Chris Patrick writes that SLAC researchers have built a neural-network method for compressing large scientific datasets while preserving fine details that conventional compression erases. The approach uses wavelet analysis to separate data features by scale, then encodes each scale separately through a neural network, enabling 10- to 100-fold file size reductions and selective decompression of only the regions a researcher needs.
- Published in Nature Machine Intelligence (August 24, 2026)
- Motivated by upcoming LCLS upgrades that will generate nearly one terabyte of data per second
- Tested successfully on X-ray diffraction data, solar magnetic field measurements, and photographs
- Neural networks trained on Perlmutter at NERSC (Lawrence Berkeley National Lab)
- Co-developers include researchers from UC Davis and Carnegie Mellon University
- Published in Nature Machine Intelligence (August 24, 2026)
- Motivated by upcoming LCLS upgrades that will generate nearly one terabyte of data per second
- Tested successfully on X-ray diffraction data, solar magnetic field measurements, and photographs
- Neural networks trained on Perlmutter at NERSC (Lawrence Berkeley National Lab)
- Co-developers include researchers from UC Davis and Carnegie Mellon University
Alibaba has open-sourced Qwen-UI-Agent, a GUI agent foundation model that operates across mobile, desktop, web, and deep-search environments on real hardware rather than relying on simulation. It achieves top benchmark results: 82.1% on MobileWorld, 79.5% on OSWorld-Verified, and first on WebArena. It also introduces MobileWorld-Real, a 400+ task benchmark on 100+ phones and 150+ apps, with a 92.2% success rate.
- Supports command-line execution alongside standard GUI operations and batches multiple actions into a single decision step to shorten trajectories.
- Built-in safety layer refuses illegal or high-risk requests outright and pauses at sensitive operations (payments, data deletion, privacy grants) for explicit user confirmation.
- Trained via online reinforcement learning on trajectories exceeding 100 steps, paired with adaptive curriculum learning to progressively tackle longer tasks.
- Supports command-line execution alongside standard GUI operations and batches multiple actions into a single decision step to shorten trajectories.
- Built-in safety layer refuses illegal or high-risk requests outright and pauses at sensitive operations (payments, data deletion, privacy grants) for explicit user confirmation.
- Trained via online reinforcement learning on trajectories exceeding 100 steps, paired with adaptive curriculum learning to progressively tackle longer tasks.
Ashish Vaswani et. al. introduce Transformers and Attention in this classic 2017 paper.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
An exploration into the history of conversational technology, tracing its roots from Joseph Weizenbaum's 1966 ELIZA experiment at MIT to modern large language models like ChatGPT and Claude. The article examines how the evolution from rule-based symbolic AI to probabilistic deep learning has changed human interaction with machines, often leading users to attribute human qualities to code. It specifically addresses the risks of "chatbot psychosis" and the danger of individuals relying on general-purpose generative models for mental health support when these systems are prone to hallucinations or reinforcing delusional beliefs.
* The transition from symbolic AI's explicit rules to modern deep learning
* Joseph Weizenbaum’s warning against humanizing machines via the ELIZA effect
* The psychological impact and risks of using large language models for emotional support
* The transition from symbolic AI's explicit rules to modern deep learning
* Joseph Weizenbaum’s warning against humanizing machines via the ELIZA effect
* The psychological impact and risks of using large language models for emotional support
Researchers demonstrate that recurrent cortical circuits generate traveling waves acting as a computational engine in the visual cortex. These neural traveling waves allow the brain to build internal representations of the external world, enabling the prediction of upcoming sensory inputs and the replay of temporal memories. This biological mechanism is functionally analogous to how large language models learn statistical structures from text; instead, these waves encode environmental regularities into synaptic networks through experience.
- Traveling waves modulate moment-to-moment perception.
- They enable the inference of causes for noisy sensory inputs.
- These dynamics allow for short-term predictions and memory replay.
- Traveling waves modulate moment-to-moment perception.
- They enable the inference of causes for noisy sensory inputs.
- These dynamics allow for short-term predictions and memory replay.
An overview of Kolmogorov-Arnold Networks (KAN) as an alternative paradigm to traditional neural network design. Unlike Multi-Layer Perceptrons (MLPs) that use fixed activation functions on nodes, KANs employ learnable univariate functions—specifically B-splines—on the edges between neurons. This approach allows for more accurate representations with fewer parameters and offers improved interpretability and potential for continual learning due to the local nature of spline basis functions.
* The mathematical distinction between Universal Approximation Theorem and Kolmogorov-Arnold Representation Theorem
* How B-splines enable learnable non-linear transformations on edges through control points
* Comparison with MLPs regarding parameter efficiency, performance, and training speed
* Advantages in interpretability and the mitigation of catastrophic forgetting via locality properties
* The mathematical distinction between Universal Approximation Theorem and Kolmogorov-Arnold Representation Theorem
* How B-splines enable learnable non-linear transformations on edges through control points
* Comparison with MLPs regarding parameter efficiency, performance, and training speed
* Advantages in interpretability and the mitigation of catastrophic forgetting via locality properties
>"One scale parameter determines accuracy in rotation-based vector quantization."
The article demonstrates how the earlier EDEN quantization method outperforms its "successor" TurboQuant by utilizing an analytically optimized scale factor for superior accuracy and bias correction.
* EDEN outperforms newer TurboQuant algorithms.
* Optimal scaling is a key differentiator.
* EDEN-biased minimizes reconstruction error (MSE).
* EDEN-unbiased ensures highly accurate estimation.
* Superior efficiency at low bit-widths.
* Ideal for LLM and KV cache optimization.
The article demonstrates how the earlier EDEN quantization method outperforms its "successor" TurboQuant by utilizing an analytically optimized scale factor for superior accuracy and bias correction.
* EDEN outperforms newer TurboQuant algorithms.
* Optimal scaling is a key differentiator.
* EDEN-biased minimizes reconstruction error (MSE).
* EDEN-unbiased ensures highly accurate estimation.
* Superior efficiency at low bit-widths.
* Ideal for LLM and KV cache optimization.
OpenMythos is an open-source PyTorch project by Kye Gomez that proposes a theoretical reconstruction of Anthropic's Claude Mythos architecture. Instead of standard transformer layers, it suggests a Recurrent-Depth Transformer (RDT) design where weights loop through multiple iterations to increase reasoning depth during inference. By combining Mixture-of-Experts with Multi-Latent Attention and stability constraints, the model achieves performance parity between 770M parameters and a 1.3B parameter standard transformer.
* open-source PyTorch reconstruction of claude mythos
* proposes recurrent-depth transformer architecture
* reasoning depth scales via inference-time loops rather than parameter count
* uses mixture-of-experts for domain breadth
* implements multi-latent attention to reduce memory usage
* employs lti injection and adaptive computation time for stability
* achieves 1.3b parameter performance with only 770m parameters
* open-source PyTorch reconstruction of claude mythos
* proposes recurrent-depth transformer architecture
* reasoning depth scales via inference-time loops rather than parameter count
* uses mixture-of-experts for domain breadth
* implements multi-latent attention to reduce memory usage
* employs lti injection and adaptive computation time for stability
* achieves 1.3b parameter performance with only 770m parameters
Personal website of Jamie Simon, a scientist specializing in fundamental theory for deep learning. He runs a research lab at the Redwood Center at UC Berkeley with funding from Imbue and recently completed his PhD under Mike DeWeese. The site serves as a hub for his scientific research, personal blog posts regarding science and life adventures, and custom-made puzzles.
Main topics:
* Deep learning fundamental theory
* Research publications
* Science and lifestyle blog
* Puzzle creation
Main topics:
* Deep learning fundamental theory
* Research publications
* Science and lifestyle blog
* Puzzle creation
A comprehensive curated collection of Large Language Model (LLM) architecture figures and technical fact sheets. This gallery provides a visual and data-driven overview of modern model designs, ranging from classic dense architectures like GPT-2 to advanced sparse Mixture-of-Experts (MoE) systems and hybrid attention models. Users can explore detailed specifications including parameter scales, context windows, attention mechanisms, and intelligence indices for various prominent models.
Key features include:
* Detailed architecture fact sheets for a wide array of models such as Llama, DeepSeek, Qwen, Gemma, and Mistral.
* An architecture diff tool to compare two different model designs side-by-side.
* Comparative analysis across dense, MoE, MLA, and hybrid decoder families.
* Links to original source articles and technical reports for deeper research.
Key features include:
* Detailed architecture fact sheets for a wide array of models such as Llama, DeepSeek, Qwen, Gemma, and Mistral.
* An architecture diff tool to compare two different model designs side-by-side.
* Comparative analysis across dense, MoE, MLA, and hybrid decoder families.
* Links to original source articles and technical reports for deeper research.
