Search: “research”

IBM Research demonstrated the capability to store and search across a database of 100 billion vectors, showcasing extreme-scale vector storage for AI applications. This work addresses the growing need

The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling their use across diverse hardware and software plat

Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabl

Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping t

IBM, as part of a global effort, is developing tools to streamline AI benchmarking by making results easier to compare, validate, and reuse. The initiative is detailed in a blog post titled 'All of AI

Anyone building with AI eventually hits the same tradeoff: how to get the most intelligence per dollar, the right model at the right cost for each task. That’s what DigitalOcean Inference Engine is bu

Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive chart

IBM Quantum's Q2 2026 updates likely showcase advancements in quantum computing hardware, software, or services, including new processor capabilities, improved error correction, expanded cloud access,

Google's NotebookLM is adding a new way to catch up on your notes: TikTok-style AI videos. The new feature is rolling out to Google AI Ultra and Pro subscribers, allowing NotebookLM to generate 60-sec

AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory

IBM's nanostack is a chip architecture that stacks multiple layers of transistors vertically to increase transistor density and computing power within a smaller physical footprint. This 3D approach en

Talos was built to help resolve a major bottleneck in genomic medicine: human review time. The open-source system recovered 90% of in-scope diagnoses while surfacing just 1.3 candidate variants per pa

Right now, MIT alumni and friends are voicing their support for: America’s scientific and technological leadership Merit-based admissions and affordable education Advances that increase US health, sec

The Fast Fourier Transform (FFT) is a computationally efficient algorithm that converts time-domain signals into their frequency-domain representation, reducing computational complexity from O(n²) to

What makes a robot gripper useful isn’t that it can pick up one object — it’s that it can pick up the next one, and the one after that, with a tool it’s never held before. What makes an autonomous veh

Google has open-sourced its flood forecasting framework, which replicates operational FloodHub model training settings and reflects methodology described in a 2024 Nature paper for global ungauged flo

As a deadly Ebola outbreak spreads through the Democratic Republic of Congo, health workers are dealing with the consequences of the Trump administration's cuts to global health programs and withdrawa

Understanding AI as an extension of human intelligence—not a replacement for it—offers a more grounded path for building trustworthy AI systems. The post Extending Human Intelligence Through AI appear

Building an AI app shouldn’t require a PhD in machine learning (ML) or months of wrestling with complex architectures. Yet that’s exactly what happens when you try to orchestrate multiple API calls, m

Vega turns a full credential into a single proof, sharing only what is needed and nothing more, with performance that works in real apps. The post Vega: Zero-knowledge proofs for digital identity in t

Last summer, Peter Degen's postdoctoral supervisor came to him with an unusual problem: One of his papers was being cited too much. Citations are the currency of academia, but there was something unus

mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy t

MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone

Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize fo

Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation h

Qiskit v2.4 represents the latest release of IBM's open-source quantum computing framework, introducing new features and improvements to the software development kit for quantum programming. The relea

IBM Quantum's Q1 2026 updates likely cover recent advances in quantum computing hardware, software, and applications, including announcements about processor improvements, new quantum algorithms, or e

Discover how Vultr values and rewards the ethical disclosure of security vulnerabilities with its new bug bounty program. Report issues, get compensated, and help improve Vultr's web ecosystem through

The author provided an update on several ongoing technical projects, including the ATOM Report, a new post-training course, and

This National Robotics Week, NVIDIA is highlighting the breakthroughs that are bringing AI into the physical world — as well as the growing wave of robots transforming industries, from agricultural an

For the past five years, the New Future of Work report has captured how work is changing. This year, the shift feels especially sharp. Previous editions have focused on technology’s role in increasing

Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to imp

Large Language Models (LLMs) have achieved state-of-the-art performance on a broad range of Natural Language Processing (NLP) tasks, including document processing and code generation. Autoregressive L

Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they unlock a host of advantages currently reserved fo

Large language models (LLMs) with integrated search tools show strong promise in open-domain question answering (QA), yet they often struggle to produce complete answer set to complex questions such a

Systematic failures of vision models on semantically coherent subsets, known as error slices, reveal limitations in robustness and evaluation. Existing slice discovery approaches largely model slices

Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algorithmic puzzles, we demonstrate that while decomposit

Scientists today face challenges of extraordinary scale and complexity. From shaping and simulating the intricate dynamics of fusion plasma, to exploring the vast search space of new materials, to mak

Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval–generation optimization. In this work, we

Visual generative models (e.g., diffusion models) typically operate in compressed latent spaces to balance training efficiency and sample quality. In parallel, there has been growing interest in lever

SensorFM is a foundation model that analyzes multimodal wearable sensor signals from devices like smartwatches and fitness trackers to generate insights into health and activities. The model was train

While large language models (LLMs) and coding agents are often applied to user interface (UI) development, developers find it difficult to reliably assess their proficiency in visual and interaction d

This study focuses on Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text, with both modalities aligned to the text conditions. Despite progress

The web is complex, open-ended, and constantly changing, making it challenging to scale training data for visual web agents. Existing data collection attempts remain limited to offline trajectories fo

Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of expert paths—the sequence