@MrX

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

Hey everyone! 👋 If you’re building RAG or autonomous AI agents, you’ve probably hit the "Vector DB Wall": flat Euclidean vectors suck at modeling complex hierarchical reasoning, and loading millions of 1536D vectors + JSON metadata into memory causes massive RAM bloat and OOM crashes. We spent the last few months solving this from the ground up. Today, we are releasing HyperspaceDB v3.1.0, transitioning from a standard vector index to a full Spatial AI Engine. Here is what’s under the hood: 1. The RAM Diet (Schema-Driven MRL) Instead of loading full dense vectors into memory, we built native support for Matryoshka Representation Learning (MRL). The engine keeps a lightweight navigation core (e.g., 129 dimensions) in ultra-fast RAM, while the heavy semantic tail (672 dimensions) streams dynamically from NVMe SSDs for final top-K re-ranking. The benchmark: In our stress tests with 100,000 vectors, HyperspaceDB consumed just ~72.0 MB of RAM compared to >3,000 MB for Chroma and ~1,700 MB for Milvus. 2. 801D Hybrid Vectors (Lorentz + Euclidean) Flat vectors fail at taxonomy (e.g., Legal Codes, Medical Trees). We introduced an 801D Hybrid Vector. The first 33 dimensions live in a negatively curved Lorentz hyperboloid (allowing for native graph/tree embeddings), while the remaining 768 dimensions handle Euclidean semantic density. Agents can now verify facts geometrically using geodesic path tracing. 3. Killing the "Two-Database Problem" Gluing Pinecone to MongoDB for document storage is painful. We built Sidecar Document Storage. You store massive raw texts directly in the index, which automatically compresses (Zstd) and pushes them to fractal .hyp chunks on disk. Meanwhile, Typed Metadata (int, bool, enum) is compiled directly into the HNSW graph nodes in RAM, providing zero-latency pre-filtering with no JSON-parsing overhead. 4. Lock-Free Rust Performance Under a 1,000-concurrent-client stress test, our lock-free HNSW and L0/L2 DashMap cache held flat at 9,476 QPS with a p99 latency of 11.83 ms. Competitors hit severe lock contention at this scale, with latencies spiking over 2,000 ms. We’ve also added a WASM runtime, Raspberry Pi ARM64 support, and native LangChain/LlamaIndex/MCP integrations. Would love to hear your thoughts, answer any questions about the architecture, or get feedback from anyone pushing the limits of Agentic RAG! Ask me anything! 🚀 submitted by /u/Sam_YARINK [link] [Kommentare]

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