<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Calls | IAAA</title><link>https://iaaa-x.github.io/calls/</link><atom:link href="https://iaaa-x.github.io/calls/index.xml" rel="self" type="application/rss+xml"/><description>Calls</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 29 Aug 2025 00:00:00 +0000</lastBuildDate><image><url>https://iaaa-x.github.io/media/logo_hu_564a4cbe8434def.png</url><title>Calls</title><link>https://iaaa-x.github.io/calls/</link></image><item><title>Call for Reviewers</title><link>https://iaaa-x.github.io/calls/call_for_reviewers/</link><pubDate>Fri, 29 Aug 2025 00:00:00 +0000</pubDate><guid>https://iaaa-x.github.io/calls/call_for_reviewers/</guid><description>&lt;p>[TODO] Add call for reviewers details here.&lt;/p></description></item><item><title>Call for Papers</title><link>https://iaaa-x.github.io/calls/call_for_papers/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://iaaa-x.github.io/calls/call_for_papers/</guid><description>&lt;p>We invite &lt;strong style="color: #92400e !important; font-weight: bold;">long (8-page) and short (4-page)&lt;/strong> paper submissions on topics including, but not limited to:&lt;/p>
&lt;ul>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">AI agent design and training for traditional human IA tasks&lt;/strong>, e.g., IR, RecSys, and QA systems&lt;/li>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">User simulation&lt;/strong> for estimating user backgrounds, goals, and information needs&lt;/li>
&lt;li>Exploration of &lt;strong style="color: #92400e !important; font-weight: bold;">human-AI agent interaction modes in information acquisition&lt;/strong>&lt;/li>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">Privacy protection strategies for personalized IA&lt;/strong> provided by AI agents&lt;/li>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">Autonomous information acquisition by AI agents&lt;/strong> (e.g., retrieval-augmented generation; RAG) for extending or updating their static and limited parametric knowledge&lt;/li>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">Agent memory mechanisms and cognitive architectures&lt;/strong> to process and organize information&lt;/li>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">Learning paradigms for the AI agent&amp;rsquo;s self-improvement through accessing new information&lt;/strong>, e.g., deep reinforcement learning&lt;/li>
&lt;li>&lt;strong style="color: #92400e !important; font-weight: bold;">Datasets, benchmarks, and hardware or software systems&lt;/strong> supporting new paradigms of AI-enhanced IA and IA-enhanced AI agents&lt;/li>
&lt;/ul>
&lt;h3 id="submission-instructions">Submission Instructions&lt;/h3>
&lt;p>We welcome two types of papers: regular workshop papers and non-archival submissions. Only regular workshop papers will be included in the workshop proceedings. The review process will be double-blind. All submissions should be in PDF format, following the [TBD] template and made through the OpenReview submission portal ([TBD]).&lt;/p>
&lt;h3 id="important-dates">Important Dates&lt;/h3>
&lt;ul>
&lt;li>[TBD]&lt;/li>
&lt;/ul></description></item></channel></rss>