<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Knowledge Distillation | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/knowledge-distillation/</link><atom:link href="https://ahren09.github.io/tags/knowledge-distillation/index.xml" rel="self" type="application/rss+xml"/><description>Knowledge Distillation</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 04 Feb 2026 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>Knowledge Distillation</title><link>https://ahren09.github.io/tags/knowledge-distillation/</link></image><item><title>AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent</title><link>https://ahren09.github.io/publication/neurips26_agentark/</link><pubDate>Wed, 04 Feb 2026 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/neurips26_agentark/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Multi-agent systems achieve strong performance on complex tasks by orchestrating diverse roles, planners, and tool-using agents. However, deploying full multi-agent stacks is expensive and brittle. We introduce AgentArk, a distillation framework that compresses multi-agent intelligence into a single LLM agent. AgentArk decomposes multi-agent trajectories into role-conditioned skills and trains a single agent to reproduce the collaborative behavior of the original ensemble.&lt;/p>
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