<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Conference | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://Ahren09.github.io/publication_types/conference/</link><atom:link href="https://Ahren09.github.io/publication_types/conference/index.xml" rel="self" type="application/rss+xml"/><description>Conference</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_85eb14b35ab132ae.png</url><title>Conference</title><link>https://Ahren09.github.io/publication_types/conference/</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"&gt;Abstract&lt;/h2&gt;
&lt;p&gt;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&gt;
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