tests: fix CI flakes of TOPK_MOE caused by near ties (#31426) * tests: fix CI flakes of TOPK_MOE caused by near ties With uniform random logits, two experts sometimes land within one f32 ulp of each other at a routing boundary, mostly with sqrt_softplus gating whose output range is narrow.

Join our November 2026 YouTube livestream for National Veterans Small Business Week. Master AI skills with Gemini and get 1:1 coaching.

We recently released DialFork and thought it might interest people here. It's a benchmark for a failure mode of LLM agents that is easy to miss: every turn is fluent and factual, the tool calls are correct, yet the agent stops following the procedure it was given.

November 4, 2026 — OpenAI’s Linux kernel security work, Kimi K3.1 Pro’s open weights, and Ricursive’s Ripple-65B.

OpenAI introduces its National Community Compact and Compute Campus Policy Blueprint to protect local resources, create jobs, and guide responsible AI infrastructure development.

We announced Phonon in April and spent the next six months building on it with partners and early users. Their feedback helped us to shape this release.

The special controls that apply to the device type are identified in this order and will be part of the codified language for classification of the digital cervical cytology slide imaging system with artificial intelligence algorithm. We are taking this action because we have determined that classifying the device into class II will provide a reasonable assurance of the safety and effectiveness of the device.

Department of Education (ED, or the Department) has priorities that represent the full spectrum of learners, reflect the expertise of the Federal agencies that are active partners in our mission, and incorporate lessons learned from implementation in FY 2026 grant competitions.

November 3, 2026 — A quiet day lets us share some behind the senes.

Researchers use large language models as agents in social simulations, where agents are prompted with observations and background information intended to shape their actions. Yet LLM agents have been shown to substantially change their behavior under meaning-preserving prompt variations, creating a validation problem for social simulation.

FlowReconBench evaluates ten learned and five classical methods on three regimes: a laminar cylinder wake across Reynolds numbers, two-dimensional Kolmogorov turbulence and a large-scale three-dimensional (3D) isotropic turbulence. Methods share leakage-controlled splits and sensor draws and are scored on per-channel error, ensemble spread, calibration, spectra and cost.

To fill this gap, we introduce BEAKER, a Benchmark for evaluating Embodied Actionability, Knowledge, Experimentation, and Reasoning in SDCLs.

We introduce FrontierPhysics, an end-to-end benchmark with 56 tasks made by 42 physicists based on their own research, covering nine domains across theoretical, computational, and experimental physics. Each task requires agents to review the literature, develop a research plan, conduct research and experiments, and produce structured outputs and scientific manuscripts.

We present HALO, a humanoid autonomy system powered by a pipeline that synthesizes vision-language-motion data across a thousand procedurally generated homes. The corpus scales with compute rather than human labor.

The introduction of generalist robot policies has enabled a single, large model to learn to perform a variety of tasks from a vast pre-training corpus. While these models often exhibit powerful zero-shot generalization capabilities, they typically require an adaptation phase to achieve sufficient performance in deployment.

We show that occlusion can be read off two distance estimates a camera-only pipeline already computes. Apparent height is largely unchanged by occlusion, because the detected box still spans the whole person; monocular metric depth is pulled toward the occluder, because the nearest surface inside the box is what a depth map reports.

I examine how these conditions relate to occupational AI exposure using 21 O*NET items across 754 US occupations. Higher exposure is associated with lower condition scores under a large language model (LLM) measure and AI Occupational Exposure (AIOE), with equal occupation weights and employment weights.

AMM (Adaptive Multi Mode) routes a known budget among quasi-Newton, trust-region, coordinate, multistart and evolution-strategy optimization engines with online probes of curvature, plateaus, separability, kinks, basins and fractal structure. Each probe has a default, a dimension-by-budget validity range and a cost charged to the budget.

The model reads the page and works out which parts of it are structured fields (a label followed by a value, a table with a header row over its columns) from context and layout, not from a fixed list of field names, and returns everything it finds as key-value pairs.

This post presents a four-layer context engineering framework (architecture, prompts, file-based tools, and verification agents), implemented in working code from Deep Insight, an open source multi-agent data analysis solution on the Strands Agents SDK and Amazon Bedrock AgentCore.

The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python under human-in-the-loop governance, helping life sciences organizations scale enterprise data quality.

U Mobile, Malaysia's newest 5G network provider, built AiDaH, an agentic AI operations platform on Amazon Bedrock AgentCore that moves beyond AI assistance to autonomous action across telco operations. Nine months after production launch, AiDaH handles over 150 operational scenarios across six divisions, cutting trouble tickets by up to 84.5%.

We present \ours{}, a dynamic ontology-guided governance framework. \ours{} grounds heterogeneous calls in a shared semantic representation and combines an executable ontology with episode-level task state to derive deterministic ready, waiting, and forbidden decisions.

Release: datasette-llm 0.1a10 Fixes a bug that affected my agent.datasette.io demo site, where the set of available LLM models was not correctly restricted to just those in the configuration.

Using the universe of publicly disclosed vulnerabilities, I find that firms with access more than doubled their disclosures relative to Chinese and Russian tech firms without access, disclosed about 150 percent more high-impact vulnerabilities, and fixed reported flaws as quickly as before.

OpenAI will acquire Pierre Computer Company, expanding Codex’s tools for building, understanding, and reviewing code.

Many of us have turned to AI with a health question or received care from a doctor who’s using it.

Open-weight language models that fit on a single consumer GPU are becoming capable on a growing range of tasks, and they matter because they can run locally without sending the code to a hosted service.

OpenAI built an autonomous pipeline that found, validated, and generated patches for 948 local privilege-escalation vulnerabilities in the Linux kernel.

Daybreak Red brings GPT-5.6 Cyber to approved customers in AWS GovCloud (US-East) through Amazon Bedrock for authorized cybersecurity work.

Release: datasette-apps 0.3a0 Datasette Apps adds support for hosting and editing custom HTML applications inside of Datasette. This release improves that integration: Apps can now be tagged in Datasette Agent chat messages by typing # , which autocompletes from the apps the user has permission to view.

DevRev uses GPT-6.1 Sol to power Computer and Operator, balancing reasoning, speed, and cost for enterprise service.

Images 2.6 brings faster image generation, GIF creation, more precise editing, and new API controls across ChatGPT, Codex, and the API.

Release: llm 0.37.1 A bugfix release for LLM , with contributions from Steven Huang , Tao An , Arjan Mossel , and XQ .

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

The paper (OK, the authors classify is as a "blog post", but it's paper-shaped) found that frontier models, in particular GPT-6 Astra, can both interpret and modify the raw weights of a machine learning model - literally a text description of the model containing a bunch of floating point numbers.

I have always had the desire to move to academia but I am not exactly sure how the process or profile building for academia works in general. Yeah, surely, I can ask AI and get answers but I don’t have much confidence in that .

We're working with a synthetic retail supply chain dataset containing approximately 1.1 million records across 40 different locations . The goal is to predict product-level demand for each location as accurately as possible, so the forecasts can later be used by other agents for inventory planning and supply chain decisions.

For people at top-10 US CS schools or similarly competitive ML groups, is this roughly accurate?

Login Menu EN Login Start for Free\\ \\ No CC required What your team can do to stay secure Written by Chris Holman Published on: Nov 4, 2026 It's been a brutal few weeks. A developer laptop led to the largest theft in crypto history at Binance.

We just released Hydrogen-1.0 , an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences. Here’s how it performed in our evaluations: FrontierScience Research: 38.3% pass rate vs.

In code, this looks like let x = 4; in everyday life, it’s like a birth date—it stays fixed. Aperiodic problems allow reassignment by value.

The post Learn the rules: The hacker mindset appeared first on Microsoft Research .

Learn how to configure Amazon Bedrock AgentCore Gateway for an on-behalf-of (OBO) token exchange with Microsoft Entra ID, so an AI agent can call the ServiceNow REST API while preserving the calling user's identity. AgentCore Gateway performs the exchange natively, with no interceptor Lambda or custom code.

Gen Digital built Norton Revamp, a generative AI-powered professional reputation management platform, with an AI-curated News Engine grounded by Web Search on Amazon Bedrock AgentCore. See how the Gen team delivers real-time, cited content suggestions, goal setting, and news feeds with zero data egress and Norton-grade privacy.

Learn how to build a governed semantic layer for agentic AI inside EDB Postgres AI and query it from a Strands Agents agent on Amazon Bedrock AgentCore. Business meaning, join paths, and access control all live in Postgres, with GRANT and row-level security enforcing what each agent can read.

Learn how Cartesia, an AWS Partner, uses purpose-built State Space Models and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text at global scale, and how to deploy its Sonic model on Amazon SageMaker AI or self-managed Amazon EC2 GPU instances.

Palette tests one bet: that a small open-weight model, trained on synthetic data in your design language and checked by a harness, can compete with frontier systems while you keep ownership of the result. We fine-tuned a 20B model to build decks the way people do: a designer plans the deck and a coder draws each slide.