AgentScope 推出 Piccolo，一系列 27B、9B、4B 大模型，專為代理與資料工作流程中的決策設計。
模型能在上下文中評估候選方案，並以機率回傳結構化決策。
AI 伴隨問答
針對這篇報導有疑問？可直接點選快捷問題或向 AI 追問背景脈絡與技術細節：
AgentScope introduces Piccolo: open-weight decision models in 27B, 9B, and 4B sizes for agent and data workflows. Piccolo evaluates candidate options in context and returns structured decisions with probabilities.
🤖 https://t.co/NWGfPX1KN5
🌐 Explore Piccolo: https://t.co/IM0quc5EV4
🎯 Decisions that drive action: Control game characters, select and review tools, retain relevant context, filter data, assess output quality, and choose the next step—all through one decision interface.
📊 Benchmark results: Piccolo-27B achieves 92.21% accuracy on JevBench Public and scores 68.59 and 77.75 on text and multimodal Decision Index Public, respectively. On our purpose-built benchmarks, it achieves 84.41% on HarnessJevBench for agent execution control and 82.60% on DataJevBench for data workflows.
🔄 Recursive self-improvement: Model errors and per-example learning signals guide data selection, synthesis, and changes to training and inference. Research agents test each change and use the results to plan the next round, within human-defined goals and budgets.
📜 Apache 2.0; third-party licenses apply.
💻 Code: https://t.co/A1pR9jQZOo
