給 agent 餵 200 萬篇論文，小模型優化多贏 0.38%
案例摘要
作者跑兩個相同 Claude Code agent 優化小語言模型：一個只靠內建知識，一個接 Paper Lantern（200 萬＋ CS 論文的 MCP 搜尋），每次嘗試前先掃文獻、看了 520 篇、提出 25 個點子。有文獻的 agent 提升 4.05% 對 3.67%，約 15 個點子有效；2 小時賽程中領先 12 分鐘以上。但留言質疑結果強度與通用性。
實際結果
4.05% vs 3.67%；約六成點子有效；方法論受質疑。
台灣／大陸場景對照
🇹🇼 台灣
研發場景的啟示：agent＋文獻庫（RAG）確實能擠出增益，但增益幅度和成本要誠實評估；適合作為進階玩法案例。
🇨🇳 大陸
大陸可用知網／arXiv 鏡像＋Dify 知識庫做同款；算力成本先算清楚再跑。
英文原文
Give a coding agent a paper corpus to find new techniques
The poster ran two identical Claude Code agents on the same job: optimize a small language model. One used only its built-in knowledge; the other was connected to Paper Lantern, an MCP search tool over 2M+ open-source computer science papers, and searched the literature before each attempt, scanning 520 papers, surfacing 25 ideas, and applying them with human-readable guidance.
The paper-enabled agent improved the model by 4.05% versus 3.67% for the baseline, and the poster says about 15 of the 25 suggested paper-based ideas worked. They also say the better config stayed 12+ minutes ahead on a 2-hour run, though commenters questioned how strong and general the result was.
來源
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