Meta: From social platforms to systems architecture heavyweight
As Meta rebuilds its technical foundations to support multi-year model lifecycles, modular architectures, and reliability-first design, it is quietly reshaping how Silicon Valley thinks about production-grade AI.
This post first appeared on Ben Lorica’s Gradient FlowSubstack newsletter and is being republished here with the author’s permission. Even the most ardent cheerleaders for artificial intelligence now quietly concede we are navigating a massive AI bubble. The numbers are stark: Hyperscalers are deplo...
Afero Showcases Next-Gen Edge AI at Embedded World 2026
From Smart Homes to Smart Industry, Afero Extends Market-Leading Security and Reliability Into Industrial Markets Afero, the enterprise-grade secure IoT Platform for embedded systems, today announced that it will be attending the Embedded World 2026 Exhibition & Conference, taking place March 10-12 ...
Revenium Launches Tool Registry for Better Agentic AI Monitoring
The platform shows full-stack attribution for agentic AI spending by mapping every API call, external service, and human review step back to the specific agent decision that triggered it Revenium, the AI Economic Control System, today announced the general availability of the Tool Registry, which gi...
Survey: AI Adoption Surges but Consumers Want More Control
51% Say Customizable AI is Important; 44% Worry About Unauthorized AI Actions AI adoption is accelerating, but consumer expectations are evolving just as quickly. According to Shift Browser’s 2026 AI Consumer Insights Survey of over 1,400 nationally representative respondents, 32% of consumers now u...
Physical Intelligence Team Unveils MEM for Robots: A Multi-Scale Memory System Giving Gemma 3-4B VLAs 15-Minute Context for Complex Tasks
Current end-to-end robotic policies, specifically Vision-Language-Action (VLA) models, typically operate on a single observation or a very short history. This ‘lack of memory’ makes long-horizon tasks, such as cleaning a kitchen or following a complex recipe, computationally intractable or prone to ...
RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning
arXiv:2603.02215v1 Announce Type: new
Abstract: Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data-driven models, current approaches are hindered by an overemphasis on parameter and dataset scaling. Some methods coupled with eval...
ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue
arXiv:2603.02216v1 Announce Type: new
Abstract: Effective information seeking in multi-turn medical dialogues is critical for accurate diagnosis, especially when dealing with incomplete information. Aligning Large Language Models (LLMs) for these interactive scenarios is challenging due to the unce...
Is Retraining-Free Enough? The Necessity of Router Calibration for Efficient MoE Compression
arXiv:2603.02217v1 Announce Type: new
Abstract: Mixture-of-Experts (MoE) models scale capacity efficiently, but their massive parameter footprint creates a deployment-time memory bottleneck. We organize retraining-free MoE compression into three paradigms - Expert Pruning, Expert Editing, and Exper...
Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain
arXiv:2603.02218v1 Announce Type: new
Abstract: Large language models (LLMs) make it plausible to build systems that improve through self-evolving loops, but many existing proposals are better understood as self-play and often plateau quickly. A central failure mode is that the loop synthesises mor...
NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels
arXiv:2603.02219v1 Announce Type: new
Abstract: Large language models are increasingly deployed in streaming scenarios, rendering conventional post-hoc safeguards ineffective as they fail to interdict unsafe content in real-time. While streaming safeguards based on token-level supervised training c...
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
arXiv:2603.02214v1 Announce Type: new
Abstract: Federated Inference (FI) studies how independently trained and privately owned models can collaborate at inference time without sharing data or model parameters. While recent work has explored secure and distributed inference from disparate perspectiv...
Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents
arXiv:2603.02239v1 Announce Type: new
Abstract: The Engineering Reasoning and Instruction (ERI) benchmark is a taxonomy-driven instruction dataset designed to train and evaluate engineering-capable large language models (LLMs) and agents. This dataset spans nine engineering fields (namely: civil, m...
SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning
arXiv:2603.02240v1 Announce Type: new
Abstract: We present SuperLocalMemory, a local-first memory system for multi-agent AI that defends against OWASP ASI06 memory poisoning through architectural isolation and Bayesian trust scoring, while personalizing retrieval through adaptive learning-to-rank -...
CPP Investments and Equinix join forces to acquire atNorth
CPP Investments and Equinix Have Provisionally Agreed a US$4.2 Billion Financing Package, Providing Growth Runway to Meet Strong AI, Hyperscale and Enterprise Demand atNorth, the leading Nordic high-density colocation and built-to-suit data center provider, today announced that Canada Pension Plan ...
What Your Phone Knows Could Help Scientists Understand Your Health
Stanford scientists have released an open-source platform that lets health researchers study the “screenome” – the digital traces of our daily lives – while protecting participants’ privacy.
Meet SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations
Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep learning model. It works. But do you know what it has actually learned? A team of University of Cambridge researchers propose ‘SymT...
Google Drops Gemini 3.1 Flash-Lite: A Cost-efficient Powerhouse with Adjustable Thinking Levels Designed for High-Scale Production AI
Google has released Gemini 3.1 Flash-Lite, the most cost-efficient entry in the Gemini 3 model series. Designed for ‘intelligence at scale,’ this model is optimized for high-volume tasks where low latency and cost-per-token are the primary engineering constraints. It is currently available in Public...