Oct 22 - Advances in AI at Virginia Tech

Join our virtual meetup to hear talks from AI researchers at Virginia Tech!

Date, Time and Location

Oct 22, 2026
9:00 AM - 11:00 AM PST
Online. Register for the Zoom!

Multi-Agent Communication: A framework, diagnostic and mechanistic perspective

Multi-agent LLM systems are increasingly used for collaborative reasoning, debate, and consensus, yet their communication dynamics remain poorly understood. This talk presents a framework for studying multi-agent communication through diagnostic and mechanistic perspectives.

I will discuss CONSENSAGENT, which improves consensus by mitigating sycophancy, alongside our diagnostic work on communication patterns and failure modes in real-world multi-agent debates. I will then present ongoing work that moves toward a mechanistic understanding of how these interaction patterns arise internally, with the broader goal of making multi-agent systems more interpretable, reliable, and controllable.

About the Speaker

Priya Pitre I am an Ph.D student in the Computer Science Department at Virginia Tech (VT), co-advised by Dr. Xuan Wang and Dr. Naren Ramakrishnan.

Exposing and Improving Fine-Grained Visual Grounding Abilities of Lightweight Multimodal LLMs

Lightweight multimodal LLMs can localize whole objects effectively, yet often struggle when a query targets a small object part or fine-grained visual detail. This talk presents a reasoning-guided framework that teaches compact models to ground parts through an explicit coarse-to-fine process: first locating the parent object, then identifying the requested part.

A part-aware reinforcement-learning objective provides stage-wise rewards for object accuracy, part containment, and the consistency of the model’s self-critique. Using these techniques, a compact 4B-parameter model achieves state-of-the-art zero-shot part grounding while preserving its object-level performance.

These advances can be used to enable lightweight MLLMs to support detail-oriented tasks in biology and robotics.

About the Speaker

Kazi Mehrab I a CS PhD candidate at Virginia Tech, where I currently focus on multimodal LLMs and computer vision tasks, including visual perception, reasoning and grounding.

Understanding Visual Generative Models for Precise Control

Despite remarkable progress in image and video generation, translating user intent into precise and consistent visual outputs remains a challenge. This talk explores how understanding the representations within generative models can enable finer control over what they create.

It connects semantic image editing with compositional generation, examining how visual concepts can be isolated, manipulated, and combined while preserving their identity and surrounding content. Building on these insights, structured visual inputs provide a way to express complex intent through subject references, poses, and spatial layouts.

The discussion then extends from images to video, where representations must evolve to preserve scene continuity while accommodating motion and change. Together, these directions establish a unified perspective on how visual representations can support controllable editing, composition, and coherent generation across space and time.

About the Speaker

Yusuf Dalva is a Ph.D. candidate at Virginia Tech, advised by Pinar Yanardag and affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics.