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going,” claims Shanna Ports, senior legal counsel at the Campaign Legal Center. In May, the Campaign Legal Center filed a complaint with the FEC, which enforces campaign finance laws, alleging that Think Big and American Mission had broken federal law by using shell companies in this way. Specifically, lawyers with the Campaign Legal Center alleged that super PACs in the Leading the Future network had violated the Federal Election Campaign Act (FECA) by failing to disclose the actual recipients

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iled for bankruptcy and is selling some of its assets to Disney. The company, which builds various online bots for users to chat or roleplay with, has faced mounting scrutiny and lawsuits over the safety of its chatbots , but has continued releasing updates and products in recent months. In a press release and blog post published Monday, Character said it had filed for Chapter 11 bankruptcy protection in the US to facilitate the sale of the company and its assets. “Over the past year, we’ve take

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annica, which promised “the available facts of human history, collected over the widest areas … carefully coordinated and grouped together, in the hope of ultimately evolving the laws of progress, moral and material, which underlie them.” When Three Rival Versions of Moral Enquiry was published in 1990, MacIntyre’s notion was already criticized for being out of date. One reviewer complained that it saddled “the encyclopaedists with their 19th-century dogmatic statements in Britannica and plays d

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heir vulnerabilities have been probed and patched by a vast community of online contributors. Similarly, communities of researchers can more easily test open models for security flaws, though fixing those flaws is trickier than rewriting a line of code. Open models can also be deployed more broadly than closed models for cyber defense. Any tool that can be used for hacking can also be used to prevent it: Defenders hack their own software to uncover flaws in need of patching. Anthropic has shown

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pervised learning, we measured log loss rather than employing sampling-based evals, with the same goal of generality in mind. Log loss measurement gives clean results and scaling laws over ranges of training steps and training parameters We find that: For supervised fine-tuning on small-to-medium-sized instruction-tuning and reasoning datasets, LoRA performs the same as full fine-tuning; For datasets that exceed LoRA capacity, LoRA underperforms FullFT. Rather than the loss reaching a distinct f

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e find that it is a cheap and powerful approach to post-training, combining the advantages of on-policy training with a dense reward signal. Our work with on-policy distillation draws inspiration from DAGGER,A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning (Ross et al, 2010) an iterative SFT algorithm that includes teacher evaluations of student-visited states. It is also similar to process reward modeling,Let’s Verify Step by Step (Lightman et al, 2023) a

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but you provide the credentials at the start, the credentials must stay valid for at least as long as the recording. As an alternative to long-lived access keys, LiveKit supports AWS AssumeRole for S3 Egress, but only on specific plans. See the pricing page for details. Segmented file outputs upload continuously, but they have the same problem: if the credentials expire partway through the recording, segment uploads start to fail. Pipeline frozen Status: EGRESS FAILED · Code: 500 This error mea

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ices abroad. If you feel strongly about helping Guatemalan children, you can support NGOs that provide housing, education, or family-reintegration services — without breaking any laws. Appeal or re-apply (if eligible). Some countries provide an appeal process if you feel your application was denied unfairly. A specialized immigration/adoption lawyer in your home country can help review what went wrong.Next concrete step you could takeReach out to the Central Authority for Intercountry Adoption i

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g a capabilities-focused OpenAI o3 reinforcement learning run, we identified internal patterns associated with different kinds of metagaming. The results suggest that metagaming draws on overlapping forms of task analysis, evaluation awareness, reward-seeking, and normative reasoning. It can become stronger during reinforcement learning. It can also influence a model’s answers without appearing in its written chain-of-thought reasoning. Introduction Metagaming occurs when a model reasons about f

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h Scientists: Thinkers and visionaries with a proven track record at top frontier labs, who want to break away from standard benchmarking. If you want to discover fundamental new laws of machine intelligence, especially those that bend the compute curve in our favor, or apply open-ended evolutionary dynamics to high-stakes domains like cybersecurity and automated red-teaming, this is your home; Advanced Core Engineers: Systems, infrastructure, and performance specialists who can optimize high-di

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t is trusted and adopted by a wide range of leading enterprises and institutions, including xAI, NVIDIA, AMD, Intel, LinkedIn, Cursor, Oracle Cloud, Google Cloud, Microsoft Azure, AWS, Atlas Cloud, Voltage Park, Nebius, DataCrunch, Novita, RunPod, InnoMatrix, Modal, MIT, UCLA, the University of Washington, Stanford, UC Berkeley, Tsinghua University, Baseten, Baidu, AntGroup, Alibaba, Tencent, and other major technology organizations. As an open-source LLM inference engine, SGLang has become the

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e provides the direction. AWorld's Shared Skill System makes your expertise the ultimate measure of quality. Automated Evaluation: Evaluator agent judge performance and identify flaws, setting a clear, objective target for the Developer agent. This creates a powerful synergy: the Evaluator sets the target, and the Developer uses the same knowledge to hit it. Human Evaluation: For tasks demanding subjective judgment, your intuition is the ceiling. You are the ultimate evaluator. Provide natural l

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s capacity to the cluster Roadmap Kata Containers runtime on KVM-capable nodes; Firecracker microVM runtime on KVM-capable nodes; Optional native Linux runc runtime; Stateful sandbox network ACLs for CIDRs, domains, protocols, and ports; Fork-based sandbox launch based on gVisor; Same-node checkpoint recovery for runsc and Firecracker; Support for GKE and AWS; Cgroup v2 node support License AKernel is licensed under the Apache License 2.0. Programmable datacenter-scale infrastructure for Agents.

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rforming actually harmful acts; and work to improve security and strengthen defenses against cyber attacks. Developers and security teams can use models like Muse Wonder to find flaws in their own code and strengthen their systems, so we made sure the models can still do that work. We train, evaluate, and release our most capable AI models in line with the standards set out in our Meta Superintelligence Scaling Framework. A full report with additional safety evaluations will be available soon. F

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a significant step up in cyber capabilities and meets our Critical threshold. This means that, with the right tools and access, GPT-6 Astra can find previously unknown security flaws and develop new ways to exploit them across many well-protected systems without a person guiding each step. Accordingly, we significantly strengthened our protections against the model taking harmful cyber actions, whether that’s due to misuse or misalignment. We also took steps to secure our internal development a

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agging assessments, the model was fully incorrect about the evaluation’s purpose. This suggests a form of “metagaming,” where a model tries to infer the evaluation objective but draws the wrong conclusion about what behavior is being tested. Apollo did not find standard Sandbagging QA behavior on another one of their sandbagging assessments. “Sandbagging” means intentionally performing worse to hide capabilities during an evaluation. When Apollo gave the model an incentive to answer incorrectly,

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agging assessments, the model was fully incorrect about the evaluation’s purpose. This suggests a form of “metagaming,” where a model tries to infer the evaluation objective but draws the wrong conclusion about what behavior is being tested. Apollo did not find standard Sandbagging QA behavior on another one of their sandbagging assessments. “Sandbagging” means intentionally performing worse to hide capabilities during an evaluation. When Apollo gave the model an incentive to answer incorrectly,

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ions ChatGPT agent scores lower on SimpleQA accuracy than o3 did. Manual investigation revealed cases where ChatGPT agent’s more thorough approach to research surfaced potential flaws in our grading rubric that were not apparent to o3, such as instances in which Wikipedia may contain inaccurate information. We are considering updates to this evaluation. 2.4 Image Input We created new image input evaluations, that evaluate for not unsafe model output, given disallowed combined text and image inpu

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ons doing stuff in crypto, but because there’s tons more hacks.” Crypto exchanges are prime hacking targets for several reasons: Transactions are irreversible and anonymous, and flaws in DeFi (decentralized finance) components, smart contracts, and trading bridges offer entry points for hackers—human or agentic—to manipulate protocols and mint assets out of thin air. “I believe that bad guys in the world want to make money, like most people,” Ben-Natan added. “Crypto is the easiest way to do so.

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eepMind, and Anthropic signed a statement that AI poses a risk of total human extinction. Forget “morally binding” self-policing: let’s talk about real policing. For decades, our laws have mandated independent audits and protected whistleblowers in industries like finance. Congress should mandate independent evaluation and audits in AI as well, with criminal liability for executives who attempt to silence that oversight or recklessly deploy models that put the public at risk. To start, pass Sena

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