<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Foundation Model | Junfei Zhan's Website</title><link>https://junfei-z.github.io/tags/foundation-model/</link><atom:link href="https://junfei-z.github.io/tags/foundation-model/index.xml" rel="self" type="application/rss+xml"/><description>Foundation Model</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 21 May 2025 00:00:00 +0000</lastBuildDate><image><url>https://junfei-z.github.io/media/icon_hu70bcee51a3cd7a7338014254a2e0c844_1401285_512x512_fill_lanczos_center_3.png</url><title>Foundation Model</title><link>https://junfei-z.github.io/tags/foundation-model/</link></image><item><title>Slide - Can Large Language Models Credibly Stand in for Humans in Game-Theoretic Experiments?</title><link>https://junfei-z.github.io/samples/1_dsp/</link><pubDate>Wed, 21 May 2025 00:00:00 +0000</pubDate><guid>https://junfei-z.github.io/samples/1_dsp/</guid><description>&lt;p>Evaluating LLMs (e.g., GPT-4o, LLaMA-3.3) in classic games such as Prisoner&amp;rsquo;s Dilemma, Ultimatum Game, and Public Goods Game.&lt;br>
We propose a multi-agent routing framework PRIME-Router that improves strategic adaptability and persona consistency across repeated interactions.&lt;/p></description></item><item><title>演示文稿 - Can Large Language Models Credibly Stand in for Humans in Game-Theoretic Experiments?</title><link>https://junfei-z.github.io/zh/samples/1_dsp/</link><pubDate>Wed, 21 May 2025 00:00:00 +0000</pubDate><guid>https://junfei-z.github.io/zh/samples/1_dsp/</guid><description>&lt;p>评估 LLMs（如 GPT-4o、LLaMA-3.3）在经典博弈中的表现，包括 Prisoner&amp;rsquo;s Dilemma、Ultimatum Game 和 Public Goods Game。
我们提出了一种多智能体路由框架 PRIME-Router，可在重复交互中提升策略适应性和角色一致性。&lt;/p></description></item></channel></rss>