analiza aiqbench
This study establishes a critical benchmark for Vision-Language Models acting as crisis operators, demonstrating that narrowcast communication strategies consistently outperform broadcast methods in reducing civilian failure rates during evacuations. The research highlights that visual input representations are essential for high-quality guidance, whereas adding graph-based adjacency data often degrades performance depending on the specific model architecture. Ultimately, the findings underscore that deploying VLMs in dynamic, embodied scenarios remains a complex challenge where input modality and communication strategy directly dictate intervention success.
Guide Me Out: A Framework to Benchmark VLM Operators Communication in Crisis Scenarios
arXiv:2606.09428v1 Announce Type: new Abstract: Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios. We address this gap with a novel benchmarking framework for evaluating Vision-Language Models (VLMs) tasked with guiding civilian agents through simulated evacuations. We test two communication strategies (narrowcast vs. broadcast), two environment representations (visual vs. graph-based), and two threat behaviors (static vs. moving) across nine maps of varying structural complexity. Our results show that Narrowcast consistently reduces civilian Fail rates compared to Broadcast across all difficulty levels. Guidance quality depends heavily on how the VLM operator represents the world: the visual modality drives performance, while adding an adjacency graph is model-dependent and often harmful. Moving threats raise Fail rates across all conditions as communication must continuously adapt over time. Together, these findings show that deploying VLMs as AI operators in evacuation scenarios remains a non-trivial challenge, where the choice of communication strategy and input representation can directly determine the success or failure of the intervention.
Abstract:Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios. We address this gap with a novel benchmarking framework for evaluating Vision-Language Models (VLMs) tasked with guiding civilian agents through simulated evacuations. We test two communication strategies (narrowcast vs. broadcast), two environment representations (visual vs. graph-based), and two threat behaviors (static vs. moving) across nine maps of varying structural complexity. Our results show that Narrowcast consistently reduces civilian Fail rates compared to Broadcast across all difficulty levels. Guidance quality depends heavily on how the VLM operator represents the world: the visual modality drives performance, while adding an adjacency graph is model-dependent and often harmful. Moving threats raise Fail rates across all conditions as communication must continuously adapt over time. Together, these findings show that deploying VLMs as AI operators in evacuation scenarios remains a non-trivial challenge, where the choice of communication strategy and input representation can directly determine the success or failure of the intervention.
Computation and Language (cs.CL)
arXiv:2606.09428 [cs.CL]
(or arXiv:2606.09428v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.09428 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 293 cuvinte
Subiecte conexe
COSMA achieves a 2.5× cost reduction via joint quantum kernel optimization
Quantum Zeitgeist · 13 iul. 2026
A Multimodal Framework for Measuring Creator Communication on Short-Form Video
arXiv · 27 oct. 2026
An Agentic AI System for Multi-Framework Communication Coding
arXiv · 14 oct. 2026
End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks
arXiv · 23 sept. 2026