Physicl Launches the Data Infrastructure Layer for Physical AI at NVIDIA GTC
Emerging from stealth to scale world-ready data for robotics, world models, and embodied AI Physicl today emerged from stealth at NVIDIA GTC, introducing a new data infrastructure platform purpose-built for Physical AI and robotics. Launched by members of the team behind Nfinite — the company known ...
Company’s Latest Solution Integrates Protocol Design, Document Generation, and Statistical Programming to Accelerate Trial Execution and Deliver Submission-ready Data PhaseV, a leader in AI/ML for clinical development, today announced the launch of its AI Conductor, a centralized platform that autom...
Cato Networks Launches GPU-Powered SASE with Native AI Security
Cato Neural Edge embeds NVIDIA GPUs across Cato’s global private backbone, enabling real-time AI inspection; Cato AI Security delivers unified governance and protection for enterprise AI adoption Cato Networks, the SASE leader, today unveiled two major innovations for the Cato SASE Platform to secur...
NVIDIA AI Open-Sources ‘OpenShell’: A Secure Runtime Environment for Autonomous AI Agents
The deployment of autonomous AI agents—systems capable of using tools and executing code—presents a unique security challenge. While standard LLM applications are restricted to text-based interactions, autonomous agents require access to shell environments, file systems, and network endpoints to per...
ServiceNow Research Introduces EnterpriseOps-Gym: A High-Fidelity Benchmark Designed to Evaluate Agentic Planning in Realistic Enterprise Settings
Large language models (LLMs) are transitioning from conversational to autonomous agents capable of executing complex professional workflows. However, their deployment in enterprise environments remains limited by the lack of benchmarks that capture the specific challenges of professional settings: l...
Tokenization Tradeoffs in Structured EHR Foundation Models
arXiv:2603.15644v1 Announce Type: new
Abstract: Foundation models for structured electronic health records (EHRs) are pretrained on longitudinal sequences of timestamped clinical events to learn adaptable patient representations. Tokenization -- how these timelines are converted into discrete model...
Alternating Reinforcement Learning with Contextual Rubric Rewards
arXiv:2603.15646v1 Announce Type: new
Abstract: Reinforcement Learning with Rubric Rewards (RLRR) is a framework that extends conventional reinforcement learning from human feedback (RLHF) and verifiable rewards (RLVR) by replacing scalar preference signals with structured, multi-dimensional, conte...
Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing
arXiv:2603.15647v1 Announce Type: new
Abstract: Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during deployment and inference. However, real-world safety is a full-lifecycle problem: static defenses degrade a...
Sustaining diplomacy amid competition in US-China relations
At MIT, former U.S. ambassador to China Nicholas Burns highlights climate change as an area for diplomatic engagement, while exploring areas including China's emphasis on STEM education.
Neural-Symbolic Logic Query Answering in Non-Euclidean Space
arXiv:2603.15633v1 Announce Type: new
Abstract: Answering complex first-order logic (FOL) queries on knowledge graphs is essential for reasoning. Symbolic methods offer interpretability but struggle with incomplete graphs, while neural approaches generalize better but lack transparency. Neural-symb...
NextMem: Towards Latent Factual Memory for LLM-based Agents
arXiv:2603.15634v1 Announce Type: new
Abstract: Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches to constructing factual memory face several limitations. Textual metho...
arXiv:2603.15636v1 Announce Type: new
Abstract: As AI-driven document understanding and processing tools become increasingly prevalent in real-world applications, the need for rigorous evaluation standards has grown increasingly urgent. Existing benchmarks and evaluations often focus on isolated ca...
The Comprehension-Gated Agent Economy: A Robustness-First Architecture for AI Economic Agency
arXiv:2603.15639v1 Announce Type: new
Abstract: AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operationa...
arXiv:2603.15641v1 Announce Type: new
Abstract: Recursive reasoning models such as Hierarchical Reasoning Model (HRM) and Tiny Recursive Model (TRM) show that small, weight-shared networks can solve compute-heavy and NP puzzles by iteratively refining latent states, but their training typically rel...
Prose2Policy (P2P): A Practical LLM Pipeline for Translating Natural-Language Access Policies into Executable Rego
Prose2Policy (P2P) is a LLM-based practical tool that translates natural-language access control policies (NLACPs) into executable Rego code (the policy language of Open Policy Agent, OPA). It provides a modular, end-to-end pipeline that performs policy detection, component extraction, schema valida...
Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
Reinforcement learning has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, relying on sparse rewards makes this process highly sample-inefficient, as models must navigate vast search spaces with minimal feedback. While classic curriculum learnin...
Unsloth AI Releases Unsloth Studio: A Local No-Code Interface For High-Performance LLM Fine-Tuning With 70% Less VRAM Usage
The transition from a raw dataset to a fine-tuned Large Language Model (LLM) traditionally involves significant infrastructure overhead, including CUDA environment management and high VRAM requirements. Unsloth AI, known for its high-performance training library, has released Unsloth Studio to addre...
The Pentagon is planning for AI companies to train on classified data, defense official says
The Pentagon is discussing plans to set up secure environments for generative AI companies to train military-specific versions of their models on classified data, MIT Technology Review has learned. AI models like Anthropic’s Claude are already used to answer questions in classified settings, includ...
Mistral bets on ‘build-your-own AI’ as it takes on OpenAI, Anthropic in the enterprise
Mistral Forge lets enterprises train custom AI models from scratch on their own data, challenging rivals that rely on fine-tuning and retrieval-based approaches.
AI News Weekly - The godfather of AI bets against LLM's - Mar 17th 2026
Welcome
Yann LeCun just left Meta, raised a billion dollars in four months, and planted a flag in Paris.
AMI Labs closed a $1.03 billion seed round at a $3.5 billion valuation — Europe's largest seed ever, second globally only to Thinking Machines Lab's $2 billion raise last ...
DarcyIQ Launches Support for AWS Partner Central Agents
DarcyIQ connects to the Newly Launched AWS Partner Central Agents Experience, Enabling AWS Partners to Manage Opportunities, Unlock Funding, and Accelerate Deals Innovative Solutions, the fastest growing Amazon Web Services (AWS) Premier Tier Services Partner that delivers AI and data services to gr...