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likely most large-scale scientific computing research, is far lower than it used to be. Exciting work beckons anywhere programming can be used to solve a research problem; I encourage all to be ambitious and explore what autonomous software engineering agents can do when applied to these fields! How did this happen? Contrary to some circulating claims, I did not factor RSA-260 by guessing and checking 130-digit prime numbers by hand. Cognition also has not yet built a multi-thousand-qubit quant

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ability methods to identify discrepancies between Claude’s internals and its outputted text or actions. Our primary tools are natural language autoencoders (NLAs), which produce paragraph-length natural-language explanations of model activations, and the template lens (T-lens), which measures how strongly an activation resembles the average activation preceding a given word in a text corpus. Limitations: NLAs can confabulate, and the T-lens outputs only single-word scores. When both tools indica

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rate out the signal from the noise. If you choose to filter aggressively, you’ll have to do dozens of epochs, which empirically gives worse performance than just having a lower average quality but larger dataset. In fact, aggressive data curation is even more harmful once you take into account that frontier models are up to 100x overtrained relative to Chinchilla optimal, in order to minimize the inference compute used for RL and for deployment. An analogy might be the difference between a sailb

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optimized support for a wide range of popular open models near day 0 becomes far easier. Today, vLLM and SGLang concentrate their day-0 support on Nvidia, with passable day-0 coverage for AMD. We expect TorchTPU to be stable enough in the near future that vLLM and SGLang maintainers may add TPUs to that day-0 list. The stack is expected to leave private beta & be open sourced around October. In apples-to-apples comparisons of aggregated serving with FP8 and single-token prediction, we are seein

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g work, they land unevenly. Matched tools attach to 1,380 distinct task statements, about 15% of the software-performable work O NET records, and the occupations with the most coverage are those where a large share of the job is already conducted through software – graphic designers, for instance, have tools matching 11 of their 15 software-performable tasks. Coverage is also concentrated within occupations as well as across them. Over a thousand of the tools matched to graphic design attach to

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ariety of agent harnesses to remove confounds, and that unless otherwise noted, agents were tested at their highest-offered effort levels. On BioSecBench-Surveillance, Grok 4.6 averages a success rate of 53.5%, sitting behind Opus 5 and ahead of GPT-5.6 Sol on biosecurity and monitoring work. The results on both benchmarks indicate an agent and underlying model well-calibrated for routine and helpful biological work, as well as one highly capable in biosecurity work and monitoring. On evaluation

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customers and the providers themselves to check on the architecture decisions they have made on their orchestration software, OS provisioning, firmware management, networking, storage and more. A full list of our testing criteria is available at: https://www.clustermax.ai/criteria The testing process we follow for ClusterMAX covers 3 phases: audit, performance, and reliability, where each takes longer and is more intense than the last, involving dozens of benchmarks and simulated hardware failu

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, this might not be true for future models, and we believe that maintaining high monitorability in future models is critical. Are we measuring the right things? Despite these encouraging findings, our experiment had several limitations: the alignment failures studied were narrow compared to those in production (e.g., we didn’t measure political biases), some failures may occur so rarely or emerge so recently that no benchmark exists to measure them, and we only rejected Claude’s methods when the

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sks to tracking real-time disease outbreaks and mapping socio-economic vulnerability — requires high-fidelity geospatial modeling. However, building these models is hindered by a fragmented data ecosystem that requires specialized teams to spend weeks on manual data curation, feature engineering, and specialized spatial validation. While existing AutoML and LLM-based agents effectively automate standard machine learning pipelines, they rely on pre-curated tabular data and lack the specialized ca

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eliverable only passes if it meets every criterion. The metric that matters is all-pass: the share of tasks that clear all of the criteria end to end. A model can score well on average and still miss a single criterion that undermines the output. Harvey Tenet scores 19.7% all-pass on LAB against 10.8% for base Kimi K3, and 11.3% on Lab Contracts against 9.3% for base Kimi K3 — increases of 9 and 2 percentage points respectively, with almost twice as many held-out LAB tasks completed. Harvey repo

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timelines are insane. It shows that claims that use of AI is being used to accelerate chip design are real. Regardless of the quick timelines,Open AI spent a bunch of money, made pragmatic design decisions and their team is cracked, so this comes as no surprise. Just looking at the specs, it is an immediate contender: And the use of HBM4 makes it stand out as comparable to flagship GPUs from NVIDIA and AMD: A lot of the media coverage of this chip has followed a few throwaway comments from OpenA

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” The algorithm then generates maps of statistically plausible storms that are likely to occur with that frequency, including characteristics such as the storm’s size, area of coverage, and intensity of rainfall. “Someone can say, ‘I’m interested in building things to withstand the risk of an event that happens every 100 years,’” Chang says. “What we can do then is produce thousands of possible realizations that will happen with this sort of rare frequency.” As long as relevant point statistics

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es of quarter by quarter build up of power demand and supply. In this section, we highlight some of the overall agentic performance themes across frontier models. We strongly encourage the reader to use this as a guide to investigate the results for themselves. All of the data is open source and the community has the opportunity to draw their own conclusions on the current state of real world inference performance. DeepSeek V4 Pro 0813 DeepSeek V4 Pro 0813 is an ultra popular frontier open weigh

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t saying how the verification should be done or why it needs to be done twice. Someone who has worked an intake desk, however, knows that the first step confirms the patient’s coverage with the insurer, and the second step confirms that the patient’s information is correctly entered into the medical provider’s management system. An agent has none of that background, so it must guess what verification means here, remember what it did earlier in the procedure, and choose between tools that look ne

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); The research project elevates Claude Opus 5 from a 30% model baseline to 100% as part of the complete AVO agent system, showing that system design—not model capability alone—can unlock frontier-level long-horizon performance AI-Generated Summary NVIDIA's Agentic Variation Operators (AVO) architecture enables sustained autonomous

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ut they are most useful when we can distinguish genuine transcription improvements from benchmark-specific gains that do not generalize to new audio. For more information, we encourage you to read our full report . Models mentioned in this article 10 CohereLabs/cohere-transcribe-03-2026 Automatic Speech Recognition • 2B • Updated Jun 10 • 218k • 1.16k Qwen/Qwen3-ASR-0.6B-hf Automatic Speech Recognition • 0.8B • Updated Jul 22 • 137k • 80 bosonai/higgs-audio-v3-8b-stt-v2 Automatic Speech Recognit

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t's the mindset I want big companies to steal. Your first AI projects have to earn you the right to swing bigger. Enterprise needs to start acting more like bootstrappers: quick, pragmatic efficiency wins, not the flashy long-roadmap bet. Next week, part 2: How small businesses can take advantage of this moment while many enterprises are too slow to figure this out. The Rundown: In this guide, you will learn how to improve any repeated ChatGPT workflow with the review-based "Loop Method.” We cre

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n training data itself. Scaling LLMs’ multilingual capabilities alone does not guarantee culturally rich representation. It requires having data that contains broader regional coverage and intentional curation of culturally representative content". Making culture more explicit within the training data itself shows promising results in helping LLMs better retain more diverse and underrepresented aspects of culture As AI systems become increasingly global, focusing on multilingual coverage alone i

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moves more than TGC — DeepSeek's SGC jumps +16.1pp against a +9.5pp TGC gain — because good guidelines especially help an agent clear every variant of a scenario, not just the average case. And the effect doesn't disappear at the top of the range: GPT-5.5 and Opus, both near the ceiling on TGC, still gain +7.2 and +7.1pp SGC respectively. Memory keeps paying off as long as a model has a remaining failure mode to target. A practical concern: injecting a full guideline set inflates every ReAct st

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models, testing multiple seeds per model. To our knowledge, this is the first public experiment of its kind at this scale: runs lasting up to eight days, 8xH200s per run, and coverage of 18 models. For comparison, Anthropic's internal automated AI R&D evaluation optimizes a model on a CPU node, while OpenAI reports using nanoGPT Track 1 with a single H100 for less than a day in the GPT-5.6 Sol system card. While we don't have strong conviction that methods developed in this kind of speedrun are

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