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前沿/AI应用/持续创业/噪音围绕/慢动作回放

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@Alina_P_I 惊喜🎉

[Alina 🩵📖✨ @Alina_P_I]

If China decides to Open Source any model on par with Mythos by End Of Year, US AI is dead.

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@jun_song 哈哈😁好主意

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@jun_song 酷，加油！

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@monitero3315 早

[Ajeet ( opensox.ai ) @jackedAJ]

someone just built a game in which YOU are the operating system. 🤣🤣🤣

you manage processes, memory and I/O events. manually.

learning by doing on steroids.

[Sebastien Bubeck @SebastienBubeck]

This is so cool! GPT-5.5-pro proved a computational lower bound (assuming SETH) showing that there are essentially no non-trivial algorithm to find the furthest pair among n points in very high dimension. And cherry on the cake the starting point of the proof is the unit distance proof!

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@qwqguo 哟

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@zarazhangrui 都不会，X上噪音太大，只适合agent浏览

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@zarazhangrui 对极啦

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@JJitsev 冲击SOTA

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@xuanmingzhangai 加油，好期待！Qwen好样的

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@jvr0x 这是个意义不明的实验模型，并不少普通的代理模型。

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@RichardZ412 难道不是和Qwen 3.6对比吗

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Qwen真是刚想什么，就能刚好造出来！太好了🥳

[Qwen @Alibaba_Qwen]

📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation.

🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves.

🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes:

1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench

2️⃣ Investigate how world modeling enhances agent training:
🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments
🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning

📑 Paper: https://t.co/Jx2l5RKq71
📖 Blog: https://t.co/7tVcKyhsx2
💻 GitHub: https://t.co/B5Lvb1UZCn
🤗 HuggingFace: https://t.co/Kw3QBL1TM5
🧩 ModelScope: https://t.co/YBnGYgMWWI

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@AdinaYakup @huggingface GGUF

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@kimmonismus Anthropic的手册写的清清楚楚，Anthropic从网上抓数据，从其他模型蒸馏。甚至回复都直���说自己是其他模型

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@populartourist 还没有可用的gguf

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@zhouyan20009 好看

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@dzhulgakov 关注