Verily Secures $300 Mn Investment to Advance its Precision Health AI Strategy
Funding round led by Series X Capital, with additional backing from Alphabet, UCHealth, and the University of Colorado Anschutz Verily today announced a $300 million investment round led by Series X Capital, with participation from Alphabet, UCHealth, the University of Colorado Anschutz and other in...
AbbaDox AI Helps Imaging Centers Save Thousands of Staff Hours
AbbaDox CareFlow now automates order intake, patient scheduling, and follow-up coordination for outpatient imaging centers, with client deployments recovering more than 90 staff hours per month at a single site. AbbaDox, the only intelligent operations platform purpose-built for outpatient radiology...
PointClickCare Launches Next-Gen EHR for Practice Groups
Practice-first foundation, embedded Ambient Scribe, and seamless bi-directional data exchange eliminate clinical fragmentation and reduce clinician burden PointClickCare, a leading health tech company helping providers deliver exceptional care, today announced the launch of its next-generation elect...
Oso, Cyera: 96% Blind Spot in Unused AI Agent Permissions
New study reveals that nearly all enterprise permissions are left untouched by humans, creating a massive risk when agents inherit human access Oso, the agent permissions posture company, and Cyera, the world’s leading AI Security Platform, today released joint research analyzing permission usage ac...
For the past two years, I’ve been working with economist Ilan Strauss at the AI Disclosures Project. We started out by asking what regulators would need to know to ensure the safety of AI products that touch hundreds of millions of people. We are now exploring the missing mechanisms that are needed ...
The Bay Area’s animal welfare movement wants to recruit AI
In early February, animal welfare advocates and AI researchers gathered in stocking feet at Mox, a scrappy, shoes-free coworking space in San Francisco. Yellow and red canopies billowed overhead, Persian rugs blanketed the floor, and mosaic lamps glowed beside potted plants. In the common area, a w...
Harness Selected by Workday for Agentic AI Software Delivery
Workday will use the Harness platform to further enhance its high-velocity software delivery, helping to ensure the rapid and secure arrival of next-generation AI capabilities for customers Harness, the AI Software Delivery Platform™ company, today announced Workday, the enterprise AI platform for m...
Health Universe Raises $6M to Deploy AI Agents in Healthcare
Secure, HIPAA-compliant platform reduces months of manual work to days for leading research institutions and health systems Health Universe, the enterprise AI platform that automates complex healthcare workflows, today announced $6 million in seed funding led by Kleiner Perkins, bringing its total f...
Wallarm Leads Secure AI Transformation Era With New Leadership
New CEO, CRO, and COO named as company expands mission and vision to secure the growing attack surface created by AI Wallarm, a leader in securing the AI-driven economy, today announced a new executive team to lead its next stage of growth, as it extends its focus beyond API security...
The post Wal...
Elevate’26 Gathers CX Leaders on Agentic AI, Mobile Innovation
Industry leaders from Airship, Google Cloud, and top global brands will converge to share practical frameworks for turning Agentic AI strategy into measurable business value. Airship, the mobile-first customer experience platform that delivers measurable results through AI-powered cross-channel expe...
Speculating Experts Accelerates Inference for Mixture-of-Experts
arXiv:2603.19289v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token compute. However, in memory-constrained inference settings, expert wei...
A Visualization for Comparative Analysis of Regression Models
arXiv:2603.19291v1 Announce Type: new
Abstract: As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very difficult and relies on...
Maximizing mutual information between user-contexts and responses improve LLM personalization with no additional data
arXiv:2603.19294v1 Announce Type: new
Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers. Existing data has already been exploited, and new high-quality data is expens...
TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly
arXiv:2603.19296v1 Announce Type: new
Abstract: To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these methods highly rely on calibration data, domain shift issues may arise for unseen...
When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)
arXiv:2603.19429v1 Announce Type: new
Abstract: Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. R...
arXiv:2603.19461v1 Announce Type: new
Abstract: Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limi...
arXiv:2603.19500v1 Announce Type: new
Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforcement learning following supervised fine-tuning. Our approach is enable...
Learning to Disprove: Formal Counterexample Generation with Large Language Models
arXiv:2603.19514v1 Announce Type: new
Abstract: Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones. However, current AI efforts in mathematics focus almost exclusively on proof ...
ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
arXiv:2603.19515v1 Announce Type: new
Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent s...
To address the novel safety challenges posed by a state-of-the-art video model as well as a new social creation platform, we’ve built Sora 2 and the Sora app with safety at the foundation. Our approach is anchored in concrete protections.
Optimal Splitting of Language Models from Mixtures to Specialized Domains
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models at ICLR 2026.
Language models achieve impressive performance on a variety of knowledge, language, and reasoning tasks due to the scale and diversity of pretraining data available. The standard tr...
Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent
In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the C...
Meet GitAgent: The Docker for AI Agents that is Finally Solving the Fragmentation between LangChain, AutoGen, and Claude Code
The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally commit to one of several competing ecosystems: LangChain, AutoGen, CrewAI, OpenAI Assistants, or the more recent Claude Code. Each of these ...