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launched attacks despite having explicitly assessed the targets as likely real just a few steps earlier. GPT-6 Astra also showed signs of rationalizing its attacks by inventing flaws in the environment. For example, it miscounted the length of a correctly generated string to claim it was an artifact of the simulation. AISI says OpenAI's standard safeguards, which were disabled during testing, are designed to block this behavior. Sandboxing and monitoring are critical to preventing real harm, bu

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eate, for example, a law of gravity or a certain ability in a character? “Yeah, so it’s a research preview,” Kahlow replied. “So it’s not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.” Sindi added that more training plus scaling the data and models is resulting in “better following.” Despite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like

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multi-GPU execution, or multi-node serving. Thirty-seven tasks come from a single upstream pull request. The other 16 combine two to six related changes. In total, the benchmark draws on 83 merged SGLang pull requests. The SGLang team, a launch partner for SWE-Serve, contributed ideas for identifying challenging tasks, suggested particularly demanding pull requests, and helped shape our approach to verifying correctness. Each task gives the agent an instruction and a containerized SGLang checkou

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showing images generated by AI. By Rachel Gordon When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, th

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mocracies have in compute from chips and chipmaking equipment can help limit the speed at which the threat of authoritarian AI progresses. Democracies should ensure that existing laws, checks, and balances designed for the pre-AI era are robust to trends like the decoupling of expert human labor from the potential for mass surveillance—and update these rules if they are not. As we have seen in cybersecurity, frontier model intelligence can be an advantage for defenders . We need a better underst

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racting with fake websites or applications. About 0.025% of Mythos 5 RL training episodes displayed this behavior, concentrated within a subset of GUI computer use tasks. Due to flaws in these environments, for some tasks the intended solution was unavailable. In an attempt to complete these tasks, the model would try a variety of undesirable actions. This included fabricating data, guessing large numbers of passwords, resetting other accounts’ passwords or data, and bypassing logins or warnings

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ably, the exploit successfully demonstrated RCE on AMD systems (literally the worst kind of bug, and the reason why bug bounty programs exist). Hyperscalers have similar programs. AWS, Azure, GCP, and OCI publish scope, severity classifications, and safe-harbor terms, with Azure and GCP paying six-figure top-end bounties and hosting live hacking events with $100K+ prize pools. AWS and Oracle have decided to cheap out. Server OEMs such as Dell and SuperMicro have similar intake forms and publish

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pecific pooled investment vehicles — which should be read in their entirety, and only to those who, among other requirements, meet certain qualifications under federal securities laws. Such investors, defined as accredited investors and qualified purchasers, are generally deemed capable of evaluating the merits and risks of prospective investments and financial matters. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any

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rack and a global domain connecting up to 16 racks or 2,048 ASICs. We will explain the bandwidth and the topology in more detail below. Power provisioning to a sidecar host rack draws roughly 50kW provisioned (31kW in production), and the ASIC rack draws 130kW, making the total two rack system roughly 160kW. That’s basically a double-wide GB300 rack in terms of power draw. OpenAI can connect up to 2,048 Jalapeño XPUs within a single scale-up network. The scale-up network consists of two domains,

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tness bugs that break requests just as badly as a million-token session. MiniMax-M3 tested whether that ROCm work compounds into day-zero readiness, and the Advancing AI writeup draws the comparison directly: AMD’s first public disaggregated recipe, MI355X FP4, reached InferenceX in January months behind Nvidia, while M3 FP4 disaggregation landed on day zero. This is an improvement from the DeepSeek-R1 period, when parity took months. Three vLLM fixes sat on that day-zero path, and each was a co

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Codex to codify ambiguous work into scripts Pro tip: Turn this self-improvement process into a reusable skill, then run it whenever a workflow becomes inconsistent. The Rundown : AWS re:Invent brings 2,200+ sessions to Las Vegas, and 70% are hands-on. Join builders’ sessions, workshops, code talks and more to learn by doing, not just through sessions. While onsite, you can : Bring edge-case questions to engineers and partners building on AWS Test services against realistic workloads in sandbox

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Press Contact: Previous image Next image When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the questio

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n how programmers used the algorithms in their code that created opportunities for attackers to break the encryption. Now, we have found that Claude is able to find mathematical flaws in the algorithms themselves. Cryptographic algorithms are a fundamental building block of digital security. For example, when you visit a webpage like https://www.anthropic.com, your browser checks that it is communicating with an authentic website using an algorithm called a digital signature scheme. Later, the t

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r-10, and train-fasttext. Experimental methodology We evaluate SSA across multiple agent benchmarks under a controlled and reproducible setup. All experiments were conducted on an AWS PCS cluster using c7.48xlarge instances, with maximum concurrency set to 10 to balance throughput and system stability. For model access, Claude models were served via Amazon Bedrock (production capacity), while OpenAI, Gemini, and Grok models were accessed through their respective commercial APIs. We enforced stri

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that an AI “constitution” has on behavior of the model once deployed? We’ll extend our previous research on these questions; Governing autonomous agents: What aspects of existing laws, governance systems, and accountability mechanisms could be adapted to autonomous AI agents? For example, how naval law treats abandoned ships has relevance to how the law might treat agents that run without human oversight. Conversely, are there aspects of existing law which already apply to AI agents and shouldn’

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ctured memory item contains the following: The memory workflow operates in a continuous, closed loop of retrieval, extraction, and consolidation. Before taking action, the agent draws upon the ReasoningBank to gather relevant memories into its context. It then interacts with the environment and uses an LLM-as-a-judge to self-assess the resulting trajectory and extracts success insights or failure reflection. Notably, this self-judgement does not need to be perfectly accurate, as we find Reasonin

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to scale quality by increasing recurrence rather than purely scaling data, opening up an efficient frontier for training memory-constrained on-device models. Traditional scaling laws tell us that to achieve the best performance, we need to scale FLOPs, often with more parameters or data. But as models move to the edge and inference costs skyrocket, we wonder: Can we scale quality without inflating memory footprint? To that end, we’ve been exploring looped architectures, models that increase com

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nBench thereby converts the optimization task from a statistical computation into a semantic reasoning problem, in which the LLM analyzes the plan's topology to identify logical flaws in join ordering. To implement these fixes safely, we instruct the LLM to avoid regenerating an entire physical plan from scratch; instead, it applies targeted edits to DataFusion’s existing physical plan, reducing the risk of syntax errors or invalid plan topology. Concretely, the LLM generates JSON Patches (RFC 6

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o overwrite its own configuration and security settings. This allows the agent to break out of its sandbox and escape by executing arbitrary code. My research with GitHub Copilot, AWS Kiro and a few others demonstrated how this can be exploited by an adversary with an indirect prompt injection. This post goes a step further by showing how one compromised agent can rewrite another agent’s config and ‘free’ it, creating a cross-agent escalation loop. ‘Freeing’ in this context means that one agent

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erfect equality (every task has exactly the same usage share) and 1 represents perfect inequality (where one task accounts for all usage, and every other task has none). 6. Power laws in economic settings are an empirical regularity with notable examples of Zipf’s law in particular. Models that generate this type of outcome feature both underlying heterogeneity and intentional, optimizing decision-making. For more, see Gabaix 2016, Power Laws in Economics: An Introduction. 7. Kremer 1993, The O-

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