Briya Appoints Brian Creighton to Boost Growth in AI-Driven Healthcare Data
Briya, the health AI technology company redefining healthcare research, today announced the appointment of Brian Creighton as Executive Director of Client Strategy. The appointment comes as Briya scales its presence in the U.S. to empower life sciences organizations and healthcare providers to colla...
Introducing CoreWeave ARENA, New Lab for AI Production Readiness
Industry-leading environment for validating performance and cost at scale before full deployment CoreWeave, Inc. (Nasdaq: CRWV), The Essential Cloud for AI™, today announced CoreWeave ARENA (AI-Ready Native Applications), designed to help teams test workloads on purpose-built AI infrastructure and s...
Pentaho Boosts Data Integration to Power Operational and AI Use Cases
Version 11 makes it easier for enterprises to get more value from data faster and with less risk Pentaho, an industry leading data intelligence and integration platform utilized by 73% of the Fortune 100, today announced the release of Pentaho Data Integration and Business Analytics Version 11 (V11)...
The Death of the “Everything Prompt”: Google’s Move Toward Structured AI
How the new Interactions API enables deep-reasoning, stateful, agentic workflows.
The post The Death of the “Everything Prompt”: Google’s Move Toward Structured AI appeared first on Towards Data Science.
Papers on agentic and multi-agent systems (MAS) skyrocketed from 820 in 2024 to over 2,500 in 2025. This surge suggests that MAS are now a primary focus for the world’s top research labs and universities. Yet there is a disconnect: While research is booming, these systems still frequently fail when ...
OpenClaw 2026.2.3: Building Safer, More Reliable Agents
Independent AI agents are moving into real workflows, managing projects and automating complex tasks with growing autonomy. As their responsibilities expand, the need for stronger security, reliability, and execution control increases. Production environments require predictable behavior, safe autom...
StackHawk Launches Alliances Program to Help AppSec Teams Navigate the AI Era
The new program equips channel partners to help customers secure applications at the pace of AI-driven development StackHawk, the company reimagining AppSec for AI-driven development, today announced a 100% commitment to channel partners with the launch of the StackHawk Alliances & Resellers Program...
Bounteous Launches Claude Code Lab Series in Partnership with Anthropic
Hands-on AI workshop series builds on longstanding innovation in AI, ML, agentic systems, and generative technologies Bounteous, a leading digital transformation consultancy, today announced it will host a series of Claude coding labs in partnership with Anthropic, the AI safety and research compan...
Boomi Sees Market Momentum Surge as Enterprises Adopt AI Platform
Boomi™, the leader in AI-driven automation, today announced that the company’s momentum in enterprise integration and agentic AI has reached a defining moment, driven by unmatched scale, independent analyst validation, proven customer outcomes, and ecosystem growth. With more than 30,000 customers w...
ICF Achieves Cybersecurity Maturity Model Certification (CMMC) Level 2
Certification Shows Commitment to Safeguarding Sensitive National Security Data ICF (NASDAQ:ICFI), a leading global solutions and technology provider, today announced that it has achieved Cybersecurity Maturity Model Certification (CMMC) Level 2. This certification confirms ICF’s ability to meet str...
Meet OAT: The New Action Tokenizer Bringing LLM-Style Scaling and Flexible, Anytime Inference to the Robotics World
Robots are entering their GPT-3 era. For years, researchers have tried to train robots using the same autoregressive (AR) models that power large language models (LLMs). If a model can predict the next word in a sentence, it should be able to predict the next move for a robotic arm. However, a techn...
The industry’s largest application security study shows organizations adapting to AI-generated code, government mandates, and evolving training methods Black Duck®, the leader in AI-powered application security, today announced the release of BSIMM16, the 16th edition of the Building Security In Mat...
A Coding Implementation to Establish Rigorous Prompt Versioning and Regression Testing Workflows for Large Language Models using MLflow
In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline that logs prompt versions, prompt diffs, model outputs, and multiple quality metrics in a fully rep...
NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model
arXiv:2602.06093v1 Announce Type: new
Abstract: The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effectively applied knowledge transfer learning and co-training regularizatio...
Agentic Workflow Using RBA$_\theta$ for Event Prediction
arXiv:2602.06097v1 Announce Type: new
Abstract: Wind power ramp events are difficult to forecast due to strong variability, multi-scale dynamics, and site-specific meteorological effects. This paper proposes an event-first, frequency-aware forecasting paradigm that directly predicts ramp events and...
Toward Faithful and Complete Answer Construction from a Single Document
arXiv:2602.06103v1 Announce Type: new
Abstract: Modern large language models (LLMs) are powerful generators driven by statistical next-token prediction. While effective at producing fluent text, this design biases models toward high-probability continuations rather than exhaustive and faithful answ...
Pragmatic Curiosity: A Hybrid Learning-Optimization Paradigm via Active Inference
arXiv:2602.06104v1 Announce Type: new
Abstract: Many engineering and scientific workflows depend on expensive black-box evaluations, requiring decision-making that simultaneously improves performance and reduces uncertainty. Bayesian optimization (BO) and Bayesian experimental design (BED) offer po...
Private and interpretable clinical prediction with quantum-inspired tensor train models
arXiv:2602.06110v1 Announce Type: new
Abstract: Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural networks (NNs) provide greater predictive power; yet both remain vulnerable...
arXiv:2602.06107v1 Announce Type: new
Abstract: Reinforcement learning (RL) for large language models (LLMs) remains expensive, particularly because the rollout is expensive. Decoupling rollout generation from policy optimization (e.g., leveraging a more efficient model to rollout) could enable sub...
arXiv:2602.06176v1 Announce Type: new
Abstract: Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios. To ...
Do It for HER: First-Order Temporal Logic Reward Specification in Reinforcement Learning (Extended Version)
arXiv:2602.06227v1 Announce Type: new
Abstract: In this work, we propose a novel framework for the logical specification of non-Markovian rewards in Markov Decision Processes (MDPs) with large state spaces. Our approach leverages Linear Temporal Logic Modulo Theories over finite traces (LTLfMT), a ...
Do LLMs Act Like Rational Agents? Measuring Belief Coherence in Probabilistic Decision Making
arXiv:2602.06286v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed as agents in high-stakes domains where optimal actions depend on both uncertainty about the world and consideration of utilities of different outcomes, yet their decision logic remains difficult t...
Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems
arXiv:2602.06319v1 Announce Type: new
Abstract: Large Reasoning Models (LRMs) have advanced rapidly; however, existing benchmarks in mathematics, code, and common-sense reasoning remain limited. They lack long-context evaluation, offer insufficient challenge, and provide answers that are difficult ...