubling Down on Open Source**
  - **Langfuse has open sourced all remaining Product Features under the MIT license**, enhancing community collaboration and accelerating development cycles for LLM applications.

- **Human Brain Cells on Chip for Sale – First biocomputing platform hits the market**
  - **Cortical Labs** has launched the **world's first biocomputer**, utilizing human brain cells on a chip, with a price tag of **$35,000** per unit, marking a significant advancement in biocomputing technology.

- **[R]Time Blindness: Why Video-Language Models Can't See What Humans Can?**
  - **VLMs struggle** with temporal patterns when spatial information is obscured, as demonstrated by the introduction of **SpookyBench**, where humans achieve over **98% accuracy** while VLMs score **0%**.

- **AGI is not multimodal**
  - **AGI cannot be achieved through multimodal models** as they fail to address the essential need for a physical understanding of the world, which is crucial for solving real-world problems like motion planning and social coordination.

- **Autonomous drone defeats human champions in racing first**
  - **TU Delft's autonomous drone** triumphed at the A2RL Drone Championship, marking a historic first by defeating human champions with speeds reaching **95.8 km/h** on a challenging track, showcasing advanced AI capabilities in real-world conditions.

- **[R] Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space**
  - **Soft Thinking** introduces a novel method that enables **human-like reasoning** in LLMs by utilizing **continuous concept space**, allowing for smoother transitions and richer representations beyond discrete token limitations.

- **NVIDIA Blackwell Delivers Breakthrough Performance in Latest MLPerf Training Results**
  - **NVIDIA Blackwell architecture** achieved **breakthrough performance** in the latest MLPerf Training, outperforming previous generations by **2.2x** on the Llama 3.1 405B pretraining benchmark, showcasing its capability to handle demanding AI workloads.

- **[N] Nvidia’s Blackwell Conquers Largest LLM Training Benchmark**
  - **Nvidia's Blackwell GPUs** have achieved unprecedented success in the latest MLPerf training results, outperforming competitors across all six benchmarks, while AMD's MI325X has shown competitive performance on the popular LLM fine-tuning benchmark.

- **🤗KV Cache from scratch in nanoVLM**
  - **KV Caching in nanoVLM** enhances autoregressive generation efficiency, achieving a **38% speedup** by caching keys and values, thus avoiding redundant computations during token generation.

- **[R] GuidedQuant: Boost layer-wise PTQ methods using the end loss guidance (Qwen3, Gemma3, Llama3.3 / 2~4bit quantization) (ICML 2025)**
  - **GuidedQuant** enhances layer-wise **PTQ** methods by incorporating **end loss guidance**, leading to improved performance in quantization tasks across various models, including Qwen3 and Llama3.3.