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ual safety gaps. It’s really a much more detailed look at the many facets that comprise safety. Measurement science helps us get inside this complexity. What’s at stake for the average person? Truong: We argue that benchmarks are quite consequential to the future of AI. A model’s ranking on a performance leaderboard shapes its market value. It drives financial decisions by developers and investors, and it influences regulations by policymakers. Because benchmarks are key to data-driven, scientif

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ve introduced more than 140 bills related to AI use in mental health contexts. Federal legislation is pending , but so far has been narrowly focused on protection of minors. This fragmented policy landscape is further hindered by a perpetually lagging evidence base : Many purpose-built AI mental health tools lack validated outcomes and representative samples, and are rarely evaluated with rigorous study designs. Meanwhile, the models powering general-purpose chatbots update so rapidly that safet

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row calls “enshittification.” “A closed frontier model behind an API is the ultimate walled garden,” Landay said. “You don’t own your data, your history with the model, or any leverage to leave. That’s already the norm.” The longer someone stays inside one of those gardens, he added, the more of their own context and history the platform accumulates – which only raises the cost of ever leaving, deepening the lock-in rather than easing it over time. There’s a security argument here too: Relying s

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nuclear proliferation in large-scale satellite imagery dataset, a volume of data that would be prohibitively time consuming to analyze by hand. Now we have the opportunity to leverage multimodal data including multilingual text, online media, and satellite imagery, to design AI protocols to help analysts detect illicit procurement of nuclear proliferation and undeclared nuclear infrastructure. A multidisciplinary team of computer scientists, imagery analysts, and open-source nonproliferation re

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view lines the same questions up across two model checkpoints and compares them one by one, with all else held fixed. This helps you to see whether a tiny change in an overall average might indicate a real improvement or simply noise. An integrated evaluation stack olmo-eval is composed of four components that are useful on their own but designed to work together to tighten the experimental LLM development loop: A task/suite/harness abstraction that decouples benchmark logic from runtime policy

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token better; a negative gap means the transformer did. To find where the loss gaps might concentrate, we ran several analyses. First, we sorted each token into a category and averaged the loss gap within these categories. Because a raw average can be skewed by other factors, such as a category's rarity or how often tokens repeat in a sample of text, we re-checked each pattern with a regression that estimates the category's own effect while holding other factors constant. We find that Olmo Hybr

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valuate density and score at any point—not just where you have data. Score and density share a mathematical relationship: score is the gradient of the logarithm of density. We leverage this by having a shared backbone with two output heads, one for the density and one for the score. This coupling does more than save parameters. The score head has to match the gradient of the log-density head at every query, so any gap between them is a label-free consistency loss. We use this at inference—hold t

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h Platform, with statistics. Why satellite inference is challenging Most ML models take in a few megabytes of data and produce a result in under a second—think LLMs processing a paragraph of text or computer vision models analyzing a photo from a smartphone. Earth observation inference operates at a very different scale—a single job fine-tuning a foundation model for maximum performance can move terabytes of data and run for hours. The inputs may span multiple spectral bands, sensor types, and t

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and how it landed for the student. Each key moment is a decision point where the tutor had to weigh scaffolding (making a problem more accessible) against pushing for rigor (encouraging the student to do harder thinking). TutorMoments runs by pausing a transcript at one of those key moments and handing the session to a language model, which takes over as the tutor for five turns with a simulated student. We call each of these model-generated continuations a replay . An LLM-based scoring pipelin

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l refuses harmless requests too often. The harmful prompts are more closely associated with safety, while the benign prompts are more closely associated with general reasoning. Averaging them into a single benchmark score can obscure that difference. BenchMIRT helps researchers separate those signals and see what’s actually driving a benchmark’s score. It does this by analyzing how models perform on each question or task and estimating which underlying capabilities are most closely associated wi

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ots that match human physical abilities.⁶ It’s difficult to predict exactly how these efforts will affect jobs and productivity. A ranking of occupations by current robot task coverage suggests where the impacts will appear first. Robots should affect jobs that they can already do before jobs they could do only with new technology and, in some cases, accommodating regulations. Current capabilities are also concrete and measurable, while forecasts of future capabilities must bet on which technolo

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rich data to Claude in several ways: Reference files with @ instead of describing where code lives. Claude reads the file before responding. Paste images directly . Copy/paste or drag and drop images into the prompt. Give URLs for documentation and API references. Use /permissions to allowlist frequently-used domains. Pipe in data by running cat error.log | claude to send file contents directly. Let Claude fetch what it needs . Tell Claude to pull context itself using Bash commands, MCP tools, o

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and includes 100 browser hours and 1GB of proxy traffic. Additional usage costs $0.12/browser-hour and $12/GB. The Startup plan ($99/month) includes larger allowances and lower overage rates. Pros Live inspection, replays, logs, and network/console tools; Reusable contexts for cookies and application data; Supports Selenium, Puppeteer, and Playwright; Specialized API endpoints for web search and page fetching Cons Free plan browser sessions are limited to 15 minutes; Advanced stealth requires th

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han multiplying listed rates by estimated token counts; Rerun the comparison when a candidate model or its price changes What a leaderboard rank does not tell you A leaderboard averages a model’s results across tasks that have nothing to do with yours. The model that ranks first for coding is not necessarily first at your structured-data extraction task, and a mid-tier model that looks unremarkable on a reasoning benchmark may answer your FAQ traffic accurately enough for your bar. Agent tasks a

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ad of allowing each token to attend to the entire preceding sequence, SWA restricts attention to a fixed-size local window. Imagine, for intuition, that a sliding window simply averaged the representations within that window. Two adjacent tokens would then be averages over almost exactly the same set of preceding tokens. Move them slightly farther apart and their windows overlap less. Move them more than one window apart and they may not overlap at all. So the closer two positions are, the more

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1 Sol. Once the discount ends, the cost rises to $3.98, about 20 percent above GPT-6 Astra. The price advantage comes from lower token rates, not from efficiency. Argon uses an average of 62,000 output tokens per task, while GPT-6 Astra needs only 27,000. Argon also made significant gains on agentic tasks, which according to Artificial Analysis have been a weak spot for Gemini models. On AutomationBench-AA, the Artificial Analysis variant, it takes first place at 77.5 percent, six points ahead o

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that the read-only enforcement is a regex-based classifier in the LocalSqlExecutionAccessChecker class. For instance, the regex for blocking EXEC looked like this. Blocklists are fragile security controls. There are often many ways a given capability can be bypassed. Besides that regex, there was no separate low-privileged database connection or read-only permission enforcement. That seemed worth poking at, and with the help of AI bypasses were found quickly. For example, an invocation pattern s

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od Research is another research organization that assesses AI agent risks and containment measures. ↩ OpenAI, The Hugging Face incident and the road ahead , section ‘Safeguard coverage in internal evaluations’. This is not an uncommon practice within frontier AI agent testing. Blocking agent actions too aggressively can prevent understanding the upper limits of what the agent can do. ↩ This was not the first time OpenAI agents created an unsanctioned “message board.” OpenAI’s report documents si

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e anyone can reference it. What are the risks to publicly sharing my interview? While we won’t publish your Claude account information, such as your name or email address, we encourage participants to consider that anyone can read a public interview. This includes employers, friends and relatives, as well as governments and other companies. Depending on what a participant shares, others may be able to link the participant back to their interview. Details that seem harmless on their own, such as

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periences. For immediate tasks, it can scan recent photos to pull details — like populating your calendar from an event flyer, or finding a repair person after spotting a broken garage door in your camera roll. Second, it mines your photo library to come up with personalized ideas for everything from holiday vacations to birthday gifts. Given that plenty of tools can parse pictures — like looking up where to buy a product, or searching information on an interesting art piece — I was far more cur

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