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cs.AI2026
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration
Adrián Szlatincsán, Filippo Ziliotto, Bruno Lepri +3
LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating a collaborator's actions in r…
cs.AI2026
Mirror, Mirror on the Wall: Can VLM Agents Tell Who They Are at All?
Filippo Ziliotto, Ciro Beneduce, Bruno Lepri +3
In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogous functional capability emer…
cs.AI2024
TANGO: Training-free Embodied AI Agents for Open-world Tasks
Filippo Ziliotto, Tommaso Campari, Luciano Serafini +1
Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. I…