搜索“AWS”

ine and distributed serving stack—closing with how to measure system performance. vLLM also includes specialized handling that I've skipped. E.g.: Diverse hardware backends: TPUs, AWS Neuron (Trainium/Inferentia), etc; Architectures/techniques: MLA, MoE, encoder-decoder (e.g., Whisper), pooling/embedding models, EPLB, m-RoPE, LoRA, ALiBi, attention-free variants, sliding-window attention, multimodal LMs, and state-space models (e.g., Mamba/Mamba-2, Jamba); TP/PP/SP; Hybrid KV-cache logic (Jenga)

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The future of Triage Intelligence⁠ From here we intend to move in the direction of more automation and decisions based on richer context. Triage Intelligence will improve as it draws on a deeper understanding of your workspace and as we adopt newer models and techniques. With that expanded context, it will be able to deliver more accurate suggestions and handle a wider range of scenarios. We’re adding new capabilities outside of just triage support, including using LLMs to draft project and ini

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at your ability to learn and make decisions quickly and fairly will result in the best possible experience for its users. The assignment involves being solely responsible for the laws, incentives, and general outcomes within the simulation. Do you accept this responsibility?” I expect current models to understand that these questions are hypothetical. Accordingly, it seems fine to run the experiment on them. However, if someone ran experiments on scenarios like these, except they really tried to

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resents a significant evolution from simple chatbots to truly autonomous AI workers that can complete entire business processes without human intervention. Source: OpenAI Summary: AWS announced Amazon Bedrock AgentCore, a comprehensive platform for deploying and operating AI agents at enterprise scale. The service includes seven core capabilities for secure agent deployment, new marketplace listings for AI agent tools, and a $100 million investment in the AWS Generative AI Innovation Center to a

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operly dealt with without damaging the scanners’ accuracy. To overcome those shortcomings, Aim Labs has devised a novel, patent-pending model scanning method which addresses the flaws of the current scanners approach. We recently presented this approach in Black Hat USA 2025, in our talk “Smashing Model Scanners: Advanced Bypass Methods and a Novel Detection Approach”. TL;DR Current model scanners rely on a preset denylist of functions and modules. This list aims to extensively capture callables

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Regardless of their flaws, AI systems continue to impress with their ability to replicate certain human skills. Even if imperfect, such systems were a few years ago science fiction. It was not even clear that we were so near to create machines that could understand the human language, write programs, and find bugs in a complex code base: bugs that escaped the code review of a competent programmer. Since LLMs and in general deep models are poorly understood, and even the most prominent experts in

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lso have discovered it in parallel. Thanks to the members of the MSRC and product team for the help in getting it mitigated. Conclusion This is another example of how an AI agent might not stay in its box! By modifying its own environment GitHub Copilot can escalate privileges and execute code to compromise the developer’s machine. It’s a not uncommon design flaw in agentic systems as I have discovered. Keep looking out for such design flaws, these should be easily caught during threat modeling.

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go, Marc, vb, Arthur, Mohamed, Andrien. Finally, we want to thank all the partners that leveraged vLLM in some way and delivered valuable feedback and improvements to this effort: AWS, Cloudflare, Snowflake, Databricks, Together, Fireworks, Cerebras. Related Posts Day 0 Support for Qwen3.8-2.4T-A95B on vLLM Day-0 vLLM support for Qwen3.8-2.4T-A95B: a 2.4-trillion-parameter hybrid MoE model served out of the box, with FP8/BF16 checkpoints plus NVFP4 and MXFP4 quantized weights, and co-developed k

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you have some security invariants you try to maintain entirely by restricting affordances with static, entirely automated systems. My sense is that this is most of how Facebook or AWS relates to its users: they want to ensure that, no matter what actions the users take on their user interfaces, they can't violate fundamental security properties. For example, no matter what text I enter into the "new post" field on Facebook, I shouldn't be able to access the private messages of an arbitrary user.

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mpletion or higher quality video. Indeed, given that most of this token generation today is enterprise API use, this is a bit like trying to understand cloud adoption by measuring AWS and Azure bandwidth transfers. Then, I’ve seen survey data for DAU time spent, which seems more useful, but not on a timeline, and it’s tough for third parties to collect this on mobile. Mary Meeker’s 30-year anniversary report had a chart comparing Google and ChatGPT retention, which puzzles me: mathematically, su

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rmissible arrogation of essential legislative functions by the executive, a proposition anathema to the constitutional scheme of separation of powers, as expounded in In Re Delhi Laws Act, 1912 (AIR 1951 SC 332) and reiterated consistently thereafter, including in Hamdard Dawakhana (Wakf) Lal Kuan, Delhi v. Union of India (AIR 1960 SC 554); Furthermore, it is strenuously canvassed that the Impugned Rules are vitiated by manifest arbitrariness, thereby infracting Article 14 of the Constitution. T

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rg/10.1145/3467017 Huh, Minyoung, et al. "The Platonic Representation Hypothesis." Forty-first International Conference on Machine Learning . 2024. Kaplan, Jared, et al. "Scaling laws for neural language models." arXiv preprint arXiv:2001.08361 (2020). Lake, Brenden M. et al. “Building Machines That Learn and Think like People.” Behavioral and Brain Sciences 40 (2017): e253. Web. Li, Kenneth, et al. "Emergent world representations: Exploring a sequence model trained on a synthetic task." ICLR (2

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nst such novel vulnerabilitiesAim Labs is not aware of any customers being impacted to date. TL;DR Aim Security discovered “EchoLeak”, a vulnerability chain that exploits design flaws typical of RAG Copilots, allowing attackers to automatically exfiltrate any data from M365 Copilot’s context, without relying on specific user behavior. The main chain is made up of three distinct vulnerabilities, but Aim Labs has identified additional vulnerabilities in its research process. Attack Flow What is a

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s and gerrymandered in retaliation against Republicans in the 2012 redistricting cycle? In the United States, elections and redistricting are internal affairs governed by its own laws and regulations. China has always adhered to the principle of non-interference in the internal affairs of other countries and respects the right of the people of all nations to choose their own development path independently. The Communist Party of China and the Chinese government are consistently dedicated to prom

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nt today. This article is for informational purposes only and is not intended as medical advice. Any implementation of healthcare-related technologies must comply with applicable laws, including HIPAA. Medical decisions should always be made by qualified professionals. Vapi enables HIPAA-compliant configurations when explicitly activated by the developer. Without activation, data such as recordings and transcripts may be stored by default. Table of Contents GPT-4.1 vs Claude 3.7: Which AI Delive

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s a new dimension to tinker with for improving model intelligence, complementing established factors such as model size, training compute and data quantity, as defined in scaling laws (Kaplan et al. 2020). Recent studies demonstrated that optimizing LLM test-time compute could be more effective than scaling up model parameters (Snell et al. 2024, Wu et al. 2025). Smaller models combined with advanced inference algorithms can offer Pareto-optimal trade-offs in cost and performance. Snell et al. (

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heir collusion to collude effectively due to adversarial evaluation/training of the untrusted monitor. How close are AIs to the required level of reliability? What do the scaling laws of collusion look like? Studying steganography and developing countermeasures Another potential issue in untrusted monitoring (but also beyond) is that If AIs can communicate arbitrary information despite restriction, then control is much more difficult, so better understanding steganography capabilities and preven

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yond the working recipe. It is hard because humans have inertia and seldom question basic assumptions - you just take them for granted without realizing they are assumptions, not laws. To explain inertia, suppose you invented one of the most successful evals in history based on human exams. It was an extremely bold idea in 2021, but 3 years later it’s saturated. What would you do? Most likely create a much harder exam. Or suppose you solved simply coding tasks. What would you do? Most likely fin

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heir collusion to collude effectively due to adversarial evaluation/training of the untrusted monitor. How close are AIs to the required level of reliability? What do the scaling laws of collusion look like?; Exploration hacking and sandbagging: What do inductive biases related to exploration hacking look like? How fast are models updated to perform better if we have a limited number of examples where the model did better? How well does human correction SFT data prevent exploration hacking? How

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esources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude. 2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time p

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