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Northwest Institute of Nuclear Technology, Xi鈥檃n, China
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Operator-residual feedback slashes the rate of misleading score-only decisions from nearly 40% to under 2%, ensuring that autonomous agents make choices grounded in physical reality.
Achieving 93.02% accuracy in molecular structure recognition, MinerU.Chem outperforms existing systems, unlocking new possibilities for AI-driven chemistry research.
A general Agent OS can boost long-horizon robotic execution and enable continual learning through structured memory management and self-evolution.
ABot-N1 redefines urban navigation by achieving a 35% boost in point-of-interest arrival rates, setting new benchmarks for visual language navigation models.
Closing the sim-to-real gap in vision-language navigation requires benchmarks grounded in realistic 3D reconstructions, not just generated scenes.
Network jitter in cloud-based robot control can be overcome by converting temporal lag into spatial pose offsets, restoring the VLA's original geometric intent without fine-tuning.
Agents can now explore environments more efficiently by thinking like humans, prioritizing key landmarks and semantic information during online memory construction.
Forget task-specific architectures: a single Vision-Language-Action foundation model, ABot-N0, now dominates embodied navigation across five distinct tasks.
