The Agent Architect · 2026-W44
The Agent Architect #44: World-Model Simulation Planning
预览:本期将于周二08:00 UTC发送。
本周模式
World-Model Simulation Planning
- 是什么:
- 智能体用 LLM 或学习得到的模型模拟各候选动作的结果,为其打分,然后在真实环境中执行最优的那个。
- 何时使用:
- 在线网站或图形界面上的不可逆操作:回退不可能或代价高昂,出错后果严重。
- 注意:
- 模拟误差会累积:推演早期的一次错误预测会层层放大,即便打分逻辑正确,动作选择依然会出错。
本周智能体AI动态
- Serving DeepSeek-V4.1-Flash at Scale: vLLM Matches DeepInfer and Scales to GB300 NVL72vLLM
With P/D disaggregation and Wide-EP on a GB300 NVL72 rack, vLLM serves DeepSeek-V4.1-Flash on SemiAnalysis AgentX at 954K tokens/s per GPU at a P90 of 196 tokens/s per user, 7.9x our aggregated single-node result.
- Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3arXiv cs.AI
Instrumental convergence is often invoked as a general reason to expect capable agents to acquire or express power-seeking subgoals: self-preservation, resource acquisition, deception, or resistance to oversight which poses an important threat to the society. For LLMs, reinforcement learning is the first stage of post-training that directly optimizes behavior against an objective; therefore, it is the stage where convergent instrumental behavior, on IC grounds, likely to appear. Does reinforcement learning with verifiable rewards (RLVR) make a model more likely to express instrumental-converge
- Voxtral 3Mistral AI
Voxtral 3: Mistral's voice agent SDK for real-time speech in 50+ languages — accurate transcription, emotional TTS, deep tool use. EU-hosted or self-deployed.
- GLM-5.3 Optimizations, Part 2: PCP, DCP, and Hybrid HiSparse on 16 H200svLLM
vLLM serves GLM-5.3 on 16 H200s with PCP and DCP prefill and Hybrid HiSparse decode, reaching 1M-token contexts and 1.7–3.2× the per-user speed of data-parallel P/D on the AgentX agentic benchmark.
- From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation TasksarXiv cs.AI
Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of using general-purpose AI models for robot manipulation without task-specific training. However, their manipulation capabilities have not been systematically evaluated on standardized robotics benchmarks. In this report, we present a unified closed-loop manipulation framework for evaluating frontier AI models on the LIBERO-10 benchmark. Our framework enables models to control a robot directly through visual observations, robot pro
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