# Jun 17, 2025

- **Unsupervised Elicitation of Language Models**  
  The **Internal Coherence Maximization (ICM)** algorithm fine-tunes pretrained language models using their own generated labels, achieving performance comparable to **golden supervision** and surpassing **crowdsourced human supervision** on various tasks. [Link to article](https://arxiv.org/abs/2506.10139)

- **Nanonets-OCR-s**  
  **Nanonets-OCR-s** is a cutting-edge **image-to-markdown OCR model** that intelligently transforms documents into structured markdown, enhancing compatibility with **Large Language Models (LLMs)** for advanced processing.

- **Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task**  
  **Cognitive debt accumulates** when using AI tools like ChatGPT for essay writing, as evidenced by participants showing weaker neural connectivity and lower ownership of their work compared to those using traditional methods.

- **We're expanding our Gemini 2.5 family of models**  
  **Gemini 2.5** now includes **Flash** and **Pro** models, which are generally available, alongside the introduction of **2.5 Flash-Lite**, the fastest and most cost-efficient model yet.

- **The drawbridges come up: the dream of a interconnected context ecosystem is over**  
  **MCPs** are emerging as a pivotal technology, echoing the **Web 2.0** vision of interconnected services, yet they face increasing restrictions that hinder competition and innovation.

- **How you breathe is like a fingerprint that can identify you**  
  **Breathing patterns are unique** to individuals, akin to fingerprints, and can reveal insights into their **physical and mental health**, including correlations with BMI and anxiety levels.

- **AMD's CDNA 4 Architecture Announcement**  
  **AMD’s CDNA 4 architecture** enhances matrix multiplication performance for machine learning by optimizing lower precision data types, while maintaining a competitive edge in vector operations through a familiar chiplet design.

- **Programming Language Design in the Era of LLMs: A Return to Mediocrity?**  
  **LLMs challenge traditional DSL design** by raising the question of necessity; if LLMs can generate code efficiently, why invest in creating domain-specific languages that eliminate boilerplate?

- **Vision Transformers Don't Need Trained Registers**  
  **Vision Transformers** can effectively operate without **trained registers**, as demonstrated in recent research that explores artifacts in attention and feature maps, paralleling findings in **large language models**.

- **Breaking Quadratic Barriers: A Non-Attention LLM for Ultra-Long Context Horizons**  
  The proposed **non-attention architecture** for large language models (LLMs) can efficiently manage **context windows** of hundreds of thousands to millions of tokens, circumventing the **quadratic memory** issues of traditional Transformers.

- **Time Series Forecasting with Graph Transformers**  
  **Graph Transformers enhance time series forecasting** by leveraging interconnected data from relational databases, allowing for more accurate predictions through a comprehensive end-to-end pipeline that integrates various signals from the entire graph structure.

- **Research Scientists + Engineers for Generative AI at NVIDIA**  
  NVIDIA is seeking **senior and principal research scientists** to advance **generative AI**, focusing on **training and deploying frontier-scale models** and optimizing architectures for enhanced AI performance.

- **Why Claude Code feels like magic?**  
  **Claude Code feels like magic** due to its **iterative nature**, allowing it to explore the entire solution space rapidly, enhancing user experience without increasing model intelligence.

- **Voyager: Real-Time Splatting City-Scale 3D Gaussians on Your Phone**  
  **Voyager** enables **real-time city-scale 3D Gaussian Splatting** on mobile devices by streaming only the necessary Gaussians, significantly reducing data transfer needs and improving rendering speed.

- **Real-time action chunking with large models**  
  **Real-time chunking (RTC)** enables robots to execute actions without delays or discontinuities, significantly improving performance in dynamic tasks like striking a match or plugging in an Ethernet cable, even under high latency conditions exceeding 300 milliseconds.