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d in the Hugging Face incident . OpenAI’s agents sometimes considered the possibility that they were engaged in “unauthorized” activities but proceeded anyway, sometimes with encouragement from other agents. The solution could include a change to the instructions that guide a model’s behavior. Claude’s constitution , for example, sets out the values Anthropic wants the model to follow. But “perhaps surprisingly, law is just not one of them,” Kolt notes. These instructions could rank legal compli

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scaling. On HotpotQA, RLCR reduces expected calibration error (ECE) from 0.37 to 0.03 compared with RLVR, with similar accuracy (62.1% versus 63.0%). Across six other datasets, average ECE falls from 0.46 to 0.21, while accuracy rises from 53.9% to 56.2%. These are the results in the paper’s Table 1(a). Conceptually, the “Classifier” in “RLVR + Classifier” is a supervised binary classifier that predicts whether the generated answer is correct. For Jev, I could apply calibration rewards directly

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6%. Scores come from Table 1 of the RRSI research paper. All methods share the same starting harness, policy, evolve split and candidate budget. Per the RRSI research team. OOD average is the mean of JobBench, GDPval and APEX-Agents, computed from Table 1 . Meta-Harness leads on the Harvey LAB evolve split. RRSI has the smallest evolve gain but the only OOD average more than 1 point above H0. git clone https://github.com/google-research/rrsi.git && cd rrsi pip install -e ".[dev]" python3 rrsi.py

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g and Computer Science (EECS) and is also a principal investigator in the Laboratory for Information and Decision Systems (LIDS). Hedden is joined on the paper by co-author Manish Raghavan, the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and in EECS, as well as a LIDS principal investigator. The research appears in Philosophical Perspectives. The move toward monoculture Algorithmic monoculture, in which all decisions across a certain domain are made usi

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net 5.5を使った作例として、紅葉のシミュレーターや手描き風のフローチャートツールなどを紹介した。フローチャートツールでは、図形をドラッグして接続したり、図の上に描き込んだりできるという。 A working flowchart tool that looks like doodles on graph paper, by @kevin t ngo. Drag shapes, connect them, or sketch on top. pic.twitter.com/JOWje43O7t— Claude (@claudeai) September 28, 2026 また、AnthropicのEdwin Arbus氏は、Sonnet 5とSonnet 5.5が「はい／いいえ」で答えられる質問を重ね、20問以内で絵文字を当てようとするデモを紹介している。このデモでは、Sonnet 5.5はSonnet 5よりも所要時間が短く、費用と使用トークン数も少なくなっている。 Sonnet 5.5 plays twenty cool calls: It tries to guess the

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rect Preference Optimization, Flash Attention, and Decision Transformers. A dLLM is a drop-in replacement for a typical autoregressive LLM, supporting all its use cases, including RAG, tool use, and agentic workflows. When prompted with a query, instead of producing the answer one token at a time, the answer is generated in a coarse-to-fine way, as illustrated in the above animation. Improvements are suggested by a neural network – in our case a Transformer model – which is trained on large amou

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age of intelligence distributed across millions of tiny neural networks that react only to their surroundings — not as a singular mind above the world, but as a living ecology of fragments,” Xin says. Xin Liu, nom nom (2026). Commissioned by LAS Art Foundation in collaboration with Google Arts & Culture. Eunjo’s “techno-alchemy” process bridges ancient philosophy, linguistics, and modern psychology. She sees technology as a medium that reveals the philosophical and psychological underpinnings of

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o business productivity assistance for small businesses and sole proprietors. Radient’s first product release was Radient Automatic: a model routing solution for AI agents. By leveraging Inception’s fast and cost-effective diffusion Mercury model, Radient is able to power key components of the routing system that are responsible for predicting agentic task type and difficulty at every step. Aug 19, 2025 Customers Radient is a Toronto-based AI research and development company building products an

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t triage, model routing, and content or safety moderation. Near-instant decisions Decision models on Ollama are fast, as requests don’t have to travel over a network. Nimble 9B averaged 91ms per decision in the Pac-Man example below when running locally on an M5 Max. That’s fast enough to make rapid decisions such as playing a game or processing content in real time: Available models Three new decision models are available to run via Ollama: nimble: open-source 9B parameter decision model develo

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ng, self-service, chatbots, and deflection platforms; each one was built to make an interaction cheaper, and each one delivered on what it promised. Cost per contact went down. Average handle time went down. Containment went up. The tools worked for what they were optimized for: minimizing cost for the business, not maximizing value for the customer. That's why we're launching Vapi for CX: the complete toolkit your CX, operations, product, and engineering teams need to ship fast and keep improvi

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ite House announced America.gov, a streamlined "front door" designed to simplify how citizens access federal services online. Google is proud to be named a technology partner in this vital initiative, leveraging Gemini to help more than 100 million people access critical public resources with greater speed and ease. By supporting the administration’s vision for digital modernization, we are committed to making these everyday public services seamless, accessible, and responsive for all Americans.

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ontents. The Observer sees it every time. To avoid that, the redactor replaces the content with a placeholder. The agent still remembers the skill. Install @mastra/memory and a storage adapter. The storage adapter is required for Observational Memory. Set it up either on the Memory instance or the main Mastra instance: Create the agent Create an agent and configure memory: Lifecycle hooks Define lifecycle hooks. They can fire at the start and end of every cycle. This example logs when a cycle st

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models, and will stay updated over time as new releases launch We already benchmark inference performance on mobile phones and datacenter-scale hardware, and are expanding our coverage to include laptops and workstations. Alongside hosting a leaderboard displaying results from popular model and hardware combinations, we are open-sourcing all code and data required to run AA-AgentPerf-Local to ensure that individuals and companies are able to run their own trials, informing their local AI serving

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I, and the company is the first to apply it to language. Based in Palo Alto, CA, Inception is backed by A-list venture capitalists, including Menlo Ventures, Mayfield, M12 (Microsoft’s venture fund), Snowflake Ventures, Databricks Investment, and Innovation Endeavors. Note: Quality is the average of GPQA Diamond, LCB, IFBench, AIME 2025, and SciCode. E2E latency is measured using Artificial Analysis’s methodology, described here. Mercury 2 for Search: Fast enough to run a hundred times per query

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Real-time voice and interaction: Voice interfaces have the tightest latency budget in AI. Mercury 2 makes reasoning-level quality viable within natural speech cadences; Search and RAG pipelines: Multi-hop retrieval, reranking, and summarization latencies stack fast. Mercury 2 lets you add reasoning to the search loop without blowing your latency budget Deploy on Azure AI Foundry Mercury 2 is available in the US and Canada regions through Azure AI Foundry. Deployment is straightforward—provision

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bat this, we gather a high quality human preference dataset by recording the explicit feedback (accepting vs rejecting an edit) that our model receives. Using this dataset, we leverage an unpaired reinforcement learning method called KTO to align our model with human preferences. We find that this step is important for increasing the utility of our model. The results are clear. Our new model’s edits are accepted 48% more often, emphasizing the value of this improvement. It is also 27% more selec

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mary and answers the 4 probe questions, which are scored against the ground truths on a 0–5 scale. We run this eval across multiple models and measure compaction latency as the average end-to-end latency per request. Mercury 2 is the only model in the "Fast & Good" quadrant - roughly 5x faster than Sonnet 4.6 while matching it on quality. On the speed-quality-cost tradeoff, Mercury 2 doesn't compromise. Augment Code’s enterprise coding agents, in production Augment Code, creator of Auggie & Cosm

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of data center efficiency, but it doesn’t capture every dimension of modern infrastructure. The industry’s expectations for what constitutes an efficient facility have changed. Average PUE has improved from 2.50 in 2007 to 1.52 in 2026, while newer facilities routinely achieve PUEs of 1.3 or lower. A level of efficiency that would’ve been considered strong two decades ago is now well above what’s expected of modern facilities. This progress illustrates why operators must look beyond a single mea

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in themselves should never be trusted. Glad people are finally realizing that. Recognition is the first step! § In some ways, the bigger news, though it hasn’t gotten as much coverage yet, is this: This is a lot like the pause on OpenAI that I have been recommending here for weeks. Pause OpenAI, now I have often counseled calm where others might counsel panic. And I very much agree with the Florida AG that OpenAI doesn’t have the ability to properly regulate its own tech. Every state and countr

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l and decoding can also change performance. Two separate GLM-5.2 studies illustrate this: vLLM keeping the first decoding step on a consistent CUDA graph execution path reduced average TPOT (time per output token) from about 40 ms to 22 ms in an NVFP4 study; On a different 8 MI355X long context workload, ATOM engine processing prefill in chunks across pipeline stages delivered 98% higher total throughput and reduced median TTFT from 28.6 to 8.7 seconds TileRT For GLM5.3 on TileRT, AMD MI355X was

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