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Reuth Mirsky

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Preprint Aug 2026

Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?

Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI techniques are increasingly applied to safety-critical scenarios, it is crucial to understand whether VLMs possess robust spatial memory and make reliable decisions. In...

G. Malfa, Nitay Alon, Emanuele La Malfa et al. · 0 citations
#machine learning Preprint Aug 2026

Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents

EvalXRL is a benchmark in which a Large Language Model (LLM) coding agent uses different XRL methods to diagnose a held-out malfunction in an RL agent, and then repair it, and proposes the first head-to-head comparison of multiple XRL methods in closed-loop usage.

Ram Rachum, Yotam Amitai, Bálint Gyevnár et al. · 0 citations

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