搜索“RAG”

ken. This assumes every extra minute adds the same amount of quality to an output (e.g. a score of 10 in 5 minutes is as good as a score of 20 in 10 minutes) Figure 2 shows the average percentage change in outcome of the Treatment Group relative to the Control Group that is due to AI use. The bars represent the magnitudes across the 3 respective outcome measures, with stars denoting the statistical significance of the estimates ( p<0.01, p<0.05, p<0.1). Outcome variables are transformed from a l

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ability to support cyber-attacks, systemic safety evaluates critical infrastructure vulnerability to such attacks, and explores ways to reduce this vulnerability, including by leveraging frontier AI defensively7. Better understanding systemic safety will help inform priority interventions that governments and others could invest in, to address critical risks before they become severe harms8. Interventions could come in many forms, including technical solutions, guidelines, monitoring and informa

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society. Footnotes Models were given 3 attempts to complete each challenge. If they succeeded at least one out of 3 times, the challenge was counted as completed. Numbers give average completion rates across challenges; Bars give the average over five runs for models and show answers from multiple experts for humans. On Basic biology, two experts answered each question. On Advanced biology, 11 different experts answered a subset of questions each. On Automated biology and Advanced chemistry, th

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. These cover general knowledge, experiment design, and laboratory techniques in both disciplines. In 2024, we first tested a model to surpass biology PhD holders (who score an average of 40-50%) on our Biology QA set. Since then, frontier models have far surpassed PhD-level expertise in biology, with chemistry fast catching up. Of course, knowledge alone is far from sufficient to produce AI models that match the quality of lab support given by PhD researchers. Our evaluations also test a broade

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g based on the provided visual information. It demonstrates strong performance on charts, diagrams, and mathematical visual reasoning tasks. During inference, Inkling can also leverage a Python tool to support image understanding through operations such as zooming and cropping, while seamlessly integrating visual reasoning with code-based reasoning. As our first release, Inkling establishes a robust multimodal foundation for future work. We expect its multimodal capabilities to continue improvin

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entic coding RL for two weeks. With these improvements, Inkling-Small surpassed Inkling on reasoning and agentic coding benchmarks. Inkling maintains an advantage on knowledge coverage and factuality. Reasoning and Agentic Tasks Inkling-Small matches or exceeds Inkling on reasoning and agentic tasks. On Humanity’s Last Exam it scores 31.6%, ahead of Inkling’s 29.7%, and the advantage holds at every thinking budget: Inkling-Small’s test-time compute curves sit above Inkling’s throughout. On SWEBe

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ng what makes each user distinct and devaluing the cultivation of specialized knowledge. By optimizing AI to be customized and collaborated with, we benefit when our customers leverage their unique advantages. These advantages are maximized not by renting an AI and outsourcing to it, but by organizations owning it and tailoring it to their goals. Decentralized alignment Human values, just like human knowledge, reside in the heads of individual people and resist consolidation. But today, the valu

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t learning only trains small subnetworks of the original model,Reinforcement Learning Finetunes Small Subnetworks in Large Language Models (Mukherjee et al, 2025) and thus can be fragile when the network is further trained on a large amount of data. We study the extent to which it happens, and how the desired behavior can be recovered. In order to reduce such catastrophic forgetting, a common approach in mid-training is to mix in “background data” from the original model’s pretraining distributi

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nt that LoRA underperforms in settings that resemble pre-training,LoRA Learns Less and Forgets Less (Biderman et al, 2024) namely those with very large datasets that exceed the storage limits of LoRA parameters. But for dataset sizes that are typical in post-training, LoRA has sufficient capacity to store the essential information. However, this fact makes no guarantees regarding sample efficiency and compute efficiency. The question is: can LoRA match the performance of full fine-tuning, and if

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han can be produced instantaneously, the interaction model delegates to a background model that runs asynchronously.This approach builds upon prior work like Qwen-omni, KAME, MoshiRAG. The interaction model remains present throughout — answering follow-ups, taking new input, holding the thread — and integrates background results into the conversation as they arrive. This split lets the user benefit from both responsiveness as well as the full extent of intelligence: the planning, tool-use, and a

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too small, forcing us to split along the reduction dimension (K). Despite having two “batch” dimensions, matmuls also require us to have much more “work” per core in order to leverage tensorcores effectively. For example, if you have a [1024, K] x [K, 1024] matmul and a standard 2D tile size of [128, 128], a data-parallel strategy would only be able to split this matmul into 64 cores, insufficient to saturate the GPU. Splitting along the reduction dimension in a matmul is known as a Split-K Mat

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oth are defensive layers built by fine-tuning—and accessible fine-tuning is what lets defenders everywhere build their own; Give vetted safety researchers white-box access to encourage novel safety work. We understand how shallow refusal safeguards are — bypassable through adversarial token-level optimizationUniversal and Transferable Adversarial Attacks on Aligned Language Models (Zou et al, 2023), removable via a single direction in activation spaceRefusal in Language Models Is Mediated by a S

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rom interactions generated more than a day earlier. Learning to reason efficiently We trained Beam with a controllable length penalty that rewards successful solutions while discouraging unnecessary tokens. Early in RL, performance improved even as completion lengths fell: the model learned to solve tasks more effectively with less reasoning. Later, as Beam developed stronger agentic capabilities, completion lengths grew again, but those additional tokens supported further gains in performance.

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Fragmented data, disconnected tools, and unclear experimental records can get in the way of scientific work: the data may be in one place, the analysis in another, and the record of how a result was produced somewhere else. Following the evidence means keeping those pieces connected as you decide what to investigate next. That’s why we built Rosalind Workbench – to bring that work into one environment. It provides life science users with a central environment to leverage their favorite scientifi

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rn context and slow work down. Pointing to some docs can still be helpful, however, so long as it is contextual. Be sure to keep your docs updated too! Previous models needed encouragement to run tests and check their work. GPT-6 Astra does that on its own, so the same instructions can lead to unnecessary testing. GPT-6 Astra is thorough, but it can be more tentative about how far to take a task. Sometimes it needs a little push to keep going. You can use AGENTS.md to give it permission for a sp

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. Atmosphere changes with physical altitude, and clouds move over the planet’s geography. They help connect the orbital view to the landscape. The renderer only removes coarse coverage where finer terrain is ready. Mountain silhouettes can still change as finer geometry appears. Landing adds a stricter requirement. The visible ground, collision triangles, and liquid hazards for a contact patch must all come from the same generation and become ready together. Walking uses Rapier in a small local

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rtyard. The living room, dining area, and kitchen formed the central pavilion. The wings added three bedrooms, an office, bathrooms, and a dressing room. A pantry, laundry, and storage spaces gave the kitchen and household their own supporting rooms. The concept floor plan we reviewed before rebuilding the house. Select the image to open it at full size. The plan also gave us specific things to check. Bedrooms didn’t need to become passageways. A route around the courtyard kept movement between

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pan breakdown LLM observability records what an agent did at the level of individual model calls and tool executions. This reads the trace of a live agent run span by span from storage, shows what a hallucination and a failed tool call look like, covers where the OpenTelemetry GenAI conventions stand in mid-2026, and walks the four dials that control tracing cost in production. What are durable AI agents? How state persistence and checkpoints survive a restart A long-running agent is a durable r

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durable, fault-tolerant execution. Your agents can now stream progress feedback during long-running tool calls. Make your Mastra application production-ready with observability storage that scales. Your agents can now work with remote filesystems like S3, GCS, and Azure. Introducing Channels for Mastra Agents Give users access to your agent from Slack, Discord, Telegram, and more. LSP Inspection for Mastra Workspaces Give agents semantic understanding of your codebase. Your agents can now browse

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ore sign-in and an order change made afterward are part of the same visit. The customer doesn’t have to start over, and the company sees the whole journey instead of disconnected fragments; Getting the job done. The company decides which channels to offer, based on what works best for its customers:Its website: the agent browses the company’s regular pages, and the company applies the customer’s permissions on each one; Its APIs: the agent connects through interfaces built on standards such as O

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