8 citations · 8 across the 8 of their papers we have counts for
8 papers
Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?
Gabriele La Malfa, Nitay Alon, Emanuele La Malfa +2
Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI techniques are increasingly…
Proceedings of the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind
Nitay Alon, Joseph M. Barnby, Reuth Mirsky +1
This volume includes a selection of papers presented at the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2026 in Singapore on 26th January…
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord acc…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
All You Need is Sally-Anne: ToM in AI Strongly Supported After Surpassing Tests for 3-Year-Olds
Nitay Alon, Joseph Barnby, Reuth Mirsky +1
Theory of Mind (ToM) is a hallmark of human cognition, allowing individuals to reason about others' beliefs and intentions. Engineers behind recent advances in Artificial Intellige…
Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning
Eitan Wagner, Nitay Alon, Joseph M. Barnby +1
Theory of Mind (ToM) capabilities in LLMs have recently become a central object of investigation. Cognitive science distinguishes between two steps required for ToM tasks: 1) deter…
-IPOMDP: Mitigating Deception in a Cognitive Hierarchy with Off-Policy Counterfactual Anomaly Detection
Nitay Alon, Joseph M. Barnby, Stefan Sarkadi +3
Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of re…
Emergent Dominance Hierarchies in Reinforcement Learning Agents
Ram Rachum, Yonatan Nakar, Bill Tomlinson +2
Modern Reinforcement Learning (RL) algorithms are able to outperform humans in a wide variety of tasks. Multi-agent reinforcement learning (MARL) settings present additional challe…