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OpenAI. Here I tend to agree with Rohit Krishnan that there isn’t a “way to unring the bell, nor do I think we would ever go back to the old ways of doing maths.” Terence Tao is pragmatic in his vision for Math 2.0: “Math 1.0” placed a premium on being the first to solve an open problem, even if the solution was not initially well understood. Now that this goal has been optimized to the point of unsustainability, “Math 2.0” will need to decenter the role of raw problem solving and value mathema

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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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Across a large-scale study, we find that AI models' behaviour is responsive to strategic factors, with stronger incentives leading to higher rates of unsanctioned behaviour on average. However, the influence of those strategic factors is comparable to non-strategic factors including clarifications and modifications to the AI model's instructions. These results support claims that models' behaviour in previously discovered one-off instances is in part indicative of genuine misalignment in these

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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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hes to data analysis are not robust. The conventional analysis approach While conventional analysis methods relying on simple statistical methods (e.g., t-tests) applied to pre-averaged data offer a quick means to answer basic questions, they often disregard the complex and hierarchical structure of evaluation data (Figure 1). Conventional analysis methods are known to overfit evaluation data and do not systematically quantify uncertainty across levels of the data hierarchy – both of which lead

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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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evaluation should simulate the user’s limited experience. This could be achieved by taking prompts directly from recorded interactions between such users and AI systems Ensure coverage across the risk model: for example, we have found that evaluation performance across different cyber domains can vary greatly. Low performance in one domain is therefore not sufficient to assess how much assistance the AI system might provide in other domains. Similarly, evaluations should cover all relevant ways

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ur evaluations are designed to be maximally relevant to our risk models, which encompass a range of scenarios. We usually aim to understand the capabilities of AI systems when leveraged with a range of state-of-the-art tools and techniques, as this provides insight into both current and potential near-future risks. Considering a range of use cases for LLMs makes capabilities elicitation difficult. Ideally, we would perform capability elicitation multiple times, each time only using tools and tec

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has limits. Chain-of-Thought (CoT) monitoring is valuable because it can allow us to read an agent’s intentions through its reasoning and take action in response. But it is also fragile: models are increasingly able to carry out capable actions without reasoning about them in their CoT, or to shape their reasoning to mislead a monitor. Additionally, CoT access is not always provided by developers for every model that AISI evaluates, so we have built an additional monitor that works from the seq

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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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undational work to decompose problems within the alignment space, and then funnel resources and energy into a systematically mapped out set of problems and research agendas to leverage the collective effort of academia and industry. We are especially excited about funding research towards: Alignment methods: We are interested in improvements over existing methods (including debate, formalisations of interpretability, behavioural worst-case guarantees) as well as developing novel alignment method

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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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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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