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University of Science and Technology, KAUST
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Autonomous research agents can achieve state-of-the-art performance and efficiency by coupling rapid, parallel evidence assessment with strategic, deliberative planning.
Instantaneous geometric scans leave robots blind to cascading physical interactions, but grounding VLM physical intuition with conformally certified backup control vaults safety rates from 43.3% to 99.3%.
A layered defense can thwart most self-state attacks on AI agents, but a hidden vulnerability persists that challenges current OS security paradigms.
Natural paper revisions can be harnessed to train AI agents for precise and context-aware editing of complex scientific diagrams.
LLM search agents can be easily manipulated, with some models endorsing false claims at alarming rates, revealing a critical gap in their safety evaluation.
