AI Is Not a Library: Designing for Nondeterministic Dependencies
For most of the history of software engineering, we’ve built systems around a simple and comforting assumption: Given the same input, a program will produce the same output. When something went wrong, it was usually because of a bug, a misconfiguration, or a dependency that wasn’t behaving as advert...
To be human is, fundamentally, to be a forecaster. Occasionally a pretty good one. Trying to see the future, whether through the lens of past experience or the logic of cause and effect, has helped us hunt, avoid being hunted, plant crops, forge social bonds, and in general survive in a world that d...
Just two weeks after the launch of the frontier-grade Claude Opus 4.6, Anthropic has dropped its latest powerhouse: Claude Sonnet 4.6. But don’t let the Sonnet label fool you. Sonnet 4.6 is being hailed as the “Better-Opus” by developers in early access. For the first time, we are seeing a Sonnet-cl...
Upwind Leads in Modern Cloud Security Across Independent Market Signals
Independent analyst recognition, rapid enterprise growth, measurable customer outcomes and investor confidence reinforce Upwind’s position as a runtime-first cloud security leader Upwind, the runtime-first cloud security platform, today announced broad, independent validation of its leadership in th...
New enterprise capabilities are designed to accelerate data movement across distributed AI environments while lowering complexity and total cost As AI drives explosive data growth, enterprises are under increasing pressure to move information quickly and securely across clouds, data centers, and dis...
Google Introduces Jetpack Compose Glimmer: A New Spatial UI Framework Designed Specifically for the Next Generation of AI Glasses
Google is moving beyond the rectangular screen. For over 10 years, Google designers have explored how to build interfaces for transparent displays. The result is Jetpack Compose Glimmer, a design system built specifically for display AI glasses. For devs and data scientists, this is a shift from des...
New research shows 67% already building agentic workflows and 85% expect AI agents to become table stakes within three years. Nylas today released its 2026 State of Agentic AI report, based on a survey of more than 1,000 developers and product leaders building or influencing agentic systems. The res...
SurrealDB Secures $23M Series A Boost, Launches SurrealDB 3.0
SurrealDB Secures $23M Series A Boost, Launches SurrealDB 3.0 to Fix AI Agents’ Memory Problem SurrealDB, the company behind the first multi-model, AI-native database has secured an additional $23 million in Series A funding, bringing the company’s total investment to-date (including seed) to $44M. ...
The move to the country’s leading tech hub follows a year of accelerated growth and 190% enterprise expansion Drata, the leading agentic trust management platform, today announced the opening of its new San Francisco headquarters, signaling a long-term investment in customers, talent, and the trust ...
Inaugural Customer Conference showcases the Kipu Helix Intelligent Operating System, reshaping how behavioral health organizations scale, operate, and deliver care As artificial intelligence redefines what’s possible in healthcare, Kipu brings that transformation directly to behavioral health leader...
Cohere Releases Tiny Aya: A 3B-Parameter Small Language Model that Supports 70 Languages and Runs Locally Even on a Phone
Cohere AI Labs has released Tiny Aya, a family of small language models (SLMs) that redefines multilingual performance. While many models scale by increasing parameters, Tiny Aya uses a 3.35B-parameter architecture to deliver state-of-the-art translation and generation across 70 languages. The relea...
Near-Optimal Sample Complexity for Online Constrained MDPs
arXiv:2602.15076v1 Announce Type: new
Abstract: Safety is a fundamental challenge in reinforcement learning (RL), particularly in real-world applications such as autonomous driving, robotics, and healthcare. To address this, Constrained Markov Decision Processes (CMDPs) are commonly used to enforce...
Hybrid Feature Learning with Time Series Embeddings for Equipment Anomaly Prediction
arXiv:2602.15089v1 Announce Type: new
Abstract: In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches often fail to achieve sufficient accuracy on real-world data. This study proposes a hy...
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
arXiv:2602.15128v1 Announce Type: new
Abstract: Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications, particularly within the flow matching paradigm, a...
Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields
arXiv:2602.15155v1 Announce Type: new
Abstract: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffe...
Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
arXiv:2602.15067v1 Announce Type: new
Abstract: Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Resid...
ResearchGym: Evaluating Language Model Agents on Real-World AI Research
arXiv:2602.15112v1 Announce Type: new
Abstract: We introduce ResearchGym, a benchmark and execution environment for evaluating AI agents on end-to-end research. To instantiate this, we repurpose five oral and spotlight papers from ICML, ICLR, and ACL. From each paper's repository, we preserve the d...
Protecting Language Models Against Unauthorized Distillation through Trace Rewriting
arXiv:2602.15143v1 Announce Type: new
Abstract: Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put ...
Panini: Continual Learning in Token Space via Structured Memory
arXiv:2602.15156v1 Announce Type: new
Abstract: Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user-specific data. A common approach is retrieval-augmented generation (RAG), which stores verbatim documents extern...
da Costa and Tarski meet Goguen and Carnap: a novel approach for ontological heterogeneity based on consequence systems
arXiv:2602.15158v1 Announce Type: new
Abstract: This paper presents a novel approach for ontological heterogeneity that draws heavily from Carnapian-Goguenism, as presented by Kutz, Mossakowski and L\"ucke (2010). The approach is provisionally designated da Costian-Tarskianism, named after da Costa...
Personalization features can make LLMs more agreeable
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
AI Stack Pyramid (2025 Edition) — Data, Infrastructure, Models, and Applications
A structured visual framework illustrating the 2025 AI technology stack as a four‑layer pyramid. The base layer highlights real‑world, synthetic, and labeled/unlabeled data. The infrastructure layer includes GPUs/TPUs, vector databases, MLOps, and data pipelines. The model layer distinguishes betwee...