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KPGrasp achieves a remarkable 76.3% grasp success rate, outpacing existing methods by a staggering 47.4% while simplifying the grasp generation process.
Forget tedious expert demonstrations: this RL approach learns autonomous parking by smartly incorporating human corrections and failed attempts, achieving impressive real-world performance.
LLM safety crumbles in low-resource languages because alignment is skin-deep; LASA fixes this by injecting safety at the semantic core, slashing attack success by 88%.
VLMs still struggle to understand our planet, as revealed by a new geospatial benchmark spanning diverse Earth observation tasks and multi-source sensing data.
A new process reward model acts as a universal geospatial verifier, scaling the performance of both specialized and general-purpose VLMs in remote sensing.
