{"version":"1.0","generated_at":"2026-10-19T15:41:54.118580","id":3501,"slug":"cohere","title":"Cohere 论文：多智能体编码团队宜由强模型领队、便宜模型只做规划","summary":"Cohere 论文：多智能体编码团队宜由强模型领队、便宜模型只做规划：这条内容来自 AIHOT 补充信号池，核心焦点是讨论数据集与基础模型。为什么值得看：它已经被上游系统筛过一轮，适合继续判断能否转化成 OPC 的选题、案例或工作流启发。","abstract":"Cohere 论文：多智能体编码团队宜由强模型领队、便宜模型只做规划 Cohere 论文：多智能体编码团队宜由强模型领队、便宜模型只做规划：这条内容来自 AIHOT 补充信号池，核心焦点是讨论数据集与基础模型。为什么值得看：它已经被上游系统筛过一轮，适合继续判断能否转化成 OPC 的选题、案例或工作流启发。 Cohere 论文：多智能体编码团队宜由强模型领队、便宜模型只做规划：这条内容来自 AIHOT 补充信号池，核心焦点是讨论数据集与基础模型。为什么值得看：它已经被上游系统筛过一轮，适合继续判断能否转化成 OPC 的选题、案例或工作流启发。 原贴提到：Cohere Labs 论文《Scaling Inference-Time Compute with Teams of Coding Agent 关键词：AI、自动化、研究、AIHOT 全量 来源：AIHOT 全量 cohere labs 论文《scaling inference-time compute with teams of coding agents》研究多智能体编码团队的算力分配，发现团队超过单 agent 但成本高出 3 到 7 倍。 aihot 分类：paper。 这条内容的真正价值，不只是“有人发布了一个新功能”，而是它揭示了 AIHOT 全量 背后的产品方向、工作流变化或竞争信号。对 OPC 来说，这种信息可以转化成持续追踪的栏目选题。 如果把《Cohere 论文：多智能体编码团队宜由强模型领队、便宜模型只做规划》放到你的内容系统里，它最大的价值在于帮助读者更快看懂“为什么值得关注”，而不是只看到一条碎片化…","access_level":"member","access_label":"免费阅读","access_mode":"full","is_preview":false,"canonical_url":"https://opc.beizhux.com/content/3501/cohere","html_url":"https://opc.beizhux.com/content/3501/cohere","json_url":"https://opc.beizhux.com/content/3501/cohere.json","published_at":"2026-10-19T13:10:44","updated_at":"2026-10-19T14:49:02","category":{"slug":"ai-skills","name":"AI技能杠杆"},"source":{"site":"x.com","author":"X：Rohan Paul (@rohanpaul_ai)","url":"https://x.com/rohanpaul_ai/status/2112048784457060471"},"tags":["AI","AIHOT 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