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Flux-OPD is proposed, an OPD paradigm that uses evolving contexts as in-training supervision to capture task preferences in open-ended domains and outperforms existing OPD paradigms, highlighting the potential to combine teacher supervision with evolving contexts.
SpikingMOT is proposed as a spike-driven tracker that adaptively models sparse trajectory dynamics with spiking neural networks (SNNs) and brings SNNs into MOT, opening a promising direction for efficient tracking.
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. How...
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