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 robot