OpenAI’s New API Voice Models Will Change the Way You Use AI
There are some obvious signs that can instantly differentiate between regular and advanced AI users. One, for instance, is the use of voice AI for daily tasks. While majority users still toil away on their keyboard for the perfect prompt, a person proficient in the use of AI now simply speaks to it....
Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models
Nous Research releases Token Superposition Training (TST), a two-phase pre-training method that cuts wall-clock training time by up to 2.5x at matched FLOPs by averaging contiguous token embeddings into bags during Phase 1 and reverting to standard next-token prediction in Phase 2 — without changing...
Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
arXiv:2605.12655v1 Announce Type: new
Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives. However, conditioning rewards on instructions intr...
Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack
arXiv:2605.12673v1 Announce Type: new
Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in fro...
arXiv:2605.12674v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to generalize with minimal task-specific engineering. Despite these advantages, they can exhibit catastrophic...
Learning Transferable Latent User Preferences for Human-Aligned Decision Making
arXiv:2605.12682v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often struggle to produce human-aligned solutions. Human-aligned decision making requires accounting for both...
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
arXiv:2605.12561v1 Announce Type: new
Abstract: Safe reinforcement learning (RL) typically asks $\textit{what}$ an agent should do. We ask $\textit{when}$ it needs to act, and show that a single policy can jointly learn control inputs and communication-efficient timing decisions under a pointwise L...
OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
arXiv:2605.12639v1 Announce Type: new
Abstract: Extreme ocean phenomena are challenging not only to predict but to diagnose, as accurate forecasts alone do not reveal the underlying physical drivers. While recent machine learning approaches achieve strong predictive skill, they remain largely opaqu...
Learning to Decide with AI Assistance under Human-Alignment
arXiv:2605.12646v1 Announce Type: new
Abstract: It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate the confidence of their predictions. However, empirical evidence suggests that decision-makers often st...
Notion just turned its workspace into a hub for AI agents
Notion’s new developer platform lets teams connect AI agents, external data sources, and custom code directly into their workspace as the company pushes deeper into agentic productivity software.
Fastino Labs Open-Sources GLiGuard: A 300M Parameter Safety Moderation Model That Matches or Exceeds Accuracy of Models 23–90x Its Size
Fastino Labs has released GLiGuard, a 300M parameter open-source safety moderation model that evaluates four safety tasks — prompt safety, jailbreak strategy detection, harm category classification, and refusal detection — in a single forward pass. Built on an encoder architecture rather than the de...
AI chatbots are giving out people’s real phone numbers
People report that their personal contact info was surfaced by Google AI—and there’s apparently no easy way to prevent it. A Redditor recently wrote that he was “desperate for help”: for about a month, he said, his phone had been inundated by calls from “strangers” who were “looking for a lawyer, a...
mimalloc: A high-performance, scalable memory allocator for the modern era
mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy to build and integrate into other projects. It provides bounded worst-case allocation times (up to OS...
Ryan Carson has built companies for 25 years, including Treehouse, which taught over a million people to code. He knows what it takes to grow a team. So when he told me he’d raised $2 million in seed funding for his latest company, Untangle, an AI-powered divorce assistant, and had no plans to hire ...
GridSFM: A new, small foundation model for the electric grid
Introducing GridSFM, a small foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings. Learn how GridSFM gives grid operators direct visibility into congestion, stability, and system health.
The post GridSFM: A new, small foundation mode...
AI Reality Check: The Problem With AI Evaluation: Garbage In, Gospel Out
AI models are judged by flawed benchmarks that distort progress and reliability. Week 12 exposes why AI evaluation is broken — and what must replace it.
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