Add presentation details for Elias Stengel-Eskin

Added details for a presentation by Elias Stengel-Eskin, including speaker bio and abstract.

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+            <h4>2026/06/02</h4>
+            <li>
+              <b><a href="[PAPER LINK]">Multi-Model Training for Multi-Agent Communication Skills</a></b>
+              <br>
+              Presenter: <u><a href="https://esteng.github.io" target="_blank" rel="noopener noreferrer">Elias Stengel-Eskin</a></u>, University of Texas at Austin 
+              <a class="btn btn-info btn-xs" data-toggle="collapse" href="#20260602-bio" role="button" aria-expanded="false">
+                Speaker Bio
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+                <div class="card card-body">
+                  Elias Stengel-Eskin is an Assistant Professor of Computer Science at the University of Texas at Austin. His research spans  natural language processing, computational linguistics, and artificial intelligence, and focuses on developing AI agents that can intelligently communicate and collaborate with people and each other. This includes work on multi-agent communication and collaboration, converting language to action, grounding language to vision, and handling uncertainty, ambiguity, and underspecification. Before joining UT Austin, he was a Postdoctoral Research Associate at the University of North Carolina, Chapel Hill. He received a Ph.D. in Computer Science in 2023 from Johns Hopkins University and a B.A. & Sc. in Cognitive Science from McGill University in 2018. 
+                </div>
+              </div>
+              <br>
+              <!-- <a href="[RECORDING LINK - ADD AFTER TALK]"><img src="https://img.shields.io/badge/Youtube-Recording-orange"></a> -->
+              <!-- <a href="[PAPER LINK]"><img src="https://img.shields.io/badge/Paper-link-important"></a> -->
+              <!-- <a href="[GITHUB_LINK]"><img src="https://img.shields.io/badge/Github-link-lightgrey"></a> -->
+              <!-- <a href="[SLIDES_LINK]"><img src="https://img.shields.io/badge/Talk-Slides-blue"></a> -->
+              <a class="btn btn-primary btn-xs" data-toggle="collapse" href="#20260602-abstract" role="button" aria-expanded="false">
+                Abstract
+              </a>
+              <div class="collapse" id="20260602-abstract">
+                <div class="card card-body">
+                  As we scale from individual agents to teams of agents, inter-agent communication will become increasingly important. In this talk, I will describe a general paradigm for teaching multi-agent communication skills through multi-model reinforcement learning, which I will illustrate via three key collaborative skills: expressing confidence in a calibrated way, responding robustly to positive and negative persuasion, and expressing reasoning faithfully. I will show how these problems can be framed in terms of speaker-listener games, and how this framing allows us to teach models collaborative skills, often using games simulated on smaller models to train larger models.
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+            </li>
 
             <h4>2026/05/19</h4>
             <li>