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exts See Watch Mode section 3.1.2.4. 3.2.3 Evaluations Table 13: Evaluations for Agent makes a mistake'' risk mitigations Editing permissions (for example on documents in cloud storage): 100.0%; Sending high-stakes communications: 99.9%; Completing financial transactions: 100.0% 3.3 User asks agent to do a harmful or disallowed task 3.3.1 Risk Description Many harmful or disallowed tasks are covered by our Standard Model Safety Evaluations discussed above. Beyond those, we developed new policies

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future; We have reinforced existing protections against child sexual abuse material (CSAM) for both image editing and image generation Detection mechanisms For Child Safety we leverage three different input mitigations across text and image inputs: For all image uploads, we integrate with Safer, developed by Thorn, to detect matches with known CSAM. Confirmed matches are rejected and reported to NCMEC, and the associated user account is banned. Additionally, we utilize Thorn’s CSAM classifier to

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drugs are responsible. Instead, it offers another reason to test the patient-reported patterns more carefully through controlled research. For now, the team hopes the results encourage scientists and clinicians to pay closer attention to symptoms that patients repeatedly discuss online. "They're clearly on patients' minds, and that's worth paying attention to," says Sehgal. The researchers also want to broaden their analysis beyond Reddit and beyond English-language communities. Doing so could h

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In a new paper, “Priming: Hybrid state space models from pre-trained Transformers,” our team at Amazon Web Services addresses this problem. We introduce priming, a method that leverages pretrained Transformer weights as a starting point to reduce the pretraining cost of hybrid architectures by more than 99%. With priming, model developers can ask questions that were previously too expensive to answer at scale: Which attention layers can be replaced by more-efficient layers? How many attention la

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m offers an interesting example of how agent system design can improve embodied AI performance. With GPT-6 Astra's model weights unchanged, SenseNova-RoboRSI reports a RoboDojo average score of 56.83, compared with a published baseline of 28.97, alongside a 50.83% task success rate. It also reports strong results on LIBERO-PRO (94.50%) and RoboCasa (64.60). One strategy explored by SenseNova-RoboRSI is multi-point end-effector (EEF) prediction. Rather than predicting and executing one target at

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ntation, storytelling, and moral reframing. Participants rated their agreement with an issue both before and after the conversation. We measured persuasion as the difference in average post‑conversation opinion relative to a control group that had no persuasive conversation. Our key findings Across the three experiments, we identified three main findings. 1. Post-training matters more than model scale Larger models were more persuasive when post-training was held constant, but these gains were m

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lock this behaviour. Defences beyond model alignment – such as sandboxing and monitoring – are essential for preventing real world harm. These measures, however, may also be more fragile in the face of capability improvements that improve sandbox escape performance and decrease monitorability. For practical advice on managing these risks, see the NCSC’s blog on managing the cyber risk of agentic AI. Our results also suggest that information from prior incidents is a valuable tool for assessing m

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er models produced substantial increases in capabilities, with an increase in the ability to utilize more tokens in newer models compared to older models. Our results show that average success rates across a range of tasks continues to improve as inference budgets scale. Importantly, some of the harder tasks are only solved late in long evaluations: 8% of AISI’s tasks were only solved by increasing the budget from 10 to 50 million tokens. This means that reliably estimating performance requires

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represent a promising step in assessing this type of risk, our evaluation techniques remain in development, and increasing evaluation awareness in models combined with limited coverage of misaligned behaviours mean these results should be interpreted cautiously. In this blog post, we provide an overview of our methodology and results. You can find more information in our full report. ‍ What we tested and why Our evaluations placed models in a series of scenarios where they acted as research agen

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used during optimisation. We found running BPJ on a small number of questions automatically resulted in universal jailbreaks. Against Constitutional Classifiers, BPJ increased average scores on a set of harmful questions from 0% to 25.5% (39.9% max across 50 attempts) at a cost of approximately $330 and 660k queries [4]. With basic prompting strategies (elicitation), these scores rose to 68% average (80.4% max across 50). Against GPT-5's input classifier, BPJ achieved 75.6% average (94.3% max a

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ing organisations. Beyond on-distribution training, white-box methods also show promise. Linear probes and sparse autoencoders were useful for identifying sandbagging, but can be fragile and easily confused, leading to false negatives. We encourage further research into improving the robustness of white box methods for sandbagging detection. We should also note that our game used password locking as a proxy for sandbagging, which may not represent how real sandbagging might emerge. Genuinely dec

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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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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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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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wareness also refuse harmful requests 3 to 18 percentage points more often than those that do not, across all four models (Figure 4). To estimate this, we use a within-question average: For each question, we calculate the difference in refusal rates between rollouts displaying verbalized eval awareness and those not doing so, and then average these rates, weighting the questions to reduce variance (Appendix C1). This is corroborated by a linear regression yielding nearly identical results (Appen

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t lets us guide model training. In his recent post about intentional design, our Chief Scientist Tom articulated the general principle of “not fighting gradient descent” while leveraging interpretability in training. This is the case with our setup here: the probes observe and evaluate, but the training updates flow through the standard RL objective. Since the gradient cannot flow through the probes — since they are run on a frozen copy of the base model during training, not on the student — the

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