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Nanyang Technological University
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OmniLLMs can now confess which specific evidence tokens rely on, enabling targeted hallucination correction without retraining.
Long-horizon agents achieve state-of-the-art performance by decoupling planning credit from execution guidance via structured subtask trees.
TFR-Net not only prevents knowledge re-emergence during continual unlearning but also enhances model capacity, achieving a remarkable trade-off between unlearning and utility retention.
PowerAtlas achieves optimal electricity-computing scheduling, ensuring compliance with grid rules while maximizing efficiency in volatile AI workloads.
