推理能力

OpenAI says Astra is its first model to reach the Critical cybersecurity capability threshold. Under its Preparedness Framework, that means Astra can, with the right tools and access, find unknown flaws and develop exploits across hardened systems without step-by-step human guidance. Hence, OpenAI now says Astra will launch with additional chain-of-thought monitoring, while classifiers can automatically stop potentially unauthorized actions. Astra reached roughly 39% exploit success at ~75K output tokens, while GPT-5.6 Sol is only around 1% there and needs nearly 140K tokens to reach ~12% i.e. Astra is dramatically more capable and token-efficient at exploit development on this internal benchmark. Expert assessments went further: Astra escaped a browser sandbox, executed host commands, and escalated an unprivileged operating-system user to root.

🚀 Hy4 preview is here. 770B, 49B active, 1M context. Built for productivity. Open source frontier. Consistent affordable price. Use it. Tell us what breaks. More on Hy blog：https://t.co/rbl1IWRk3C HuggingFace：https://t.co/mE9wevH5XR Github：https://t.co/pyl9zckpoL https://t.co/4iW6gSuZKr

OxAlpha is a new iteration of GLM, from China’s Z .ai And it will change how you run long running agent fast. --- bloomberg .com/news/articles/2026-08-26/china-s-z-ai-made-ox-alpha-stealth-model-that-rivals-deepseek https://t.co/ZOTlnBgOsy

Model Architecture Four core upgrades for maximum capability, efficiency, capacity, and stability: - Attention: GDN + QSA Hybrid. Gated DeltaNet (GDN) compresses history. Qwen Sparse Attention (QSA) uses a lightweight indexer for micro-block context selection. Lower the cost of attention on long sequences. - Residual: Gated Residual (GR) widens the residual stream to 4 branches with a dynamic read and write gating, strengthening cross-layer information flow and significantly improving training stability. - Embedding: N-gram Embedding uses local context lookups to expand model capacity at minimal compute cost, while keeping the embedding table in host memory with asynchronous prefetching. - Optimization: Muon optimizer. Refines Muon through improved orthogonalization, smarter parameter assignment between Muon and AdamW, and fused-parameter splitting, with scaling laws refitted for the new architecture.

Qwen 3.8 Flash-Next official released: A 6B-active open model just beat Claude Opus 4.6 Max across 8 of 9 comparable benchmarks! Qwen3.8-Flash-Next is a highly sparse MoE: • 125B model parameters • 51B additional n-gram embeddings • Only 6B parameters active per token It scores: • 62.5 SWE-bench Pro • 81.0 SWE-bench Multilingual • 73.9 CoworkBench • 55.7 JobBench • 73.5 Toolathlon • 81.3 IFBench • 91.7 GPQA Diamond • 91.9 LiveCodeBench It also outperforms Qwen3.8-27B and DeepSeek-V4-Flash across most of the table. Super cool release!!