Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual information remain...
Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been driven lar...
We study parallel test-time scaling for long-horizon agentic tasks such as agentic search and deep research, where multiple rollouts are generated in parallel a...
Language change both reflects and shapes social processes, and the semantic evolution of foundational concepts provides a measurable trace of historical and soc...
Continuous diffusion models have achieved strong performance across domains such as images. However, in language modeling, prior continuous diffusion language m...
Kullback-Leibler (KL) divergence is a fundamental concept in information theory that quantifies the discrepancy between two probability distributions. In the co...
Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that imp...
General first-order methods (GFOM) are a flexible class of iterative algorithms which update a state vector by matrix-vector multiplications and entrywise nonli...
This paper reports an unexpected finding: in a deterministic hyperdimensional computing (HDC) architecture based on Galois-field algebra, a path-dependent seman...
Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest adva...
Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflow...
Matrix extensions have emerged as an essential feature in modern CPUs to address the surging demands of AI workloads. However, existing designs often incur subs...
Spiking Transformers, which combine the scalability of Transformers with the sparse, energy-efficient property of Spiking Neural Networks (SNNs), have achieved ...
The generation of sustained, open-ended complexity from local interactions remains a fundamental challenge in artificial life. Differentiable multi-agent system...
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often ...
As high-performance computing and AI workloads become increasingly dependent on GPUs, maintaining high performance across rapidly evolving hardware generations ...
This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through local in...
Cloud native architecture is about building and running scalable microservice applications to take full advantage of the cloud environments. Managed Kubernetes ...
We present this as a negative result with an explanatory mechanism, not as a formal upper bound. Predictive coding networks (PCNs) admit a K-way energy probe in...
The newly introduced continuous checkpointing feature in Orbax and MaxText is designed to optimize the balance between reliability and performance during model...
Large language models LLMs have fixed knowledge, being trained at a specific point in time. Software engineering practices are fast‑paced and change often, with...
Background Harvard‑trained neuroscientist and former Harvard Medical School professor Gideon Kreiman is leading a startup that aims to give humans “perfect and...
At the HumanX AI conference in San Francisco this week, thousands of tech professionals gathered at the Moscone Center to discuss how agentic AI is reshaping bu...
The Problem: Intelligence ≠ Empathy Modern AI is trained on massive datasets and refined through techniques like reinforcement learning from human feedback. Mo...
Tired of spending hours manually measuring photos, sourcing material prices, and calculating quotes? For handyman businesses, this back‑office work is a major p...
The Agent Development Kit ADK SkillToolset introduces a 'progressive disclosure' architecture that allows AI agents to load domain expertise on demand, reducing...
The newly introduced continuous checkpointing feature in Orbax and MaxText is designed to optimize the balance between reliability and performance during model...
In the previous articlehttps://dev.to/rijultp/understanding-transformers-part-4-introduction-to-self-attention-45bg we explored the self‑attention concept for t...
The Security Pretext The core of the Mythos marketing campaign is its “zero‑trust” security architecture. Anthropic insists that the model is designed to opera...
Image classification sounds easy until you remember that a computer never sees “objects.” It only sees pixel arrays. This post explains why that makes k‑NN a us...
MLP = A Function Not Layers Most people think neural networks are stacks of layers. They are wrong. An MLP is: y = fx; θ 👉 A learnable function. Start Simple...
Overview Every time I ask ChatGPT something simple, it gives me a clean, direct, confident answer. I find this deeply suspicious. Real thinking doesn’t work th...
The Real Problem Low training loss ≠ good model. The real goal: generalization. Optimization = Learning Optimization reduces loss by updating parameters. Witho...
The illustration for The New Yorker’s profile of OpenAI CEO Sam Altman is a jump scare. Altman stands in a blue sweater with a blank expression. Around his head...
!Cover image for Building Igris: Crafting My Personal AI Agent & Knowledge Codexhttps://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto...
“Every frontier model we evaluated lost money over the season and many experienced ruin,” the authors of the paper concluded, with the AI “systematically underp...
!https://www.bleepstatic.com/content/hl-images/2023/03/24/ChatGPT-logo.jpg OpenAI has rolled out a new Pro subscription that costs $100, matching Anthropic’s Cl...
Large language models LLMs have fixed knowledge, being trained at a specific point in time. Software engineering practices are fast‑paced and change often, with...
!https://9to5google.com/wp-content/uploads/sites/4/2024/03/Pixelated-cover.jpg?quality=82&strip=all&w=1600 Welcome to episode 95 of Pixelated, a podcast by 9to5...
Large language models (LLMs) undergo alignment training to avoid harmful behaviors, yet the resulting safeguards remain brittle: jailbreaks routinely bypass the...
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evide...
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content contains...
Vision-language models (VLMs) still struggle with visual perception tasks such as spatial understanding and viewpoint recognition. One plausible contributing fa...
Large Vision Language Models (LVLMs) achieve strong multimodal reasoning but frequently exhibit hallucinations and incorrect responses with high certainty, whic...
Recent advances in large language models (LLMs) have enabled the large-scale generation of highly fluent and deceptive news-like content. While prior work has o...
Donald Trump has spun the recent rescue of a downed airmanhttps://www.cbsnews.com/live-updates/iran-war-us-trump-warns-more-coming-oil-gas-strait-hormuz/ whose...
Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models can inf...
Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases. In practic...
