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New research by the Times shows that thousands of fatal crashes involving pedestrians could have been avoided with lower hood heights and improved visibility.
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Hi everyone, For the past couple of weeks I have been working on a simulator project considering the shortcomings of MuJoCo. There are things that people like and also don't like about MuJoCo, like the CPU dependency on MuJoCo which makes the simulation not parallelizable beyond a certain limit (depending on the hardware). I know there exists MJX which is GPU accelerated, however, it is not really made for vision based RL pipelines and training. There is also NVIDIA Isaac ecosystem, but that requires a powerful GPU, thus making it limited in terms of accessibility, let alone it requires license. This is why I worked out this new simulator (still working on it, so there will be significant bugs which require fixing). I call it **MuJoFil** \- MuJoCo + Google's Filament Render Engine. Basically I used Nvidia's Newton Physics Engine (which itself is based on MuJoCo's physics engine but is GPU native), clubbed it with Google's Filament render engine (both of these are open-source), modified Filament significantly to support working natively on GPU to render multiple simulations in parallel, and worked on optimizing it for performance. So what is MuJoFil? It is supposed to be an open-source high visual fidelity simulator optimised for a highly parallelized RL training pipeline so that users can use it to train Vision based Policies. Besides, it offers PBR textures support and also a simple to use plug and play functionality, where you can use any environments available online and support formats such as GLB, OpenUSD, etc. for setting environments for your robots. Basically, now you aren't just limited to environments native to MuJoCo, but rather you can use any environments available online from sketchfab, polyhaven, etc. and use it as a practical robot simulation environment. Check it out for yourself in the video. I would really appreciate it if you guys could tell how you feel about it and suggest ideas for what all things I can incorporate into it as this is going to be a fully open-source and free to use simulator that I have been working on for weeks. PS: While I have a couple of published research papers at top RL and AI/ML venues in the field of RL, I still consider myself a learner in this field who is continuously trying, learning, and building stuff, so there will be things in this hugely ambitious project which I might have missed to work on, and that is where I want help from you people who understand this field well. Sorry for this lengthy post and thanks if you read it till here🙇🙇🙏, I would really appreciate if you could share your thoughts on it. Also, I will make its code repo public on GitHub, but till then you can definitely check it out on PyPI. There are 2 separate packages, one can be installed using: "pip install mujofil" This is the CPU based variant, whereas there is a CUDA supporting GPU native variant about which I mentioned above, you can currently install it using: "pip install mujofil-warp" I am planning on changing its name to mujofil-cuda instead of mujofil-warp as that apparently sounds more intuitive to my direct peers but you can suggest this name as well. Thank you for the support❤️. submitted by /u/MT1699 [link] [Kommentare]
DeepSWE delivers four advances over existing public benchmarks: Contamination free: Tasks are written from scratch, not adapted from existing commits or PRs, so no model has seen the solution during pretraining. High diversity: Tasks span a broad pool of 91 repositories across 5 languages. Real-world complexity: Prompts are ~half the length of SWE-bench Pro's, yet solutions require 5.5x more code and ~2x more output tokens. Reliable verification: Verifiers are hand-written to test software behavior rather than implementation details. The result is a benchmark that reflects how today's frontier coding agents actually perform in software engineering work. https://preview.redd.it/lacvagyr159h1.png?width=1373&format=png&auto=webp&s=6514340a15d51d7f03da733f08fb3f6a302cac75 It's open-source: https://github.com/datacurve-ai/deep-swe submitted by /u/we_are_mammals [link] [Kommentare]
A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigate how world modeling based on language models can further push the boundaries of general agents. (i) We first focus on building foundation models for agentic environment simulation. We introduce Qwen-AgentWorld-35B-A3B and Qwen-AgentWorld-397B-A17B, the first language world models capable of simulating agentic environments covering 7 domains via long chain-of-thought reasoning. Leveraging more than 10M environment interaction trajectories of 7 domains in real-world environments, we develop Qwen-AgentWorld through a three-stage training pipeline: CPT injects general-purpose world modeling capabilities from the state transition dynamics and augmented professional corpora, SFT activates next-state-prediction reasoning, and RL sharpens simulation fidelity through a tailored framework with hybrid rubric-and-rule rewards. To evaluate language world models, we present AgentWorldBench, a comprehensive benchmark constructed from real-world interactions of 5 frontier models on 9 established benchmarks. Empirical results demonstrate that Qwen-AgentWorld significantly outperforms existing frontier models. (ii) Beyond foundation models, we further investigate two complementary paradigms through which world modeling enhances general agents. First, as a decoupled environment simulator, Qwen-AgentWorld supports scalable and controllable simulation of thousands of real-world environments for agentic RL, yielding gains that surpass real-environment training alone. Second, as a unified agent foundation model, world-model training acts as a highly effective warm-up that improves downstream performance across 7 agentic benchmarks. Code: https://github.com/QwenLM/Qwen-AgentWorld
Security, reliability, and maintainability reports for SaaS founders and AI-agent-built projects.
For four days, dozens of Romanian hospitals went offline, as cyber-experts sought to defeat the hackers.
@notch If you are asking “Why push back against anti-datacenter efforts?” I consider it a tragedy that anti-nuclear efforts largely strangled nuclear power in the US based on vibes, and I don’t want to see that happen to AI. Public opinion matters, and it shouldn’t be ceded
