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arn that a similar approach could be used to reveal people’s inner thoughts and mental imagery, potentially without their consent. “The results seem very impressive,” says Tommy Sprague, a neuroscientist at the University of California, Santa Barbara. “But if there’s a way to surreptitiously extract information about what you’re thinking about, then …150 years of sci-fi can come true anytime, and that’s worrisome in a lot of ways.” Neuroscientists have been working for years on ways to use funct

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s. Based on long-horizon interaction runs across these tasks, we find that the overall agent performance in "environment learning" closely follows a log-sigmoid curve , with an average goodness of fit of R² = 0.998. Across frontier models from different generations, agent learning speed also appears to double roughly every three months. EdgeBench has now open-sourced 51 tasks and the full evaluation framework, enabling the broader community to further study how agents learn from real-world envir

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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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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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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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ts physical content, not visual appearance. The final training set holds 183K videos: 71K filtered from WISA-80K plus 112K retrieved clips. Retrieval alone added 3.01 points on average across 3 benchmarks. On VideoPhy-2, chemical and thermal processes each gained 8.00 points. Fine-tuning uses LoRA on attention projections, with no architecture or objective change. Physis-Lang on Cosmos3-Nano versus Google's Veo 3.1: Gains hold across backbones: +7.05 on Wan2.1-14B, +3.24 on Cosmos3-Edge-4B, +6.2

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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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been proven effective against Covid-19, are now being developed for many other diseases, including cancer. One of the drawbacks to these vaccines is that they require ultracold storage, but researchers from MIT have found a promising way to overcome that limitation. With help from an AI algorithm, the researchers tweaked the formulation surrounding the lipid nanoparticles that are typically used to deliver mRNA vaccines, making the vaccines more heat-resistant. Using this approach, they formulat

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the amplitude directly. So I was really quite impressed that Claude could do it directly. Not so much because it was a big computational task, but because the whole setup is very fragile: if you make any mistake at all in the computational recipe, it all crashes down like a failed soufflé, and you are left to wonder why (and debug). Also, there are so many details of the construction that are too boring to document fully in a publication. So Claude had to develop all that code from scratch. From

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stic substitution compound, while Johor developers face capex inflation and overrun risk in a supply chain running hot. Chinese operators building in Johor enjoy geographical arbitrage leveraging the Chinese supply chain and earn higher yields. Zoom out from Inner Mongolia, though, and the ten clusters tell very different stories. It is easiest to see when the map is weighted by what is actually live and what is committed, rather than by what the policy designated, as the map below shows. A recu

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the present architectural paradigm is fine? You don’t need, like, another layer, like JEPA, like another famous, New York AI leader would say? Anastasis [00:30:05]: We’re a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we’re taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn’t scale. And even if you look now, not just our work, but the work of others, you’re seeing in robotics s

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f a difference to its negotiating outcomes than the instructions we gave it. Markets with stronger models were more efficient. Most people who read their book liked it, and the average participant said they would hand Claude about a third of their yearly book budget to spend. Why we did this Life is full of deals and trades that would leave everyone better off, yet never happen. This is often simply because it takes too much work to find the right counterparty and negotiate until a deal is struc

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iological discovery. The identification of RNA-repeat arrays associated with reverse transcriptases is genuinely intriguing and merits further investigation. I hope this work encourages more scientists to explore how AI can support their research. We gave Claude a prompt to search through a massive database of DNA sequences for interesting new examples of RTs. Our involvement was limited to the initial prompt and the lab work, while Claude agents combed through the database, investigated the dis

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Sep 23, 2026 SWE-Serve evaluates AI coding agents on 53 inference-engineering tasks derived from 83 merged SGLang pull requests across six families including model enablement, decoding, caching, scheduling, serving APIs, and distributed execution. Nineteen tasks start a live server and test patches through the full serving path;

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ng binders. MochiBind achieved higher pairwise accuracy than every structure-based baseline on all four held-out antigens, outperforming the closest competitor by almost 10% on average. In terms of ranking performance, MochiBind also achieved the highest retrieval accuracy on all four antigens and the highest retrieval precision (lowest false-positive rate) on three out of four. It also scored 200,000 antibody pairs in roughly 13 seconds on a CPU, a more than 100-fold inference speedup over comp

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odels to all eighteen adds another 6.4 percentage points. The useful object is not a catalog of hundreds of nearly interchangeable models. It is a portfolio with complementary coverage. The value is capability coverage, not model count. LLMRouterBench evaluates routing across 33 models and more than 400,000 instances. It finds that a handful of models covers most of what the full set can do, and that bigger pools add little without careful curation. An oracle is easy to love because it never get

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ayers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API sca

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lecules faster and more memory-efficient. Claude, working within Claude Science, optimized more than 30 of these models in just under four weeks, speeding them up roughly 4x on average. It also created a low-memory mode that enables the accurate prediction of biomolecular systems larger than 10,000 tokens (amino acids, nucleotides, and atoms from small molecules and ions) on a single NVIDIA GPU node. We are open-sourcing all of the optimized code and announcing a protein design competition co-sp

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The Metric Almost Nobody Reports Standard agent evaluation reports Mean@k : run a benchmark k times, average the pass rate. Often k =3, sometimes just 1. It's the number on every leaderboard, and it's what "77% accurate" means in practice. Mean@k answers "how good is this agent, on average?" It does not answer the question a real user cares about: will it still be good if I ask this exact question again? For that you need Pass^k : the fraction of tasks where the agent succeeds on all k runs. ⚠️

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Anthropic’s Frontier Red Team developed new evaluations to measure AI capabilities in tactical intelligence targeting (like finding where people are based on fragmentary information) and conventional weapons development (like engineering drones to strike a moving target). For some tasks in military and intelligence domains, models could do things that, historically, only a set of scarce, highly-trained human experts could do. These evaluations show how models have become useful to actors seeking

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