5 Powerful Python Decorators to Build Clean AI Code
This article outlines five particularly useful Python decorators that, based on developers' experience, haven proven themselves effective to make AI code cleaner.
TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.
IBM Releases Two Granite Speech 4.1 2B Models: Autoregressive ASR with Translation and Non-Autoregressive Editing for Fast Inference
IBM Releases Granite Speech 4.1 2B and Its Non-Autoregressive Twin — Compact ASR Models Built for Enterprise
The post IBM Releases Two Granite Speech 4.1 2B Models: Autoregressive ASR with Translation and Non-Autoregressive Editing for Fast Inference appeared first on MarkTechPost.
This AI knew the answers but didn’t understand the questions
For decades, psychologists have debated whether the human mind can be explained by one unified theory or must be broken into separate parts like memory and attention. A recent AI model called Centaur seemed to offer a breakthrough, claiming it could mimic human thinking across 160 different cognitiv...
Cursor Introduces a TypeScript SDK for Building Programmatic Coding Agents With Sandboxed Cloud VMs, Subagents, Hooks, and Token-Based Pricing
Cursor Launches TypeScript SDK to Let Developers Build and Deploy Programmatic Coding Agents
The post Cursor Introduces a TypeScript SDK for Building Programmatic Coding Agents With Sandboxed Cloud VMs, Subagents, Hooks, and Token-Based Pricing appeared first on MarkTechPost.
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
arXiv:2604.25942v1 Announce Type: new
Abstract: Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework that combines engineered 12-lead ECG timeseries f...
A Randomized PDE Energy driven Iterative Framework for Efficient and Stable PDE Solutions
arXiv:2604.25943v1 Announce Type: new
Abstract: Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineering applications, yet existing numerical solvers rely heavily on matrix based discretizations, while learning based methods require costly trai...
A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
arXiv:2604.25972v1 Announce Type: new
Abstract: In multi-agent reinforcement learning (MARL), the integration of a communication mechanism, allowing agents to better learn to coordinate their actions and converge on their objectives by sharing information. Based on an interaction graph, a subclass ...
Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective
arXiv:2604.25975v1 Announce Type: new
Abstract: Key-value (KV) caching is essential for large language model inference, yet its memory overhead poses a critical bottleneck for long-context generation. Existing eviction policies predominantly rely on empirical heuristics, lacking a rigorous theoreti...
Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital
arXiv:2604.26091v1 Announce Type: new
Abstract: We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX Terminal Pro, a 21-day deployment in which 3,505 user-funded agents traded real ETH in a bounded on...
Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
arXiv:2604.26095v1 Announce Type: new
Abstract: {Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under strict time constraints.} {The core challenge lies in the belief-space ob...
Evaluating Strategic Reasoning in Forecasting Agents
arXiv:2604.26106v1 Announce Type: new
Abstract: Forecasting benchmarks produce accuracy leaderboards but little insight into why some forecasters are more accurate than others. We introduce Bench to the Future 2 (BTF-2), 1,417 pastcasting questions with a frozen 15M-document research corpus in whic...
Hierarchical Multi-Persona Induction from User Behavioral Logs: Learning Evidence-Grounded and Truthful Personas
arXiv:2604.26120v1 Announce Type: new
Abstract: Behavioral logs provide rich signals for user modeling, but are noisy and interleaved across diverse intents. Recent work uses LLMs to generate interpretable natural-language personas from user logs, yet evaluation often emphasizes downstream utility,...
OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms
arXiv:2604.26211v1 Announce Type: new
Abstract: In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that starts at idea generation and ends with executable code. Our system combines structured meta-prompt...
Amazon’s cloud business is surging — and so is its capital spending
The e-commerce giant is making more money than expected from AWS but it's also spending a lot, and will continue to do so in the near term, its chief executive said.
AI Weekly Issue #488: OpenAI lost three things in five days
Quick Hits
Musk takes the stand in the OpenAI trial: "If we make it OK to loot..." — Jury selection wrapped Monday; Musk testified Tuesday in the $134B suit to return OpenAI's assets to the nonprofit and oust Altman. Live coverage tracked every hour. The case will set the legal template for what co...
Bootstrapping Sign Language Annotations with Sign Language Models
AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to the prohibitive costs...
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, stat...