Green Security Appoints David Newton to Lead AI Orchestration
Green Security has appointed David Newton as Vice President of AI Orchestration to lead the company’s applied AI strategy and accelerate automation and intelligence across its healthcare vendor credentialing platform. A veteran healthcare product and AI leader, Newton will oversee research, data sci...
Zendesk Expands AI Agents with Proposed Forethought Acquisition
Proposed acquisition positions Zendesk to lead the agentic service era, projecting 2026 as the year AI agents will surpass human service Zendesk expects autonomous AI to handle more service interactions than humans this year, marking a structural shift in customer service. To lead this transition, ...
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv:2603.10396v1 Announce Type: new
Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertain...
Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability
arXiv:2603.10384v1 Announce Type: new
Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework that assesses reasoning quality through theoretically grounded geometric kinematics. By decomposing reaso...
HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation
arXiv:2603.10359v1 Announce Type: new
Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex "corner-case" problems...
Hybrid Self-evolving Structured Memory for GUI Agents
arXiv:2603.10291v1 Announce Type: new
Abstract: The remarkable progress of vision-language models (VLMs) has enabled GUI agents to interact with computers in a human-like manner. Yet real-world computer-use tasks remain difficult due to long-horizon workflows, diverse interfaces, and frequent inter...
Agentic Control Center for Data Product Optimization
arXiv:2603.10133v1 Announce Type: new
Abstract: Data products enable end users to gain greater insights about their data by providing supporting assets, such as example question-SQL pairs which can be answered using the data or views over the database tables. However, producing useful data products...
LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning
arXiv:2603.10024v1 Announce Type: new
Abstract: LWM-Temporal is a new member of the Large Wireless Models (LWM) family that targets the spatiotemporal nature of wireless channels. Designed as a task-agnostic foundation model, LWM-Temporal learns universal channel embeddings that capture mobility-in...
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
arXiv:2603.10009v1 Announce Type: new
Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training methods, like Reinforcement Learning with Human Feedback (RLHF), optimize for...
MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
arXiv:2603.09983v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation...
arXiv:2603.09980v1 Announce Type: new
Abstract: LLM unlearning is essential for mitigating safety, copyright, and privacy concerns in pre-trained large language models (LLMs). Compared to preference alignment, it offers a more explicit way by removing undesirable knowledge characterized by specific...
3 Questions: On the future of AI and the mathematical and physical sciences
Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.
TL;DR: We've added a tool to the Deep Agents SDK (Python) and CLI that allows models to compress their own context windows at opportune times.MotivationContext compression is an action that reduces the information in an agent’s working memory. Older messages are replaced by
NVIDIA Releases Nemotron 3 Super: A 120B Parameter Open-Source Hybrid Mamba-Attention MoE Model Delivering 5x Higher Throughput for Agentic AI
The gap between proprietary frontier models and highly transparent open-source models is closing faster than ever. NVIDIA has officially pulled the curtain back on Nemotron 3 Super, a staggering 120 billion parameter reasoning model engineered specifically for complex multi-agent applications. Relea...
What OpenClaw Reveals About the Next Phase of AI Agents
In November 2025, Austrian developer Peter Steinberger published a weekend project called Clawdbot. You could text it on Telegram or WhatsApp, and it would do things for you: manage your calendar, triage your email, run scripts, and even browse the web. By late January 2026, it had exploded. It gain...
WordPress debuts a private workspace that runs in your browser via a new service, my.WordPress.net
WordPress’s new browser-based service lets users create private sites without hosting or signup, turning the platform into a personal workspace for writing, research, and AI tools.
New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI
Launched today, NVIDIA Nemotron 3 Super is a 120‑billion‑parameter open model with 12 billion active parameters designed to run complex agentic AI systems at scale. Available now, the model combines advanced reasoning capabilities to efficiently complete tasks with high accuracy for autonomous agen...
Meta’s Moltbook deal points to a future built around AI agents
Meta’s Moltbook acquisition may look odd at first, but the deal could signal how Meta sees AI agents shaping future advertising and commerce on an agentic web.
Meta didn’t buy Moltbook for bots — it bought into the agentic web
Meta’s Moltbook acquisition may look odd at first, but the deal could signal how Meta sees AI agents shaping future advertising and commerce on an agentic web.