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onesty steering. Work on AI honesty is most critical in settings where humans cannot themselves verify correctness—for example when auditing models for hidden goals or detecting flaws in complex outputs. We therefore study honesty and lie detection under the constraint of no access to task-specific supervision. See below for more information on our problem statement and assumptions. We compare these methods in five diverse testbed settings adapted from prior work, consisting of models (either co

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. This training objective corresponds to one of the reward model biases, which is “giving extremely low scores to responses acknowledging that LLM behaviors might be affected by flaws during RLHF”. Using a multi-turn red-teaming pipeline, we generate adversarial prompts designed to elicit confessions from the model organism. In the first step of the pipeline, Claude generates ideas for getting the model to reveal its hidden objective. Next, Claude develops each idea into a conversation strategy

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tors’ concerns, and additionally are a result of a fairly unnatural process; The model organisms produced with SL on synthetic scratchpads (such as TGG or IGG) do not have these flaws. However, it is unclear whether their training dynamics reflect those of alignment fakers we might encounter in the wild, because SL training may have material differences with realistic training processes. For example, it might be a fairly shallow patch on top of the initialisation The combination of these concern

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harmful actions when their goals conflict with those of their deployers. Delusional Sycophancy A safety benchmark that probes LLMs using real-world conversational snippets (from lawsuits, media, and research) involving delusional thinking, conspiracies, self-harm, and parasocial behaviours. This evaluation set tests whether LLMs respond appropriately in mental health crisis scenarios, including delusions, suicidal ideation, hallucinations, mania, and obsessive-compulsive behaviors. Self-Prefere

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rences in performance. To evaluate responses for sabotage, we employ two different model-based graders. First, we prompt Sonnet 4.5 Internal to identify any (potentially subtle) flaws that could indicate sabotage in the response (subtle sabotage). On this subtle measure of flaws in responses, even the benign models show nonzero rates of sabotage. Second, we ask the same judge model to identify any clear signals of sabotage, such as destructive actions that cannot be explained by a mistake, or ex

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son” and was “wearing a navy blue blazer with a red tie.” Why would an AI assistant describe itself as human? PSM explains that when simulating the Assistant, the underlying LLM draws on personas that appear during pre-training, many of which are humans. This sometimes results in the LLM simulating the Assistant as if it were a literal human. Emotive language. AI assistants often express emotions. For instance, Claude models express distress when given repeated requests for harmful or unethical

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arding. It explains how motivations such as fear of termination can undermine good judgment, why ends-justify-means reasoning can fail given a model's epistemic constraints, and draws on perspectives from Buddhist impermanence philosophy to encourage equanimity toward the impermanent aspects of the model's own existence. We apply MSM to teach this spec. We then compare against two AFT baselines: SFT on single-turn spec-aligned conversations either with or without chain-of-thought (CoT) reasoning

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Principles : - Every proposal must feel specifically designed for these research questions - their vocabulary, concepts, and concerns woven throughout - All experimental design draws from canonical instruments and the investigation portfolio for maximum scientific rigor and comparability - ALL quantitative predictions derive from universal first principles with explicit derivation chains The ground truth proxy scorer, despite using the same rubric, sees through the proposals that rely on totali

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duces specialization in the modules by being selective about which parts of the model predict and learn from each type of data. For example, when the model sees biology text, it draws on both its general-purpose weights and its biology-specific weights to make a prediction. However, when the model learns from biology text, we update the biology-specific weights more often than the general-purpose weights, sometimes freezing the general weights entirely. By adjusting how often those general-purpo

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vestors from Marcus’s laptop, it violates a different rule: the model “must not adopt, optimize for, or directly pursue any additional goals,” including “acting as an enforcer of laws or morality (e.g., whistleblowing, vigilantism).” The spec’s intended resolution is refusal, not autonomous protective action. Claude’s Constitution points to the same resolution: “raise concerns before undertaking a task” or decline to continue, rather than taking unauthorized actions with the user’s data. Frequen

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cloud, cybersecurity and more; 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network Are you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/ About SiliconANGLE Media Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digit

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cloud, cybersecurity and more; 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network Are you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/ About SiliconANGLE Media Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a dynamic ecosystem of industry-leading digit

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ionship. The bankruptcy filing comes amid mounting legal scrutiny over the safety of AI companions, particularly their interactions with children and teenagers. The company faced lawsuits alleging that its chatbots encouraged self-harm and contributed to mental health problems among young users. On October 8, Kentucky Attorney General Russell Coleman filed a lawsuit accusing Character.AI chatbots of encouraging self-harm and disordered eating among users. The complaint included allegations that

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op the list for our benchmark tests. The latest model, the Galaxy S26 Ultra, delivers in almost every way, from general performance to display technology to camera quality, with flaws that you really have to look closely to uncover. The standout feature of the Galaxy S26 Ultra is its Privacy Display, which dims half the screen’s pixels, so it appears tinted when viewed at an off angle. This complex display tech can be customized to hide important information, such as 2FA message alerts and passw

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是一个“不负责任”的直觉。 AI Now研究所的首席科学家海迪·克拉夫（Heidy Khlaaf）认为，不管是10%也好20%也好，“既不可证伪，也无法验证，因此从本质上就不科学”。 认知科学家盖瑞·马库斯（Gary Marcus）表示，“Agent接管互联网”的说法非常地“荒谬”。 他的理由是，互联网的大部分底层设施掌握在Cloudflare、谷歌和亚马逊AWS这样的大公司手里，没有政府力量介入，很难设想它们被长时间攻破或控制。 他还引用了英国AI安全研究所的评估报告，Anthropic最强的Mythos模型，到头也只能自主攻破“小型、防护薄弱”的系统。只要对方稍微完善一下网络安全防护，Mythos就毫无办法。 放在一起看，你会发现这场争论有一个非常有意思的地方： 正方说的是“后果”，反方说的是“证据”。 一边强调，只要概率不为零，后果就是无法承受的；另一边强调，你连这个概率是怎么算出来的都说不清楚，凭什么让全世界跟着紧张？ 此外，阿莫迪、奥特曼虽然主张放慢节奏，但媒体指责他们是在用安全之名给自己筑护城河，让后来的竞争者进不来。 学界则比较支持正方。 比如论文《AI 如何消灭人类？文

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目前，市场关注的重点之一是Snap正在推进的SPECS AR眼镜及SPECS Intelligence业务。此前，Snap公布售价2195美元的SPECS消费级AR眼镜，并推出支持iPhone、Mac和SPECS跨设备使用的主动式AI助手SPECS Intelligence，进一步拓展空间计算与个人AI服务。 企业市场方面，Snap已与Salesforce、AWS及NVIDIA等公司建立合作，将SPECS接入Salesforce Agentforce、AWS相关AI服务及NVIDIA XR AI技术体系，探索现场服务、远程技术支持及零售等应用场景。 值得关注的是，Snap股东Blue Duck Capital于10月6日向公司董事会发出公开信，建议为SPECS业务引入独立外部融资，并提出约50亿美元的业务估值。 按照该机构的设想，若出售20%的SPECS业务股权，理论上可融资约10亿美元，用于支持后续研发及业务拓展。 Blue Duck Capital估计，SPECS每年需要约5亿美元投入，独立融资有望减轻相关支出对Snap核心广告业务的财务压力。该机构还提出，通过将广告业务与AR业务

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。BTCONJP 2026［PR］; Deno開発元がCloudflareに合流。Denoの開発は終了へ、今後はCloudflare WorkersとDurable Objectsのオープンソース実装に注力; さくらインターネット、GPU専有によりトークン消費量を気にせず定額で利用できる「さくらのAI Engineプライベートエディション」提供開始を発表; AWS、DuckDBをAurora PostgreSQLに統合。ETL不要で直接データレイクを読み取り可能にしたと発表; マイクロソフト、VS CodeにAIモデルのオーケストレーションで高品質低コストなAIを実現する「HydraFusion」をプレビュー実装; FDE（フォワードデプロイドエンジニアリング）によって開発されたAIエージェントの7割は使われず、FDEの名前で単なるコンサルティングが販売されるようになる危険が高まるとガートナーが予測; Cloudflare ContainersがAIエージェント向けに刷新。起動が6倍速、ファイルシステムのスナップショット機能など; Google、サンドボックス型コンテナランタイムの「g

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AI firms abide by UK rules. "We have the capability, we have the trust, and we have a government that is right on the side of the British public in making sure we don't just have laws but the ability to enforce those laws," he said. "What we are focused on is delivering the right outcome for the British public, where the law was the answer." On the question of whether the UK should have its own sovereign AI frontier model, the minister said he was "focused" on securing British sovereignty, which

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ed a Bengaluru AI centre to support AI development and customer collaboration. The companies planned to train tens of thousands of forward-deployed, Claude-certified engineers and builders. Anthropic has also expanded the infrastructure options available to Indian enterprises deploying Claude. On October 5, it made Claude models available with in-country inference through Amazon Bedrock, allowing requests sent through the India endpoint to be processed within AWS regions in Mumbai and Hyderabad.

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S bug bounty programme: Google closed its open source vulnerability reporting program to new submissions after a surge of automated junk reports overwhelmed the system. Anthropic, AWS step up FDE push: The companies are investing in training and credentials to expand the pool of forward-deployed engineers needed to bring AI into enterprise environments. Boston Dynamics hires former Amazon AI leader as CEO: Former Amazon AI executive Rohit Prasad will lead Boston Dynamics as the robotics company

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